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Batteries, Volume 9, Issue 3 (March 2023) – 47 articles

Cover Story (view full-size image): Electrolyte filling and wetting are crucial steps in battery cell production, influencing quality and costs. To identify common parameters affecting wetting behavior, we conducted a systematic literature review, analyzing 39 fully labeled articles out of 544 records. We found research gaps, including a lack of a holistic view on measurement methods, underrepresented studies on series production, and the need for research targeting the transferability of results from material to cell level. We discussed limitations of our study and suggested potential further research topics. View this paper
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18 pages, 431 KiB  
Article
Efficient Reallocation of BESS in Monopolar DC Networks for Annual Operating Costs Minimization: A Combinatorial-Convex Approach
by Luis Fernando Grisales-Noreña, Oscar Danilo Montoya and Jesús C. Hernández
Batteries 2023, 9(3), 190; https://doi.org/10.3390/batteries9030190 - 22 Mar 2023
Cited by 4 | Viewed by 2118
Abstract
This article deals with the solution of a mixed-integer nonlinear programming (MINLP) problem related to the efficient reallocation of battery energy storage systems (BESS) in monopolar direct current (DC) grids through a master–slave optimization approach. The master stage solves the integer nature of [...] Read more.
This article deals with the solution of a mixed-integer nonlinear programming (MINLP) problem related to the efficient reallocation of battery energy storage systems (BESS) in monopolar direct current (DC) grids through a master–slave optimization approach. The master stage solves the integer nature of the MINLP model, which is related to the nodes where the BESS will be located. In this stage, the discrete version of the vortex search algorithm is implemented. To determine the objective function value, a recursive convex approximation is implemented to solve the nonlinear component of the MINLP model (multi-period optimal power flow problem) in the slave stage. Two objective functions are considered performance indicators regarding the efficient reallocation of BESS in monopolar DC systems. The first objective function corresponds to the expected costs of the annual energy losses, and the second is associated with the annual expected energy generation costs. Numerical results for the DC version of the IEEE 33 bus grid confirm the effectiveness and robustness of the proposed master–slave optimization approach in comparison with the solution of the exact MINLP model in the General Algebraic Modeling System (GAMS) software. The proposed master–slave optimizer was programmed in the MATLAB software. The recursive convex solution of the multi-period optimal power flow problem was implemented in the convex discipline tool (CVX) with the SDPT3 and SEDUMI solvers. The numerical reductions achieved with respect to the benchmark case in terms of energy loss costs and energy purchasing costs were 7.2091% and 3.2105%, which surpassed the results reached by the GAMS software, with reductions of about 6.0316% and 1.5736%. Full article
(This article belongs to the Collection Advances in Battery Energy Storage and Applications)
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Figure 1

Figure 1
<p>Single-line diagram of the IEEE 33-node grid.</p>
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<p>Energy loss behavior for the benchmark case and the solution methods.</p>
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<p>Output generation in terminals of the substation bus.</p>
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25 pages, 10489 KiB  
Article
Investigation of the Electrochemical Behaviour of Al Current Collector Material Polarised Highly Anodically and Located in Butyltrimethylammonium Bis(trifluoromethylsulfonyl)imide Room-Temperature Ionic Liquid
by Jaanus Kruusma, Tanel Käämbre, Arvo Tõnisoo, Vambola Kisand, Karmen Lust and Enn Lust
Batteries 2023, 9(3), 189; https://doi.org/10.3390/batteries9030189 - 22 Mar 2023
Viewed by 1717
Abstract
The electrochemical behaviour of Al, used as a current collector in supercapacitors and in Li-ion and Na-ion electrochemical power sources, was investigated for the first time using the in situ soft X-ray photoelectron spectroscopy (XPS) method, collecting the information directly at the electrolyte-covered [...] Read more.
The electrochemical behaviour of Al, used as a current collector in supercapacitors and in Li-ion and Na-ion electrochemical power sources, was investigated for the first time using the in situ soft X-ray photoelectron spectroscopy (XPS) method, collecting the information directly at the electrolyte-covered Al current collector polarised electrochemically at high anodic potentials. Cyclic voltammetry, electrochemical impedance spectroscopy, and synchrotron in situ soft XPS methods were applied to collect physical and electrochemical information characterising the electrochemically polarised Al-current-collector RTIL interface soaked into the butyltrimethylammonium bis(trifluoromethylsulfonyl)imide (N4111(TFSI)) room-temperature ionic liquid. The obtained data show the start of intensive oxidation processes, including aluminium oxidation and the formation of an insoluble Al(TFSI)3 surface layer in N4111(TFSI) at E ≥ 3.0 V (vs. Ag-QRE). Very intensive electro-oxidation of TFSI anions at E ≥ 6.5 V (vs. Ag-QRE) has been observed. CV data indicate that the electrochemical oxidation of once-activated Al is possible in N4111(TFSI) at 1.1 V < E < 1.6 V (vs. Ag-QRE). Therefore, the oxidation of Al starts at E ≥ 2.05 V (vs. Ag-QRE) if the Al surface is modified with electro-oxidation products of TFSI anions. Full article
(This article belongs to the Special Issue Operando, In Situ and Ex Situ Studies of Battery Materials)
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Figure 1

Figure 1
<p>In situ XPS (i.e., core-electron binding energy, <span class="html-italic">BE</span>) data measured at variable Al-electrode potentials (marked in figures) for C 1s, tick mark labels 100 counts s<sup>−1</sup> (<b>a</b>); for N 1s, tick mark labels 20 counts s<sup>−1</sup> (<b>b</b>); for O 1s, tick mark labels 30 counts s<sup>−1</sup> (<b>c</b>); for F 1s, tick mark labels 40 counts s<sup>−1</sup> (<b>d</b>); and for S 2p, tick mark labels 400 counts s<sup>−1</sup> (<b>e</b>).</p>
Full article ">Figure 1 Cont.
<p>In situ XPS (i.e., core-electron binding energy, <span class="html-italic">BE</span>) data measured at variable Al-electrode potentials (marked in figures) for C 1s, tick mark labels 100 counts s<sup>−1</sup> (<b>a</b>); for N 1s, tick mark labels 20 counts s<sup>−1</sup> (<b>b</b>); for O 1s, tick mark labels 30 counts s<sup>−1</sup> (<b>c</b>); for F 1s, tick mark labels 40 counts s<sup>−1</sup> (<b>d</b>); and for S 2p, tick mark labels 400 counts s<sup>−1</sup> (<b>e</b>).</p>
Full article ">Figure 1 Cont.
<p>In situ XPS (i.e., core-electron binding energy, <span class="html-italic">BE</span>) data measured at variable Al-electrode potentials (marked in figures) for C 1s, tick mark labels 100 counts s<sup>−1</sup> (<b>a</b>); for N 1s, tick mark labels 20 counts s<sup>−1</sup> (<b>b</b>); for O 1s, tick mark labels 30 counts s<sup>−1</sup> (<b>c</b>); for F 1s, tick mark labels 40 counts s<sup>−1</sup> (<b>d</b>); and for S 2p, tick mark labels 400 counts s<sup>−1</sup> (<b>e</b>).</p>
Full article ">Figure 2
<p>The cyclic voltammogram (CV) measured within the potential range of 0.00 V to 1.00 V and reverse at the potential sweep rate of 1.00 mV s<sup>−1</sup>. The second CV sweep is presented; the potential sweep directions are indicated in the figures with arrows.</p>
Full article ">Figure 3
<p>The cyclic voltammograms (CV), shown in different <span class="html-italic">i</span> and <span class="html-italic">E</span> scales, measured within the potential range from 0.00 V to the final potentials (indicated in the figure) and reverse at the potential sweep rate of 1.00 mV s<sup>−1</sup> (<b>a</b>,<b>b</b>). The second CV sweeps are presented; the potential sweep directions are indicated in the figures with arrows. <span class="html-italic">I<sub>p</sub></span> vs. <span class="html-italic">v</span><sup>1/2</sup> relationship, for the electro-oxidation process at <span class="html-italic">E</span> = 7.8 V, is shown in (<b>b</b>).</p>
Full article ">Figure 3 Cont.
<p>The cyclic voltammograms (CV), shown in different <span class="html-italic">i</span> and <span class="html-italic">E</span> scales, measured within the potential range from 0.00 V to the final potentials (indicated in the figure) and reverse at the potential sweep rate of 1.00 mV s<sup>−1</sup> (<b>a</b>,<b>b</b>). The second CV sweeps are presented; the potential sweep directions are indicated in the figures with arrows. <span class="html-italic">I<sub>p</sub></span> vs. <span class="html-italic">v</span><sup>1/2</sup> relationship, for the electro-oxidation process at <span class="html-italic">E</span> = 7.8 V, is shown in (<b>b</b>).</p>
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<p>Nyquist plots, shown in various scales, for the N4111(TFSI)|Al/Al<sub>2</sub>O<sub>3</sub> system measured at fixed potentials noted in the figures (<b>a</b>–<b>c</b>).</p>
Full article ">Figure 4 Cont.
<p>Nyquist plots, shown in various scales, for the N4111(TFSI)|Al/Al<sub>2</sub>O<sub>3</sub> system measured at fixed potentials noted in the figures (<b>a</b>–<b>c</b>).</p>
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<p>Bode plots for the N4111(TFSI)|Al/Al<sub>2</sub>O<sub>3</sub> system measured at fixed Al-electrode potentials noted in the figures: log |<span class="html-italic">Z</span>″| vs. log <span class="html-italic">ν</span> (<b>a</b>); log |<span class="html-italic">Z</span>| vs. log <span class="html-italic">ν</span> (<b>b</b>), and −<span class="html-italic">δ</span> vs. log <span class="html-italic">ν</span> (<b>c</b>).</p>
Full article ">Figure 5 Cont.
<p>Bode plots for the N4111(TFSI)|Al/Al<sub>2</sub>O<sub>3</sub> system measured at fixed Al-electrode potentials noted in the figures: log |<span class="html-italic">Z</span>″| vs. log <span class="html-italic">ν</span> (<b>a</b>); log |<span class="html-italic">Z</span>| vs. log <span class="html-italic">ν</span> (<b>b</b>), and −<span class="html-italic">δ</span> vs. log <span class="html-italic">ν</span> (<b>c</b>).</p>
Full article ">Figure 6
<p>Series capacitance (<span class="html-italic">C<sub>s</sub></span>) vs. Al-electrode-potential (<span class="html-italic">E</span>) relationship at the electrochemical-impedance-spectroscopy modulation frequency of 0.10 Hz.</p>
Full article ">Figure 7
<p>Parallel capacitance (<span class="html-italic">C<sub>p</sub></span>) vs. Al-electrode potential (<span class="html-italic">E</span>) relationship at the electrochemical-impedance-spectroscopy modulation frequency 0.10 Hz.</p>
Full article ">Figure 8
<p>The ratio of parallel (<span class="html-italic">C<sub>p</sub></span>) and series (<span class="html-italic">C<sub>s</sub></span>) capacitance (<span class="html-italic">C<sub>p</sub></span> <span class="html-italic">C<sub>s</sub></span><sup>−1</sup>) vs. Al-electrode potential (<span class="html-italic">E</span>), applied; relationship at the electrochemical-impedance-spectroscopy modulation frequency of 0.10 Hz.</p>
Full article ">Figure 9
<p>Imaginary capacitance (<span class="html-italic">C</span>″) vs. potential (<span class="html-italic">E</span>) applied to the Al current collector. <span class="html-italic">C</span>″ values were calculated using impedance-spectroscopy data measured at the modulation frequency of 0.1 Hz.</p>
Full article ">Figure 10
<p>The series-resistivity (<span class="html-italic">R<sub>s</sub></span>) (<b>a</b>) and parallel-resistivity (<span class="html-italic">R<sub>p</sub></span>) (<b>b</b>) relationships vs. Al-electrode potential (<span class="html-italic">E</span>) applied.</p>
Full article ">Figure 11
<p>Mid-frequency relaxation time constant (log [t(rel, 1)]) vs. <span class="html-italic">E</span> (<b>a</b>) and low-frequency relaxation time constant (log [t(rel, 2)]) vs. <span class="html-italic">E</span> relationships (<b>b</b>).</p>
Full article ">Scheme 1
<p>The equivalent scheme (model) used to describe the electrochemical behaviour of the N4111(TFSI)|Al interface. <span class="html-italic">R<sub>s</sub></span>—series resistance, <span class="html-italic">R<sub>p</sub></span>—parallel resistance, and <span class="html-italic">CPE</span>—constant-phase element.</p>
Full article ">Scheme 2
<p>The equivalent circuit to describe the N4111(TFSI)|Al/Al<sub>2</sub>O<sub>3</sub> system at <span class="html-italic">E</span> = 7.00 V (<b>a</b>) and at <span class="html-italic">E</span> = 8.00 V (<b>b</b>).</p>
Full article ">
13 pages, 6058 KiB  
Article
Pulsed Current Constructs 3DM Cu/ZnO Current Collector Composite Anode for Free-Dendritic Lithium Metal Batteries
by Zhenkai Zhou, Qiang Chen, Yang Wang, Guangya Hou, Jianli Zhang and Yiping Tang
Batteries 2023, 9(3), 188; https://doi.org/10.3390/batteries9030188 - 22 Mar 2023
Cited by 5 | Viewed by 3003
Abstract
Although lithium metal is an ideal anode material for achieving high-energy-density lithium-based batteries, the uneven deposition/exfoliation process of lithium during cycling easily triggers the formation of lithium dendrites and dead lithium, which leads to a low Coulombic efficiency and safety issues. In this [...] Read more.
Although lithium metal is an ideal anode material for achieving high-energy-density lithium-based batteries, the uneven deposition/exfoliation process of lithium during cycling easily triggers the formation of lithium dendrites and dead lithium, which leads to a low Coulombic efficiency and safety issues. In this paper, a lithiophilic 3D copper mesh current collector is designed by using lithiophilic ZnO and pulsed current plating and is applied to a lithium metal battery composite anode. Under the action of the pulsed current field, the novel lithium metal composite anode battery achieved the homogeneous deposition of lithium ions. The lithium-to-copper half cells assembled with the 3DM Cu/ZnO current collector from the pulsed current deposition presented a Coulombic efficiency as high as 97.8% after 1 min of activation at 3 mA cm?2 follow by 10 cycles at a stripping current of 0.5 mA cm?2. Moreover, the symmetric cell could be stable for 1500 h at 1 mA cm?2 with a limited capacity of 1 mAh cm?2, and the assembled full cell (LiFePO4 as the cathode) maintained a Coulombic efficiency of about 90% for the 30th cycle at 1 C. This novel mechanism is an advanced strategy to improve cyclic stability and is crucial for designing stable lithium metal batteries. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Schematic diagram of (<bold>a</bold>) commercial copper mesh and (<bold>b</bold>) fabrication of 3DM Cu/ZnO. Optical photograph of (<bold>c</bold>) 3DM Cu and (<bold>d</bold>) 3DM Cu/ZnO. SEM images of (<bold>e</bold>,<bold>f</bold>) 3DM Cu and (<bold>h</bold>,<bold>i</bold>) 3DM Cu/ZnO. EDS mapping of (<bold>g</bold>) 3DM Cu and (<bold>j</bold>) 3DM Cu/ZnO.</p>
Full article ">Figure 2
<p>(<bold>a</bold>) The voltage distribution profiles of Li||Cu and Li||Cu/ZnO cells upon galvanostatic Li deposition. SEM images of (<bold>b</bold>–<bold>d</bold>) 3DM Bare Cu and (<bold>e</bold>–<bold>g</bold>) 3DM Cu/ZnO current collectors along 1 (stage Ⅰ), 3 (stage Ⅱ), and 6 mAh cm<sup>−2</sup> (stage Ⅲ) of lithium deposition. The insets show the photographs of 3DM Bare Cu and 3DM Cu/ZnO current collectors after 1, 3, and 6 mAh cm<sup>−2</sup> of lithium deposition.</p>
Full article ">Figure 3
<p>Schematic diagram of lithium plating on the (<bold>a</bold>) 3DM Bare Cu with normal Li deposition and (<bold>b</bold>) 3DM Cu/ZnO current collector with an applied pulsed current. Digit photo images of the Li deposit (<bold>c</bold>) 3DM Bare Cu with normal and (<bold>d</bold>) 3DM Cu/ZnO with an applied pulsed current after different deposition durations.</p>
Full article ">Figure 4
<p>The CE of Li-Cu half cells with activation: (<bold>a</bold>) normal deposition on 3DM Bare Cu, (<bold>b</bold>) normal deposition on 3DM Cu/ZnO, and (<bold>c</bold>) pulsed current deposition on Cu/ZnO. (<bold>d</bold>) The specific capacity and nucleation potential of the half cells discharge at a current density of 0.5 mA cm<sup>−2</sup>. SEM images of (<bold>e</bold>,<bold>f</bold>) Cu@Li-N anode and (<bold>g</bold>,<bold>h</bold>) Cu/ZnO@Li-P anode after 25th and 50th cycles of lithium stripping.</p>
Full article ">Figure 5
<p>(<bold>a</bold>) Rate performance of 3DM Cu@Li and Cu/ZnO@Li-P composite anode. (<bold>b</bold>) Corresponding voltage profiles from (<bold>a</bold>). (<bold>c</bold>) Cycle performance of 3DM Bare Cu@Li and Cu/ZnO@Li-P composite anode at 1 mA cm<sup>−2</sup> and 1 mAh cm<sup>−2</sup>. (<bold>d</bold>) Voltage profiles of the 100, 200, 300, and 400 h cycles from (<bold>c</bold>).</p>
Full article ">Figure 6
<p>(<bold>a</bold>) Rate capability of the full cell with 3DM Cu@Li and 3DM Cu/ZnO@Li-P composite anode at different rates ranging from 0.1 to 2 C. (<bold>b</bold>,<bold>c</bold>) Voltage profiles during the rate testing at different current density. (<bold>d</bold>) Cycling performance of full cells with 3DM Cu@Li and Cu/ZnO@Li-P composite anode at 1 C with three activation cycles at 0.1 C in the beginning. EIS curves of two current collectors on lithium-free anodes: (<bold>e</bold>) before cycling, (<bold>f</bold>) after 20 cycles at 1 C. SEM images of (<bold>g</bold>,<bold>h</bold>) 3DM Cu@Li composite anode and (<bold>j</bold>,<bold>k</bold>) 3DM Cu/ZnO@Li-P composite anode after 20 cycles lithium stripping. The SEM images of cathodes: (<bold>i</bold>) 3DM Cu@Li composite anode and (<bold>l</bold>) 3DM Cu/ZnO@Li-P composite anode full cells.</p>
Full article ">
29 pages, 47772 KiB  
Article
An Accurate Activate Screw Detection Method for Automatic Electric Vehicle Battery Disassembly
by Huaicheng Li, Hengwei Zhang, Yisheng Zhang, Shengmin Zhang, Yanlong Peng, Zhigang Wang, Huawei Song and Ming Chen
Batteries 2023, 9(3), 187; https://doi.org/10.3390/batteries9030187 - 21 Mar 2023
Cited by 13 | Viewed by 3770
Abstract
With the increasing popularity of electric vehicles, the number of end-of-life (EOF) electric vehicle batteries (EVBs) is also increasing day by day. Efficient dismantling and recycling of EVBs are essential to ensure environmental protection. There are many types of EVBs with complex structures, [...] Read more.
