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World Electr. Veh. J., Volume 14, Issue 11 (November 2023) – 18 articles

Cover Story (view full-size image): In response to the growing need for sustainable urban mobility, this paper explores the design details of electric powertrains for light electric vehicles (LEVs) tailored to the specific demands of city and suburban environments. Unlike traditional approaches that overlook driving missions, this study emphasizes the role of driving cycles from the outset in optimizing powertrains for multipurpose LEVs. The proposed modular and scalable electric powertrain is finely tuned for European urban settings, accommodating varying motor specifications and the energy capacity of the battery. With a focus on motor, battery, and charging requirements, this research contributes valuable insights for the efficient deployment of LEVs in the pursuit of sustainable urban transportation. View this paper
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24 pages, 6127 KiB  
Article
Multi-Mode Switching Control Strategy for IWM-EV Active Energy-Regenerative Suspension Based on Pavement Level Recognition
by Zhigang Zhou, Zhichong Shi and Xinqing Ding
World Electr. Veh. J. 2023, 14(11), 317; https://doi.org/10.3390/wevj14110317 - 20 Nov 2023
Viewed by 1915
Abstract
Aiming at the problems of poor overall vibration reduction and high energy consumption of in-wheel motor-driven electric vehicle (IWM-EV) active suspension on mixed pavement, a multi-mode switching control strategy based on pavement identification and particle swarm optimization is proposed. First, the whole vehicle [...] Read more.
Aiming at the problems of poor overall vibration reduction and high energy consumption of in-wheel motor-driven electric vehicle (IWM-EV) active suspension on mixed pavement, a multi-mode switching control strategy based on pavement identification and particle swarm optimization is proposed. First, the whole vehicle dynamic model containing active energy-regenerative suspension and the reference model was established, and the sliding mode controller and PID controller designed, respectively, to suppress the vertical vibration of the vehicle and the in-wheel motor. Second, a road grade recognition model based on the dynamic travel signal of the suspension and the road grade coefficient was established to identify the road grade, and then the dynamic performance and energy-feedback characteristics of suspension were optimized by particle swarm optimization. According to the results of pavement identification, the optimal solution of the suspension controller parameters under each working mode was divided and selected to realize the switch of the suspension working mode. The simulation results show that the control strategy can accurately identify the grade of road surface under the condition of mixed road surface, and the ride index of the optimized active energy-regenerative suspension is obviously improved, while some energy is recovered. Full article
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<p>Single wheel configuration of active regenerative suspension.</p>
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<p>Dynamic model of vehicle active energy-regenerative suspension.</p>
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<p>Vertical force of in-wheel motor.</p>
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<p>Active energy-regenerative circuit model.</p>
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<p>Class B pavement time domain model.</p>
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<p>Power spectral density of Class B pavement.</p>
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<p>Time domain model of composite pavement.</p>
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<p>Recognition results of composite pavement.</p>
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<p>Particle swarm optimization algorithm block diagram.</p>
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<p>Control strategy of active regenerative suspension based on particle swarm optimization algorithm.</p>
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<p>Evolution curve of fitness value.</p>
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<p>Vehicle control strategy block diagram.</p>
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<p>Mixed grade pavement input.</p>
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<p>The vertical acceleration of the center of mass.</p>
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<p>Dynamic deflection of the suspension.</p>
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<p>Tire runout.</p>
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<p>Dynamic displacement of in-wheel motor.</p>
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<p>Vertical acceleration of in-wheel motor.</p>
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<p>Angular acceleration of roll angle.</p>
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<p>Angular acceleration of pitch angle.</p>
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<p>The circuit recovers energy.</p>
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25 pages, 13842 KiB  
Article
Research into the Peculiarities of the Individual Traction Drive Nonlinear System Oscillatory Processes
by Alexander V. Klimov, Baurzhan K. Ospanbekov, Andrey V. Keller, Sergey S. Shadrin, Daria A. Makarova and Yury M. Furletov
World Electr. Veh. J. 2023, 14(11), 316; https://doi.org/10.3390/wevj14110316 - 20 Nov 2023
Cited by 2 | Viewed by 1631
Abstract
Auto-oscillations may occur in moving vehicles in the area where the tire interacts with the support base. The parameters of such oscillations depend on the sliding velocity in the contact patch. As they negatively affect the processes occurring in the electric drive and [...] Read more.
Auto-oscillations may occur in moving vehicles in the area where the tire interacts with the support base. The parameters of such oscillations depend on the sliding velocity in the contact patch. As they negatively affect the processes occurring in the electric drive and the mechanical transmission, reducing their energy efficiency, such processes can cause failures in various elements. This paper aims to conduct a theoretical study into the peculiarities of oscillatory processes in the nonlinear system and an experimental study of the auto-oscillation modes of an individual traction drive. It presents the theoretical basis used to analyze the peculiarities of oscillation processes, including their onset and course, the results of simulation mathematical modeling and the experimental studies into the oscillation phenomena in the movement of the vehicle towards the supporting base. The practical value of this study lies in the possibility to use the results in the development of algorithms for the exclusion of auto-oscillation phenomena in the development of vehicle control systems, as well as to use the auto-oscillation processes onset and course analysis methodology to design the electric drive of the driving wheels. Full article
(This article belongs to the Topic Advanced Electric Vehicle Technology)
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<p>Calculation scheme of interaction of an elastic wheel with a solid support base: 1—mass <span class="html-italic">m1</span> of sprung parts of the car falling on the wheel; 2—mass <span class="html-italic">m2</span> of the wheel; 3—rollers; 4—elastic element characterizing the pliability of the tire in the longitudinal direction; 5—support base; 6—rotating wheel; 7—traction motor; c—spring stiffness.</p>
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<p>Dependence of friction force F on relative sliding velocity <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mrow> <mn>2</mn> <mi>s</mi> <mi>k</mi> </mrow> </msub> </mrow> </semantics></math> for the Coulomb dry friction model (<b>a</b>) and for Paseika’s “magic formula” (<b>b</b>).</p>
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<p>Dependence of friction force F on relative sliding velocity V<sub>2<span class="html-italic">sk</span></sub> for model (8).</p>
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<p>Trajectory in a corner on dry asphalt.</p>
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<p>Process of time variation in speed in a turn on dry asphalt.</p>
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<p>Angular velocities of the driving wheels in a turn on dry asphalt: 1—left rear wheel; 2—right rear wheel.</p>
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<p>Process of time variation in torques on the driving wheels in a turn on dry asphalt: 1—left rear wheel; 2—right rear wheel.</p>
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<p>Trajectory of movement in a turn on ice with snow.</p>
