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Review

An Overview About Second-Life Battery Utilization for Energy Storage: Key Challenges and Solutions

by
Hua Song
,
Huaizhi Chen
,
Yanbo Wang
* and
Xiang-E Sun
School of Electronics and Information, Yangtze University, Jingzhou 434025, China
*
Author to whom correspondence should be addressed.
Energies 2024, 17(23), 6163; https://doi.org/10.3390/en17236163
Submission received: 2 October 2024 / Revised: 19 November 2024 / Accepted: 2 December 2024 / Published: 6 December 2024
(This article belongs to the Special Issue Advancements in Smart Electric Mobility Systems: Integration of Renewable Energy and Energy Storage)
Browse Figures
Figure 1
<p>Diagram of the lifetime of an EV battery.</p> "> Figure 2
<p>The potential application of second-life batteries in future power grids.</p> "> Figure 3
<p>The phenomena, mechanisms, and control methods for battery safety [<a href="#B9-energies-17-06163" class="html-bibr">9</a>,<a href="#B10-energies-17-06163" class="html-bibr">10</a>,<a href="#B11-energies-17-06163" class="html-bibr">11</a>,<a href="#B12-energies-17-06163" class="html-bibr">12</a>,<a href="#B13-energies-17-06163" class="html-bibr">13</a>,<a href="#B14-energies-17-06163" class="html-bibr">14</a>,<a href="#B15-energies-17-06163" class="html-bibr">15</a>,<a href="#B16-energies-17-06163" class="html-bibr">16</a>].</p> "> Figure 4
<p>SOC inconsistency of different cells in a battery module.</p> "> Figure 5
<p>A diagram of battery balancing methods. (<b>a</b>) Active balancing method. (<b>b</b>) Passive balancing method.</p> "> Figure 6
<p>The potential compatibility problem for second-life battery utilization in energy storage systems.</p> "> Figure 7
<p>The concept, impact and stakeholders of DP technology.</p> ">
Versions Notes

Abstract

:
This article provides a comprehensive overview of the potential challenges and solutions of second-life batteries. First, safety issues of second-life batteries are investigated, which is highly related to the thermal runaway of battery systems. The critical solutions for the thermal runaway problem are discussed, including structural optimization, parameter identification, advanced BMS, and artificial intelligence (AI)-based control strategies. Furthermore, the cell inhomogeneity problem of second-life battery systems is analyzed, where the passive balancing strategy and active balancing strategy are reviewed, respectively. Then, the compatibility issue of second-life batteries is investigated to determine whether electrical dynamic characteristics of a second-life battery can meet the performance requirements for energy storage. In addition, date security and protection methods are reviewed, including digital passport, smart meters and Internet of Things (IoT). The future trends and solutions of key challenges for second-life battery utilization are discussed.

1. Introduction

1.1. Background

The increasing penetration of electric vehicles (EVs) has led to the rapid development and application of power batteries. In an EV, the battery is one of the most important components, providing electrical power, which takes up 30–40% of the whole cost of the electric vehicle [1]. It is reported that the battery market has been up to USD 6000 billion in 2023, and battery applications are being significantly increased throughout the world in the next decades [2]. EV power batteries include different types, such as lead–acid batteries, nickel–metal hydride batteries, lithium ion batteries, and solid state batteries [3,4,5,6,7,8,9]. Lithium ion batteries are increasingly utilized due to their relatively high energy density and long lifespan [3,4,5,6,7,8,9]. It is predicted that up to 250,000 metric tons of EV batteries are expected to reach the end of their lifetime in 2025 [10]. If the retired EV batteries can be recycled and reused, the lifetime of the batteries can be extended to serve as energy storage for electrical energy systems [11,12,13,14,15,16,17]. In fact, there still exists 70–80% of their original capacity after EV batteries are retired, which can be continuously used for energy storage and backup scenarios to maximize the utilization of their remaining lifetime [18,19]. By standardized recycling measures, including detection, classification, repair, and reconstruction, etc., the retired power batteries can be utilized as energy storage systems (ESSs) [20,21], which can be applied in photovoltaic power stations [22], microgrids [23,24], wind power generators [25] and uninterrupted power supplies (UPSs) [26], etc. Therefore, the remaining life of power batteries can be fully utilized to extend their whole lifetime, which reduces the total cost of power batteries and energy storage for second-life users such as grid operators [27,28].