With the increasing popularity of electric vehicles, the number of end-of-life (EOF) electric vehicle batteries (EVBs) is also increasing day by day. Efficient dismantling and recycling of EVBs are essential to ensure environmental protection. There are many types of EVBs with complex structures, and the current automatic dismantling line is immature and lacks corresponding dismantling equipment. This makes it difficult for some small parts to be disassembled precisely. Screws are used extensively in batteries to fix or connect modules in EVBs. However, due to the small size of screws and differences in installation angles, screw detection is a very challenging task and a significant obstacle to automatic EVBs disassembly. This research proposes a systematic method to complete screw detection called “Active Screw Detection”. The experimental results show that with the YOLOX-s model, the improved YOLOX model achieves 95.92% and 92.14% accuracy for both mAP50 and mAP75 positioning after autonomous adjustment of the robotic arm attitude. Compared to the method without autonomous adjustment of the robotic arm, mAP50 and mAP75 improved by 62.81% and 57.67%, respectively. In addition, the improved YOLOX model improves mAP50 and mAP75 by 0.19% and 3.59%, respectively, compared to the original YOLOX model. Full article
(This article belongs to the Special Issue Recycling of Lithium-Ion Batteries: Current Status and Future Outlook)
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Figure 1

Figure 1
<p>A passive and compliant pneumatic torque actuator with vision sensing.</p>
Full article ">Figure 2
<p>Framework of the “Activate Screw Detection”.</p>
Full article ">Figure 3
<p>Pictures of screw taken by the robot arm from different angles. The red arrow represents the normal vector on the upper surface of the screw.</p>
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<p>Flowchart of the robot arm adaptive adjustment algorithm.</p>
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<p>Schematic diagram of the screw and background parts in the prediction box, where the gray part is the screw body area, and the orange part is the background area.</p>
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<p>Architecture of YOLOX model.</p>
Full article ">Figure 7
<p>IoU faces the case of a box contained in another box. Blue box is <math display="inline"><semantics> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math>, and the red box is <span class="html-italic">B</span>. (<b>a</b>–<b>c</b>) denote the different <span class="html-italic">B</span> contained in the same <math display="inline"><semantics> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math> inner where the area of <span class="html-italic">B</span> is constant. The IoU is the same in all three cases.</p>
Full article ">Figure 8
<p>Demo diagram of GIoU calculations. <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>∪</mo> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </mrow> </semantics></math> is the green part, <span class="html-italic">C</span> is the outermost rectangular part, and <math display="inline"><semantics> <mrow> <mi>C</mi> <mo>−</mo> <mo>(</mo> <mi>B</mi> <mo>∪</mo> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> <mo>)</mo> </mrow> </semantics></math> is the orange part.</p>
Full article ">Figure 9
<p>GIoU faces the case of a box contained in another box. (<b>a</b>–<b>c</b>) denote the different <span class="html-italic">B</span> contained in the same <math display="inline"><semantics> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math> inner where the area of <span class="html-italic">B</span> remains unchanged. Both <math display="inline"><semantics> <mrow> <mi>B</mi> <mo>∪</mo> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </mrow> </semantics></math> (the green part) and <span class="html-italic">C</span> (the orange part) are the same, resulting in the same value for GIoU.</p>
Full article ">Figure 10
<p>Calculation of DIoU. <math display="inline"><semantics> <msup> <mi>b</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math> is the centroid of the ground-truth box, <span class="html-italic">b</span> is the centroid of the prediction box, the green line is <math display="inline"><semantics> <mrow> <mi>ρ</mi> <mo>(</mo> <mi>b</mi> <mo>,</mo> <msup> <mi>b</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> <mo>)</mo> </mrow> </semantics></math>, and the black line is <span class="html-italic">c</span>.</p>
Full article ">Figure 11
<p>DIoU faces the case where one box is contained in another and the centroids of the two boxes coincide. (<b>a</b>–<b>c</b>) denote the cases where different <span class="html-italic">B</span> coincide with the centroid of the same <math display="inline"><semantics> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math> and <span class="html-italic">B</span> is contained inside <math display="inline"><semantics> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math>. Leads to the same result for DIoU.</p>
Full article ">Figure 12
<p>The case of box-to-box symmetry. <span class="html-italic">B</span> at each of the three positions is symmetrical about the midline of <math display="inline"><semantics> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math>, then the value of each version of IoU calculated from <span class="html-italic">B</span> and <math display="inline"><semantics> <msup> <mi>B</mi> <mrow> <mi>g</mi> <mi>t</mi> </mrow> </msup> </semantics></math> is the same.</p>
Full article ">Figure 13
<p>The ground-truth and prediction boxes are transferred from the Cartesian coordinate system (<b>left</b>) to the polar coordinate system (<b>right</b>).</p>
Full article ">Figure 14
<p>RGB images (<b>a</b>) and depth images (<b>b</b>) captured by the vision sensor in the initial state.</p>
Full article ">Figure 15
<p>Results of the initial positioning of the screws.</p>
Full article ">Figure 16
<p>OTSU-based binary thresholding process: (<b>a</b>) RGB original image after relaxed crop out; (<b>b</b>) binary image after OTSU-based binary thresholding; (<b>c</b>) binary image after convex wrapping of what is considered to be the screw part (white area), and the black part is considered to be the background.</p>
Full article ">Figure 17
<p>RGB image (<b>a</b>) and depth image (<b>b</b>) captured by the vision sensor after adaptive pose and position adjustment.</p>
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<p>Screw positioning results based on images collected after attitude correction by robotic arm.</p>
Full article ">Figure 19
<p>Change of loss value during training.</p>
Full article ">Figure 20
<p>Effect of the original YOLOX (<b>a</b>) and the improved YOLOX (<b>b</b>) on screw detection tasks.</p>
Full article ">Figure 20 Cont.
<p>Effect of the original YOLOX (<b>a</b>) and the improved YOLOX (<b>b</b>) on screw detection tasks.</p>
Full article ">
49 pages, 15657 KiB  
Review
All-Solid-State Thin Film Li-Ion Batteries: New Challenges, New Materials, and New Designs
by Baolin Wu, Chunguang Chen, Dmitri L. Danilov, Rüdiger-A. Eichel and Peter H. L. Notten
Batteries 2023, 9(3), 186; https://doi.org/10.3390/batteries9030186 - 21 Mar 2023
Cited by 24 | Viewed by 12882
Abstract
All-solid-state batteries (ASSBs) are among the remarkable next-generation energy storage technologies for a broad range of applications, including (implantable) medical devices, portable electronic devices, (hybrid) electric vehicles, and even large-scale grid storage. All-solid-state thin film Li-ion batteries (TFLIBs) with an extended cycle life, [...] Read more.
All-solid-state batteries (ASSBs) are among the remarkable next-generation energy storage technologies for a broad range of applications, including (implantable) medical devices, portable electronic devices, (hybrid) electric vehicles, and even large-scale grid storage. All-solid-state thin film Li-ion batteries (TFLIBs) with an extended cycle life, broad temperature operation range, and minimal self-discharge rate are superior to bulk-type ASSBs and have attracted considerable attention. Compared with conventional batteries, stacking dense thin films reduces the Li-ion diffusion length, thereby improving the rate capability. It is vital to develop TFLIBs with higher energy density and stability. However, multiple challenges, such as interfacial instability, low volumetric energy density, and high manufacturing cost, still hinder the widespread application of TFLIBs. At present, many approaches, such as materials optimization and novel architecture design, have been explored to enhance the stability and energy density of TFLIBs. An overview of these discoveries and developments in TFLIBs is presented in this review, together with new insights into the intrinsic mechanisms of operation; this is of great value to the batteries research community and facilitates further improvements in batteries in the near future. Full article
(This article belongs to the Special Issue Advancements towards Practical All-Solid-State Batteries)
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<p>Schematic view of the development of battery materials for TFLIBs: (<b>a</b>) Anode materials, (<b>b</b>) cathode materials, (<b>c</b>) electrolytes, and (<b>d</b>) current collectors.</p>
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<p>Schematic diagram of TFLIBs and electrode/SSEs interface issues [<a href="#B21-batteries-09-00186" class="html-bibr">21</a>].</p>
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<p>(<b>a</b>) Schematic diagram of graphite film||Li cells; (<b>b</b>) Electrode energy density change with increasing area capacity of LiCoO<sub>2</sub>||Csi full-cells with different current collectors [<a href="#B83-batteries-09-00186" class="html-bibr">83</a>] (<b>c</b>) Scanning electron microscopy images of a dendritic copper current collector [<a href="#B84-batteries-09-00186" class="html-bibr">84</a>]. (<b>d</b>) Top and side views of VA-CuO-Cu current collectors [<a href="#B77-batteries-09-00186" class="html-bibr">77</a>].</p>
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<p>Scheme of the dilemma of Li-metal anodes (<b>a</b>) [<a href="#B90-batteries-09-00186" class="html-bibr">90</a>]; and Si thin film anodes (<b>b</b>) in rechargeable batteries [<a href="#B95-batteries-09-00186" class="html-bibr">95</a>].</p>
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<p>(<b>a</b>) Representative strategies for volume-change-accommodating Si electrode [<a href="#B117-batteries-09-00186" class="html-bibr">117</a>], (<b>b</b>) SEM photographs of patterned Si film electrodes [<a href="#B118-batteries-09-00186" class="html-bibr">118</a>], (<b>c</b>) SEM top and tilted view photographs of as-prepared (top) and fully lithiated silicon honeycomb structure (bottom). (<b>d</b>) Morphological changes of the Si honeycomb structure as a function of Li-content [<a href="#B119-batteries-09-00186" class="html-bibr">119</a>].</p>
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<p>Schematic representation of the crystal structure of various cathode materials: (<b>a</b>) layered, (<b>b</b>) spinel, (<b>c</b>) NASICON, (<b>d</b>) Olivine, and (<b>e</b>) Tavorite [<a href="#B188-batteries-09-00186" class="html-bibr">188</a>].</p>
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<p>Schematic illustration of the crystal structure of electrolytes: (<b>a</b>) Perovskite; (<b>b</b>) NASICON [<a href="#B239-batteries-09-00186" class="html-bibr">239</a>]; (<b>c</b>) LISICON; (<b>d</b>) Thio-LISICON; (<b>e</b>) Garnets [<a href="#B240-batteries-09-00186" class="html-bibr">240</a>]; (<b>f</b>) Sufides [<a href="#B241-batteries-09-00186" class="html-bibr">241</a>].</p>
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<p>Scheme of different batteries architectures. (<b>a</b>) Planar TFLIBs; (<b>b</b>) Bipolar LIBs [<a href="#B293-batteries-09-00186" class="html-bibr">293</a>]; (<b>c</b>) Anode-free TFLIBs; (<b>d</b>) 3D TFLIBs [<a href="#B112-batteries-09-00186" class="html-bibr">112</a>]; (<b>e</b>) Flexible and (<b>f</b>) stretchable LIBs [<a href="#B294-batteries-09-00186" class="html-bibr">294</a>].</p>
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<p>(<b>a</b>) Structure of a triple-layered bipolar stacked all-solid-state LIBs; (<b>b</b>) 1st and 100th charge-discharge profiles (left) and cycling properties of double-layered all-solid-state LIBs at 35 °C at 0.1 C (red), 0.2 C (green) and 0.5 C (blue) rates (right) [<a href="#B300-batteries-09-00186" class="html-bibr">300</a>]; (<b>c</b>) Schematic process for the preparation of bipolar batteries (<b>d</b>) Discharge curves of a 12 V bipolar batteries at high temperatures [<a href="#B301-batteries-09-00186" class="html-bibr">301</a>].</p>
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<p>3D structures. (<b>a</b>) SEM image of aluminum nanorods directly grown on Al substrate [<a href="#B312-batteries-09-00186" class="html-bibr">312</a>]; (<b>b</b>) 3D VO<sub>x</sub> film on Si pillars [<a href="#B313-batteries-09-00186" class="html-bibr">313</a>]; (<b>c</b>) SEM images of Ni/LMO on the TiN-coated Si microstructured substrate [<a href="#B314-batteries-09-00186" class="html-bibr">314</a>]; (<b>d</b>) 3D schematic illustration of the as-fabricated check-patterned Cu foil (left) and SEM images of Si electrodes on patterned Cu foil (right) [<a href="#B316-batteries-09-00186" class="html-bibr">316</a>]; (<b>e</b>) Layer interfaces EDX-STEM elemental map of the stacked layers consisted of Si-3D/Al<sub>2</sub>O<sub>3</sub>/Pt/TiO<sub>2</sub>/Li<sub>3</sub>PO<sub>4</sub>/SiO<sub>2</sub>-Li<sub>3</sub>PO<sub>4</sub> layer [<a href="#B317-batteries-09-00186" class="html-bibr">317</a>]; (<b>f</b>) Batteries testing through contact with the top electrode and cathode current collector layers (left), cross-sectional TEM image and overview of all-ALD solid-state batteries with 40 nm Ru/70 nm LiV<sub>2</sub>O<sub>5</sub>/50 nm Li<sub>2</sub>PO<sub>2</sub>N/10 nm SnN<sub>x</sub>/25 nm TiN (right) [<a href="#B192-batteries-09-00186" class="html-bibr">192</a>].</p>
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31 pages, 5611 KiB  
Review
Survey on Battery Technologies and Modeling Methods for Electric Vehicles
by Mehroze Iqbal, Amel Benmouna, Mohamed Becherif and Saad Mekhilef
Batteries 2023, 9(3), 185; https://doi.org/10.3390/batteries9030185 - 20 Mar 2023
Cited by 12 | Viewed by 7245
Abstract
The systematic transition of conventional automobiles to their electrified counterparts is an imperative step toward successful decarbonization. Crucial advances in battery storage systems (BSS) and related technologies will enable this transition to proceed smoothly. This requires equivalent developments in several interconnected areas, such [...] Read more.
The systematic transition of conventional automobiles to their electrified counterparts is an imperative step toward successful decarbonization. Crucial advances in battery storage systems (BSS) and related technologies will enable this transition to proceed smoothly. This requires equivalent developments in several interconnected areas, such as complete battery cycles and battery management systems (BMS). In this context, this article critically examines state-of-the-art battery technologies from the perspective of automakers, provides insightful discussions, and poses open questions with possible answers. The generations of BSS (traditional, current, and futuristic) are first reviewed and analyzed via two distinct qualitative factors (DQFs): key design markers and performance indicators. Based on the introduced DQFs, major development trends and probable evolutions are forecasted. Thereafter, recent modeling and state estimation methods are comprehensively reviewed in relation to high-performance BMS. Accordingly, promising modeling methods are identified as futuristic solutions, leading to an accurate and timely decision for reliable and safer user experience. This article is concluded by presenting a techno-economic assessment of what to expect, as well as highlighting future challenges and opportunities for industry, academia, and policy makers. Full article
(This article belongs to the Special Issue Feature Papers to Celebrate the First Impact Factor of Batteries)
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<p>GHG emissions contributions<sup>+</sup> (2020). (<b>a</b>) Country-wise. (<b>b</b>) Sector-wise. <sup>+</sup>Data collected from Rhodium Group official webpage [<a href="#B1-batteries-09-00185" class="html-bibr">1</a>].</p>
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<p>The specific power/energy of electrochemical sources.</p>
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<p>Powertrain configurations of electrified automobiles. (<b>a</b>) r-HEV (Nissan e-Power, etc.). (<b>b</b>) BEV (Renault Zoe, etc.). (<b>c</b>) GHEV (Toyota Prius, etc.). (<b>d</b>) FCHEV (Toyota Mirai, etc.). Legend: S-Ac (semi/active), FC (fuel cell), B (battery), SC (supercap), b12 (converters).</p>
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<p>Powertrain configurations of electrified automobiles. (<b>a</b>) r-HEV (Nissan e-Power, etc.). (<b>b</b>) BEV (Renault Zoe, etc.). (<b>c</b>) GHEV (Toyota Prius, etc.). (<b>d</b>) FCHEV (Toyota Mirai, etc.). Legend: S-Ac (semi/active), FC (fuel cell), B (battery), SC (supercap), b12 (converters).</p>
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<p>Contents and contributions of this article in a nutshell.</p>
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<p>The complete battery cycle from the perspective of automakers.</p>
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<p>Generational evolution of batteries for EV applications.</p>
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<p>Composition of battery cell (traditional generation). (<b>a</b>) Lead-acid cell. (<b>b</b>) Ni-MH cell.</p>
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<p>Composition of battery cell from current ((<b>a</b>): Li-ion LFP type cell) and future generation ((<b>b</b>): Na-S cell).</p>
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<p>Composition of battery cell (future generation). (<b>a</b>) Zn-air cell. (<b>b</b>) Li-S cell.</p>
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<p>Conceptual illustration of battery management and power control unit.</p>
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<p>Graphical illustration of battery modeling techniques. (<b>a</b>) Electrical equivalent (basic, extended, and generalized). (<b>b</b>) Electrochemical. (<b>c</b>) Data-driven (neural network).</p>
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<p>Generation-wise comparison among batteries for EV applications. (<b>a</b>) Key performance indicators. (<b>b</b>) Key design markers.</p>
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23 pages, 1866 KiB  
Review
Electrode Fabrication Techniques for Li Ion Based Energy Storage System: A Review
by Veena Singh, Sudhanshu Kuthe and Natalia V. Skorodumova
Batteries 2023, 9(3), 184; https://doi.org/10.3390/batteries9030184 - 20 Mar 2023
Cited by 11 | Viewed by 5686
Abstract
Development of reliable energy storage technologies is the key for the consistent energy supply based on alternate energy sources. Among energy storage systems, the electrochemical storage devices are the most robust. Consistent energy storage systems such as lithium ion (Li ion) based energy [...] Read more.