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<p>Process of time variation in speed in a turn on snow-covered ice.</p>
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<p>Angular velocities of the driving wheels in a turn on ice with snow: 1—left rear wheel; 2—right rear wheel.</p>
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<p>Process of change in time of torques on the driving wheels in a turn on ice with snow: 1—left rear wheel; 2—right rear wheel.</p>
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<p>Trajectory of motion during braking in a turn on ice with snow.</p>
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<p>Process of change in time of movement speed during braking in a turn on ice with snow.</p>
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<p>The process of change in time of regenerative moments given to the driving wheels of an electric bus during braking in a turn on ice with snow: 1—left rear wheel; 2—right rear wheel.</p>
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<p>Process of change in time of angular velocities of the driving wheels during braking in a turn on ice with snow: 1—left rear wheel; 2—right rear wheel.</p>
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<p>Fragments of the traction motor torque (<b>a</b>), the driving wheel rotation angular velocity (<b>b</b>) and the radial tire deformation (<b>c</b>) observed during the electric bus straight-on acceleration on dry asphalt.</p>
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<p>Spectral energy densities of traction motor torque (<b>a</b>), angular velocity of the driving wheel rotation (<b>b</b>) and radial tire deformation (<b>c</b>) during the straight-on acceleration of an electric bus on dry asphalt.</p>
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<p>General view of the tested vehicle (<b>a</b>); traction electric drive scheme (<b>b</b>).</p>
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<p>Characteristic fragment of the realization of traction electric torque, obtained during testing of the electric bus.</p>
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<p>Characteristic fragment of the realization of traction electric torque, obtained as a result of modeling of electric bus motion.</p>
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<p>Spectral energy density of the traction motor torque energy for the realization obtained experimentally.</p>
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<p>A characteristic fragment of the wheel angular velocity obtained during the electric bus tests.</p>
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<p>A characteristic fragment of the wheel angular velocity, obtained as a result the electric bus motion modeling.</p>
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<p>Energy spectral density of the angular velocity of the wheel obtained experimentally.</p>
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<p>Realizations of the position of the travel pedal (<b>a</b>), electric torque of the rear left driving wheel, reduced to the wheel (<b>b</b>); angular speed of rotation of the rotor of the rear left electric motor (<b>c</b>).</p>
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<p>Spectral energy density for torque realization of <a href="#wevj-14-00316-f025" class="html-fig">Figure 25</a>b.</p>
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<p>Spectral energy density for realization of angular speed of rotor rotation of <a href="#wevj-14-00316-f025" class="html-fig">Figure 25</a>c.</p>
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<p>Variation in torques on the shaft of electric motors, angular velocities of wheels and rotors, as well as motor currents in time during the acceleration up to 20 km/h and subsequent complex braking.</p>
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<p>Variation in traction motor torques, angular velocities of wheels and rotors, and motor currents in time during the acceleration to maximum speed with increased wheel slippage.</p>
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<p>Dependences of torques and currents of traction motors on time at slippage of the driving wheels.</p>
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<p>Fragment of torque realization of traction electric motors when driving on an urban route.</p>
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18 pages, 5295 KiB  
Article
Real-Time Energy Management Strategy of Hydrogen Fuel Cell Hybrid Electric Vehicles Based on Power Following Strategy–Fuzzy Logic Control Strategy Hybrid Control
by Ke Zou, Wenguang Luo and Zhengjie Lu
World Electr. Veh. J. 2023, 14(11), 315; https://doi.org/10.3390/wevj14110315 - 20 Nov 2023
Cited by 6 | Viewed by 2384
Abstract
Fuel cell hybrid electric vehicles have the advantages of zero emission, high efficiency and fast refuelling, etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power [...] Read more.
Fuel cell hybrid electric vehicles have the advantages of zero emission, high efficiency and fast refuelling, etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power distribution. The traditional power following strategy has the advantage of a real-time operation, but the power correction is usually based only on the state of charge of a lithium battery, which causes the operating point of the fuel cell to be in the region of a low efficiency. To solve this problem, this paper proposes a hybrid power-following-fuzzy control strategy, where a fuzzy logic control strategy is used to optimise the correction module based on the power following strategy, which regulates the state of charge while correcting the output power of the fuel cell towards the efficient operating point. The results of the joint simulation with Matlab + Advisor under the Globally Harmonised Light Vehicle Test Cycle Conditions show that the proposed strategy still ensures the advantages of real-time energy management, and for the hydrogen fuel cell, the hydrogen consumption is reduced by 13.5% and 4.1% compared with the power following strategy and the fuzzy logic control strategy, and the average output power variability is reduced by 14.6% and 5.1%, respectively, which is important for improving the economy of the whole vehicle and prolonging the lifetime of fuel cell. Full article
(This article belongs to the Topic Advanced Electric Vehicle Technology)
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<p>System structure of FCHEV.</p>
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<p>PEMFC work efficiency chart.</p>
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<p>Battery equivalent circuit.</p>
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<p>Motor MAP diagram.</p>
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<p>Fuzzy control framework.</p>
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<p>PEMFC workspace division.</p>
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<p>Membership function for fuel cell output power.</p>
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<p>Correction coefficient membership function.</p>
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<p>Battery SOC membership function.</p>
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<p>Power following strategy model.</p>
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<p>SOC power correction module in PFS-FLCS hybrid strategy.</p>
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<p>Basic framework of the PFS-FLCS hybrid control strategy model.</p>
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<p>Vehicle top-level simulation model.</p>
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<p>Vehicle dynamics validation with different control strategies under WLTC.</p>
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<p>The simulation results of battery SOC with different control strategies.</p>
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<p>The simulation results of PEMFC output power.</p>
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<p>Hydrogen consumption with different control strategies.</p>
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16 pages, 1833 KiB  
Systematic Review
Managing Transitions to Autonomous and Electric Vehicles: Scientometric and Bibliometric Review
by Milan Todorovic, Abdulaziz Aldakkhelallah and Milan Simic
World Electr. Veh. J. 2023, 14(11), 314; https://doi.org/10.3390/wevj14110314 - 20 Nov 2023
Cited by 2 | Viewed by 4011
Abstract
This paper presents a scientometric and bibliometric literature review of the research on transitions to autonomous and electric vehicles. We discuss the main characteristics, evolution, and various transitional issues, identifying potential trends for future research. The Scopus and WoS search for relevant research [...] Read more.