1.2. The Definition and Potential Application of Second-Life EV Batteries

A secondary battery, also named a second life battery, refers to a power battery that can be continuously used when its first life as an EV battery ends, where the 70–80% of its initial capacity is still applicable after it is retired as an EV. Figure 1 shows the typical lifetime stage of an EV power battery, which includes its primary life phase, second-life phase, and recycling phase [29,30,31,32].
  • Phase 1 (First life): The power battery is used to supply an EV when the capacity retention rate is between 100% to 80%, where the capacity retention rate refers to the ratio of practical capacity after several cycles to the initial capacity value.
  • Phase 2 (Second life): When the capacity retention rate is lower than 80%, the power battery must be retired but can be utilized for energy storage. By second life utilization, the overall lifetime of EV batteries can be maximized. It can be seen that the second life stage is a relatively long duration in the whole lifetime of an EV battery.
  • Phase 3 (Recycling): Once the energy retention rate of an EV battery is lower than 30%, EV batteries must be recycled and decomposed.
A second-life battery is a promising solution for energy storage in a sustainable and cost-effective way. The advantages of a second-life battery in terms of tech-economic analysis are clarified as follows [33,34,35]:
  • Sustainability: A second-life battery can be recycled and reused as ESS to realize energy conservation.
  • Cost-effectiveness: The average expense of power batteries during the whole lifetime can be significantly reduced due to an extended lifetime.
  • High flexibility: The power of a second-life battery can be flexibly controlled to meet different energy storage application needs.

2. The Potential Applications of Second-Life Batteries

2.1. Possible Applications

In this section, the potential applications of a second-life battery are investigated (Table 1). Figure 2 shows the potential applications of second-life batteries in future power grids. The critical technical challenges of second-life batteries are summarized.
(1)
Power smoothing for renewable energy systems
The fluctuation of renewable power plants such as wind power plants and PV power plants has a significant effect on power system stability and security. Second-life batteries can be applied to smooth the power fluctuations of renewable energy resources within different time scales [36,37,38]. In long-term operation, the excessive electricity can be stored when wind energy is sufficient. Once wind power is insufficient, the stored power can be released to realize a power balance. Hence, renewable energies can be efficiently utilized to provide a sustainable and smoothing power supply [25,39]. The utilization of a second-life battery can promote the penetration of renewable energies in a cost-effective way and further reduces dependence on traditional energy sources and greenhouse gas emissions [20,40,41,42].
(2)
Commercial charging station
In a future power grid, commercial charging stations are important components to supply electrified loads such as electric vehicles and buses. Second-life batteries can be utilized to build commercial charging stations, which provide a cost-effective solution to realize electricity transactions. To establish market mechanisms with the power grid, charging stations can reserve and sell electricity considering the power grid demands, where second-life batteries can participate in the market bidding under peak-to-valley electricity prices to obtain economic benefits [43,44]. Furthermore, the flexibility of demand side responses can be utilized to obtain economic benefits based on flexible electricity prices in different time periods.
(3)
Backup power supply
Second-life batteries can be combined and utilized as a backup uninterruptible power supply (UPS) to provide a reliable power supply for critical loads such as hospitals, data centers, residential buildings, and microgrids, etc. in an efficient and cost-effective way, which also reduces the dependence of local loads on traditional power sources [45]. The backup power source can enhance the reliability of a power supply in the presence of power deficits or grid faults [46], ensuring that the equipment will not be shut down to reduce the data loss risk due to power interruptions [47].
(4)
Auxiliary service capability
Auxiliary services can be provided by a second-life based energy storage system, providing important capabilities such as frequency regulation, inertia support and voltage regulation. A second-life battery can quickly respond to fluctuations in the grid frequency and provide frequency regulation by charging and discharging operations, which reduces the requirements for peak shaving equipment and realizes the sustainable utilization of electricity [23,48,49,50,51].

2.2. The Potential Technical Challenges of Second-Life Batteries

Although the utilization of a second-life battery is a promising solution for energy storage in future power grids, there exists several key technical challenges due to the degraded performance of EV batteries after long-term operation, which can be clarified as follows:
  • Safety and reliability: After long-term operation of the second-life battery system, the capacity and performance will be attenuated, which may destroy the thermal balance of the battery and bring some risks and hazards. Therefore, the safety and reliability of second-life battery use is a key issue.
  • Cell inhomogeneity in battery system: The inequality problem of cells in a battery package is an important challenge. An unbalance between the cells may occur, which causes performance differences of cells in the battery pack.
  • Compatibility issue: The performance of different battery packs may be different. The compatibility of different battery packs is critical to realize energy storage. Furthermore, cost-effective maintenance and management technologies are also important aspects.
  • Data safety and protection strategy: The usage data of second-life batteries can be used to optimize the battery performance, extend the service life, predict potential risks, and even support the dispatch and management of smart grids.

3. Safety Management of Second-Life Battery

Safety of a second-life battery is a primary concern in energy storage applications during long-term operation, which is highly related to the thermal runaway of a battery system [52]. In energy storage applications, the battery temperature may rise rapidly due to runaway of the electrochemical reaction under a harsh working environment, which further weakens the performance of a second-life battery [53]. For example, the capacity retention of a battery can be attenuated, where the internal resistance of the battery is increased, and energy efficiency is decreased [54]. Meanwhile, in a second-life battery system with multiple cells, the cell inconsistency is probably aggregated, so that some cells overheat and this further causes thermal runaway of the entire battery pack [55]. Therefore, the avoidance of the thermal runaway issue is essential to ensure secure and reliable operation of a second-life battery system. In this section, the mechanism of thermal runaway is first analyzed. Further, the state detection and control methods for thermal runaway are reviewed and analyzed. Figure 3 shows the phenomena, mechanisms, and control methods for the thermal runaway problem. Generally, several external phenomena and interface characteristic can indicate the potential risks of thermal runaway such as a surface temperature rise and voltage rapid drop [56], smoke dissipation and pungent odor [57], shell swelling [58], localized discoloration [59], etc.