Development of reliable energy storage technologies is the key for the consistent energy supply based on alternate energy sources. Among energy storage systems, the electrochemical storage devices are the most robust. Consistent energy storage systems such as lithium ion (Li ion) based energy storage has become an ultimate system utilized for both domestic and industrial scales due to its advantages over the other energy storage systems. Considering the factors related to Li ion-based energy storage system, in the present review, we discuss various electrode fabrication techniques including electrodeposition, chemical vapor deposition (CVD), stereolithography, pressing, roll to roll, dip coating, doctor blade, drop casting, nanorod growing, brush coating, stamping, inkjet printing (IJP), fused deposition modelling (FDM) and direct ink writing (DIW). Additionally, we analyze the statistics of publications on these fabrication techniques and outline challenges and future prospects for the Li ion battery market. Full article
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<p>Classification of energy storage system (ESS) [<a href="#B8-batteries-09-00184" class="html-bibr">8</a>,<a href="#B9-batteries-09-00184" class="html-bibr">9</a>,<a href="#B13-batteries-09-00184" class="html-bibr">13</a>,<a href="#B14-batteries-09-00184" class="html-bibr">14</a>].</p>
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<p>Advantages of Li ion-based energy storage system [<a href="#B23-batteries-09-00184" class="html-bibr">23</a>,<a href="#B24-batteries-09-00184" class="html-bibr">24</a>,<a href="#B25-batteries-09-00184" class="html-bibr">25</a>].</p>
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<p>Schematic illustration of Li ion-based energy storage system.</p>
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<p>Electrode fabrication techniques for Li ion-based energy storage system.</p>
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<p>The number of research articles vs. years for different electrode fabrication techniques [<a href="#B111-batteries-09-00184" class="html-bibr">111</a>].</p>
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13 pages, 5822 KiB  
Article
Investigation on the Air Stability of P2-Layered Transition Metal Oxides by Nb Doping in Sodium Ion Batteries
by Yanyan Chen, Qinhao Shi, Shengyu Zhao, Wuliang Feng, Yang Liu, Xinxin Yang, Zhenwei Wang and Yufeng Zhao
Batteries 2023, 9(3), 183; https://doi.org/10.3390/batteries9030183 - 20 Mar 2023
Cited by 5 | Viewed by 2801
Abstract
Sodium-ion batteries are regarded as a substitution for lithium-ion batteries for its abundant resources, wide distribution, low cost, etc. The P2-layered sodium transition metal oxides (P2-NaxTMO2) have attracted extensive attention due to their high rate and cycling properties. However, [...] Read more.
Sodium-ion batteries are regarded as a substitution for lithium-ion batteries for its abundant resources, wide distribution, low cost, etc. The P2-layered sodium transition metal oxides (P2-NaxTMO2) have attracted extensive attention due to their high rate and cycling properties. However, P2-NaxTMO2 often undergoes structural transformations when exposed in ambient air, which restricts its practical applications. Herein we studied the effect of Nb doping on the air stability of P2-NaxTMO2. We demonstrated that the Nb-induced surface preconstructed layer inhibited the surface dissolution of the P2 material in the electrochemical reaction and formed a stable and thin (cathode–electrolyte interphase) CEI film, which prevented water molecules from entering the P2-NaxTMO2 lattice. Na0.67Mn0.67Ni0.33Nb0.03O2 could exhibit superior rate performance (a reversible capacity of 72.5 mAh g−1 at 20 C) and outstanding cycling performance (84.43% capacity retention after 1000 cycles at 5 C) in a half cell after exposed in a moisture atmosphere (RH93%) for 20 days. Full article
(This article belongs to the Special Issue High-Performance Materials for Sodium-Ion Batteries)
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<p>Rietveld refinement plots of (<bold>a</bold>) P2-Na<sub>0.67</sub>MN and (<bold>b</bold>) P2-Na<sub>0.67</sub>MNNb. (<bold>c</bold>) The crystal structures of P2-Na<sub>0.67</sub>MN and P2-Na<sub>0.67</sub>MNNb.</p>
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<p>The XRD patterns of (<bold>a</bold>) P2-Na<sub>0.67</sub>MN and (<bold>b</bold>) P2-Na<sub>0.67</sub>MNNb samples exposed in different atmospheres after 1 day of exposure. The XRD patterns of (<bold>c</bold>) P2-Na<sub>0.67</sub>MN and (<bold>d</bold>) P2-Na<sub>0.67</sub>MNNb samples exposed in different atmospheres after 8 days of exposure.</p>
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<p>The SEM images of (<bold>a</bold>) pristine P2-Na<sub>0.67</sub>MN and (<bold>b</bold>,<bold>c</bold>) P2-Na<sub>0.67</sub>MN samples exposed in RH93% humid environment after different days of exposure. The SEM images of (<bold>d</bold>,<bold>g</bold>) pristine P2-Na<sub>0.67</sub>MNNb. (<bold>e</bold>,<bold>h</bold>) P2-Na<sub>0.67</sub>MNNb samples exposed in RH93% humid environment after 8 days of exposure. (<bold>f</bold>,<bold>i</bold>) P2-Na<sub>0.67</sub>MNNb samples exposed in RH93% humid environment after 20 days of exposure.</p>
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<p>(<bold>a</bold>,<bold>b</bold>) The TEM and (<bold>c</bold>) the HRTEM of P2-Na<sub>0.67</sub>MNNb. Enlarged images of the (<bold>d</bold>) red and (<bold>e</bold>) purple boxes in (<bold>c</bold>). (<bold>f</bold>) SAED pattern of P2-Na<sub>0.67</sub>MNNb (the SAED pattern was selected from the blue box in (<bold>b</bold>)). (<bold>g</bold>) HAADF-EDS elemental mappings of P2-Na<sub>0.67</sub>MNNb (including Na-K edge, Mn-K edge, Ni-K edge and Nb-L edge).</p>
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<p>(<bold>a</bold>,<bold>b</bold>) Galvanostatic charge/discharge voltage profiles of the pristine P2-Na<sub>0.67</sub>MNNb and exposed P2-Na<sub>0.67</sub>MNNb at the first three cycles in the voltage range of 2–4 V, respectively (exposed P2-Na<sub>0.67</sub>MNNb refers to exposing pristine P2-Na<sub>0.67</sub>MNNb to RH93% humid environment for 20 days). (<bold>c</bold>) Rate capability of pristine P2-Na<sub>0.67</sub>MNNb and exposed P2-Na<sub>0.67</sub>MNNb. (<bold>d</bold>) Rate capability of pristine P2-Na<sub>0.67</sub>MN and exposed P2-Na<sub>0.67</sub>MN. Charge and discharge curves of (<bold>e</bold>) pristine P2-Na<sub>0.67</sub>MN and (<bold>f</bold>) exposed P2-Na<sub>0.67</sub>MN at different current densities. (<bold>g</bold>) Electrochemical cycling performance of pristine P2-Na<sub>0.67</sub>MN, exposed P2-Na<sub>0.67</sub>MN, pristine P2-Na<sub>0.67</sub>MNNb and exposed P2-Na<sub>0.67</sub>MNNb at a rate of 5 C for 1000 cycles. (<bold>h</bold>) Nyquist plot of the coin cells that based on pristine P2-Na<sub>0.67</sub>MN, exposed P2-Na<sub>0.67</sub>MN, pristine P2-Na<sub>0.67</sub>MNNb and exposed P2-Na<sub>0.67</sub>MNNb electrode materials after 1000 cycles at a rate of 5 C.</p>
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<p>The HRTEM images of (<bold>a</bold>) P2-Na<sub>0.67</sub>MNNb and (<bold>b</bold>) P2-Na<sub>0.67</sub>MN after 500 cycles at 1 C and 25 °C in coin cells.</p>
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19 pages, 7127 KiB  
Article
In Situ Metal Organic Framework (ZIF-8) and Mechanofusion-Assisted MWCNT Coating of LiFePO4/C Composite Material for Lithium-Ion Batteries
by Priyatrisha Mathur, Jeng-Ywan Shih, Ying-Jeng James Li, Tai-Feng Hung, Balamurugan Thirumalraj, Sayee Kannan Ramaraj, Rajan Jose, Chelladurai Karuppiah and Chun-Chen Yang
Batteries 2023, 9(3), 182; https://doi.org/10.3390/batteries9030182 - 20 Mar 2023
Cited by 11 | Viewed by 3344
Abstract
LiFePO4 is one of the industrial, scalable cathode materials in lithium-ion battery production, due to its cost-effectiveness and environmental friendliness. However, the electrochemical performance of LiFePO4 in high current rate operation is still limited, due to its poor ionic- and electron-conductive [...] Read more.
LiFePO4 is one of the industrial, scalable cathode materials in lithium-ion battery production, due to its cost-effectiveness and environmental friendliness. However, the electrochemical performance of LiFePO4 in high current rate operation is still limited, due to its poor ionic- and electron-conductive properties. In this study, a zeolitic imidazolate framework (ZIF-8) and multiwalled carbon nanotubes (MWCNT) modified LiFePO4/C (LFP) composite cathode materials were developed and investigated in detail. The ZIF-8 and MWCNT can be used as ionic- and electron-conductive materials, respectively. The surface modification of LFP by ZIF-8 and MWCNT was carried out through in situ wet chemical and mechanical alloy coating. The as-synthesized materials were scrutinized via various characterization methods, such as XRD, SEM, EDX, etc., to determine the material microstructure, morphology, phase, chemical composition, etc. The uniform and stable spherical morphology of LFP composites was obtained when the ZIF-8 coating was processed by the agitator [A], instead of the magnetic stirrer [MS], condition. It was found that the (optimum of) 2 wt.% ZIF-8@LFP [A]/MWCNT composite cathode material exhibited outstanding improvement in high-rate performance; it maintained the discharge capacities of 125 mAh g−1 at 1C, 110 mAh g−1 at 3C, 103 mAh g−1 at 5C, and 91 mAh g−1 at 10C. Better cycling stability with capacity retention of 75.82% at 1C for 100 cycles, as compared to other electrodes prepared in this study, was also revealed. These excellent results were mainly obtained because of the improvement of lithium-ion transport properties, less polarization effect, and interfacial impedance of the LFP composite cathode materials derived from the synergistic effect of both ZIF-8 and MWCNT coating materials. Full article
(This article belongs to the Special Issue High Energy Lithium-Ion Batteries)
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<p>SEM images of the (<b>A</b>) bare LFP, (<b>B</b>) 2 wt.% ZIF-8@LFP [MS], (<b>C</b>,<b>D</b>) 2 wt.% ZIF-8@LFP [A], and (<b>E</b>,<b>F</b>) 2 wt.% ZIF-8@LFP [A]/MWCNT samples.</p>
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<p>(<b>A</b>) EDX spectral analysis of the 2 wt.% ZIF-8@LFP [A]/MWCNT composite. (<b>B</b>) EDX mapping results of the 2 wt.% ZIF-8@LFP [A]/MWCNT sample and its corresponding elements of (<b>C</b>) iron, (<b>D</b>) phosphorus, (<b>E</b>) oxygen, (<b>F</b>) zinc, (<b>G</b>) nitrogen, and (<b>H</b>) carbon.</p>
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<p>(<b>A</b>) XRD patterns of the pure ZIF-8 (a), bare LFP (b), 2 wt.% ZIF-8@LFP [A] (c), and 2 wt.% ZIF-8@LFP [A]/MWCNT (d) samples. (<b>B</b>) Micro-Raman spectra of the pure ZIF-8 (a), bare LFP (b), 2 wt.% ZIF-8@LFP [A] (c), and 2 wt.% ZIF-8@LFP [A]/MWCNT (d) samples.</p>
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<p>(<b>A</b>) Full survey spectra of the bare LFP and LFP composite (2% ZIF-8@LFP [A]/MWCNT). The deconvolution spectra of (<b>B</b>) C 1s, (<b>C</b>) N 1s, and (<b>D</b>) Zn 2p peaks of the bare LFP and LFP composite (2% ZIF-8@LFP [A]/MWCNT).</p>
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<p>(<b>A</b>) First charge–discharge cycles of the bare LFP (a), 2 wt.% ZIF-8@LFP [MS], 2 wt.% ZIF-8@LFP [A], and 2 wt.% ZIF-8@LFP [A]/MWCNT electrodes at 0.1C/0.1C. (<b>B</b>) The estimation of voltage polarization from the magnified view at the selected area of <a href="#batteries-09-00182-f004" class="html-fig">Figure 4</a>A.</p>
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<p>Cycle stability curves of the (a) bare LFP, (b) 2 wt.% ZIF-8@LFP [MS], (c) 2 wt.% ZIF-8@LFP [A] and (d) 2 wt.% ZIF-8@LFP [A]/MWCNT electrodes at 0.1C/0.1C rate for 30 cycles (<b>A</b>), and at 1C/1C rate for 100 cycles (<b>B</b>).</p>
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<p>Charge–discharge curves of (<b>A</b>) bare LFP, (<b>B</b>) 2 wt.% ZIF-8@LFP [MS], (<b>C</b>) 2 wt.% ZIF-8@LFP [A], and (<b>D</b>) 2 wt.% ZIF-8@LFP [A]/MWCNT electrodes at 1C/1C rate for 100 cycles.</p>
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<p>(<b>A</b>) High-rate profiles of the (a) bare LFP, (b) 2 wt.% ZIF-8@LFP [MS], (c) 2 wt.% ZIF-8@LFP [A], and (d) 2 wt.% ZIF-8@LFP [A]/MWCNT electrodes at various current rates of 0.2C, 0.5C, 1C, 3C, 5C, and 10C. (<b>B</b>) Nyquist plots of the (a) bare LFP, (b) 2 wt.% ZIF-8@LFP [MS], (c) 2 wt.% ZIF-8@LFP [A], and (d) 2 wt.% ZIF-8@LFP [A]/MWCNT electrodes after high-rate (<span class="html-italic">ca.</span> 0.2C–10C). (<b>C</b>) The magnified view of <a href="#batteries-09-00182-f004" class="html-fig">Figure 4</a>B. (<b>D</b>) The linear dependence of Z’ versus w<sup>−1/2</sup> for after high rate (<span class="html-italic">ca.</span> 0.2C–10C) cycles.</p>
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23 pages, 10317 KiB  
Article
A Data-Driven LiFePO4 Battery Capacity Estimation Method Based on Cloud Charging Data from Electric Vehicles
by Xingyu Zhou, Xuebing Han, Yanan Wang, Languang Lu and Minggao Ouyang
Batteries 2023, 9(3), 181; https://doi.org/10.3390/batteries9030181 - 20 Mar 2023
Cited by 14 | Viewed by 4776
Abstract
The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are [...] Read more.
The accuracy of capacity estimation is of great importance to the safe, efficient, and reliable operation of battery systems. In recent years, data-driven methods have emerged as promising alternatives to capacity estimation due to higher estimation accuracy. Despite significant progress, data-driven methods are mainly developed by experimental data under well-controlled charge–discharge processes, which are seldom available for practical battery health monitoring under realistic conditions due to uncertainties in environmental and operational conditions. In this paper, a novel method to estimate the capacity of large-format LiFePO4 batteries based on real data from electric vehicles is proposed. A comprehensive dataset consisting of 85 vehicles that has been running for around one year under diverse nominal conditions derived from a cloud platform is generated. A classification and aggregation capacity prediction method is developed, combining a battery aging experiment with big data analysis on cloud data. Based on degradation mechanisms, IC curve features are extracted, and a linear regression model is established to realize high-precision estimation for slow-charging data with constant-current charging. The selected features are highly correlated with capacity (Pearson correlation coefficient < 0.85 for all vehicles), and the MSE of the capacity estimation results is less than 1 Ah. On the basis of protocol analysis and mechanism studies, a feature set including internal resistance, temperature, and statistical characteristics of the voltage curve is constructed, and a neural network (NN) model is established for multi-stage variable-current fast-charging data. Finally, the above two models are integrated to achieve capacity prediction under complex and changeable realistic working conditions, and the relative error of the capacity estimation method is less than 0.8%. An aging experiment using the battery, which is the same as those equipped in the vehicles in the dataset, is carried out to verify the methods. To the best of the authors’ knowledge, our study is the first to verify a capacity estimation model derived from field data using an aging experiment of the same type of battery. Full article
(This article belongs to the Special Issue Battery Energy Storage in Advanced Power Systems)
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<p>A cloud-based framework for battery capacity estimation in EV applications.</p>
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<p>The histogram distribution of several features collected and calculated based on the vehicle data: (<b>a</b>) Cycle number distribution; (<b>b</b>) Total ampere-hour throughput distribution; (<b>c</b>) Operation time distribution; (<b>d</b>) Start charging voltage distribution of all the 25,031 charging processes; (<b>e</b>) End charging voltage distribution of all the charging processes; (<b>f</b>) Charging capacity distribution of all the charging processes.</p>
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<p>A typical example of a slow-charging protocol. Current, voltage, and temperature versus timestamp is given. The current is approximately constant, with a magnitude of 10.5 A, and temperature either rises slowly or fluctuates in a small range.</p>
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<p>A typical piece of data sample, i.e., raw data in the dataset. Some data quality problems including NaN, data discontinuity, and data mismatch according to timestamps appear in the raw data.</p>
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<p>The flowchart of the data preprocessing process.</p>
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<p>Voltage curve and IC curve of the fresh cell, derived from the constant-current charging process with the use of cycling devices in the laboratory, with a current of 10.5 A. There are three obvious peaks in the IC curve, which correspond to the phase transformation processes of the graphite anode. (<b>a</b>) Voltage curve of the fresh cell; (<b>b</b>) IC curve of the fresh cell.</p>
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<p>The specific definition and acquisition method of Peak 1. The IC curve is derived from a stochastically selected charging data piece from the real vehicle dataset. The whole peak should be in the voltage range from 3.34 V to 3.4 V. The peak point corresponds to the point with the highest IC value in the voltage range from 3.34 V to 3.4 V.</p>
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<p>Health indicator estimation results based on total ampere-hour throughput of six sample EVs. (<b>a</b>) vin8. (<b>b</b>) vin29. (<b>c</b>) vin35. (<b>d</b>) vin36. (<b>e</b>) vin49. (<b>f</b>) vin56.</p>
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<p>Health indicator estimation absolute error based on IC features of six sample EVs. (<b>a</b>) vin8. (<b>b</b>) vin29. (<b>c</b>) vin35. (<b>d</b>) vin36. (<b>e</b>) vin49. (<b>f</b>) vin56.</p>
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<p>The results of battery aging experiment: (<b>a</b>) Capacity retention plotted as a function of cycle number; (<b>b</b>) Capacity retention plotted as a function of total ampere-hour throughput; (<b>c</b>) Evolution of IC curve in intervals of 100 cycles for cycle aging in between, where the inset shows a detailed view of Peak 1; (<b>d</b>) Degradation of the envelope area of Peak 1 as a function of total ampere-hour throughput. The linear relation acquired from real vehicle data is verified.</p>
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<p>Capacity estimation results based on total ampere-hour throughput of six sample EVs. (<b>a</b>) vin8. (<b>b</b>) vin29. (<b>c</b>) vin35. (<b>d</b>) vin36. (<b>e</b>) vin49. (<b>f</b>) vin56.</p>
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<p>The four most typical fast-charging protocols: (<b>a</b>) Multi-stage constant-current fast-charging protocol; (<b>b</b>) Current limiting at high-temperature protocol; (<b>c</b>) Current limiting at low-temperature protocol; (<b>d</b>) Mild fast-charging protocol.</p>
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<p>The features selected from multi-stage constant-current fast-charging process. The internal resistance is calculated based on the voltage and current changes at stage switch point. The temperature rise rate is calculated based on a linear regression method.</p>
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<p>Observed and predicted results of neural network model: (<b>a</b>) Observed and predicted total ampere-hour throughput; (<b>b</b>) Observed and predicted capacity.</p>
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20 pages, 4977 KiB  
Article
Lithium-Ion Battery State-of-Charge Estimation Using Electrochemical Model with Sensitive Parameters Adjustment
by Jingrong Wang, Jinhao Meng, Qiao Peng, Tianqi Liu, Xueyang Zeng, Gang Chen and Yan Li
Batteries 2023, 9(3), 180; https://doi.org/10.3390/batteries9030180 - 20 Mar 2023
Cited by 22 | Viewed by 4970
Abstract
State-of-charge (SOC) estimation of lithium-ion (Li-ion) batteries with good accuracy is of critical importance for battery management systems. For the model-based methods, the electrochemical model has been widely used due to its accuracy and ability to describe the internal behaviors of the battery. [...] Read more.