This paper presents a scientometric and bibliometric literature review of the research on transitions to autonomous and electric vehicles. We discuss the main characteristics, evolution, and various transitional issues, identifying potential trends for future research. The Scopus and WoS search for relevant research articles generated a corpus of 4693 articles, which we analyzed using VOSviewer visualization software. This result shows that the transition research is interdisciplinary, with 54 scientific areas identified. Analysis requires an understanding of the broader aspects of the automotive industry, trends related to sustainability, environment protection, road safety, public policies, market factors and other business, and legal and management issues. This study highlights the need for more research to address the challenges of this global transition in the automotive industry. Topics for future research are constant improvements in AI algorithms used in AVs, innovations in green energy sources, and storage solutions for EVs. This is leading to new innovative business models and platforms. Further to that, the conclusion is that the impact of the transition to a shared economy, the emergency of mobility as a service, and public acceptance of the technology have to be comprehensively considered. The vehicle of the future is seen as a smart electric car, running on green energy, and utilizing various levels of automation up to full autonomy. Full article
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<p>Analysis of the papers by the type and the research area. (<b>a</b>) Documents by the type of publication show that the most of the publications are journal articles covering 64.5% of all; (<b>b</b>) Documents by subject area show that engineering publications are dominant covering 21.6% of all publications.</p>
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<p>Co-citations analysis with network visualization of minimum three co-citations.</p>
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<p>Citation analysis for 191 articles. The first author is in the circle.</p>
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<p>Papers analysis by terms co-occurrence for the set of 191 articles.</p>
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<p>Optimal EV research, development, and production timeline.</p>
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13 pages, 464 KiB  
Article
Modeling EV Charging Station Loads Considering On-Road Wireless Charging Capabilities
by Walied Alfraidi, Mohammad Shalaby and Fahad Alaql
World Electr. Veh. J. 2023, 14(11), 313; https://doi.org/10.3390/wevj14110313 - 19 Nov 2023
Cited by 3 | Viewed by 2377
Abstract
Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless [...] Read more.
Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades. Full article
(This article belongs to the Special Issue Fast-Charging Station for Electric Vehicles: Challenges and Issues)
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<p>Flow chart of the developed Electric Vehicle Decision Tree (EVDT).</p>
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<p>Proposed Monte Carlo simulation approach for modeling an EV charging station, considering an on-road wireless charging system.</p>
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<p>Averaged EVs charged using the rapid charging station at location-14 or via on-road wireless charging.</p>
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<p>Averaged EVs charged using the rapid charging station at location-21 or via on-road wireless charging.</p>
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<p>Averaged EVs charged using the rapid charging station at location-24 or via on-road wireless charging.</p>
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<p>Averaged EVs charged using the rapid charging station at location-30 or via on-road wireless charging.</p>
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<p>EV expected charging station loads at location 14, with and without on-road wireless charging capability.</p>
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<p>EV expected charging station loads at location 21 with and without on-road wireless charging capabilities.</p>
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<p>EV expected charging station loads at location 24 with and without on-road wireless charging capabilities.</p>
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<p>EV expected charging station loads at location 30 with and without on-road wireless charging capabilities.</p>
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<p>Expected load of the on-road EV wireless charging system at location 14.</p>
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<p>Expected load of the on-road EV wireless charging system at location 21.</p>
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<p>Expected load of the on-road EV wireless charging system at location 24.</p>
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<p>Expected load of the on-road EV wireless charging system at location 30.</p>
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31 pages, 20172 KiB  
Article
Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis
by Jordy Alexander Hernández, Efrén Fernández and Hugo Torres
World Electr. Veh. J. 2023, 14(11), 312; https://doi.org/10.3390/wevj14110312 - 17 Nov 2023
Cited by 3 | Viewed by 2804
Abstract
The state of charge of a battery depends on many magnitudes, but only voltage and intensity are included in mathematical equations because other variables are complex to integrate into. The contribution of this work was to obtain a model to determine the state [...] Read more.
The state of charge of a battery depends on many magnitudes, but only voltage and intensity are included in mathematical equations because other variables are complex to integrate into. The contribution of this work was to obtain a model to determine the state of charge with these complex variables. This method was developed considering four models, the multilayer feed-forward backpropagation models of two and three input variables used supervised training, with the variable-learning-rate backpropagation training function, five and seven neurons in the hidden layer, respectively, achieving an optimal training. Meanwhile, the radial basis neural network models of two and three input variables were trained with the hybrid method, the propagation constant with a value of 1 and 80 neurons in the hidden layer. As a result, the radial basis neural network with the variable-learning-rate training function, considering the discharge temperature, was the one with the best performance, with a correlation coefficient of 0.99182 and a confidence interval of 95% (0.98849; 0.99516). It is then concluded that artificial neural networks have high performance when modeling nonlinear systems, whose parameters are difficult to measure with time variation, so estimating them in formulas where they are omitted is no longer necessary, which means an accurate SOC. Full article
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<p>Methods for performance analysis.</p>
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<p>RC circuit used to develop the model.</p>
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<p>Inputs and outputs of the artificial neural network of the first model.</p>
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<p>Inputs and outputs of the artificial neural network of the second model.</p>
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<p>Test station for data acquisition.</p>
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<p>Voltage and temperature in the discharge process.</p>
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<p>Current and temperature in the discharge process.</p>
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<p>Structure of the feed-forward backpropagation neural network of the second method with 2 layers.</p>
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<p>Feed-forward backpropagation training phase.</p>
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<p>Radial basis hybrid training phase.</p>
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<p>Standard neural structure.</p>
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<p>Best validation performance in terms of MSE.</p>
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<p>Training state plot comprises gradient, scalar μ, and validation check.</p>
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<p>Difference between the actual and the target output.</p>
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<p>Regression relation of the FBNN network.</p>
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<p>Feed-forward in a three-layer network. The superscripts represent the layer number.</p>
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<p>Regression relation of the RBNN network.</p>
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<p>FBNN and RBNN results with temperature.</p>
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<p>FBNN and RBNN results without temperature.</p>
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<p>Performance comparison of the developed methods.</p>
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<p>SOC comparisons of different FBNN configurations with temperature.</p>
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<p>SOC comparisons of different FBNN configurations without temperature.</p>
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<p>SOC interval graph of the different models developed, 95% confidence interval of the mean.</p>
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<p>Difference of the means for the different methods.</p>
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15 pages, 4385 KiB  
Article
Speed Stability and Anti-Disturbance Performance Improvement of an Interior Permanent Magnet Synchronous Motor for Electric Vehicles
by Zhongxian Chen, Xianglin Dai and Munawar Faizan
World Electr. Veh. J. 2023, 14(11), 311; https://doi.org/10.3390/wevj14110311 - 16 Nov 2023
Cited by 2 | Viewed by 1808
Abstract
To enhance the speed stability and anti-interference performance of the interior permanent magnet synchronous motor (IPMSM) in electric vehicles, a composite control strategy, incorporating sliding mode control (SMC) and extended state observer (ESO), was implemented to regulate the IPMSM’s speed. Firstly, three simulation [...] Read more.