3.1. Mechanism Analysis of the Thermal Runaway Issue

According to the analysis for the current work [56,57,58,59], the physical process of electrothermal runaway mainly includes the following four stages.
(1) Excitation stage: The abnormal situations such as overcharge, short circuit, and internal material defect, etc., cause the local temperature to rise within the battery, causing a rapid voltage drop of the battery [56].
(2) Thermal dissemination stage: Once the internal temperature rises, the heat spreads rapidly to the surroundings, which causes a rapid temperature rise in the battery system. Then, the chemical reaction intensifies to produce massive heat and gas with a pungent odor [57].
(3) Thermal runaway acceleration stage: As the internal temperature and pressure increase, the thermal stress is dramatically increased, which may cause the battery shell to be placed under high pressure and bulge [58].
(4) Combustion and explosion stage: When the internal pressure is beyond a certain level, the battery may start to burn, which releases a large amount of heat and toxic gases [59].
As shown in Figure 3, the thermal runaway mechanism is highly correlated with mechanical abuse [60,61], oxidation-reduction [62,63], low-temperature fast charging [5,64,65], and electrolyte decomposition [66,67]. The analysis in [60] shows that mechanical abuse can cause external structural deformation, leading to internal diaphragm rupture [61], resulting in internal short circuits and a sharp increase in battery temperature. Redox refers to the reaction between the oxygen released by the phase transition of the positive electrode material [62] and the reducibility of the negative electrode during the crosstalk process of the positive and negative electrodes of a second-life battery, which generates a large amount of heat [63]. When second-life batteries are rapidly charged at low temperatures, the negative electrode potential is prone to become too low, causing lithium ions to not be properly embedded in the negative electrode material [64]. The lithium deposited on the negative electrode of the battery will undergo a violent reaction with the electrolyte to release heat [5]. And in low-temperature environments, the cooling system of the battery will be affected [65]. The accumulation of these factors will cause the battery temperature to reach the initial temperature of self-heating. Then, the solid electrolyte interface membrane and electrolyte begin to decompose with slight gas generation [66]. This will cause an imbalance between charging and discharging of the second-life battery, leading to a further increase in temperature [67].
The aforementioned factors can cause a rapid rise of the internal temperature of a second-life battery. When the temperature exceeds the critical value, the thermal runaway issue can be induced.