State-of-charge (SOC) estimation of lithium-ion (Li-ion) batteries with good accuracy is of critical importance for battery management systems. For the model-based methods, the electrochemical model has been widely used due to its accuracy and ability to describe the internal behaviors of the battery. However, the uncertainty of parameters and the lack of correction from voltage also induce errors during long-time calculation. This paper proposes a particle filter (PF) based method to estimate Li-ion batteries’ SOC using electrochemical model, with sensitive parameter identification achieved using the particle swarm optimization (PSO) algorithm. First, a single particle model with electrolyte dynamics (SPME) is used in this work to reduce the computational burden of the battery electrochemical model, whose sensitive parameters are selected through the elementary effect test. Then, the representative sensitive parameters, which are difficult to measure directly, are adjusted by PSO for a high efficiency. Finally, a model-based SOC estimation framework is constructed with PF to achieve accurate Li-ion battery SOC. Compared with extended Kalman filter and equivalent circuit model, the proposed method shows high accuracy under three different driving cycles. Full article
(This article belongs to the Special Issue Advanced Lithium-Ion Battery Management in Renewable Energy Systems)
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<p>Schematics of (<b>a</b>) the P2D model and (<b>b</b>) the SPME.</p>
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<p>Flowchart of PSO.</p>
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<p>Flowchart of PF.</p>
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<p>The framework of the online SOC estimation based on electrochemical model using PF4.</p>
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<p>Battery experimental testing equipment.</p>
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<p>Elementary test result of 14 electrochemical parameters of ICR18650-26J battery under C-rates at (<b>a</b>) 0.5C and (<b>b</b>) 1C.</p>
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<p>Model simulation results of voltage, current and the voltage error under (<b>a</b>) NEDC conditions, (<b>b</b>) FTP conditions and (<b>c</b>) UDDS conditions.</p>
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<p>SOC estimation results using SPME+PF, SPME and ECM+EKF (<b>a</b>) NEDC conditions, (<b>b</b>) FTP conditions and (<b>c</b>) UDDS conditions.</p>
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<p>SOC estimation results using SPME+PF, SPME and ECM+EKF (<b>a</b>) NEDC conditions, (<b>b</b>) FTP conditions and (<b>c</b>) UDDS conditions.</p>
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<p>Errors comparison while the evaluation criteria are (<b>a</b>) MAE and (<b>b</b>) RMSE.</p>
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13 pages, 2485 KiB  
Article
Dehydrogenation of Alkali Metal Aluminum Hydrides MAlH4 (M = Li, Na, K, and Cs): Insight from First-Principles Calculations
by Rui Zhou, Xiaohua Mo, Yong Huang, Chunyan Hu, Xiaoli Zuo, Yu Ma, Qi Wei and Weiqing Jiang
Batteries 2023, 9(3), 179; https://doi.org/10.3390/batteries9030179 - 19 Mar 2023
Cited by 5 | Viewed by 2163
Abstract
Complex aluminum hydrides with high hydrogen capacity are among the most promising solid-state hydrogen storage materials. The present study determines the thermal stability, hydrogen dissociation energy, and electronic structures of alkali metal aluminum hydrides, MAlH4 (M = Li, Na, K, and Cs), [...] Read more.
Complex aluminum hydrides with high hydrogen capacity are among the most promising solid-state hydrogen storage materials. The present study determines the thermal stability, hydrogen dissociation energy, and electronic structures of alkali metal aluminum hydrides, MAlH4 (M = Li, Na, K, and Cs), using first-principles density functional theory calculations in an attempt to gain insight into the dehydrogenation mechanism of these hydrides. The results show that the hydrogen dissociation energy (Ed-H2) of MAlH4 (M = Li, Na, K, and Cs) correlates with the Pauling electronegativity of cation M (χP); that is, the Ed-H2 (average value) decreases, i.e., 1.211 eV (LiAlH4) < 1.281 eV (NaAlH4) < 1.291 eV (KAlH4) < 1.361 eV (CsAlH4), with the increasing χP value, i.e., 0.98 (Li) > 0.93 (Na) > 0.82 (K) > 0.79 (Cs). The main reason for this finding is that alkali alanate MAlH4 at higher cation electronegativity is thermally less stable and held by weaker Al-H covalent and H-H ionic interactions. Our work contributes to the design of alkali metal aluminum hydrides with a favorable dehydrogenation, which is useful for on-board hydrogen storage. Full article
(This article belongs to the Special Issue Advances in Carbon-Based Materials for Energy Storage)
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<p>The crystal models of MAlH<sub>4</sub> (M = Li, Na, K, and Cs) with 2 × 1 × 1 supercell: (<b>a</b>) LiAlH<sub>4</sub>, (<b>b</b>) NaAlH<sub>4</sub>, (<b>c</b>) KAlH<sub>4</sub>, and (<b>d</b>) CsAlH<sub>4</sub>. Li, Na, K, Cs, Al, and H atoms are denoted by red, green, blue, orange, pink, and white spheres, respectively. The H atoms labeled as H<sub>A</sub>, H<sub>B</sub>, H<sub>C</sub>, H<sub>D</sub>, H<sub>E</sub>, H<sub>F</sub>, H<sub>G</sub>, and H<sub>H</sub> in two [AlH<sub>4</sub>] units are considered for hydrogen desorption.</p>
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<p>The formation enthalpy (ΔH) as a function of cation electronegativity (χ<sub>P</sub>) for MAlH<sub>4</sub> (M = Li, Na, K, and Cs) alanates. The straight line, ΔH = 296.033χ<sub>P</sub> − 402.19, indicates least square fitting.</p>
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<p>The formed AlH<sub>3</sub> group, hydrogen dissociation energy (E<sub>d</sub>-H<sub>2</sub>), and total energy (E) for MAlH<sub>4</sub> (M = Li, Na, K, and Cs) with H<sub>A</sub> and H<sub>X</sub> desorption: (<b>a</b>) H<sub>A</sub> and H<sub>X</sub> from one [AlH<sub>4</sub>] unit; (<b>b</b>) H<sub>A</sub> and H<sub>X</sub> from two [AlH<sub>4</sub>] units. The bond lengths between Al and H atoms in AlH<sub>3</sub> group are described (in Ǻ).</p>
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<p>The total (TDOS) and partial density of states (PDOS) for MAlH<sub>4</sub> (M = Li, Na, K, and Cs), with Fermi level (E<sub>F</sub>, marked with vertical dotted line) at 0 eV and H atoms (H<sub>A</sub> and H<sub>B</sub>) from considered [AlH<sub>4</sub>] unit in <a href="#batteries-09-00179-f001" class="html-fig">Figure 1</a>: (<b>a</b>) LiAlH<sub>4</sub>, (<b>b</b>) NaAlH<sub>4</sub>, (<b>c</b>) KAlH<sub>4</sub>, and (<b>d</b>) CsAlH<sub>4</sub>. The TDOS labeled by I and II are mainly contributed by Al and H electronic states.</p>
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<p>The total (TDOS) and partial density of states (PDOS) for MAlH<sub>4</sub> (M = Li, Na, K, and Cs), with Fermi level (E<sub>F</sub>, marked with vertical dotted line) at 0 eV and H atoms (H<sub>A</sub> and H<sub>B</sub>) from considered [AlH<sub>4</sub>] unit in <a href="#batteries-09-00179-f001" class="html-fig">Figure 1</a>: (<b>a</b>) LiAlH<sub>4</sub>, (<b>b</b>) NaAlH<sub>4</sub>, (<b>c</b>) KAlH<sub>4</sub>, and (<b>d</b>) CsAlH<sub>4</sub>. The TDOS labeled by I and II are mainly contributed by Al and H electronic states.</p>
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<p>The electronic density contours for MAlH<sub>4</sub> (M = Li, Na, K, and Cs) with the contour line from 0.03 to 0.18 electrons/Ǻ<sup>3</sup>: (<b>a</b>) LiAlH<sub>4</sub>, (<b>b</b>) NaAlH<sub>4</sub>, (<b>c</b>) KAlH<sub>4</sub>, and (<b>d</b>) CsAlH<sub>4</sub>. Li, Na, K, Cs, Al, and H atoms are denoted by red, green, blue, orange, pink, and white spheres, respectively. The shortest distances between Al and H atoms and H and H atoms in this figure are described (in Ǻ).</p>
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11 pages, 18562 KiB  
Article
Textile PAN Carbon Fibers Cathode for High-Voltage Seawater Batteries
by João Ferreira, Tiago Salgueiro, Jossano Marcuzzo, Eduardo Arruda, João Ventura and Joana Oliveira
Batteries 2023, 9(3), 178; https://doi.org/10.3390/batteries9030178 - 18 Mar 2023
Cited by 3 | Viewed by 3051
Abstract
Rechargeable sodium seawater batteries (SWBs) are gaining the world leadership of high voltage energy storage devices for marine environments. With natural seawater as the source of active material, SWBs can be supplied infinitely with Na cations. Because of their open-structured cathode, the cathode [...] Read more.
Rechargeable sodium seawater batteries (SWBs) are gaining the world leadership of high voltage energy storage devices for marine environments. With natural seawater as the source of active material, SWBs can be supplied infinitely with Na cations. Because of their open-structured cathode, the cathode material’s specific surface area, porosity and wettability need to be optimized to achieve a high-performance cell. In this work, activated textile polyacrylonitrile (PAN) fibers were used to produce an activated carbon felt with a facile manufacturing process. The easy and low-cost production of these fibers makes them excellent candidates for energy storage applications involving oxygen evolution and reduction reactions. The electrochemical performance results of the fabricated activated PAN fibers and of commercial carbon felts were measured and compared, being characterized through galvanostic charge discharge cycles, electrochemical impedance spectroscopy and cyclic voltammetries. A performance improvement was observed with PAN activated carbon felt as half cell with a capacitance increase (about 9000%), and as full cell with a smaller voltage gap (about 10%) and increased gravimetric capacitance (about 260%) when compared to the commercial carbon felt. The successful implementation of PAN activated carbon felts in an aqueous environment opens new paths toward high performance seawater battery’s cathodes. Full article
(This article belongs to the Special Issue Anode and Cathode Materials for Lithium-Ion and Sodium-Ion Batteries)
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<p>Scheme of a seawater battery (SWB) cell (<b>a</b>) structure and operating mechanism during the (<b>b</b>) charge and the (<b>c</b>) discharge processes.</p>
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<p>Photographs of the used carbon felts: (<b>a</b>) commercial (CCF) and (<b>d</b>) activated (ACF) and their SEM images: CCF surface at (<b>b</b>) 1 mm and (<b>c</b>) 100 μm scales, where the white regions are the NaCl deposited, and ACF surface at (<b>e</b>) 1 mm and (<b>f</b>) 100 μm scales.</p>
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<p>Scheme of the contact angles of the drops used to measure the wettability on top of the (<b>a</b>) CCF and (<b>b</b>) ACF.</p>
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<p>Electrochemical impedance spectroscopy (EIS) fitted spectrum and cyclic voltammetry of CCF ((<b>a</b>,<b>b</b>), respectively) and of ACF with one (ACF1), two (ACF2) and three (ACF3) layers ((<b>c</b>,<b>d</b>), respectively), in a three electrode configuration [Pt/(Ag/AgCl)/CCC] in a seawater solution.</p>
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<p>Galvanostic charge–discharge cycles of a SWB cell with (<b>a</b>) CCF and (<b>b</b>) ACF3, at a current of 0.025 mA, (<b>c</b>) charge–discharge profiles of a SWB cell with ACF3 at different currents with a 5 h duration, in a two electrode connection, and (<b>d</b>) cyclic voltammetries of a SWB cell with CCF and ACF3.</p>
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18 pages, 3500 KiB  
Article
Accurate Prediction Approach of SOH for Lithium-Ion Batteries Based on LSTM Method
by Lijun Zhang, Tuo Ji, Shihao Yu and Guanchen Liu
Batteries 2023, 9(3), 177; https://doi.org/10.3390/batteries9030177 - 18 Mar 2023
Cited by 40 | Viewed by 8773
Abstract
The deterioration of the health state of lithium-ion batteries will lead to the degradation of the battery performance, the reduction of the maximum available capacity, the continuous shortening of the service life, the reduction of the driving range of electric vehicles, and even [...] Read more.
The deterioration of the health state of lithium-ion batteries will lead to the degradation of the battery performance, the reduction of the maximum available capacity, the continuous shortening of the service life, the reduction of the driving range of electric vehicles, and even the occurrence of safety accidents in electric vehicles driving. To solve the problem that the traditional battery management system is difficult to accurately manage and predict its health condition, this paper proposes the mechanism and influencing factors of battery degradation. The battery capacity is selected as the characterization of the state of health (SOH), and the long short-term memory (LSTM) model of battery capacity is constructed. The intrinsic pattern of capacity degradation is detected and extracted from the perspective of time series. Experimental results from NASA and CALCE battery life datasets show that the prediction approach based on the LSTM model can accurately predict the available capacity and the remaining useful life (RUL) of the lithium-ion battery. Full article
(This article belongs to the Special Issue Battery Safety: Recent Advances and Perspective)
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<p>Battery discharge voltage curves at different cycle times.</p>
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<p>Battery health status curve at different cycle times.</p>
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<p>Curve of potential drop of SEI layer with the number of cycles.</p>
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<p>Variation curve of electrolyte volume fraction with number of cycles.</p>
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<p>Capacity degradation of NASA batteries.</p>
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<p>Capacity degradation of lithium batteries tested by CALCE.</p>
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<p>Network structure of RNN.</p>
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<p>Gating unit structure of LSTM.</p>
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<p>Architecture of a LSTM cell.</p>
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<p>LSTM-based SOH prediction flowchart for power cells.</p>
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<p>Structural diagram of the LSTM network model.</p>
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<p>LSTM model prediction results: (<bold>a</bold>) B5 cell; (<bold>b</bold>) B6 cell.</p>
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<p>LSTM-based prediction results for CALCE battery capacity: (<bold>a</bold>) predicted results for CS-37 at T = 40%; (<bold>b</bold>) predicted results for CS-38 at T = 40%; (<bold>c</bold>) predicted results for CS-37 at T = 50%; (<bold>d</bold>) predicted results for CS-38 at T = 50%; (<bold>e</bold>) predicted results for CS-37 at T = 60%; (<bold>f</bold>) predicted results for CS-38 at T = 60%.</p>
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13 pages, 4823 KiB  
Article
Comparing the Cold-Cranking Performance of Lead-Acid and Lithium Iron Phosphate Batteries at Temperatures below 0 °C
by Sophia Bauknecht, Florian Wätzold, Anton Schlösser and Julia Kowal
Batteries 2023, 9(3), 176; https://doi.org/10.3390/batteries9030176 - 17 Mar 2023
Cited by 4 | Viewed by 3705
Abstract
Six test cells, two lead–acid batteries (LABs), and four lithium iron phosphate (LFP) batteries have been tested regarding their capacity at various temperatures (25 °C, 0 °C, and −18 °C) and regarding their cold crank capability at low temperatures (0 °C, −10 °C, [...] Read more.
Six test cells, two lead–acid batteries (LABs), and four lithium iron phosphate (LFP) batteries have been tested regarding their capacity at various temperatures (25 °C, 0 °C, and −18 °C) and regarding their cold crank capability at low temperatures (0 °C, −10 °C, −18 °C, and −30 °C). During the capacity test, the LFP batteries have a higher voltage level at all temperatures than LABs, which results in a higher power and energy output. Moreover, LFP batteries have a lower capacity decline and a lower energy decline for decreasing temperature. Regarding the cold-cranking test definition, the LABs passed the test at 0 °C, −10 °C, and −18 °C, but not at −30 °C. The LFP batteries passed the test at 0 °C and −10 °C. At −18 °C, only two of the four LFP batteries passed, while all LFP batteries failed the test at −30 °C. For comparability between technologies, it is suggested to redefine the requirements of the standard test in terms of power or energy. With this redefinition, the LFP battery can generate comparable cold-cranking results till −18 °C. Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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<p>C<sub>20</sub> capacity at (<b>a</b>) 25 °C, (<b>b</b>) 0 °C, and (<b>c</b>) −18 °C.</p>
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<p>C<sub>20</sub> capacity test (<b>a</b>) measured capacity and (<b>b</b>) useable energy.</p>
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<p>Cold-cranking test (<b>a</b>) at 0 °C and (<b>b</b>) at −10 °C.</p>
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<p>First pulse of the cold-cranking test at −18 °C: (<b>a</b>) voltage, (<b>b</b>) current, and (<b>c</b>) power.</p>
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<p>First pulse of the cold-cranking test at −30 °C: (<b>a</b>) voltage, (<b>b</b>) current, and (<b>c</b>) power.</p>
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<p>Illustration of the voltages used for determining (<b>a</b>) the total energy and (<b>b</b>) the usable energy during the first pulse of the cold-cranking test.</p>
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<p>During the first pulse of cold-cranking test (<b>a</b>) total energy and (<b>b</b>) usable energy.</p>
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<p>Energy efficiency during first pulse of cold-cranking test.</p>
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<p>Internal resistance: (<b>a</b>) absolute values and (<b>b</b>) normalized values.</p>
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<p>Complete cold-cranking test at −30 °C: (<b>a</b>) voltage, (<b>b</b>) current, and (<b>c</b>) power.</p>
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<p>Complete cold-cranking test at 0 °C: (<b>a</b>) voltage, (<b>b</b>) current, and (<b>c</b>) power.</p>
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<p>Complete cold-cranking test at −10 °C: (<b>a</b>) voltage, (<b>b</b>) current, and (<b>c</b>) power.</p>
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<p>Complete cold-cranking test at −18 °C: (<b>a</b>) voltage, (<b>b</b>) current, and (<b>c</b>) power.</p>
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14 pages, 4534 KiB  
Article
Density Functional Theory Study of Oxygen Evolution Reaction Mechanism on Rare Earth Sc-Doped Graphene
by Yiwen Liu, Mengqi Liao, Yuting Hu, Tae-Gwan Lee, Ravindranadh Koutavarapu, Shaik Gouse Peera and Chao Liu
Batteries 2023, 9(3), 175; https://doi.org/10.3390/batteries9030175 - 17 Mar 2023
Cited by 4 | Viewed by 3063
Abstract
The development of a stable catalyst with excellent catalytic performance for the oxygen evolution reaction (OER) in alkaline environments is a key reaction in various electrochemical technologies. In this work, single-atom catalysts (SACs) systems in which scandium (Sc), a rare earth metal, with [...] Read more.