To enhance the speed stability and anti-interference performance of the interior permanent magnet synchronous motor (IPMSM) in electric vehicles, a composite control strategy, incorporating sliding mode control (SMC) and extended state observer (ESO), was implemented to regulate the IPMSM’s speed. Firstly, three simulation analysis models of the IPMSM were established based on its electrical parameters. The current-loop regulator was a PI regulator, while the speed-loop regulators consisted of a basic SMC regulator, a linear SMC–ESO regulator, and a nonlinear SMC–ESO regulator. The simulation analysis results demonstrated that all three speed-loop regulators effectively ensured the speed stability of the IPMSM. However, the nonlinear SMC–ESO regulator exhibited superior performance in terms of enhancing the IPMSM’s resistance to disturbances. Secondly, a hardware testing platform was constructed to validate the simulation analysis findings. The hardware testing results, when compared to the simulation analysis results, revealed the need for optimization of the PI regulator’s control parameters to maintain the speed stability of the IPMSM. Moreover, contrary to the simulation analysis results, the hardware testing results indicated minimal difference in the anti-disturbance performance of the IPMSM between the linear SMC–ESO regulator and the nonlinear SMC–ESO regulator. Finally, the differences between the simulation analysis results and the hardware testing results are thoroughly discussed and analyzed, providing valuable insights for the practical implementation of IPMSM in electric vehicle drive systems. Full article
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<p>The basic structure of linear second-order ESO.</p>
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<p>System frame of simulation model of IPMSM.</p>
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<p>Start-up response of IPMSM with no-load.</p>
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<p>Anti-disturbance performance of IPMSM with step load.</p>
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<p>Feedback q-axis current of IPMSM step load.</p>
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<p>Experimental setup of the IPMSM system.</p>
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<p>Experimental setup of the IPMSM system.</p>
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<p>Speed stability comparison of IPMSM (measured by the current sensor and collected by the software system of PC, see <a href="#wevj-14-00311-f006" class="html-fig">Figure 6</a>a,b).</p>
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<p>Start-up response test of IPMSM (measured by the power meter and displayed by the oscilloscope).</p>
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<p>Anti-disturbance performance test of IPMSM.</p>
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<p>The simulation results of current-loop regulator with original and improved PI parameters.</p>
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16 pages, 2771 KiB  
Article
Optimizing Voltage Stability in Distribution Networks via Metaheuristic Algorithm-Driven Reactive Power Compensation from MDHD EVs
by Chen Zhang, Kourosh Sedghisigarchi, Rachel Sheinberg, Shashank Narayana Gowda and Rajit Gadh
World Electr. Veh. J. 2023, 14(11), 310; https://doi.org/10.3390/wevj14110310 - 15 Nov 2023
Cited by 2 | Viewed by 2231
Abstract
The deployment of medium-duty and heavy-duty (MDHD) electric vehicles (EVs), characterized by their substantial battery capacity and high charging power demand, poses a potential threat to voltage stability within distribution networks. One possible solution to voltage instability is reactive power compensation from charging [...] Read more.
The deployment of medium-duty and heavy-duty (MDHD) electric vehicles (EVs), characterized by their substantial battery capacity and high charging power demand, poses a potential threat to voltage stability within distribution networks. One possible solution to voltage instability is reactive power compensation from charging MDHD EVs. However, this process must be carefully facilitated in order to be effective. This paper introduces an innovative distribution network voltage stability solution by first identifying the network’s weakest buses and then utilizing a metaheuristic algorithm to schedule reactive power compensation from MDHD EVs. In the paper, multiple metaheuristic algorithms, including genetic algorithms, particle swarm optimization, moth flame optimization, salp swarm algorithms, whale optimization, and grey wolf optimization, are subjected to rigorous evaluation concerning their efficacy in terms of voltage stability improvement, power loss reduction, and computational efficiency. The proposed methodology optimizes power flow with the salp swarm algorithm, which was determined to be the most effective tool, to mitigate voltage fluctuations and enhance overall stability. The simulation results, conducted on a modified IEEE 33 bus distribution system, convincingly demonstrate the algorithm’s efficacy in augmenting voltage stability and curtailing power losses, supporting the reliable and efficient integration of MDHD EVs into distribution networks. Full article
(This article belongs to the Special Issue Electric Vehicles and Smart Grid Interaction)
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<p>Flowchart of the proposed approach.</p>
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<p>Modified IEEE 33 bus distribution system with the location of MDHD EV chargers.</p>
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<p>The voltage deviation values of various algorithms across 30 implementations.</p>
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<p>The voltage deviation values of different algorithms for the first 20 iterations within a single implementation.</p>
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<p>Voltage magnitude profile of the 33-bus system with/without MDHD EVs and with MDHD EVs plus reactive power compensation.</p>
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<p>Voltage magnitude profile of bus 11 with/without reactive power compensation from MDHD EVs.</p>
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12 pages, 3463 KiB  
Article
Modular and Scalable Powertrain for Multipurpose Light Electric Vehicles
by Mehrnaz Farzam Far, Damijan Miljavec, Roman Manko, Jenni Pippuri-Mäkeläinen, Mikaela Ranta, Janne Keränen, Jutta Kinder and Mario Vukotić
World Electr. Veh. J. 2023, 14(11), 309; https://doi.org/10.3390/wevj14110309 - 11 Nov 2023
Cited by 2 | Viewed by 3007
Abstract
Light electric vehicles are best suited for city and suburban settings, where top speed and long-distance travel are not the primary concerns. The literature concerning light electric vehicle powertrain design often overlooks the influence of the associated driving missions. Typically, the powertrain is [...] Read more.