3.2. Safety Improvement and Management Method

To avoid thermal runaway and enhance the safety of second-life batteries, various operation, control, and optimization strategies have been developed [68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93], including battery structure optimization, parameter identification and optimization, battery management systems (BMSs) and artificial intelligence (AI)-based methods.
(1)
Material and Structure Optimization
By optimizing and reconstructing the physical structure of battery cells, the mechanical strength of the battery can be increased, so that the mechanical damage during the charging/discharging process can be reduced. The electrolyte is directly related to the safety of the second-life battery under a high temperature operation, where the electrode material can influence the capacity ratio and cycle stability.
In [68,69], new anode materials such as lithium titanate and hard carbon are proposed for a second-life battery, and metal ion coatings or doped elements are introduced into the electrode materials to improve the thermal stability. In [70], LiCoO2 is proposed as the cathode material of a second-life battery, and the coating modification can limit the side reactions between the electrolyzer and the cathode material, stabilize the chemical structure of the electrolyzer, and improve its thermal stability. In [71], a ceramic-coated separator or non-woven separator is proposed to improve heat resistance and mechanical strength of the separator, so that it can maintain good isolation between the positive electrode and negative electrode under high temperature operation and improve the thermal stability of a second-life battery. In [72], a composite phase change material design method for the thermal management of a second-life battery is proposed, which has a good thermal conductivity. Also, this method can effectively control the internal temperature of battery modules with superior thermal management ability.
In [73], a state of health (SOH) estimation method of regenerated lithium ion battery packs is proposed. This method can accurately and quickly estimate the SOH of battery cells in a battery pack, which is valuable to address the problem of cell structure degradation. However, it is difficult to implement this method in practical operation. In [74], a three-dimensional thermal simulation model for lithium ion second-life batteries is proposed. This method improves thermal radiation by filling all spaces in the battery with liquid electrolyte, which thus enhances the safety of a second-life battery.
In [75], second-life battery health feature extraction and efficient sorting were proposed. This method is based on the OPTICS algorithm, a second life battery sorting method that identifies clustering structures using sorting points. By establishing a three-dimensional vector as health features (including capacity features, DC resistance, and polarization resistance), the aging degree of the second-life battery is mapped. In [76], a consistency evaluation method for series battery systems based on actual operating data is proposed. This method is based on the Thevenin equivalent circuit model to describe the dynamic characteristics of batteries, and it combines the Mahalanobis distance with a density-based spatial clustering method to effectively evaluate the inconsistency of battery cells.
After long-term use, the physical structure and mechanical strength of a battery may be deteriorated. For second-life battery applications, this issue may be aggregated. Therefore, optimization and reconstruction of a battery structure is an effective method to improve battery safety, where the abnormal cells can be separated from the battery package and the normal cells can be optimally integrated to enhance the safety of the second-life system.
(2)
Parameter Identification and Optimization
The online identification of electro-thermal parameters is a critical step to improve the safety of a second-life battery, such as temperature, capacity retention, and thermal resistance, etc., where the thermal dynamics can be predicted in real-time to prevent thermal runaway. Furthermore, the abnormal cells can be effectively identified and switched off to mitigate the risk of thermal runaway in the battery module. Table 2 summarizes the main parameters, identification methods and impacts from recent work.
In [77], a real-time parameter identification method for an EV battery is proposed based on recursive least-squares estimation. This method can effectively identify abnormal cells by detecting the parameters of an equivalent circuit model (ECM) of a second-life battery, so as to evaluate its capacity retention, state of charge (SOC), and open-circuit voltage (OCV). However, the accuracy of parameter identification depends on model parameters. In [78], a method with nonlinear parameter identification capability was developed to identify the parameters of lithium ion batteries. This method is based on the ANFIS model’s neural fuzzy method, which identifies the parameters of the second-life battery based on the load voltage, measured voltage, and temperature of the lithium ion battery. However, it is difficult to accurately collect the temperature of a second-life battery due to the differences among the battery cells. In [79], a pseudo-random noise method was developed to identify battery parameters. The cell parameters are obtained by electrochemical impedance spectroscopy, including series resistance, charge transfer resistance, and electric double-layer capacitance. However, all of the parameters of a battery can be dynamically changed as the variation of temperature and operating conditions. In [80], a second-life battery open circuit voltage (OCV) estimation algorithm based on a Kalman filtering algorithm was developed. Thus, the parameter curve of the OCV-SOC can be plotted to provide assistance for measuring SOH values. However, the estimation accuracy can be limited. In [81], a SOC estimation method based on the adaptive neurons networks (ADALINE) is proposed, where a parameter recognition technology is adopted to realize accurate SOC estimation by utilizing actual current and voltage data. This method can improve thermal stability. In [82], a lithium ion battery state estimation method based on an online fitting algorithm was proposed. This method obtains battery impedance through online pseudo-random sequence (PRS) measurement, and estimates SOC and SOH based on the parameter curve of resistance. A parameter identification method based on an Extended Kalman Filter (EKF) is proposed in [83], which can estimate SOC by measuring the voltage and current of a second-life battery. However, this method fails to consider the effects of temperature and cell ageing. In [84], a pulse parameter testing method for second-life batteries was proposed. This method uses capacity/energy testers and ultrasonic waves to predict the health status of second-life batteries by using simple pulses with a duration of a few seconds.
However, it is not easy to identify abnormal battery cells of a second-life battery in an accurate and real-time way, especially for a large-capability battery system with multiple battery cells. The accuracy of parameter and cell identification should be further improved.
(3)
Battery Management System (BMS) for a Second-life Battery
A BMS is an important component to realize energy management and protect the battery system in an efficient and secure way. In a BMS, several critical performance indexes such as SOC and SOH are detected to evaluate the operating status of a battery. In a second-life battery, a BMS is adopted to detect battery ageing and balance various cells to ensure the overall performance of the battery module.
In [85], an advanced battery management system is proposed to realize thermal balance, where non-invasive measurement is adopted to collect the voltage, current, and temperature so as to estimate the critical parameters of the battery system. The BMS can achieve the thermal balance of a second-life battery through accurate characterization of the parameters and robust management control. However, the applicability of this method for different battery types from different manufacturers should be further investigated. In [86], a battery thermal management system for a second-life battery using an air-cooled method is proposed to minimize the temperature difference among battery cells. However, it is difficult to meet the thermal management requirements for large-scale energy storage systems. In [87], a passive thermal management system was proposed. By measuring the DC resistance of the battery and calculating the heating rate, it is used to control the maximum temperature of the second-life battery. However, the thermal balance effect of the system is limited. In [88], a low-cost battery management system is proposed based on a low-cost, high-performance bare metal server, which predicts battery resistivity, volume, SOC, SOH, power drop, and remaining lifetime by employing three non-intrusive battery measurements, including voltage, current, and temperature. However, it is difficult to identify the nonlinearity of the aging process of second-life batteries using this method.
In [89], a BMS controller architecture for second-life batteries was proposed based on the use of a universal and flexible BMS control module. This method can effectively monitor the thermal parameters of multiple cells with inconsistent parameters in the second-life battery pack, and provide solutions based on the degree of damage, thereby ensuring the safe use of the second-life battery. In [90], a topology structure for BMS in a second-life battery energy storage system was proposed. By enabling BMS to balance the wear and tear between batteries and the resulting parameter differences, the self-healing effect of the batteries has been achieved to a certain extent. However, such a BMS requires a strong electrical structure to support it.
The above work mainly focuses on the monitoring and management of the overall second-life batteries. Compared to BMS for first-life batteries, the BMS of second-life batteries should be able to monitor and manage a battery package with multiple battery cells to improve the safety of the battery system. Meanwhile, the accurate identification and management of abnormal cells in a second-life battery system is essential for safety improvement.
(4)
AI-based operation and control strategies
The nonlinear characteristics of a battery after long-term operation such as parameters ageing weakens the identification accuracy of conventional methods. To improve the identification accuracy of parameters in a second-life battery, AI-based identification and control methods have attracted increasing concerns. AI-based methods can perceive the nonlinear dynamics of a battery in real time and predict the safety risks of a battery system based on massive data analysis by intelligent learning algorithms. Meanwhile, AI-based identification and intelligent control can adaptively optimize charging/discharging management of a battery to improve the safety of a battery by establishing a prediction mechanism.
In [91], an intelligent control method of battery packages was proposed to optimize charging/discharging management, which shortens the charging time and reduces the charging temperature. It thus improves the safety of a second-life battery by optimizing charging/discharging. In [92], a dynamic artificial neural network prediction method was proposed to realize thermal behavior optimization of a lithium ion battery, which is based on a dynamic artificial neural network method of the nonlinear autoregressive exogenous (NARX) model to accurately identify various thermal parameters. In [93], a switching algorithm for optimizing the charging and discharging of electric vehicle battery packs was proposed. By precisely controlling the voltage and current levels of a second-life battery, this method can improve the performance and extend the life of a battery system, which also can avoid overcharging and reduce the over discharging risk of battery cells.
Compared to traditional control methods, AI-based methods can handle nonlinear dynamics of second-life batteries to improve identification accuracy. Meanwhile, the risk prediction mechanism of second-life batteries can be established by AI-based methods to improve battery safety.