The development of a stable catalyst with excellent catalytic performance for the oxygen evolution reaction (OER) in alkaline environments is a key reaction in various electrochemical technologies. In this work, single-atom catalysts (SACs) systems in which scandium (Sc), a rare earth metal, with different N/C coordination environments (ScNxC3−x@SACs and ScNxC4−x@SACs of Sc) were systematically studied with the help of density functional theory (DFT) calculations. The results of the structural thermodynamic stability analysis indicated that the ScNxC3−x@SACs and ScNxC4−x@SACs systems are more stable with increasing N atom doping concentration around Sc. The ScN3, ScN3C, and ScN4 with better stability were selected as the objects of subsequent research. However, ScN3 and ScN4 form Sc(OH)2N3 and Sc(OH)2N4 structures with double-hydroxyl groups as ligands because of the strong adsorption of OH species, whereas the strong adsorption of OH species by ScN3C causes structural instability. Here, the overpotential (η) of Sc(OH)2N3 was 1.03 V; Sc(OH)2N4 had two reaction paths and the η of path 1 was 0.80 V, which was 0.30 V lower than that of path 2. Therefore, Sc(OH)2N4 can be used as a stable and promising OER catalyst with easy desorption of O2 and good cycle performance. The hydroxyl ligand modification of Sc-NxC3−x@SACs and Sc-NxC4−x@SACs provides a method for studying the catalytic performance of other rare earth elements. Full article
(This article belongs to the Special Issue Research Focuses on Zinc-Air Batteries)
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<p>Optimized geometries of the (<b>a</b>) Sc-N<sub>x</sub>C<sub>3−x</sub>@SACs and (<b>b</b>) Sc-N<sub>x</sub>C<sub>4−x</sub>@SACs structures.</p>
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<p>Formation energy (<math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mi>E</mi> <mi>f</mi> </msub> <mo>,</mo> <mi>eV</mi> </mrow> </semantics></math>) of the various structures of (<b>a</b>) ScN<sub>x</sub>C<sub>3−x</sub>@SACs and (<b>b</b>) ScN<sub>x</sub>C<sub>4−x</sub>@SACs.</p>
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<p>Partial densities of states (PDOS) of various structures of (<b>a</b>–<b>d</b>) ScN<sub>x</sub>C<sub>3−x</sub>@SACs and (<b>e</b>–<b>k</b>) ScN<sub>x</sub>C<sub>4−x</sub>@SACs.</p>
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<p>Mulliken charge analysis of (<b>a</b>) ScN<sub>3</sub> and (<b>b</b>) ScN<sub>4</sub>.</p>
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<p>Adsorption energy (<math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mi>E</mi> <mrow> <mi>a</mi> <mi>d</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>, eV) of various (<b>a</b>) ScN<sub>x</sub>C<sub>3−x</sub>@SACs and (<b>b</b>) ScN<sub>x</sub>C<sub>4−x</sub>@SACs structures with adsorbed OH species.</p>
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<p>The adsorption energy (<math display="inline"><semantics> <mrow> <mo>∆</mo> <msub> <mi>E</mi> <mrow> <mi>a</mi> <mi>d</mi> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math>, eV) of ScN<sub>3</sub> and ScN<sub>4</sub> with different numbers of OH species.</p>
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<p>Diagram of the structures of (<b>a</b>) Sc(OH)<sub>2</sub>N<sub>3</sub> and (<b>b</b>) Sc(OH)<sub>2</sub>N<sub>4</sub>.</p>
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<p>Adsorption energy (ΔE, eV) of ScN<sub>3</sub> and ScN<sub>4</sub> with different numbers of OH ligands for adsorbed O<sub>2</sub>.</p>
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<p>Possible reaction pathways of (<b>a</b>) Sc(OH)<sub>2</sub>N<sub>3</sub> and (<b>b</b>) Sc(OH)<sub>2</sub>N<sub>4</sub> structures. Pink atoms: Sc, gray atoms: C, blue atoms: N, red atoms: O, white atoms: H.</p>
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<p>The PDOS of (<b>a</b>) Sc(OH)<sub>2</sub>N<sub>3</sub>-center part, (<b>b</b>) Sc(OH)<sub>2</sub>N<sub>3</sub>, (<b>c</b>) Sc(OH)<sub>2</sub>N<sub>4</sub>-center part, and (<b>d</b>) Sc(OH)<sub>2</sub>N<sub>4</sub>.</p>
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36 pages, 8774 KiB  
Review
MXene-Based Materials for Multivalent Metal-Ion Batteries
by Chunlei Wang, Zibing Pan, Huaqi Chen, Xiangjun Pu and Zhongxue Chen
Batteries 2023, 9(3), 174; https://doi.org/10.3390/batteries9030174 - 17 Mar 2023
Cited by 21 | Viewed by 5598
Abstract
Multivalent metal ion (Mg2+, Zn2+, Ca2+, and Al3+) batteries (MMIBs) emerged as promising technologies for large-scale energy storage systems in recent years due to the abundant metal reserves in the Earth’s crust and potentially low [...] Read more.
Multivalent metal ion (Mg2+, Zn2+, Ca2+, and Al3+) batteries (MMIBs) emerged as promising technologies for large-scale energy storage systems in recent years due to the abundant metal reserves in the Earth’s crust and potentially low cost. However, the lack of high-performance electrode materials is still the main obstacle to the development of MMIBs. As a newly large family of two-dimensional transition metal carbides, nitrides, and carbonitrides, MXenes have attracted growing focus in the energy storage field because of their large specific surface area, excellent conductivity, tunable interlayer spaces, and compositional diversity. In particular, the multifunctional chemistry and superior hydrophilicity enable MXenes to serve not only as electrode materials but also as important functional components for heterojunction composite electrodes. Herein, the advances of MXene-based materials since its discovery for MMIBs are summarized, with an emphasis on the rational design and controllable synthesis of MXenes. More importantly, the fundamental understanding of the relationship between the morphology, structure, and function of MXenes is highlighted. Finally, the existing challenges and future research directions on MXene-based materials toward MMIBs application are critically discussed and prospected. Full article
(This article belongs to the Special Issue Rechargeable Multivalent Metal-Ion Batteries)
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<p>(<b>a</b>) Periodic table fragments marked with the “M”, “A” and “X” elements in known MAX and MXene phases. (<b>b</b>) Currently known MXene compositions, ignoring the terminal here.</p>
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<p>(<b>a</b>) The history of MXene synthesis [<a href="#B41-batteries-09-00174" class="html-bibr">41</a>]. (<b>a</b>) Reproduced with permission from the American Chemical Society. (<b>b</b>) Schematic diagram of molten fluorine salt etching Ti<sub>4</sub>N<sub>3</sub>T<sub>x</sub> [<a href="#B34-batteries-09-00174" class="html-bibr">34</a>]. (<b>b</b>) Reproduced with permission from RSC Pub. (<b>c</b>) Nb<sub>2</sub>CT<sub>x</sub> MXene delamination process [<a href="#B38-batteries-09-00174" class="html-bibr">38</a>]. (<b>c</b>) Reproduced with permission from WILEY-VCH.</p>
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<p>(<b>a</b>) Preparation process of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene by reaction between Ti<sub>3</sub>SiC<sub>2</sub> and CuCl<sub>2</sub>; (<b>b</b>) Diagram of redox potential and Gibbs free energy (700 °C) [<a href="#B46-batteries-09-00174" class="html-bibr">46</a>]. (<b>a</b>,<b>b</b>) Reproduced with permission from MDPI AG. (<b>c</b>) Schematics for etching MAX phases with Lewis acidic molten salts and high-angle annular dark-field (HAADF) image of Ti<sub>3</sub>C<sub>2</sub>Br<sub>2</sub> MXene; (<b>d</b>) HAADF diagram of changing the end group by displacement and elimination reactions [<a href="#B47-batteries-09-00174" class="html-bibr">47</a>]. (<b>c</b>,<b>d</b>) Reproduced with permission from AAAS.</p>
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<p>(<b>a</b>) PA-MXene/CNT preparation scheme using unidirectional freeze-drying; (<b>b</b>–<b>d</b>) SEM image of PA-MXene/CNT [<a href="#B61-batteries-09-00174" class="html-bibr">61</a>]. (<b>a</b>–<b>d</b>) Reproduced with permission from WILEY-VCH. (<b>e</b>) Comparison of MXene films naturally sedimented and conventional filtered under vacuum [<a href="#B57-batteries-09-00174" class="html-bibr">57</a>]. (<b>e</b>) Reproduced with permission from Springer Nature. (<b>f</b>) Schematic showing the two different processes of V<sub>4</sub>C<sub>3</sub>T<sub>x</sub> MXene [<a href="#B58-batteries-09-00174" class="html-bibr">58</a>]. (<b>f</b>) Reproduced with permission from ELSEVIER BV. (<b>g</b>) Diagram showing the chemical engraving of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> flakes to produce a porous structure [<a href="#B60-batteries-09-00174" class="html-bibr">60</a>]. (<b>g</b>) Reproduced with permission from John Wiley and Sons. (<b>h</b>) Diagram of the porous, free-standing, and flexible 3D MXene foam prepared with an S template [<a href="#B59-batteries-09-00174" class="html-bibr">59</a>]. (<b>h</b>) Reproduced with permission from WILEY VCH.</p>
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<p>Band structure diagram of the -OH, -F terminations and bare Ti<sub>2</sub>C MXene monolayers, showing that MXene changes from metal to semiconductor due to changes in surface chemistry [<a href="#B70-batteries-09-00174" class="html-bibr">70</a>]. Reproduced with permission from WILEY VCH.</p>
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<p>Galvanostatic charge/discharge (GCD) curves of (<b>a</b>) Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> and (<b>b</b>) Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>/CTAB electrode at different cycles at 0.05 A g<sup>−1</sup>; Top view of the structures for (<b>c</b>) an O atom and (<b>d</b>) a Mg atom adsorbed on 1 × 1 Ti<sub>3</sub>C<sub>2</sub> surface at the top site, body-centered cubic (bcc) site, and face-centered cubic (fcc) site, respectively. Top view of (<b>e</b>) Mg<sup>2+</sup> and (<b>g</b>) CTA<sup>+</sup> as well as (<b>f</b>) side view of CTA<sup>+</sup> adsorbed on the 3 × 3 Ti<sub>3</sub>C<sub>2</sub>O surface; (<b>h</b>) Diffusion profile of Mg<sup>2+</sup> on Ti<sub>3</sub>C<sub>2</sub>O and CTA<sup>+</sup>/Ti<sub>3</sub>C<sub>2</sub>O surface in nudged elastic band calculations [<a href="#B40-batteries-09-00174" class="html-bibr">40</a>]. Reproduced with permission from ACS Nano.</p>
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<p>FESEM and TEM images of (<b>a</b>,<b>b</b>) MoS<sub>2</sub> and (<b>c</b>,<b>d</b>) MoS<sub>2</sub>/Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> composite. The illustrations show the corresponding image with different magnifications. (<b>e</b>) Cycle performance and (<b>f</b>) charge/discharge curves of MoS<sub>2</sub> and MoS<sub>2</sub>/Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> electrodes in MIBs after stabilization [<a href="#B89-batteries-09-00174" class="html-bibr">89</a>]. (<b>a</b>–<b>f</b>) Reproduced with permission from Elsevier. (<b>g</b>) Schematic illustration of the Ti<sub>3</sub>C<sub>2</sub>/CoSe<sub>2</sub> synthesis process and half-cell mechanism. (<b>h</b>) Rate capability at different current densities and (<b>i</b>) cycling performance at 50 mAg<sup>−1</sup> of Ti<sub>3</sub>C<sub>2</sub>/CoSe<sub>2</sub> [<a href="#B91-batteries-09-00174" class="html-bibr">91</a>]. (<b>g</b>–<b>i</b>) Reproduced with permission from Elsevier BV.</p>
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<p>(<b>a</b>) Schematic diagram of TBAOH intercalated ML-V<sub>2</sub>CT<sub>x</sub> for interlayer expansion; (<b>b</b>) XRD patterns of ML-V<sub>2</sub>CT<span class="html-italic"><sub>x</sub></span> and TBAOH treated ML-V<sub>2</sub>CT<sub>x</sub>, showing an increased interlayer spacing of about 5.73 Å after TBAOH treatment; (<b>c</b>) Charge/discharge curves of TBAOH treated ML-V<sub>2</sub>CT<sub>x</sub> for the first five cycles; (<b>d</b>) Cycling performance of TBAOH treated ML-V<sub>2</sub>CT<sub>x</sub> cathode over 100 cycles at 200 mA g<sup>−1</sup>; (<b>e</b>) Rate performance of a TBAOH treated ML-V<sub>2</sub>CT<sub>x</sub> cathode [<a href="#B111-batteries-09-00174" class="html-bibr">111</a>]. (<b>a</b>–<b>e</b>) Reproduced with permission from ACS Nano. (<b>f</b>) Schematic diagram of the synthesis process of D-Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>@S@TiO<sub>2</sub>; (<b>g</b>) The working mechanism of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> as a cathode material for AIBs; (<b>h</b>) Cycling performances of D-Ti<sub>3</sub>C<sub>2</sub>T<span class="html-italic"><sub>x</sub></span>@S@TiO<sub>2</sub> [<a href="#B114-batteries-09-00174" class="html-bibr">114</a>]. (<b>f</b>–<b>h</b>) Reproduced with permission from RSC Pub.</p>
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<p>Schematic diagram of Zn deposition on the (<b>a</b>) Zn foil and (<b>j</b>) Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene@Zn paper; SEM images of Zn deposition after plating capacity up to (<b>b</b>,<b>f</b>) 1, (<b>c</b>,<b>g</b>) 10, (<b>d</b>,<b>h</b>) 20 mAh cm<sup>−2</sup> on Zn foil and Ti<sub>3</sub>C<sub>2</sub>Tx MXene@Zn paper at 1 mA cm<sup>−2</sup>, respectively; Cross-sectional SEM images of (<b>e</b>) Zn foil and (<b>i</b>) Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> MXene@Zn paper corresponding to d and h, respectively [<a href="#B125-batteries-09-00174" class="html-bibr">125</a>]. (<b>a</b>–<b>j</b>) Reproduced with permission from ACS Nano. (<b>k</b>) Schematic illustration of the Zn deposition process on Ti<sub>3</sub>C<sub>2</sub>Cl<sub>2</sub>; (<b>l</b>) SEM images and EDX mapping of the Ti<sub>3</sub>C<sub>2</sub>Cl<sub>2</sub>-Zn electrode after cycling with a flat and smooth surface; (<b>m</b>) Long cycling performance of symmetric batteries; (<b>n</b>) Comparison of rate performance of bared Zn metal and Ti<sub>3</sub>C<sub>2</sub>Cl<sub>2</sub>–Zn symmetric batteries with a capacity of 1 mAh cm<sup>−2</sup> [<a href="#B131-batteries-09-00174" class="html-bibr">131</a>]. (<b>k</b>–<b>n</b>) Reproduced with permission from ACS Nano.</p>
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<p>(<b>a</b>) Schematic of 3D Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>@MnO<sub>2</sub> microflower synthesis progress. (<b>b</b>,<b>c</b>) SEM images of 3D Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>@MnO<sub>2</sub> microflowers with different magnifications. (<b>d</b>) Contact angles of droplets of electrolyte on the surface of MnO<sub>2</sub> and 3D Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>@MnO<sub>2</sub> microflower. (<b>e</b>) Comparison of EIS for MnO<sub>2</sub> and 3D Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>@MnO<sub>2</sub> microflower cathodes in aqueous ZIBs. (<b>f</b>) Long-term cycling stability at 500 mA g<sup>−1</sup>; (<b>g</b>) rate performance of 3D Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>@MnO<sub>2</sub> microflowers in aqueous ZIBs [<a href="#B100-batteries-09-00174" class="html-bibr">100</a>]. (<b>a</b>–<b>g</b>) Reproduced with permission from the Royal Society of Chemistry. (<b>h</b>) Schematic diagram of possible structural changes in the V<sub>2</sub>CT<sub>x</sub> cathode during cycling. (<b>i</b>) Long-term cycling performance of V<sub>2</sub>CT<sub>X</sub> cathode at 10 A g<sup>−1</sup> [<a href="#B158-batteries-09-00174" class="html-bibr">158</a>]. (<b>h</b>,<b>i</b>) Reproduced with permission from ACS Nano.</p>
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<p>(<b>a</b>) Synthesis schematic of 1 T-MoS<sub>2</sub>/Ti<sub>3</sub>C<sub>2</sub> MXene. (<b>b</b>) Cycle performance at 1.00 A g<sup>−1</sup> of 1 T-MoS<sub>2</sub>/Ti<sub>3</sub>C<sub>2</sub> MXene and 1 T-MoS<sub>2</sub> [<a href="#B167-batteries-09-00174" class="html-bibr">167</a>]. (<b>a</b>,<b>b</b>) Reproduced with permission from ELSEVIER BV. (<b>c</b>) Schematic of S-Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>/PANI preparation. (<b>d</b>) Rate performance of S-Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub>/PANI [<a href="#B170-batteries-09-00174" class="html-bibr">170</a>]. (<b>c</b>,<b>d</b>) Reproduced with permission from ACS Nano.</p>
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<p>Cyclic voltammetry curve (CV) of (<b>a</b>) Ti<sub>3</sub>C<sub>2</sub>(OF), Ti<sub>3</sub>C<sub>2</sub>Cl<sub>2</sub>, (<b>b</b>) Ti<sub>3</sub>C<sub>2</sub>Br<sub>2</sub>, Ti<sub>3</sub>C<sub>2</sub>I<sub>2</sub>, (<b>c</b>) Ti<sub>3</sub>C<sub>2</sub>(BrI), and Ti<sub>3</sub>C<sub>2</sub>(ClBrI) at 1 mV s<sup>−1</sup>. (<b>d</b>) GCD curves of Ti<sub>3</sub>C<sub>2</sub>T<sub>x</sub> with terminals corresponding to (<b>a</b>–<b>c</b>) at 0.5 A g<sup>−1</sup> [<a href="#B172-batteries-09-00174" class="html-bibr">172</a>]. (<b>a</b>–<b>d</b>) Reproduced with permission from ACS Nano.</p>
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14 pages, 6573 KiB  
Article
The Effects of Silicon Anode Thickness on the Electrochemical Performance of Li-Ion Batteries
by Matea Raić, Krešimir Kvastek, Lara Mikac, Nikola Baran and Mile Ivanda
Batteries 2023, 9(3), 173; https://doi.org/10.3390/batteries9030173 - 17 Mar 2023
Cited by 4 | Viewed by 4466
Abstract
The electrode configuration is an important element in the development of Li-ion cells. The energy density is proportional to the loading of the active material. Therefore, increasing the electrode thickness is the simplest way to achieve higher capacities. In this paper, we compare [...] Read more.