Light electric vehicles are best suited for city and suburban settings, where top speed and long-distance travel are not the primary concerns. The literature concerning light electric vehicle powertrain design often overlooks the influence of the associated driving missions. Typically, the powertrain is initially parameterized, established, and then evaluated with an ex-post-performance assessment using driving cycles. Nevertheless, to optimize the size and performance of a vehicle according to its intended mission, it is essential to consider the driving cycles right from the outset, in the powertrain design. This paper presents the design of an electric powertrain for multipurpose light electric vehicles, focusing on the motor, battery, and charging requirements. The powertrain design optimization is realized from the first stages by considering the vehicle’s driving missions and operational patterns for multipurpose usage (transporting people or goods) in European urban environments. The proposed powertrain is modular and scalable in terms of the energy capacity of the battery as well as in the electric motor shaft power and torque. Having such a possibility gives one the flexibility to use the powertrain in different combinations for different vehicle categories, from L7 quadricycles to light M1 vehicles. Full article
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<p>Simulated speed and elevation profiles of the Helsinki region with a mix of urban and suburban driving conditions.</p>
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<p>(<b>a</b>) Power–speed and (<b>b</b>) torque–speed profiles of an electrical machine for the Helsinki driving cycle with a total vehicle weight of 750 kg.</p>
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<p>(<b>a</b>) Power–speed and (<b>b</b>) torque–speed profiles of an electrical machine for the Helsinki driving cycle with a total vehicle weight of 1200 kg (including a 450 kg payload).</p>
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<p>(<b>a</b>) 2D cross-section, (<b>b</b>) schematic 3D view, and (<b>c</b>) winding layout (with three phases A, B, and C, small letter denoting the opposite direction of a coil side) of the proposed traction motor.</p>
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<p>Modular battery for different vehicle classes (L7 to M1).</p>
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<p>(<b>a</b>) Distribution of the simulated daily distance of each vehicle and (<b>b</b>) maximum available charging time between the trips.</p>
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<p>Efficiency maps of motor variants with different core lengths: (<b>a</b>) Motor 1 (200 mm); (<b>b</b>) Motor 2 (150 mm); and (<b>c</b>) Motor 3 (100 mm). The dashed lines represent the power–speed profiles respecting the 15-kW continuous power limit.</p>
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<p>Influence of (<b>a</b>) power–speed and (<b>b</b>) torque–speed curves on (<b>c</b>) the vehicle’s acceleration.</p>
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18 pages, 8018 KiB  
Article
Evaluation of Electric Vehicle Charging Usage and Driver Activity
by Justin Anthony Mahlberg, Jairaj Desai and Darcy M. Bullock
World Electr. Veh. J. 2023, 14(11), 308; https://doi.org/10.3390/wevj14110308 - 8 Nov 2023
Cited by 4 | Viewed by 3375
Abstract
As the country moves toward electric vehicles (EV), the United States is in the process of investing over USD 7.5 billion in EV charging stations, and Indiana has been allocated $100 million to invest in their EV charging network. In contrast to traditional [...] Read more.
As the country moves toward electric vehicles (EV), the United States is in the process of investing over USD 7.5 billion in EV charging stations, and Indiana has been allocated $100 million to invest in their EV charging network. In contrast to traditional “gas stations”, EV charging times, depending on the charger power delivery rating, can require considerably longer dwell times. As a result, drivers tend to pair charging with other activities. This study looks at two EV public charging locations and monitors driver activity while charging, charge time, and station utilization over a 2-month period in Lafayette, Indiana. Over 4000 charging sessions at stations with varying power levels (350 kW, 150 kW, and 50 kW) were monitored, and the median charge time was between 28 and 36 min. A large variation in station utilization was observed at Electrify America charging stations that had a range of stations with 350 kW, 150 kW, and 50 kW available. The highest utilization rates by hour of day on average were observed at 25% at the 150 kW Tesla station. Driver activity during charging influenced dwell times, with the average dwell time of drivers who waited in their vehicles to charge being 10 min shorter than those who would travel to the shops. Rain in the forecast also impacted the number of users per day. Although there are no published metrics for EV utilization and associated driver activities, we believe examining this relationship will produce best practices for planning future investments in EV charging infrastructure as public and private sector partners develop a nationwide charging network. Full article
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<p>Charging station study location. Callout i is the Electrify America charging stations, callout ii is the Tesla charging stations. (The left map is produced with Leaflet; The right map is updated with the Google Map Attribution.)</p>
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<p>Charging station layout and charging output. (<b>a</b>) Electrify America charging stations; (<b>b</b>) Tesla charging stations (150 kW).</p>
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<p>Monitoring of different driver activities, the red circle marks the EV driver. (<b>a</b>) Driver entering shop; (<b>b</b>) driver remaining in vehicle; (<b>c</b>) driver picked up and leaving premises.</p>
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<p>Driver activity while charging the vehicle. (<b>a</b>) Electrify America; (<b>b</b>) Tesla.</p>
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<p>Boxplot of dwell times by driver activity.</p>
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<p>Station utilization. (<b>a</b>) Electrify America; (<b>b</b>) Tesla.</p>
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<p>Surface rain accumulations around charging stations.</p>
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<p>Average charge counts by time of day, comparing moderate to greater and minimal to no rain days.</p>
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<p>Boxplot of dwell time by station type.</p>
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<p>Electric vehicle charging utilization by time of day. (<b>a</b>) Tesla, 150 kW; (<b>b</b>) Electrify America, 350 kW; (<b>c</b>) Electrify America, 150 kW; (<b>d</b>) Electrify America, 50 kW.</p>
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<p>Electric vehicle charging utilization by time of day. (<b>a</b>) Tesla, 150 kW; (<b>b</b>) Electrify America, 350 kW; (<b>c</b>) Electrify America, 150 kW; (<b>d</b>) Electrify America, 50 kW.</p>
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<p>Peak utilization of Tesla 150 kW charging stations sorted in rank order. (<b>a</b>) All Tesla 150 kW percent utilization by date and hour; (<b>b</b>) top 30 Tesla 150 kW percent utilization by date and hour.</p>
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<p>Average utilization of a Tesla 150 kW charging station by weekday.</p>
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17 pages, 3719 KiB  
Article
Tuning Window Size to Improve the Accuracy of Battery State-of-Charge Estimations Due to Battery Cycle Addition
by Dewi Anggraeni, Budi Sudiarto, Ery Fitrianingsih and Purnomo Sidi Priambodo
World Electr. Veh. J. 2023, 14(11), 307; https://doi.org/10.3390/wevj14110307 - 8 Nov 2023
Viewed by 1864
Abstract
The primary indicator of battery level in a battery management system (BMS) is the state of charge, which plays a crucial role in enhancing safety in terms of energy transfer. Accurate measurement of SoC is essential to guaranteeing battery safety, avoiding hazardous scenarios, [...] Read more.