4. Cell Inhomogeneity in a Power Battery Module

In a battery module, multiple cells are connected in a form of series and parallel. After long-term operation, the initial performance of different cells has been differentiated. The difference of battery cells may be aggregated, which further affects the performance of a battery module and even reduces its lifespan [94]. Therefore, the effect of cell inhomogeneity for second-life battery utilization is an important concern.

4.1. Mechanism Analysis of Cell Inhomogeneity

The cell inhomogeneity in a second-life battery is mainly manifested in battery capacity, voltage, and internal resistance [95]. The differences of battery cells can be reflected in the inconsistency of the state of charge (SOC), which means that the depth of charge/discharge of each individual cell is not consistent. The cells with a smaller capacity are fully charged first, and the cells with a larger capacity are not fully charged, which thus causes the risk of overcharging for cells with a smaller capacity [96]. The inconsistency in capability and internal resistance further causes differences in the voltage and current of different battery cells, which deteriorates the inconsistency problem of charging/discharging depths [97]. Figure 4 shows the inconsistency of different cells in a battery module, where the cells have different capacity retentions and internal resistances. Therefore, how to deal with the differences between battery cells is critical to maintain the cell homogeneity and ensure the overall performance of the second-life battery. In this section, the mechanism of cell inhomogeneity is first investigated.
The analysis in [98] shows that battery cells share the same terminal voltage when connected in parallel. Due to inconsistencies in the parameters, structure, and connection methods of battery cells, an imbalance among multiple cells may be caused. A long-term imbalance can cause variations in real capacity retention and SOH of second-life batteries, thereby accelerating cell inhomogeneity. In [99], it was proposed that aging of second-life batteries can lead to an increase in cell impedance, which reduces the charging/discharging efficiency and increases the energy loss of the batteries. And these impedance differences will intensify with the aging of the battery, which further causes cell inhomogeneity. The analysis in [100] shows that due to differences in capacity retention, internal resistance, and electrochemical characteristics among different lithium ion battery cells, there exists inconsistent energy consumption and collection rates during the charging/discharging process, which causes an uneven energy distribution and excessive discharge of the battery cells. The analysis in [101,102] indicates that the electrode materials of lithium ion batteries can be deteriorated during long-term charging/discharging processes, which causes the capacity and internal resistance differences among battery cells. Therefore, it can cause cell inhomogeneity and weaken the security of second-life batteries.
Additionally, the analysis in [103] indicates that the characteristics of battery cells are highly related to temperature. Low temperature can reduce the diffusion of lithium ions, so as to increase the internal resistance of battery cells. High temperature can accelerate battery aging and trigger a potential thermal runaway phenomenon. Hence, the temperature change may cause cell inhomogeneity, affecting the secure use of a second-life battery. The analysis in [104] indicates that the electrolyte can be decomposed during the charging/discharging process. After long-term operation, the electrolyte is degraded, causing a decrease in battery performance. In addition, there exists differences in the degree of electrolyte aging among battery cells, which can exacerbate cell inhomogeneity.