The electrode configuration is an important element in the development of Li-ion cells. The energy density is proportional to the loading of the active material. Therefore, increasing the electrode thickness is the simplest way to achieve higher capacities. In this paper, we compare the effects of three different thicknesses of Ag-decorated Si electrode anode (HCSi) on the electrochemical performances such as the SEI layer formation, impedances, and mass capacitances. We prepared three different silicon electrode thicknesses to optimize the electrodes: 20, 40 and 60 µm and measured in situ galvanostatic electrochemical impedance spectroscopy (GEIS). Using GEIS, we studied the intercalation mechanism of Li+ ions in detail and found that despite having the same capacities (≈3500 mAh g−1), the thinnest electrode, HCSi20, allows diffusion of Li+ ions into the bulk, whereas thicker layers prevent smooth diffusion into the bulk of the silicon electrode due to increased layer resistance. The Voigt model was used to analyze the anomaly of the frequency dependence of the measured impedance, in which, the classical Randles circuit is connected in series with one or two R ‖ C parallel combinations. One R ‖ C circuit could be the result of the SEI formation, and the second R ‖ C circuit could be the contribution of Li. To increase the number of charge and discharge cycles, we improved the electrolyte by adding fluoroethylene carbonate (FEC), which reduced the capacity of the HCSi20 electrode to 50% of the initial capacity (≈3500 mAh g−1) after 60 cycles, whereas it dropped to 20% of the initial capacity after 10 cycles without the addition of FEC. Full article
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<p>“Home-made” 2-electrode system for electrochemical measurements.</p>
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<p>SEM images of the cross-sections of electrodes after testing: (<b>a</b>,<b>b</b>) HCSi20, (<b>c</b>,<b>d</b>) HCSi40, (<b>e</b>,<b>f</b>) HCSi60.</p>
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<p>SEM images of (<b>a</b>) active material HCSi before testing, electrodes: (<b>b</b>) HCSi20, (<b>c</b>) HCSi40, (<b>d</b>) HCSi60 after testing.</p>
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<p>Chronopotentiometic curves for electrodes HCSi20, HCSi40 and HCSi60 at constant current of −100 µA. Galvanostatic discharge with specific capacities for electrodes HCSi20, HCSi40 and HCSi60 (inset).</p>
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<p>Galvanostatic charge / discharge at constant current 300 µA for: (<b>a</b>) HCSi20 electrode. Stability over 10 cycles (inset), (<b>b</b>) HCSi20 with FEC electrode. Stability over 60 cycles (inset).</p>
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<p>In situ GEIS impedance spectra for HCSi20 electrode in (<b>a</b>) Nyquist plot, (<b>b</b>) Bode plot.</p>
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<p>In situ GEIS impedance spectra for HCSi40 electrode in (<b>a</b>) Nyquist plot, (<b>b</b>) Bode plot.</p>
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<p>In situ GEIS impedance spectra for HCSi60 electrode in (<b>a</b>) Nyquist plot, (<b>b</b>) Bode plot.</p>
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<p>In situ GEIS impedance spectra compared for all three thicknesses after 20 h of lithiation in Nyquist plot.</p>
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<p>In situ GEIS impedance spectra compared for all three thicknesses with FEC in Nyquist plot.</p>
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<p>Impedance spectra of symmetric cell (Li-Li) at OCP 0.0 V and asymmetric cell. (Li-HCSi20) at OCP +3.5 V in (<b>a</b>) Nyquist plot, (<b>b</b>) Bode plot.</p>
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<p>Time-dependance PEIS spectra of HCSi20 cell at +0.05 V in (<b>a</b>) Nyquist plot, (<b>b</b>) Bode plot.</p>
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<p>Time-dependance PEIS spectra of HCSi20 with FEC cell at +0.05 V in (<b>a</b>) Nyquist plot, (<b>b</b>) Bode plot.</p>
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<p>Charge transfer resistance (R<sub>ct</sub>) change over long period of lithiation for electrode HCSi20 and HCSi20 with the addition of FEC.</p>
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<p>Time-dependance PEIS spectra of HCSi20 cell at +0.05 V in (<b>a</b>) Nyquist plot, (<b>b</b>) residuals.</p>
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16 pages, 2104 KiB  
Article
Aging Determination of Series-Connected Lithium-Ion Cells Independent of Module Design
by Thiemo Hein, David Oeser, Andreas Ziegler, Daniel Montesinos-Miracle and Ansgar Ackva
Batteries 2023, 9(3), 172; https://doi.org/10.3390/batteries9030172 - 17 Mar 2023
Cited by 5 | Viewed by 2801
Abstract
In this work, a battery consisting of eight commercial NMC/graphite cells connected in series was cycled to 60% of its initial capacity. During the test, special care was taken to ensure that the results were not influenced by either the module assembly or [...] Read more.
In this work, a battery consisting of eight commercial NMC/graphite cells connected in series was cycled to 60% of its initial capacity. During the test, special care was taken to ensure that the results were not influenced by either the module assembly or the module design. For this purpose, the cells were virtually connected in a laboratory environment with the help of the test device as if they were operated together in a battery. Extrinsic influences that affect cell aging were thus reduced to a minimum. Differential Voltage Analysis (DVA), Electrochemical Impedance Spectrum (EIS), and relaxation measurements were performed to analyze the aging behavior of each cell. The results show that despite a theoretically perfect module design, Cell-to-Cell Variations (CtCV) occurred during aging. The shifting Depth of Discharge (DoD) values among the cells further amplify CtCV. Lithium plating was also observed in the faster aging cells after cyclic aging, suggesting that this aging effect contributes significantly to the development of CtCV. After the aging test, the battery was equipped with an active balancing system that maximizes capacity utilization. More important, the balancing charges which are calculated iteratively within the used balancing algorithm show a strong correlation to the pure capacity losses and thus provide a new way to determine the capacity values of each cell individually without disassembling the battery. Full article
(This article belongs to the Section Battery Performance, Ageing, Reliability and Safety)
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<p>(<b>a</b>) Histograms of cell capacity and (<b>b</b>) histogram of internal resistances of 200 cells measured in an initial characterization test.</p>
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<p>(<b>a</b>) Relative capacity of the module; (<b>b</b>) capacity values of all 8 cells obtained every 20 cycles.</p>
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<p>Voltages at the end of the discharging phase.</p>
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<p>Relative internal resistance of all 8 cells.</p>
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<p>Impedance spectra at 25 °C and 50% SoC of all 8 cells (<b>a</b>) before and (<b>b</b>) after 174 cycles shown in a Nyquist plot.</p>
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<p>Differential voltage analysis of all 8 cells (<b>a</b>) before and (<b>b</b>) after 174 cycles.</p>
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<p>Relaxation measurement of all 8 aged cells and one new cell: (<b>a</b>) Voltage during relaxation; (<b>b</b>) derivation of the voltage.</p>
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<p>Voltage curves of the 8 cells during discharge with active balancing.</p>
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<p>Correlation between balancing charges of the active balancing algorithm and the capacity losses of the 8 cells with the corresponding Pearson correlation coefficient.</p>
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18 pages, 24402 KiB  
Article
Implementing Binder Gradients in Thick Water-Based NMC811 Cathodes via Multi-Layer Coating
by Lukas Neidhart, Katja Fröhlich, Franz Winter and Marcus Jahn
Batteries 2023, 9(3), 171; https://doi.org/10.3390/batteries9030171 - 16 Mar 2023
Cited by 7 | Viewed by 3433
Abstract
Multi-layer coating of electrodes with different material compositions helps unlock the full potential of high-loaded electrodes. Within this work, LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes with an areal capacity of >8.5 mA h cm−2 and tuned binder concentrations [...] Read more.
Multi-layer coating of electrodes with different material compositions helps unlock the full potential of high-loaded electrodes. Within this work, LiNi0.8Mn0.1Co0.1O2 (NMC811) cathodes with an areal capacity of >8.5 mA h cm−2 and tuned binder concentrations were fabricated by using an industrially relevant roll-to-roll process. Rate capability tests revealed an increase in practical specific discharge capacity independent from the C-rate for cathodes with reduced binder concentration in the top layer. At high current densities (C-rate of 1C) an improved performance of up to 27% was achieved. Additionally, at lower C-rates, binder gradients perpendicular to the current collector have beneficial effects on thick electrodes. However, surface analysis and electrochemical impedance spectroscopy revealed that without an adequate connection between the active material particles through a carbon-binder domain, charge transfer resistance limits cycling performance at high current densities. Full article
(This article belongs to the Special Issue Emerging Technologies and Electrode Materials for Metal Batteries)
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<p>SEM images of (<b>a</b>,<b>b</b>) 0PMA (NMC811 particles in purple, KS6L graphite in green and CB in yellow; larger image shown in <a href="#batteries-09-00171-f0A2" class="html-fig">Figure A2</a>), (<b>c</b>,<b>d</b>) 25PMA and (<b>e</b>,<b>f</b>) 50PMA at two different magnifications each.</p>
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<p>(<b>a</b>) Raman spectra comparison of measured intensities, (<b>b</b>) normalized Raman spectra, (<b>c</b>) zoomed-in section between 900 and 1300 <math display="inline"><semantics> <mi mathvariant="normal">c</mi> </semantics></math><math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math><sup>−1</sup>, (<b>d</b>) zoomed-in section between 1500 and 1700 <math display="inline"><semantics> <mi mathvariant="normal">c</mi> </semantics></math><math display="inline"><semantics> <mi mathvariant="normal">m</mi> </semantics></math><sup>−1</sup>.</p>
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<p>(<b>a</b>) Rate capability test including ML samples from a previous study [<a href="#B8-batteries-09-00171" class="html-bibr">8</a>] and (<b>b</b>) results of 100 cycles at 0.2C. A plot including error bars is attached to the Appendix (<a href="#batteries-09-00171-f0A5" class="html-fig">Figure A5</a>).</p>
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<p>Comparison of EIS spectra for different electrode types with included fit of (<b>a</b>) 1st cycle and (<b>b</b>) 2nd cycle at 100% depth of discharge. Zoomed in spectra comparing 1st and 2nd cycle of (<b>c</b>) 0PMA, (<b>d</b>) 25PMA and (<b>e</b>) 50PMA.</p>
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<p>Voltage profiles of all samples at different C-rates (<b>a</b>) <math display="inline"><semantics> <mrow> <mn>0.2</mn> </mrow> </semantics></math>C, (<b>b</b>) <math display="inline"><semantics> <mrow> <mn>0.5</mn> </mrow> </semantics></math>C, (<b>c</b>) 1C.</p>
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<p>Galvanostatic (CC) and potentiostatic (CV) contributions to the specific charge capacities for 0.2C, 0.5C, and 1C. A plot including error bars is attached to the <a href="#app1-batteries-09-00171" class="html-app">Appendix A</a> (<a href="#batteries-09-00171-f0A7" class="html-fig">Figure A7</a>).</p>
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<p>Differential plots for (<b>a</b>) 0.2C, (<b>b</b>) 0.5C, and (<b>c</b>) 1C.</p>
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<p>Equivalent circuit model used for EIS fitting.</p>
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<p>Comparison of contributions (<b>a</b>) R<sub>S</sub>, (<b>b</b>) R<sub>CEI</sub>, (<b>c</b>) R<sub>e</sub> and (<b>d</b>) R<sub>ct</sub> to resistance of 0PMA, 25PMA and 50PMA cells at 100%DOD.</p>
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<p>Raman spectra of all materials used for slurry mixing (<b>a</b>) NMC811, (<b>b</b>) KS6L, (<b>c</b>) CB65, (<b>d</b>) PMA, (<b>e</b>) CMC.</p>
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<p>Top-view SEM image of 0PMA. Species are colored in purple (NMC811), green (KS6L graphite), and yellow (CBD).</p>
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<p>Cross-section images of (<b>a</b>,<b>b</b>) 0PMA, (<b>c</b>,<b>d</b>) 25PMA and (<b>e</b>,<b>f</b>) 50PMA. The left column shows images taken via digital microscope. The right column shows SEM images. Dotted red lines indicate the interface of the two layers, while green circles highlight examples for pores.</p>
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<p>I–V curves resulting from chronoamperometry measurements.</p>
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<p>(<b>a</b>) Rate capability test including ML samples from a previous study [<a href="#B8-batteries-09-00171" class="html-bibr">8</a>], (<b>b</b>) results of 100 cycles at 0.2C including coulombic efficiencies and statistical errors.</p>
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<p>Voltage curves associated to the investigated EIS cycles. (<b>a</b>) 1st cycle and (<b>b</b>) 2nd cycle.</p>
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<p>Galvanostatic (CC) and potentiostatic (CV) contributions to the specific charge capacities for 0.2C, 0.5C, and 1C.</p>
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18 pages, 3691 KiB  
Article
Influence of the Thermal Treatment on the Structure and Cycle Life of Copper Hexacyanoferrate for Aqueous Zinc-Ion Batteries
by Mohsen Baghodrat, Giorgia Zampardi, Jens Glenneberg and Fabio La Mantia
Batteries 2023, 9(3), 170; https://doi.org/10.3390/batteries9030170 - 15 Mar 2023
Cited by 7 | Viewed by 2487
Abstract
Copper hexacyanoferrate (CuHCF) has become an attractive Zn2+ insertion material as a positive electrode in aqueous zinc-ion batteries thanks to its high reversibility towards Zn2+ (de-)insertion, its simple, inexpensive and easily scalable synthesis route, its low toxicity, and its high working [...] Read more.
Copper hexacyanoferrate (CuHCF) has become an attractive Zn2+ insertion material as a positive electrode in aqueous zinc-ion batteries thanks to its high reversibility towards Zn2+ (de-)insertion, its simple, inexpensive and easily scalable synthesis route, its low toxicity, and its high working potential. It is known that the physiochemical properties of CuHCF can be modified by manipulating its synthesis parameters. However, the effect of these parameters on the material’s electrochemical performance and cycle life needs further investigation. Here, the structure and composition of CuHCF treated at different temperatures are studied through crystallographic, compositional, and thermogravimetric analyses. The resulting CuHCF powders were galvanostatically cycled to assess their electrochemical performance in relation to their annealing temperature. The results showed that the annealed CuHCF electrodes exhibited longer cycle life while maintaining a coulombic efficiency ≥ 99.5%. The longest cycle life was achieved by annealing the CuHCF electrodes at 100 °C. Full article
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<p>Schematic representation of the open-framework structure of a general PBA, where: A: alkaline cation, M,M’: transition metals, C: carbon, N: nitrogen. The structural water molecules have been omitted for the sake of clarity.</p>
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<p>Thermogravimetric curve (black) and differential-scanning-calorimetry curve (red) of the synthesized CuHCF powder.</p>
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<p>The XRPD patterns of the CuHCF powders thermally treated at different temperatures.</p>
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<p>(<b>a</b>) Average lattice constant of the CuHCF powder as a function of the temperature employed during the thermal treatment, (<b>b</b>) average variation in X-ray-diffraction-intensity ratio between 220 and 200 planes (I<sub>220</sub>/I<sub>200</sub>) of the CuHCF powder as a function of the temperature employed during the thermal treatment. The mean values and standard deviations were estimated by comparing at least two different samples resulting from two different thermally treated CuHCF powders.</p>
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<p>SEM image of untreated CuHCF powder.</p>
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<p>The ATR-FTIR spectra of the synthesized CuHCF powders thermally treated at different temperatures.</p>
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<p>Fraction of Fe<sup>III</sup> with respect to the total iron content of the lattice as a function of the temperature employed during the treatment of the CuHCF powder.</p>
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<p>Mean value and standard deviation of (<b>a</b>) specific discharge-capacity retention and (<b>b</b>) specific discharge-energy retention of the synthesized CuHCF-based electrodes galvanostatically cycled at 1C annealed at different temperatures. The mean value and the standard deviations were calculated according to at least two different measurements of two different synthesis batches for each annealing temperature.</p>
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<p>Galvanostatic cycles of (<b>a</b>) untreated and (<b>b</b>) 100 °C-annealed CuHCF electrodes, recorded at a C-rate of 1C.</p>
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<p>Differential charge plots for (<b>a</b>) untreated and (<b>b</b>) 100 °C-annealed CuHCF electrodes.</p>
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<p>The mean value and standard deviation of coulombic efficiency of the synthesized CuHCF-based electrodes, annealed at different temperatures and galvanostatically cycled at 1C. The mean value and the standard deviations were calculated according to at least two different measurements of two different synthesis batches for each annealing temperature.</p>
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30 pages, 5730 KiB  
Article
End-to-End Direct-Current-Based Extreme Fast Electric Vehicle Charging Infrastructure Using Lithium-Ion Battery Storage
by Vishwas Powar and Rajendra Singh
Batteries 2023, 9(3), 169; https://doi.org/10.3390/batteries9030169 - 14 Mar 2023
Cited by 16 | Viewed by 6424
Abstract
An urgent need to decarbonize the surface transport sector has led to a surge in the electrification of passenger and heavy-duty fleet vehicles. The lack of widespread public charging infrastructure hinders this electric vehicle (EV) transition. Extreme fast charging along interstates and highway [...] Read more.
An urgent need to decarbonize the surface transport sector has led to a surge in the electrification of passenger and heavy-duty fleet vehicles. The lack of widespread public charging infrastructure hinders this electric vehicle (EV) transition. Extreme fast charging along interstates and highway corridors is a potential solution. However, the legacy power grid based on alternating current (AC) beckons for costly upgrades that will be necessary to sustain sporadic fast charging loads. The primary goal of this paper is to propose a sustainable, low-loss, extremely fast charging infrastructure based on photovoltaics (PV) and co-located lithium-ion battery storage (BESS). Lithium-ion BESS plays a pivotal role in our proposed design by mitigating demand charges and operating as an independent 16–18 h power source. An end-to-end direct current power network with high voltage direct current interconnection is also incorporated. The design methodology focuses on comprehensive hourly EV-load models generated for different types of passenger vehicles and heavy-duty fleet charging. Appropriate PV-BESS sizing, optimum tilt, and temperature compensation techniques based on 15 years of irradiation data were utilized in the design. The proposed grid-independent DC power networks can significantly improve well-to-wheels efficiency by minimizing total system losses for fast charging networks. The network power savings for low, medium, and high voltage use cases were evaluated. Our results demonstrate 17% to 25% power savings compared to the traditional AC case. Full article
(This article belongs to the Collection Recent Advances in Battery Management Systems)
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<p>Trends in photovoltaic and electric vehicle sales bolstering interdependency [<a href="#B13-batteries-09-00169" class="html-bibr">13</a>].</p>
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<p>Interconnection queues for capacity generation from different energy sources. (<b>a</b>) Expected yearly distribution for capacity to become online. * Also includes co-located/hybrid storage capacity for some projects. (<b>b</b>) Total capacity in different stages of queues as of 2021 where IA = interconnection agreement [<a href="#B30-batteries-09-00169" class="html-bibr">30</a>].</p>
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<p>Proposed end-to-end DC fast charging EV infrastructure.</p>
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<p>Role of lithium-ion-based battery energy storage systems in existing AC grid infrastructure vs. proposed DC power networks.</p>
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<p>Existing HVDC transmission systems with the AC grid vs. proposed HVDC network with high-efficiency DC-DC converters.</p>
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<p>Average charging profiles for different EVs on a 250 kW charger [<a href="#B53-batteries-09-00169" class="html-bibr">53</a>].</p>
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<p>Modelled charging load profiles for light-duty passenger EVs (LEV), medium-duty passenger EVs (MEV), and heavy-duty truck fleet EVs (HEV) for a random week in February 2029.</p>
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<p>PV farms’ irradiation profiles for different days of sunshine. (<b>a</b>) For the ID#2 HVDC-connected Rexford 1 and 2 farm; (<b>b</b>) for the ID #1 MVDC-connected Mount Signal 1, 2, 3 farm.</p>
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<p>Three-phase LVAC vs. bipolar LVDC distribution losses with respect to cable distance for maximum Type #1 charging station hourly EV load profile.</p>
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<p>Modified 5-bus radial MVDC distribution system.</p>
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<p>Losses for different MVAC and MVDC voltages, with shaded regions indicating the power and distance range, where DC transmission results in lower losses. The dotted lines represent the limits of the transmission scheme (maximal current or maximal losses) [<a href="#B79-batteries-09-00169" class="html-bibr">79</a>].</p>
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<p>Percentage distribution of EV Arrival Rates at a charging station in a day.</p>
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<p>Charging profile with seasonal variations with different months of the year.</p>
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19 pages, 5376 KiB  
Article
The Role of the Precursor on the Electrochemical Performance of N,S Co-Doped Graphene Electrodes in Aqueous Electrolytes
by Rodrigo Braga, Diana M. Fernandes, Alberto Adán-Más, Teresa M. Silva and M. F. Montemor
Batteries 2023, 9(3), 168; https://doi.org/10.3390/batteries9030168 - 13 Mar 2023
Cited by 7 | Viewed by 2320
Abstract
The introduction of pillared agents or dopants to the graphene used as the electroactive material in supercapacitor electrodes can be an efficient way to facilitate ion transfer, mitigate re-stacking, and improve electrochemical performance. We evaluated the effect of different precursors containing nitrogen (N) [...] Read more.