The primary indicator of battery level in a battery management system (BMS) is the state of charge, which plays a crucial role in enhancing safety in terms of energy transfer. Accurate measurement of SoC is essential to guaranteeing battery safety, avoiding hazardous scenarios, and enhancing the performance of the battery. To improve SoC accuracy, first-order and second-order adaptive extended Kalman filtering (AEKF) are the best choices, as they have less computational cost and are more robust in uncertain circumstances. The impact on SoC estimation accuracy of increasing the cycle and its interaction with the size of the tuning window was evaluated using both models. The research results show that tuning the window size (M) greatly affects the accuracy of SoC estimation in both methods. M provides a quick response detection measurement and adjusts the estimation’s character with the actual value. The results indicate that the precision of SoC improves as the value of M decreases. In addition, the application of first-order AEKF has practical advantages because it does not require pre-processing steps to determine polarization resistance and polarization capacity, while second-order AEKF has better capabilities in terms of SoC estimation. The robustness of the two techniques was also evaluated by administering various initial SoCs. The examination findings demonstrate that the estimated trajectory can approximate the actual trajectory of the SoC. Full article
(This article belongs to the Topic Battery Design and Management)
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<p>Equivalent 1st-order circuit model.</p>
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<p>Equivalent 2nd-order circuit model.</p>
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<p>Schematic scheme for AEKF algorithm.</p>
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<p>Lithium-ion battery’s jump-rebound feature.</p>
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<p>Flowchart of the SoC estimation with cycle and M variable.</p>
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<p>SoC estimation in specific <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> </mrow> </semantics></math> in cycle 1.</p>
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<p>The AEKF 1st- and 2nd-order ECM performance estimation result for (<b>a</b>) 1 cycle; (<b>b</b>) 100 cycles; (<b>c</b>) 200 cycles; (<b>d</b>) 300 cycles; (<b>e</b>) 400 cycles; and (<b>f</b>) 500 cycles.</p>
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<p>SoC estimation for the first-order AEKF accuracy over different cycles and M numbers.</p>
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<p>SoC estimation for the second-order AEKF accuracy over different cycles and M numbers.</p>
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<p>SoC estimation derived from various initial SoCs.</p>
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20 pages, 6513 KiB  
Article
Experimental Investigation on Affecting Air Flow against the Maximum Temperature Difference of a Lithium-Ion Battery with Heat Pipe Cooling
by Chokchai Anamtawach, Soontorn Odngam and Chaiyut Sumpavakup
World Electr. Veh. J. 2023, 14(11), 306; https://doi.org/10.3390/wevj14110306 - 7 Nov 2023
Viewed by 2077
Abstract
Research on battery thermal management systems (BTMSs) is particularly significant since the electric vehicle sector is growing in importance and because the batteries that power them have high operating temperature requirements. Among them, heat pipe (HP)-based battery thermal management systems have very high [...] Read more.
Research on battery thermal management systems (BTMSs) is particularly significant since the electric vehicle sector is growing in importance and because the batteries that power them have high operating temperature requirements. Among them, heat pipe (HP)-based battery thermal management systems have very high heat transfer performance but fall short in maintaining uniform temperature distribution. This study presented forced air cooling by an axial fan as a method of improving the cooling performance of flat heat pipes coupled with aluminum fins (FHPAFs) and investigated the impact of air velocity on the battery pack’s maximum temperature differential (ΔTmax). All experiments were conducted on lithium nickel manganese cobalt oxide (NMC) pouch battery cells with a 20 Ah capacity in seven series connections at room temperature, under forced and natural convection, at various air velocity values (12.7 m/s, 9.5 m/s, and 6.3 m/s), and with 1C, 2C, 3C, and 4C discharge rates. The results indicated that at the same air velocity, increasing the discharge rate increases the ΔTmax significantly. Forced convection has a higher ΔTmax than natural convection. The ΔTmax was reduced when the air velocity was increased during forced convection. Full article
(This article belongs to the Special Issue Lithium-Ion Battery Diagnosis: Health and Safety)
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<p>Number of new EV registrations in Thailand [<a href="#B3-wevj-14-00306" class="html-bibr">3</a>].</p>
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<p>Components of the cooling unit: (<b>a</b>) aluminum fins; (<b>b</b>) three sets of FHPAFs.</p>
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<p>The NMC battery pouch cell.</p>
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<p>Assembling the FHPAFs in the battery pack: (<b>a</b>) filling thermal grease; (<b>b</b>) installation of FHPAFs.</p>
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<p>The experimental setup.</p>
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<p>The testing procedure flowchart.</p>
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<p>The air velocity measurement method: (<b>a</b>) The air velocity measurement point; (<b>b</b>) Air velocity measurement.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at 1C discharge rate under different convection conditions. (<b>a</b>) The T<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under natural convection. (<b>c</b>) The T<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>e</b>) The T<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>f</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>g</b>) The T<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>h</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at 1C discharge rate under different convection conditions. (<b>a</b>) The T<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under natural convection. (<b>c</b>) The T<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>e</b>) The T<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>f</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>g</b>) The T<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>h</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at 2C discharge rate under different convection conditions. (<b>a</b>) The T<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under natural convection. (<b>c</b>) The T<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>e</b>) The T<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>f</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>g</b>) The T<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>h</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at 2C discharge rate under different convection conditions. (<b>a</b>) The T<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under natural convection. (<b>c</b>) The T<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>e</b>) The T<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>f</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>g</b>) The T<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>h</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at 3C discharge rate under different convection conditions. (<b>a</b>) The T<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under natural convection. (<b>c</b>) The T<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>e</b>) The T<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>f</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>g</b>) The T<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>h</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at 3C discharge rate under different convection conditions. (<b>a</b>) The T<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under natural convection. (<b>c</b>) The T<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>e</b>) The T<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>f</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>g</b>) The T<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>h</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at 4C discharge rate under different convection conditions. (<b>a</b>) The T<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under natural convection. (<b>c</b>) The T<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s. (<b>e</b>) The T<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>f</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>g</b>) The T<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>h</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s.</p>
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<p>The ΔT<sub>max</sub> at different convection conditions and various discharge rates. (<b>a</b>) The ΔT<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>c</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s.</p>
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<p>The ΔT<sub>max</sub> at different convection conditions and various discharge rates. (<b>a</b>) The ΔT<sub>max</sub> under natural convection. (<b>b</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 6.3 m/s. (<b>c</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 9.5 m/s. (<b>d</b>) The ΔT<sub>max</sub> under forced convection at an air velocity value of 12.7 m/s.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at various discharge rates and different convection conditions. (<b>a</b>) The T<sub>max</sub> at 1C discharge rate. (<b>b</b>) The ΔT<sub>max</sub> at 1C discharge rate. (<b>c</b>) The T<sub>max</sub> at 2C discharge rate. (<b>d</b>) The ΔT<sub>max</sub> at 2C discharge rate. (<b>e</b>) The T<sub>max</sub> at 3C discharge rate. (<b>f</b>) The ΔT<sub>max</sub> at 3C discharge rate. (<b>g</b>) The T<sub>max</sub> at 4C discharge rate. (<b>h</b>) The ΔT<sub>max</sub> at 4C discharge rate.</p>
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<p>The T<sub>max</sub> and ΔT<sub>max</sub> at various discharge rates and different convection conditions. (<b>a</b>) The T<sub>max</sub> at 1C discharge rate. (<b>b</b>) The ΔT<sub>max</sub> at 1C discharge rate. (<b>c</b>) The T<sub>max</sub> at 2C discharge rate. (<b>d</b>) The ΔT<sub>max</sub> at 2C discharge rate. (<b>e</b>) The T<sub>max</sub> at 3C discharge rate. (<b>f</b>) The ΔT<sub>max</sub> at 3C discharge rate. (<b>g</b>) The T<sub>max</sub> at 4C discharge rate. (<b>h</b>) The ΔT<sub>max</sub> at 4C discharge rate.</p>
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22 pages, 1768 KiB  
Review
A Review of Non-Destructive Techniques for Lithium-Ion Battery Performance Analysis
by Ximena Carolina Acaro Chacón, Stefano Laureti, Marco Ricci and Gregorio Cappuccino
World Electr. Veh. J. 2023, 14(11), 305; https://doi.org/10.3390/wevj14110305 - 3 Nov 2023
Cited by 7 | Viewed by 5934
Abstract
Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time [...] Read more.