4.2. Balance Strategy of Battery Cells

To address cell inhomogeneity and ensure reliable operation of battery modules, cell balancing strategies have been frequently developed, mainly including active balance strategies and passive balance strategies. Figure 5 shows the basic principle of an active balancing strategy and a passive balancing strategy. Active balancing strategies were developed to regulate and balance power among battery cells through power conversion devices. Also, passive balancing strategies are realized by consuming slightly larger capacity battery cells through balancing devices to achieve an overall balance.
(1)
Active Balancing Strategy
Figure 5a shows the diagram of active balancing strategies, which achieves the balance of each battery cell (Cell1/Cell2/Cell3) in the battery pack through a DC/DC converter system. It can be seen that the unbalanced SOC of different cells can be redistributed by DC/DC converters.
In [105], an active dynamic balance strategy was proposed, which effectively deals with the dynamic imbalance problem of second-life battery and reduces the impacts of cell capacity mismatch. In [106], a cooling optimization method of a battery pack based on electro-thermal modeling and particle swarm optimization was proposed. The proposed optimization method results in a lower maximum cell temperature and smaller temperature difference among cells. Meanwhile, the branch current difference can be reduced to mitigate the effect of cell inhomogeneity. In [107], a three-dimensional temperature field reconstruction method for a lithium ion battery pack was proposed based on a distributed Kalman Filter. This method can accurately detect and manage the thermal distribution among the battery cells so as to mitigate the effects of cell inhomogeneity. However, the availability of this method in a complex battery package with multiple cells should be further investigated.
In [108], a balancing method was proposed to minimize the battery charge balancing (BCE) time through battery module reconstruction, where the module based bounded reconfiguration (BR) and complete reconfiguration (CR) algorithms were developed to calculate the optimal BCE time for achieving charge balance in a second-life battery. In [109], a dual-level passive charger for a second-life EV battery was proposed, which can increase battery capacity retention. However, the measured experimental data is very limited to validate its effectiveness. In [97], a non-contact battery capacity detection method based on small millimeter-wave radar and edge artificial intelligence is proposed, which can measure charge/discharge data and the remaining capacity of each cell to improve the compatibility of different cells. A coordinated operation strategy combining a charger and a battery equalizer was proposed in [110], which can achieve a high balancing efficiency without using an iterative calculation optimization algorithm. By coordinating the operation of battery equalizers and chargers, the SOC of multiple battery cells can be properly balanced. In [89], a battery management controller for second-life batteries was developed to integrate multiple battery modules with differences in performance and parameters in order to mitigate the effects of cell inhomogeneity for an energy storage system.
In [111], a current sharing method for battery cells in series was proposed, which can integrate cells with different capacities or even different chemical compositions to realize a proper balance. However, the efficiency, current, and capacity measurement accuracy of DC/DC converters need to be improved. In [112], a current equalization method for series-connected battery cells was proposed by a series current sharing circuit of the DC/DC converter, and each cell current was controlled based on cell capability. It thus improves the energy utilization rate of the battery pack. However, the effectiveness of this method for battery packs with multiple parallel cells should be further investigated. In [113], a charge balancing algorithm for lithium ion batteries was proposed based on a bidirectional flyback DC/DC converter to monitor and balance the battery cells, which can effectively balance the SoC within the safe area operating range. However, it requires more switches and intelligent control. In [114], a novel integrated cascaded bidirectional DC-DC converter was proposed to balance batteries of different voltage polarities by controlling the operating state of the converter.
In [115], a fast battery voltage equalizer based on a bidirectional flyback converter was proposed to realize current balancing and voltage balancing with high efficiency. In [116], a centralized balancing system for series battery packs based on equalization current ripple cancellation (ECRC) converters was proposed. Compared with traditional methods, the balancing system based on an ECRC inverter realizes the zero ripple balancing current to mitigate the adverse effects of a high current ripple on battery cells. In addition, the ECRC converter also gives the balancing system advantages such as a small size, low cost, and high efficiency. In [117], a system method for deriving an integrated cascaded multiport converter (ICMPC) was proposed, in which configuring one converter can achieve energy exchange between multiple unbalanced batteries and battery packs. In [118], a system topology derivation method for developing integrated cascaded bidirectional (ICB) converters was proposed, which can optimize centralized charge balancing systems.
(2)
Passive Balancing Strategy
Apart from an active balancing strategy, passive balancing strategies are also important solutions. Figure 5b shows a diagram of passive balancing strategies, which mainly relies on the resistance discharge method to release the electricity in batteries with higher voltage in the form of heat energy, thereby creating more charging time for other batteries.
In [119], a novel passive balancing strategy based on a dynamic resistance equalizer balancing method for a parallel battery configuration was proposed. This method can regulate the branch current based on the SOC of battery cells by adjusting the impedance of a parallel branch, so as to balance the current differences among battery cells. In [120], a distributed periodic event consistency control method for a time-lag battery management system is proposed, which can balance the SOC of a second-life battery and the output power supply of each battery cell. In [121], a multi-layer SOH balance control method was proposed to realize the SOH balance for the paralleled cells and series-connected cells of a battery system by utilizing active equalization circuits to transfer the energy between the cells with a different SOH. Compared to a traditional active SOC balancing control, this proposed method can significantly prolong the cycle life of a battery system.
In [122], virtual resistance control was first proposed to provide dynamic compensation for changes in the terminal battery voltage. Secondly, thermal management should be implemented to achieve a more uniform temperature distribution within the battery pack. Thirdly, onboard diagnostic or fault detection tools, such as performing characterization tests or identifying or even isolating problematic cells, should be applied. In [123], a Battery Balance System (BBS) was proposed. In passive equilibrium, excess energy in battery cells in a high charging state is converted into heat through resistance until it is equal to battery cells in a low charging state. The passive balancing method is simple, easy to apply, and cost-effective, but its balancing efficiency is low, and the heat generation is large. In [124], a passive balancing circuit was proposed that utilizes resistors to consume excess energy from higher units to balance lower units. However, the passive method of battery balancing leads to a reduction in available battery pack capacity, thus requiring additional thermal management measures. In [125], an adaptive passive battery balancing method was proposed. Excess charge is released as heat through shunt components, and it must be able to pass as much current as the charger to protect the battery from overcharging. However, passive balancing wastes energy by converting excess energy into heat and cannot provide balance when the battery is depleted.
In [126], a passive battery balancing method was proposed to use shunt resistors to dissipate the additional charge of batteries with high SOC, aiming to match batteries with lower SOC. However, the existing energy dissipation in passive battery balancing methods leads to a reduction in the available capacity of the battery pack and requires additional thermal management systems. In [127], a passive balancing method for switch resistance of lithium ion battery packs was proposed. It used resistors in the circuit to balance and eliminate excess voltage based on the available voltage values at the battery cell terminals. It proposed using intelligent technology to attempt battery cell balancing. In [128], a novel multi module collaborative balancing system for battery packs was proposed. The system combines active and passive balancing and also includes a fast discharge function for balancing modules through power resistors. However, the equalizer structure needs further optimization and improved circuit integration. In [129], a machine learning based battery balancing optimization mechanism for electric vehicle battery management systems was proposed. Variable resistors are used in passive balancing systems to optimize power loss and achieve optimal thermal characteristics. In [130], a capacitor based shuttle battery balancing circuit was proposed, which consumes battery cells with higher energy through capacitors. However, there is significant energy loss during capacitor charging, with a maximum efficiency of only 50%.
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Comparative analysis of different balancing strategies
A comparative analysis of different balancing strategies has been conducted. In [131], a comparative analysis of passive and active balance performance of battery cells is given. In [132], a comparative analysis of three different types of equalizers is given, including Active Equalizer (AEQ), passive equalizer (PEQ), and Bilevel Equalizer (BEQ). When the battery capacity is greater than 80% of its original capacity, the PEQ is suitable for use in operation of electric vehicles (EVs). In second-life operation, the BEQ is much more efficient and cost-effective compared to other equalizers, where BEQ is a combination of AEQ and PEQ to reduce the driver number in a battery pack compared to AEQ. In [133], it was proposed that active balancing can reduce the aging rate of batteries and achieve better utilization compared to passive balancing. The voltage difference at the end of discharge is reduced by more than five times, the discharge capacity is increased by 3.1%, and the service life is extended by 7.7%.
Table 3 gives a comparative analysis between an active balancing strategy and a passive balancing strategy, respectively, where the main methods, impacts and advantages are summarized. The advantages of active balancing methods include high balancing efficiency, fast balancing speed, and high energy utilization, while the disadvantages include complex technology, high cost, and difficult implementation. As the well-proven technologies, the advantages of passive balancing strategies include low cost and high reliability, while the disadvantages include low balancing efficiency and low energy utilization. The active balancing strategy always requires a large number of transformers, power switches, and driver circuitry, and the size of traditional equalizers can be enlarged, which causes high operating losses and a long equalization cycle.