The introduction of pillared agents or dopants to the graphene used as the electroactive material in supercapacitor electrodes can be an efficient way to facilitate ion transfer, mitigate re-stacking, and improve electrochemical performance. We evaluated the effect of different precursors containing nitrogen (N) and sulfur (S) atoms to dope graphene flake (GF) lattices. The electrochemical performance of the doped GF was assessed in 1 M KOH and 1 M Na2SO4 electrolytes. N- and S-doped GF flakes were synthesized via mechanochemical synthesis, also known as ball milling. After being ground, the materials were calcined under N2. The physicochemical characterization of the materials evidenced the co-doping of both S and N into the graphene backbone, as corroborated by the results of Raman spectroscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and transmission electron microscopy (TEM). As shown by the results, the nature of the precursors influences the ratio of S and N in the doped graphene flakes and, consequently, the response of the electroactive electrode material. The co-doping obtained using 4-amino-3-hydrazino-5-mercapto-1,2,4-triazole revealed a specific capacitance of 48 F.g−1 at 1.0 A∙g−1 and over 90% capacitance retention after 10,000 cycles at 10.0 A∙g−1 in Na2SO4. Full article
(This article belongs to the Special Issue Feature Papers to Celebrate the First Impact Factor of Batteries)
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<p>Representative TEM micrographs of S<sub>3</sub>N<sub>3−</sub>GF (<b>a</b>,<b>b</b>).</p>
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<p>Raman spectra of S<sub>3</sub>N<sub>3−</sub>GF (black), S<sub>3</sub>N<sub>2−</sub>GF (green), and SN<sub>6−</sub>GF (blue).</p>
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<p>Deconvolutions of: (<b>a</b>–<b>c</b>) C1s photoionization; (<b>d</b>–<b>f</b>) O1s photoionization; (<b>g</b>–<b>i</b>) N1s photoionization; (<b>j</b>–<b>l</b>) S2p photoionization.</p>
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<p>Deconvolutions of: (<b>a</b>–<b>c</b>) C1s photoionization; (<b>d</b>–<b>f</b>) O1s photoionization; (<b>g</b>–<b>i</b>) N1s photoionization; (<b>j</b>–<b>l</b>) S2p photoionization.</p>
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<p>Electrochemical results for different dopants and different dopant ratios in 1 M Na<sub>2</sub>SO<sub>4</sub> electrolyte, for SN<sub>6−</sub>GF (25:75), S<sub>3</sub>N<sub>2−</sub>GF (25:75), and S<sub>3</sub>N<sub>3−</sub>GF (25:75): (<b>a</b>) cyclic voltammetry at 50 mV∙s<sup>−1</sup>; (<b>b</b>) discharge curves at 1.0 A∙g<sup>−1</sup>; (<b>c</b>) specific capacitance at specific current from 0.4 A∙g<sup>−1</sup> to 10.0 A∙g<sup>−1</sup>; and capacitance retention after 10,000 cycles of continuous charge–discharge at 10.0 A∙g<sup>−1</sup> for (<b>d</b>) SN<sub>6−</sub>GF (25:75), (<b>e</b>) S<sub>3</sub>N<sub>2−</sub>GF (25:75) and (<b>f</b>) S<sub>3</sub>N<sub>3−</sub>GF (25:75).</p>
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<p>Electrochemical results for different dopants and different dopant ratios in 1 M Na<sub>2</sub>SO<sub>4</sub> electrolyte, for SN<sub>6−</sub>GF (25:75), S<sub>3</sub>N<sub>2−</sub>GF (25:75), and S<sub>3</sub>N<sub>3−</sub>GF (25:75): (<b>a</b>) cyclic voltammetry at 50 mV∙s<sup>−1</sup>; (<b>b</b>) discharge curves at 1.0 A∙g<sup>−1</sup>; (<b>c</b>) specific capacitance at specific current from 0.4 A∙g<sup>−1</sup> to 10.0 A∙g<sup>−1</sup>; and capacitance retention after 10,000 cycles of continuous charge–discharge at 10.0 A∙g<sup>−1</sup> for (<b>d</b>) SN<sub>6−</sub>GF (25:75), (<b>e</b>) S<sub>3</sub>N<sub>2−</sub>GF (25:75) and (<b>f</b>) S<sub>3</sub>N<sub>3−</sub>GF (25:75).</p>
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<p>Electrochemical results for different dopants and different dopant ratios in 1 M KOH electrolyte for SN<sub>6−</sub>GF (25:75), S<sub>3</sub>N<sub>2−</sub>GF (25:75), and S<sub>3</sub>N<sub>3−</sub>GF (25:75): (<b>a</b>) cyclic voltammetry at 50 mV∙s<sup>−1</sup>; (<b>b</b>) discharge curves at 1.0 A∙g<sup>−1</sup>; (<b>c</b>) specific capacitance at different applied specific current from 0.4 A∙g<sup>−1</sup> to 10.0 A∙g<sup>−1</sup>; and capacitance retention after 10,000 cycles of charge–discharge at 10.0 A∙g<sup>−1</sup> for (<b>d</b>) SN<sub>6−</sub>GF (25:75), (<b>e</b>) S<sub>3</sub>N<sub>2−</sub>GF (25:75), and (<b>f</b>) S<sub>3</sub>N<sub>3−</sub>GF (25:75).</p>
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<p>Electrochemical results for different dopants and different dopant ratios in 1 M KOH electrolyte for SN<sub>6−</sub>GF (25:75), S<sub>3</sub>N<sub>2−</sub>GF (25:75), and S<sub>3</sub>N<sub>3−</sub>GF (25:75): (<b>a</b>) cyclic voltammetry at 50 mV∙s<sup>−1</sup>; (<b>b</b>) discharge curves at 1.0 A∙g<sup>−1</sup>; (<b>c</b>) specific capacitance at different applied specific current from 0.4 A∙g<sup>−1</sup> to 10.0 A∙g<sup>−1</sup>; and capacitance retention after 10,000 cycles of charge–discharge at 10.0 A∙g<sup>−1</sup> for (<b>d</b>) SN<sub>6−</sub>GF (25:75), (<b>e</b>) S<sub>3</sub>N<sub>2−</sub>GF (25:75), and (<b>f</b>) S<sub>3</sub>N<sub>3−</sub>GF (25:75).</p>
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<p>SN<sub>6−</sub>GF (25:75): (<b>a</b>) cyclic voltammetry at scan rates ranging from 10 mV∙s<sup>−1</sup> to 400 mV∙s<sup>−1</sup>. Potential window from −0.10 V to 1.0 V vs. SCE (1 M Na<sub>2</sub>SO<sub>4</sub>) electrolytes; (<b>b</b>) galvanostatic charge−discharge at applied specific currents from 0.4 to 10 A∙g<sup>−1</sup> in 1 M Na<sub>2</sub>SO<sub>4</sub> electrolytes. Electrochemical performance of the SN<sub>6−</sub>GF//SN<sub>6−</sub>GF symmetric cell in 1 M Na<sub>2</sub>SO<sub>4</sub> electrolyte: (<b>c</b>) CV curves at scan rates from 10 mV∙s<sup>−1</sup> to 400 mV∙s<sup>−1</sup>; (<b>d</b>) galvanostatic charge−discharge at applied specific currents of 0.4 to 10 A∙g<sup>−1</sup>; (<b>e</b>) specific capacitance at applied specific current from 0.4 A∙g<sup>−1</sup> to 10.0 A∙g<sup>−1</sup>; (<b>f</b>) capacitance retention after 10,000 cycles of continuous charge−discharge at 10.0 A∙g<sup>−1</sup>; (<b>g</b>) Ragone plots of SN<sub>6−</sub>GF//SN<sub>6−</sub>GF symmetric device.</p>
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<p>SN<sub>6−</sub>GF (25:75): (<b>a</b>) cyclic voltammetry at scan rates ranging from 10 mV∙s<sup>−1</sup> to 400 mV∙s<sup>−1</sup>. Potential window from −0.10 V to 1.0 V vs. SCE (1 M Na<sub>2</sub>SO<sub>4</sub>) electrolytes; (<b>b</b>) galvanostatic charge−discharge at applied specific currents from 0.4 to 10 A∙g<sup>−1</sup> in 1 M Na<sub>2</sub>SO<sub>4</sub> electrolytes. Electrochemical performance of the SN<sub>6−</sub>GF//SN<sub>6−</sub>GF symmetric cell in 1 M Na<sub>2</sub>SO<sub>4</sub> electrolyte: (<b>c</b>) CV curves at scan rates from 10 mV∙s<sup>−1</sup> to 400 mV∙s<sup>−1</sup>; (<b>d</b>) galvanostatic charge−discharge at applied specific currents of 0.4 to 10 A∙g<sup>−1</sup>; (<b>e</b>) specific capacitance at applied specific current from 0.4 A∙g<sup>−1</sup> to 10.0 A∙g<sup>−1</sup>; (<b>f</b>) capacitance retention after 10,000 cycles of continuous charge−discharge at 10.0 A∙g<sup>−1</sup>; (<b>g</b>) Ragone plots of SN<sub>6−</sub>GF//SN<sub>6−</sub>GF symmetric device.</p>
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<p>Electrochemical impedance spectroscopy results for co-doped graphene sample in 1 M Na<sub>2</sub>SO<sub>4</sub> electrolyte, SN<sub>6−</sub>GF (25:75): (<b>a</b>) magnitude Bode plots; (<b>b</b>) Bode plots vs. phase angle; (<b>c</b>) Nyquist plot; and (<b>d</b>) complex capacitance analysis.</p>
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11 pages, 5336 KiB  
Article
Solid Electrolytes in the N-Propyl-N-methyl-pyrrolidinium Tetrafluoroborate—Lithium Tetrafluoroborate System
by Artem Ulihin, Dmitry Novozhilov and Nikolai Uvarov
Batteries 2023, 9(3), 167; https://doi.org/10.3390/batteries9030167 - 10 Mar 2023
Cited by 5 | Viewed by 2076
Abstract
Solid electrolytes prepared by the addition of LiBF4 to the plastic phase of [N13pyr]BF4 were prepared, and their physical and electrical properties were investigated. The electrolytes [N13pyr]BF4-LiBF4 containing 8–20 wt% LiBF4 are solid [...] Read more.
Solid electrolytes prepared by the addition of LiBF4 to the plastic phase of [N13pyr]BF4 were prepared, and their physical and electrical properties were investigated. The electrolytes [N13pyr]BF4-LiBF4 containing 8–20 wt% LiBF4 are solid at temperatures below 80 °C and have a high ionic conductivity ~10−3–10−2 S cm−1 at 60 °C. Based on the results of DSC and conductivity studies, the phase diagram of the [N13pyr]BF4-LiBF4 binary system was plotted, and the formation of a new compound, 3[N13pyr]BF4·2LiBF4 was proposed. The existence of the new phase was supported by X-ray diffraction data. Electrochemical measurements of cells with lithium electrodes were carried out to test the applicability of these materials in lithium batteries. The electrochemical window was determined to be more than 5 V. In contrast to earlier data obtained for similar systems, the preconditioning effect was not observed. Nevertheless, the solid electrolyte [N13pyr]BF4-LiBF4 system has high ionic conductivity and may be used in solid-state lithium-ion batteries. Full article
(This article belongs to the Special Issue Solid-State Electrolytes for Safe Batteries)
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<p>TG (<b>a</b>) and DSC (<b>b</b>) curves obtained for the [N<sub>13</sub>pyr]BF<sub>4</sub>-LiBF<sub>4</sub> samples containing different amounts of the LiBF<sub>4</sub> additive.</p>
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<p>Temperature dependences of the conductivity of [N<sub>13</sub>pyr]BF<sub>4</sub>-LiBF<sub>4</sub> samples containing different amounts of the LiBF<sub>4</sub> additive.</p>
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<p>Phase diagram of the [N<sub>13</sub>pyr]BF<sub>4</sub>-LiBF<sub>4</sub> binary system proposed in the work and based on the DSC (black points) and conductivity (empty symbols) data.</p>
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<p>X-ray diffraction patterns of pure [N<sub>13</sub>pyr]BF<sub>4</sub> (1), pure LiBF<sub>4</sub> (6), and [N<sub>13</sub>pyr]BF<sub>4</sub>-LiBF<sub>4</sub> samples containing 2, 12, 26 and 40 wt% LiBF<sub>4</sub>, with curves 2, 3, 4 and 5, respectively.</p>
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<p>The volt-ampere dependences obtained for [N<sub>13</sub>pyr]BF<sub>4</sub>-LiBF<sub>4</sub> samples containing 10 and 16 wt% LiBF<sub>4</sub> were obtained using a potentiodynamic method in the cell with metallic lithium and steel electrodes.</p>
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<p>Galvanostatic cycling curve obtained for Li/electrolyte/Li cells with the [N<sub>13</sub>pyr]BF<sub>4</sub>-LiBF<sub>4</sub> electrolyte containing 10 wt% LiBF<sub>4</sub> at a current density of 0.01 mA/cm<sup>2</sup> (<b>a</b>) and some cycles represented for a short time interval (<b>b</b>). The artefact observed in the time interval from 100 to 120 min was caused by a temporary power outage.</p>
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<p>Nyquist curves obtained at 40 °C for Li/electrolyte/Li cells with the [N<sub>13</sub>pyr]BF<sub>4</sub>-LiBF<sub>4</sub> electrolyte containing 10 wt% LiBF<sub>4</sub> before and after galvanostatic cycling in linear (<b>a</b>) and logarithmic (<b>b</b>) scales. The symbols are experimental data, and the lines are the fitting curves. The equivalent circuit used for the data fitting (<b>c</b>) and the change in the electrolyte resistance (<span class="html-italic">R</span><sub>e</sub>), the charge transfer resistance (<span class="html-italic">R</span><sub>1</sub>), and the SEI resistance (<span class="html-italic">R</span><sub>2</sub>) as a result of the galvanostatic cycling (<b>d</b>).</p>
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14 pages, 3749 KiB  
Article
Ion Transport Regulated Lithium Metal Batteries Achieved by Electrospun ZIF/PAN Composite Separator with Suitable Electrolyte Wettability
by Ting Liu, Xuemei Hu, Yadong Zhang, Ting He, Jianping Zhou and Junqiang Qiao
Batteries 2023, 9(3), 166; https://doi.org/10.3390/batteries9030166 - 9 Mar 2023
Cited by 9 | Viewed by 3658
Abstract
Lithium metal battery (LMB) is a topic receiving growing attention due to the high theoretical capacity, while its practical application is seriously hindered by the lithium dendrites issue. As the physical barrier between two electrodes, the separator can achieve dendrite suppression by means [...] Read more.
Lithium metal battery (LMB) is a topic receiving growing attention due to the high theoretical capacity, while its practical application is seriously hindered by the lithium dendrites issue. As the physical barrier between two electrodes, the separator can achieve dendrite suppression by means of providing higher mechanical strength, regulating ion transport and facilitating homogeneous lithium deposition. Based on this, a composite separator is fabricated with zeolitic imidazolate framework (ZIF-8) and polyacrylonitrile (PAN) via electrospinning techniques, and its physical properties and electrochemical performances, together with its dendrite suppression mechanism, are investigated. The ZIF8-PAN separator possesses a unique 3D interconnected porous skeleton, displaying higher electrolyte uptake, preferable electrolyte wettability, and lower thermal shrinkage compared with the commercial polypropylene separator. In addition, a battery assembled with the ZIF8-PAN separator can effectively regulate ion transport and suppress dendrites growth, which exhibits an enhanced ionic conductivity (1.176 mS/cm), an increased lithium-ion transference number (0.306), a wider electrochemical stability window (5.04 V), and superior cycling stability (over 600 h with voltage hysteresis of 30 mV). This work offers a promising strategy to realize safe separator for dendrite suppression in LMB. Full article
(This article belongs to the Special Issue Advanced Electrolytes for Metal Ion Batteries)
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<p>Schematic diagram of: (<b>a</b>) non-uniform deposition of Li ions after passing through PP separator; (<b>b</b>) uniform deposition of Li ions after redistribution through the obtained ZIF8-PAN separator.</p>
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<p>(<b>a</b>) Fabrication diagram and optical photograph of ZIF8-PAN separator. The SEM images of ZIF8-PAN composite membrane before hot-pressing: (<b>b</b>) top-view, (<b>e</b>) cross-section; after hot-pressing: (<b>c</b>,<b>d</b>) top-view; (<b>f</b>) cross-section. (<b>g</b>) TEM image of ZIF8-PAN separator. (<b>h</b>) EDS element mapping distribution of ZIF8-PAN separator.</p>
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<p>The results of (<b>a</b>) TGA; (<b>b</b>) DTG measurement of ZIF-8 powder and three kinds of separators. The comparison of (<b>c</b>,<b>d</b>) heat shrinkage tests; (<b>e</b>) electrolyte contact angle of PP, PAN and ZIF8-PAN separators.</p>
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<p>Electrochemical characterization of the cells assembled with three different separators at 25 °C. (<b>a</b>) Electrochemical impedance spectra for SS//SS cells. (<b>b</b>) LSV of SS//Li cells. (<b>c</b>) Chronoamperometry profile and (<b>d</b>) Nyquist plot before and after polarization for symmetric battery assembled with ZIF8-PAN separator.</p>
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<p>SEM images of (<b>a</b>,<b>d</b>) top-surface of Cu foil; (<b>b</b>,<b>e</b>) cross-section of Cu foil; (<b>c</b>,<b>f</b>) the surface of the separators disassembled from Li//Cu half cells with PP and ZIF8-PAN separator, respectively. (<b>g</b>) The CE comparison of Li//Cu half cells with PP and ZIF8-PAN separator for a current density of 1.0 mA/cm<sup>2</sup> with an area capacity of 1.0 mAh/cm<sup>2</sup>.</p>
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<p>(<b>a</b>) The cycling stability of the Li//Li symmetric cells assembled with PP and PAN separators under current density of 1.0 mA/cm<sup>2</sup> with area capacity of 1.0 mAh/cm<sup>2</sup> at 25 °C. (<b>b</b>) Corresponding magnified voltage profiles at the 10th, 100th and 200th cycle. (<b>c</b>) The SEM analysis of 100th cycled Li electrodes tested in Li//Li symmetric cells using certain separator.</p>
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<p>The electrochemical performances of Li//LiFePO<sub>4</sub> full cells assembled with PP and ZIF8-PAN separators. (<b>a</b>) Voltage profiles for the first and 270th cycle at 0.5 C. (<b>b</b>) Charge/discharge performance of ZIF8-PAN-based full cells under different current densities. (<b>c</b>,<b>d</b>) EIS results of pristine and cycled full cells. (<b>e</b>) Cyclic performance of two types of cells at 0.5 C.</p>
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15 pages, 5507 KiB  
Article
Prediction of the Heat Generation Rate of Lithium-Ion Batteries Based on Three Machine Learning Algorithms
by Renfeng Cao, Xingjuan Zhang and Han Yang
Batteries 2023, 9(3), 165; https://doi.org/10.3390/batteries9030165 - 9 Mar 2023
Cited by 11 | Viewed by 3928
Abstract
The heat generation rate (HGR) of lithium-ion batteries is crucial for the design of a battery thermal management system. Machine learning algorithms can effectively solve nonlinear problems and have been implemented in the state estimation and life prediction of batteries; however, limited research [...] Read more.