Lithium-ion batteries are considered the most suitable option for powering electric vehicles in modern transportation systems due to their high energy density, high energy efficiency, long cycle life, and low weight. Nonetheless, several safety concerns and their tendency to lose charge over time demand methods capable of determining their state of health accurately, as well as estimating a range of relevant parameters in order to ensure their safe and efficient use. In this framework, non-destructive inspection methods play a fundamental role in assessing the condition of lithium-ion batteries, allowing for their thorough examination without causing any damage. This aspect is particularly crucial when batteries are exploited in critical applications and when evaluating the potential second life usage of the cells. This review explores various non-destructive methods for evaluating lithium batteries, i.e., electrochemical impedance spectroscopy, infrared thermography, X-ray computed tomography and ultrasonic testing, considers and compares several aspects such as sensitivity, flexibility, accuracy, complexity, industrial applicability, and cost. Hence, this work aims at providing academic and industrial professionals with a tool for choosing the most appropriate methodology for a given application. Full article
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<p>Typical representation of electrochemical impedance spectroscopy (EIS) measurements of a LIB presented in a test setup.</p>
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<p>Nyquist plot for a ECM of a LIB half-cell system. Reprinted from [<a href="#B28-wevj-14-00305" class="html-bibr">28</a>]—Copyright © 2023 by The Korean Electrochemical Society—CC BY-NC 4.0.</p>
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<p>Basic test setup of infrared thermography in reflection mode.</p>
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<p>A sketch of a general XCT setup.</p>
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<p>Ultrasonic testing (<b>a</b>) pulse-echo; (<b>b</b>) through-transmission. T<sub>x</sub> and R<sub>x</sub> stand for transmitter and receiver transducer, respectively.</p>
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<p>Comparative analysis of the selected NDT methods.</p>
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24 pages, 799 KiB  
Article
Examining the Determinants of Electric Vehicle Acceptance in Jordan: A PLS-SEM Approach
by Dana Abudayyeh, Malek Almomani, Omar Almomani, Douha Jaber and Eman Alhelo
World Electr. Veh. J. 2023, 14(11), 304; https://doi.org/10.3390/wevj14110304 - 3 Nov 2023
Cited by 3 | Viewed by 3134
Abstract
Recently, technologies for electric mobility have developed rapidly. Since the introduction and spread of Electric Vehicles (EVs), several studies have attempted to investigate the benefits and risks that impact on the growth of the EV market by evaluating data gathered from various drivers. [...] Read more.
Recently, technologies for electric mobility have developed rapidly. Since the introduction and spread of Electric Vehicles (EVs), several studies have attempted to investigate the benefits and risks that impact on the growth of the EV market by evaluating data gathered from various drivers. However, some variables were disregarded such as: Public Involvement, Knowledge of EVs, Perceived Risk, Behavioural Intention, and EV acceptance. These variables are considered vital when analysing the intention to use EVs. Therefore, this study compiles the above mentioned variables to evaluate their effect on the intention to use EVs in Jordan. 501 collected responses were examined using the Smart PLS-Structural Equation Model algorithm. In general, the analysis revealed high levels of EV acceptance. The study proposed twelve direct relationship hypotheses. Out of these hypotheses, ten hypotheses were supported and two were rejected. The final conclusions are that an increase in public involvement is associated with an increase in knowledge of EVs, and an increase in their perceived risk. Moreover, the knowledge of EVs has positively and significantly influenced the perceived usefulness and perceived ease of use, along with EV acceptance. However, no relationships were found between the following: 1. the knowledge of EVs and perceived risk; and 2. perceived risk and behavioural intention. Full article
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<p>A path diagram for the development of the hypotheses.</p>
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<p>Sample size calculation with statistical power analysis.</p>
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<p>Structural and measurement model results.</p>
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19 pages, 5936 KiB  
Review
Annotated Survey on the Research Progress within Vehicle-to-Grid Techniques Based on CiteSpace Statistical Result
by Ruifeng Shi, Shuaikang Peng, Tai Chang and Kwang Y. Lee
World Electr. Veh. J. 2023, 14(11), 303; https://doi.org/10.3390/wevj14110303 - 2 Nov 2023
Cited by 4 | Viewed by 3779
Abstract
Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes [...] Read more.
Vehicle-to-grid (V2G) technology has received a lot of attention as a smart interconnection solution between electric vehicles and the grid. This paper analyzes the relevant research progress and hotpots of V2G by using CiteSpace 6.1.R6 software to construct a visualization graph, which includes keyword co-occurrence, clustering, and burstiness, and further systematically summarizes the main trends and key results of V2G research. First, the connection between electric vehicles and the grid is outlined and the potential advantages of V2G technology are emphasized, such as energy management, load balancing, and environmental sustainability. The important topics of V2G, including renewable energy consumption, power dispatch, regulation and optimization of the grid, and the smart grid, are discussed. This paper also emphasizes the positive impacts of V2G technologies on the grid, including reduced carbon emissions, improved grid reliability, and the support for renewable energy integration. Current and future challenges for V2G research, such as standardization, policy support, and business models, are also considered. This review provides a comprehensive perspective for scholars and practitioners in V2G research and contributes to a better understanding of the current status and future trends of V2G technology. Full article
(This article belongs to the Special Issue Electric Vehicles and Smart Grid Interaction)
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<p>V2G service diagram.</p>
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<p>Annual trend of publications.</p>
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<p>Knowledge mapping for the keywords’ co-occurrence.</p>
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<p>Analysis atlas of keyword clustering.</p>
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<p>The evolution view of the keywords’ co-occurrence network.</p>
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<p>Top 25 keywords with the strongest citation bursts.</p>
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18 pages, 8494 KiB  
Article
Assessment of an Electric Vehicle Drive Cycle in Relation to Minimised Energy Consumption with Driving Behaviour: The Case of Addis Ababa, Ethiopia, and Its Suburbs
by Tatek Mamo, Girma Gebresenbet, Rajendiran Gopal and Bisrat Yoseph
World Electr. Veh. J. 2023, 14(11), 302; https://doi.org/10.3390/wevj14110302 - 31 Oct 2023
Viewed by 2555
Abstract
Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is [...] Read more.