5. Compatibility Issue of Second-Life Battery

Undoubtedly, the electrochemical, materials and electrical characteristics of a second-life battery have been degraded after long-term operation. However, the dynamic characteristics of a second-life battery must be aligned with a grid code for energy storage application [134]. Furthermore, the efficiency and stability of an energy system may be affected in the presence of the incompatibility of information transmission and control performance between a second-life battery and an energy management system (EMS) [135]. In this section, the compatibility of a second-life battery with EMS is discussed in terms of electrical characteristics and control performance, respectively.
Figure 6 shows the application of a second-life battery based-energy storage system on the generation side and demand side, where second-life batteries are adopted to perform power smoothing of a wind power plant and PV plant. Also, second-life batteries can be employed to provide a secure and reliable power supply for demand sides such as commercial buildings and EV stations.

5.1. Compatibility of Electrical Characteristics for Energy Storage

Due to performance degradation of a battery system after long-term operation, it is essential to investigate whether a second-life battery can meet dynamic performance requirements for energy storage such as fluctuation suppression and transient power regulation. Furthermore, the coordinated control and operation strategies of energy storage systems based on second-life batteries should be developed. In [136], a second-life battery energy storage system based on real-time synchronous data (SBESS-RSD) was proposed, where the performance differences of second-life batteries are considered. This method can realize power regulation, power fluctuation smoothing, and peak shaving, etc. by establishing different operating modes and scheduling strategies, which enhances the compatibility of second-life batteries in energy storage systems. In [25], the availability of second-life batteries in a wind farm is analyzed based on a dynamic degradation model of batteries. And the model predictive control is adopted to deal with optimal wind scheduling problems and maximize the profit of wind farm owners.
However, the existing work slightly concerns the effects of electrical performance degradation of a second-life battery on the operation performance of an energy storage system. The compatibility of a second-life battery is essential to ensure the operation performance for energy storage, where the electrical characteristics of a second-life battery must meet dynamic performance requirements by developing advanced control strategies such as module predictive control. It is essential to compensate the performance degradation of second-life batteries to improve the applicability of a second-life battery in an energy storage system.