The heat generation rate (HGR) of lithium-ion batteries is crucial for the design of a battery thermal management system. Machine learning algorithms can effectively solve nonlinear problems and have been implemented in the state estimation and life prediction of batteries; however, limited research has been conducted on determining the battery HGR through machine learning. In this study, we employ three common machine learning algorithms, i.e., artificial neural network (ANN), support vector machine (SVM), and Gaussian process regression (GPR), to predict the battery HGR based on our experimental data, along with cases of interpolation and extrapolation. The results indicated the following: (1) the prediction accuracies for the interpolation cases were better than those of extrapolation, and the R2 values of interpolation were greater than 0.96; (2) after the discharge voltage was added as an input parameter, the prediction of the ANN was barely affected, whereas the performance of the SVM and GPR were improved; and (3) the ANN exhibited the best performance among the three algorithms. Accurate results can be obtained by using a single hidden layer and no more than 15 neurons without the additional input, where the R2 values were in the range of 0.89–1.00. Therefore, the ANN is preferable for predicting the HGR of lithium-ion batteries. Full article
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<p>Architecture of the ANN model.</p>
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<p>Comparison of the estimated and actual HGRs: (<b>a</b>) 0.5 C discharge without discharge voltage as an input; (<b>b</b>) 0.5 C discharge with discharge voltage as an input; (<b>c</b>) 1 C discharge without discharge voltage as an input; (<b>d</b>) 1 C discharge with discharge voltage as an input; (<b>e</b>) 1.5 C discharge without discharge voltage as an input; (<b>f</b>) 1.5 C discharge with discharge voltage as an input.</p>
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<p>Comparison of the estimated and actual HGRs: (<b>a</b>) 0.5 C discharge without discharge voltage as an input; (<b>b</b>) 0.5 C discharge with discharge voltage as an input; (<b>c</b>) 1 C discharge without discharge voltage as an input; (<b>d</b>) 1 C discharge with discharge voltage as an input; (<b>e</b>) 1.5 C discharge without discharge voltage as an input; (<b>f</b>) 1.5 C discharge with discharge voltage as an input.</p>
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<p>RMSE of training and testing, and the relative error of the average HGR, <span class="html-italic">δ</span>: (<b>a</b>) RMSE values of 0.5 C discharge; (<b>b</b>) <span class="html-italic">δ</span> of 0.5 C discharge; (<b>c</b>) RMSE values of 1 C discharge; (<b>d</b>) <span class="html-italic">δ</span> of 1 C discharge; (<b>e</b>) RMSE values of 1.5 C discharge; (<b>f</b>) <span class="html-italic">δ</span> of 1.5 C discharge.</p>
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<p>RMSE of training and testing, and the relative error of the average HGR, <span class="html-italic">δ</span>: (<b>a</b>) RMSE values of 0.5 C discharge; (<b>b</b>) <span class="html-italic">δ</span> of 0.5 C discharge; (<b>c</b>) RMSE values of 1 C discharge; (<b>d</b>) <span class="html-italic">δ</span> of 1 C discharge; (<b>e</b>) RMSE values of 1.5 C discharge; (<b>f</b>) <span class="html-italic">δ</span> of 1.5 C discharge.</p>
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<p>Comparison of the estimated and actual HGRs during discharge: (<b>a</b>) at 20 °C without discharge voltage as an input; (<b>b</b>) at 20 °C with discharge voltage as an input; (<b>c</b>) at 30 °C without discharge voltage as an input; (<b>d</b>) at 30 °C with discharge voltage as an input; (<b>e</b>) at 45 °C without discharge voltage as an input; (<b>f</b>) at 45 °C with discharge voltage as an input.</p>
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<p>Comparison of the estimated and actual HGRs during discharge: (<b>a</b>) at 20 °C without discharge voltage as an input; (<b>b</b>) at 20 °C with discharge voltage as an input; (<b>c</b>) at 30 °C without discharge voltage as an input; (<b>d</b>) at 30 °C with discharge voltage as an input; (<b>e</b>) at 45 °C without discharge voltage as an input; (<b>f</b>) at 45 °C with discharge voltage as an input.</p>
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<p>RMSE of training and testing, and the relative error of the average HGR, <span class="html-italic">δ</span>: (<b>a</b>) RMSE values of discharge at 20 °C; (<b>b</b>) <span class="html-italic">δ</span> of discharge at 20 °C; (<b>c</b>) RMSE values of discharge at 30 °C; (<b>d</b>) <span class="html-italic">δ</span> of discharge at 30 °C; (<b>e</b>) RMSE values of discharge at 45 °C; (<b>f</b>) <span class="html-italic">δ</span> of discharge at 45 °C.</p>
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22 pages, 47786 KiB  
Review
A Systematic Literature Analysis on Electrolyte Filling and Wetting in Lithium-Ion Battery Production
by Nicolaj Kaden, Ricarda Schlimbach, Álvaro Rohde García and Klaus Dröder
Batteries 2023, 9(3), 164; https://doi.org/10.3390/batteries9030164 - 9 Mar 2023
Cited by 14 | Viewed by 7446
Abstract
Electrolyte filling and wetting is a quality-critical and cost-intensive process step of battery cell production. Due to the importance of this process, a steadily increasing number of publications is emerging for its different influences and factors. We conducted a systematic literature review to [...] Read more.
Electrolyte filling and wetting is a quality-critical and cost-intensive process step of battery cell production. Due to the importance of this process, a steadily increasing number of publications is emerging for its different influences and factors. We conducted a systematic literature review to identify common parameters that influence wetting behavior in experimental settings, specifically focusing on material, processes, and experimental measurement methods but excluding simulation studies. We reduced the initially found 544 records systematically to 39 fully labeled articles. Our profound analysis guided by attributed labelings revealed current research gaps such as the lack of a holistic view on measurement methods for filling and wetting, underrepresented studies relevant to series production, as well as the negligence of research targeting the transferability of results from the material to the cell level, while also examining the measured variables’ interactions. After comparatively illustrating and discussing implications of our findings, we also discussed limitations of our contribution and suggested ideas for potential further research topics. Full article
(This article belongs to the Section Battery Processing, Manufacturing and Recycling)
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<p>Schematic illustration of the electrolyte filling process with its sub-processes.</p>
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<p>Schematic illustration of the process chain of battery cell production with the state-of-the-art process steps. The dashed boxes show the properties relevant to wetting, which are influenced by this particular process step.</p>
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<p>Schematic illustration of the common measurement methods used to measure the wetting properties of cell composite materials.</p>
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<p>Schematic illustration of the common measurement methods for measuring the wetting properties of assembled battery cells.</p>
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<p>Paper selection process along the PRISMA statement.</p>
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<p>Illustration of the examined publications sorted by years. In addition, we assigned the publications to the corresponding main group labels. Since one publication can be assigned to various foci groups (e.g., material and further processes), its cumulation (right bar) sometimes exceeds the total amount of papers (left bar). The publications from 2022 include only those published up to the month of June.</p>
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<p>Representation of the labels and sub labels used in relation to their frequency of occurrence.</p>
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<p>Illustration of the number of measurement methods used in the publications examined, with a classification of the measurements into material and cell levels.</p>
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<p>(<b>a</b>) Illustration of the measurement method used in the publication by Davoodabadi et al.; (<b>b</b>) an extension of the method for double-sided coated electrodes. [<a href="#B53-batteries-09-00164" class="html-bibr">53</a>].</p>
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13 pages, 4531 KiB  
Article
SOC Estimation Based on Combination of Electrochemical and External Characteristics for Hybrid Lithium-Ion Capacitors
by Xiaofan Huang, Renjie Gao, Luyao Zhang, Xinrong Lv, Shaolong Shu, Xiaoping Tang, Ziyao Wang and Junsheng Zheng
Batteries 2023, 9(3), 163; https://doi.org/10.3390/batteries9030163 - 9 Mar 2023
Cited by 2 | Viewed by 2039
Abstract
Hybrid lithium-ion capacitors (HyLICs) have received considerable attention because of their ability to combine the advantages of high-energy lithium-ion batteries and high-power supercapacitors. State of charge (SOC) is the main factor affecting the practical application of HyLICs; therefore, it is essential to estimate [...] Read more.
Hybrid lithium-ion capacitors (HyLICs) have received considerable attention because of their ability to combine the advantages of high-energy lithium-ion batteries and high-power supercapacitors. State of charge (SOC) is the main factor affecting the practical application of HyLICs; therefore, it is essential to estimate the SOC accurately. In this paper, a partition SOC-estimation method that combines electrochemical and external characteristics is proposed. The discharge process of the HyLICs was divided into three phases based on test results of electrochemical characteristics. To improve the estimation accuracy and reduce the amount of calculation, the Extended Kalman Filter (EKF) method was applied for SOC estimation at the interval where the capacitor energy storage characteristics dominated, and the Ampere-hour (Ah) method was used to estimate the SOC at the interval where battery energy storage characteristics dominated. The proposed method is verified under different operating conditions. The experimental results show good agreement with the estimation results, which indicates that the proposed method can estimate the SOC of the HyLICs accurately. Full article
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<p>Electrochemical impedance equivalent circuit model.</p>
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<p>Electrochemical impedance spectroscopy of HyLICs.</p>
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<p>Capacitor impedance changes with terminal voltage of HyLICs.</p>
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<p>Classification results based on electrochemical characteristics of HyLICs.</p>
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<p>The first-order RC equivalent circuit model.</p>
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<p>The OCV–SOC curve after the parameter identification.</p>
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<p>SOC partition-estimation method based on electrochemical characteristics.</p>
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<p>HyLICs Simulink model.</p>
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<p>Comparison chart of predicted terminal voltage and actual value.</p>
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<p>Comparison chart of (<b>a</b>) predicted SOC and actual value; (<b>b</b>) the difference through discharge OCV test.</p>
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<p>Comparison chart of (<b>a</b>) predicted SOC and actual value; (<b>b</b>) the difference through HPPC test.</p>
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<p>The I-t curve at the loading profiles of NEDC.</p>
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<p>Comparison chart of (<b>a</b>) predicted terminal voltage and actual value; (<b>b</b>) the difference through NEDC cycle test.</p>
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16 pages, 5099 KiB  
Article
Symmetric and Asymmetric Supercapacitors of ITO Glass and Film Electrodes Consisting of Carbon Dot and Magnetite
by Misganu Chewaka Fite, Po-Jen Wang and Toyoko Imae
Batteries 2023, 9(3), 162; https://doi.org/10.3390/batteries9030162 - 8 Mar 2023
Cited by 3 | Viewed by 2291
Abstract
To enhance the energy density, hybrid supercapacitors were fabricated, and their electrochemical features were investigated using a two-electrode configuration. By assembling nitrogen-doped graphene/magnetite (NG/Fe3O4) on indium tin oxide-coated (ITO) glass as a cathode and NG/carbon dots(Cdots)/Fe3O4 [...] Read more.
To enhance the energy density, hybrid supercapacitors were fabricated, and their electrochemical features were investigated using a two-electrode configuration. By assembling nitrogen-doped graphene/magnetite (NG/Fe3O4) on indium tin oxide-coated (ITO) glass as a cathode and NG/carbon dots(Cdots)/Fe3O4 on ITO glass as an anode, a much higher gravimetric specific capacitance of 252.2 F/g, at a current density of 0.5 A/g, was obtained from this asymmetric supercapacitor compared with that (212.0 F/g) of a symmetric supercapacitor (NG/Cdots/Fe3O4)//(NG/Cdots/Fe3O4). A gravimetric energy density of 90.1 Wh/kg was obtained for an asymmetric ITO glass device at a specific power density of 400.0 W/kg. On the other hand, when an asymmetric two-electrode cell was fabricated with a Cdots/polypyrrole (PPy)/Fe3O4/TEMPO-oxidized cellulose nanofiber (TOCNF)-film electrode and a Cdots/PPy/TOCNF-film electrode, the specific capacitance (107.1 F/g) at a current density of 0.8 A/g was lower than that (456.4 F/g) of a symmetric (Cdots/PPy/Fe3O4/TOCNF)//(Cdots/PPy/Fe3O4/TOCNF)-film cell. Subsequently, a gravimetric energy density of 40.6 Wh/kg was achieved for a symmetric-film device at a specific power density of 320 W/kg. These results suggest that our method offers an efficient approach to developing symmetric and asymmetric devices consisting of hybrid materials for meeting the ever-increasing demands on energy-storage devices. Full article
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<p>(<b>A</b>) A scheme and (<b>B</b>) an image of two-electrode configuration. (<b>C</b>) An image of a two-electrode cell.</p>
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<p>(<b>A</b>) An XRD pattern of Fe<sub>3</sub>O<sub>4</sub> NWs, (<b>B</b>) an image of Fe<sub>3</sub>O<sub>4</sub> NWs on a magnet, (<b>C</b>) images of an Fe<sub>3</sub>O<sub>4</sub>/TOCNF film on a magnet and (<b>D</b>) a,b IR absorption spectra of Cdots and various TOCNF films.</p>
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<p>(<b>A</b>) Stress–strain curves and (<b>B</b>) a list of mechanical properties of TOCNF, Fe<sub>3</sub>O<sub>4</sub>/TOCNF and Cdots/PPy/Fe<sub>3</sub>O<sub>4</sub>/TOCNF films.</p>
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<p>(<b>a</b>) An image of a bent Cdots/PPy/Fe<sub>3</sub>O<sub>4</sub>/TOCNF composite film. The FESEM images of Cdots/PPy/Fe<sub>3</sub>O<sub>4</sub>/TOCNF composite film (<b>b</b>) in a pristine state, (<b>c</b>) after bending 50 times and (<b>d</b>) after electrochemical measurement.</p>
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<p>(<b>A</b>) CV profiles at different scan rates, (<b>B</b>) the specific capacitance as a function of scan rate, (<b>C</b>) CV profiles for different potential windows scanned at 5 mV/s and (<b>D</b>) the specific capacitance as a function of potential window at 5 mV/s for symmetric and asymmetric supercapacitors.</p>
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<p>(<b>A</b>) The GCD profiles at different current densities and (<b>B</b>) gravimetric capacitances as a function of current density of (a–c) symmetric and (d–f) asymmetric supercapacitors. (<b>C</b>) The GCD profiles for different potential windows and (<b>D</b>) gravimetric capacitances as a function of potential window of (c) symmetric and (f) asymmetric supercapacitors at 0.5 A/g current density. (<b>E</b>) Ragone plots of (a–c) symmetric and (d–f) asymmetric supercapacitors. Supercapacitors: (a) (NG/Fe<sub>3</sub>O<sub>4</sub>)//(NG/Fe<sub>3</sub>O<sub>4</sub>), (b) (NG/Cdots)/NG/Cdots), (c) NG/Cdots/Fe<sub>3</sub>O<sub>4</sub>)//(NG/Cdots/Fe<sub>3</sub>O<sub>4</sub>), (d) (NG/Cdots)//(NG/Fe<sub>3</sub>O<sub>4</sub>), (e) (NG/Cdots)//(NG/Cdots/Fe<sub>3</sub>O<sub>4</sub>) and (f) (NG/Fe<sub>3</sub>O<sub>4</sub>)//(NG/Cdots/Fe<sub>3</sub>O<sub>4</sub>).</p>
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<p>(<b>A</b>) Nyquist plots, (<b>B</b>) Bode phase plots and (<b>C</b>) capacitance retention of (c) symmetric (NG/Cdots/Fe<sub>3</sub>O<sub>4</sub>)//(NG/Cdots/Fe<sub>3</sub>O<sub>4</sub>)) and (f) asymmetric ((NG/Fe<sub>3</sub>O<sub>4</sub>)//(NG/Cdots/Fe<sub>3</sub>O<sub>4</sub>)) devices.</p>
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<p>(<b>A</b>) CV and (<b>B</b>) GCD curves of (<b>a</b>) symmetric (Cdots/PPy/Fe<sub>3</sub>O<sub>4</sub>/TOCNF)//(Cdots/PPy/Fe<sub>3</sub>O<sub>4</sub>/TOCNF) cell and (<b>b</b>) asymmetric (Cdots/PPy/Fe<sub>3</sub>O<sub>4</sub>/TOCNF)//(Cdots/PPy/TOCNF) cell and (<b>c</b>) specific capacitance at various scan rates. (<b>C</b>) Cycle stabilities and (<b>D</b>) a Ragone diagram of symmetric and asymmetric cells.</p>
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2 pages, 171 KiB  
Correction
Correction: Vogt et al. Production and Characterisation of Fibre-Reinforced All-Solid-State Electrodes and Separator for the Application in Structural Batteries. Batteries 2022, 8, 55
by Daniel Vogt, Peter Michalowski and Arno Kwade
Batteries 2023, 9(3), 161; https://doi.org/10.3390/batteries9030161 - 8 Mar 2023
Cited by 1 | Viewed by 1269
Abstract
There was an error in the original publication [...] Full article
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