Battery electric vehicles (BEV) are suitable alternatives for achieving energy independence and meeting the criteria for reducing greenhouse emissions in the transportation sector. Evaluating their performance and energy consumption in the real-data driving cycle (DC) is important. The purpose of this work is to develop a BEV DC for the interlinked urban and suburban route of Addis Ababa (AA) in Ethiopia. In this study, a new approach of micro-trip random selection-to-rebuild with behaviour split (RSBS) was implemented, and its effectiveness was compared via the k-means clustering method. When comparing the statistical distribution of velocity and acceleration with measured real data, the RSBS cycle shows a minimum error of 2% and 2.3%, respectively. By splitting driving behaviour, aggressive drivers were found to consume more energy because of frequent panic stops and subsequent acceleration. In braking mode, coast drivers were found to improve the regenerative braking possibility and efficiency, which can extend the range by 10.8%, whereas aggressive drivers could only achieve 3.9%. Also, resynthesised RSBS with the percentage of behaviour split and its energy and power consumption were compared with standard cycles. A significant reduction of 14.57% from UDDS and 8.9% from WLTC-2 in energy consumption was achieved for the AA and its suburbs DC, indicating that this DC could be useful for both the city and suburbs. Full article
(This article belongs to the Topic Advanced Electric Vehicle Technology)
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<p>Road network of the study area of AA and suburbs.</p>
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<p>Step-by-step flow chart of data filtration and driving cycle development.</p>
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<p>Speed-acceleration distribution of the unified and filtered dataset.</p>
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<p>Distance and duration distributions of real-time trip cycles.</p>
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<p>Comparison of driving speeds.</p>
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<p>The distribution of low-speed short trip duration.</p>
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<p>Acceleration and deceleration distribution of low, medium, and high-speed trip cycles.</p>
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<p>Cumulative distribution function and its quartile score of acceleration.</p>
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<p>Speed-acceleration distributions of ACSU representative DC by the RSBS method.</p>
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<p>Speed-acceleration of the DC by the RSBS method for aggressive and combined behaviours.</p>
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<p>Contribution and influence of driving features on PCs and their variance explained.</p>
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<p>Score of silhouette value and distribution of width for five and six clusters.</p>
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<p>Comparison of speed distribution between real data and DCs of RSBS and k-means.</p>
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<p>Comparison of energy consumption for different driving behaviours of AASU.</p>
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<p>Regenerative potential of mild and aggressive driving behaviours representative of AASU DCs.</p>
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<p>Comparison of energy consumption between WLTC-2, UDDS, and AASU candidate DCs by RSBS and k-means.</p>
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<p>Comparison of power consumption between WLTC-2, UDDS, and AASU candidate DCs by RSBS and k-means methods.</p>
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14 pages, 2135 KiB  
Article
Utility Factor Curves for Plug-in Hybrid Electric Vehicles: Beyond the Standard Assumptions
by Karim Hamza and Kenneth P. Laberteaux
World Electr. Veh. J. 2023, 14(11), 301; https://doi.org/10.3390/wevj14110301 - 31 Oct 2023
Cited by 2 | Viewed by 4540
Abstract
The utility factor (UF) of a plug-in hybrid electric vehicle (PHEV) refers to the ratio of miles traveled in electric mode to the total miles traveled. Standard UF curves provide a prediction of the expected achievable UF by a PHEV given its all-electric [...] Read more.
The utility factor (UF) of a plug-in hybrid electric vehicle (PHEV) refers to the ratio of miles traveled in electric mode to the total miles traveled. Standard UF curves provide a prediction of the expected achievable UF by a PHEV given its all-electric range (AER), but such predictions entail assumptions about both the driving patterns (distance traveled and energy intensity) and charging behavior. Studies have attempted to compare the real-world UF achieved by PHEVs to their standard values, but deviations can stem from deviations in assumptions about: (i) achievable electric range, (ii) travel distance and (iii) charging frequency. In this paper, we derive analytical models for modified utility factor curves as a function of both AER and charging behavior. We show that average charging frequency is insufficient to exactly predict UF but can still estimate bounds. Our generalized model can also provide insights into the efficacy of PHEVs in reducing carbon emissions. Full article
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<p>Illustration of three categories of assumptions mismatch between standard UF curves and real-world.</p>
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<p>Reference UF curves via CHTS and SAE J2841 (based on NHTS-2001).</p>
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<p>Reference UF curves and various models for <span class="html-italic">λ</span> = 0.5.</p>
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<p>Plausible and Upper/Lower UF curves for select modelled charging frequency.</p>
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<p>Select carbon emissions offset scenarios relative to an equivalent conventional vehicle.</p>
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18 pages, 4067 KiB  
Article
Waste from Electric Vehicle: A Bibliometric Analysis from 1995 to 2023
by Arief Nurdini, Rahmat Nurcahyo and Anton Satria Prabuwono
World Electr. Veh. J. 2023, 14(11), 300; https://doi.org/10.3390/wevj14110300 - 27 Oct 2023
Cited by 4 | Viewed by 3022
Abstract
The introduction of electric vehicles (EVs) represents a promising solution for addressing urban air pollution, particularly CO2 emissions in the transportation sector. Numerous countries are actively promoting EV adoption and the electrification of transportation systems, leading to a surge in research on [...] Read more.
The introduction of electric vehicles (EVs) represents a promising solution for addressing urban air pollution, particularly CO2 emissions in the transportation sector. Numerous countries are actively promoting EV adoption and the electrification of transportation systems, leading to a surge in research on EV-related topics. This study employs bibliometrics as a valuable tool to investigate the research landscape in electric vehicle waste management. Drawing from a dataset of 593 documents retrieved from SCOPUS from 1995 to 20 September 2023, this research employs descriptive analysis and bibliometric mapping techniques. Notably, China stands out as the leading contributor to publications, with Tsinghua University being a prominent research institution in this field. An examination of keyword trends reveals dynamic shifts in research focus. In 2023, the most frequently occurring topic is “closed loop”. “Recycling” is the dominant keyword, appearing 681 times. Additionally, TreeMaps and VOSviewer results indicate that the most commonly used keywords are “electronic waste” and “recycling”. Projections suggest that “recycling materials” will gain prominence in mid-2023, further highlighting the evolving nature of this research field. Researchers in recycling materials disciplines can leverage these insights to explore new research avenues and contribute to sustainable waste management practices in the context of electric vehicles. Full article
(This article belongs to the Topic Zero Carbon Vehicles and Power Generation)
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<p>Methodological Flowchart for Bibliometric.</p>
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<p>Number of Publications.</p>
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<p>Number of Citations.</p>
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<p>Most Relevant Sources.</p>
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<p>Corresponding Author’s Countries.</p>
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<p>Most Relevant Affiliations.</p>
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<p>Subject Categories according to the Research Area.</p>
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<p>Most Relevant Authors’.</p>
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<p>Author’s Production over Time.</p>
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<p>Most Frequent Words.</p>
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<p>Trending Topics based on Authors’ Keywords.</p>
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<p>TreeMap Words.</p>
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<p>Bibliometric VOSviewer.</p>
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