5.2. Compatibility of Different Second-Life Batteries with Energy Management Systems

In energy systems with second-life batteries, the compatibility of a second-life battery with an energy management system (EMS) of energy system operators is another important concern, where the EMS can handle energy management of second-life batteries from different manufacturers. And an EMS can efficiently manage charging/discharging behaviors of energy storage systems by monitoring and managing data and information of second-life batteries. Hence, an EMS with good scalability and flexibility is essential for compatibly with various second-life batteries in practical applications.
In [137], a wireless lithium ion battery management system (BMS) was developed to perform charging/discharging management, where the microcontroller was adopted to change the charging/discharging rates of a DC/DC converter. Also, wireless transmission was employed to detect the charging/discharging states of batteries, which further improved the compatibility of a second-life battery and energy management system. In [138], an online monitoring method of second-life batteries was proposed to integrate EMS and second-life batteries, where artificial neural networks and a Kalman filter were adopted to monitor information and data in a reliable and accurate manner. In [139], an enhanced monitoring system was proposed to estimate the health status of second-life batteries and enhance information management. Meanwhile, a Kalman Filter was adopted to improve the estimation accuracy and ensure the reliable operation of a second-life battery. In [140], a distributed battery management system (DBMS) with customized hardware components was proposed, where a decentralized control architecture considering individual battery status was developed. The proposed DBMS can be compatible with various types of second-life batteries with good scalability and flexibility. In [141], a security-oriented data collection method based on BMS was proposed, where data can be transmitted to cloud and terminal systems. This method can improve performance of the BMS and realize better application of second-life batteries in energy storage systems. In [64], an onboard electrochemical impedance spectroscopy-based method was proposed to improve compatibility with second-life batteries. This method enables the BMS to accurately predict the SOH of second-life batteries to improve the performance of second-life batteries in energy storage systems.
However, the existing work slightly addresses the compatibility between EMS and second-life batteries with consideration of safety under various environmental conditions. Therefore, data and information communication between second-life batteries and EMS is critical to improve scalability and flexibility, such as optimizing control structures and establishing standardized interfaces for second-life batteries.

6. Data Safety and Protection Strategies for Second-Life Batteries

In this section, the potential data safety issues for second-life battery utilization are first analyzed. Then, the data protection strategies are investigated, including digital passport (DP) technology, smart metering and IoT technology. The advantages and disadvantages of the different data protection strategies are discussed. In addition, the relevant regulations and rules for second-life battery utilization are investigated. The operating data of EV batteries are always collected and stored by a BMS in real-time. Hence, a BMS is a huge database with massive information about battery manufacturers, consumers, and operators, etc., which are undoubtedly related to consumer privacy and commercial secrets. In practical applications, the security problems of second-life battery-based energy storage systems may be caused once the relevant data are abused [19,142]. For example, the charging/discharging behaviors may be intentionally manipulated to mitigate the security of energy storage systems. In [48], the effects of data security of second-life batteries on power grids are analyzed. The malicious attackers may manipulate the charging and discharging behaviors of second-life batteries, which disrupts the stability of a power grid, and even cause large-scale power outages. Also, the personal information such as travel patterns, driving habits and consumer addresses may be abused [40]. In addition, the key parameters such as the charging cycle and SOH in an EV battery are related to the commercial secrets of manufacturers. If these data are illegally obtained and abused by competitors, the product technology may be obtained to violate intellectual property and make competitive strategies [1]. Traditional BMSs for certain battery systems are difficult to detect and protect data in second-life utilization. Therefore, it is essential to develop advanced data protection strategies to enhance the security of energy storage systems with second-life batteries.

6.1. Digital Passport (DP) Technology

DP is an emerging concept and technology [143] to utilize EV batteries in a secure and efficient way, where data during the entire life cycle of a second-life battery can be tracked in a digitalized way, since it is equipped with a unique identity (ID) chip. The critical information of a second-life battery is recorded, including production date, date of second-life use, manufacturer, and material system, etc. Furthermore, the health status of a second-life battery such as cycle number, capacity attenuation, internal resistance variation, electro-thermal features, etc. throughout the lifespan are detected and tracked. The European Commission has published a new regulation on power batteries, which explicitly require the implementation of a DP for electric vehicle batteries (EVBs) from January 2026 [144]. The rapid development of an electric vehicle battery (EVB) market poses the requirements for recycling and reuse of EV batteries. DP is an emerging and effective solution to fill the information gap along the value chain to support actors’ sustainable product management (SPM) decisions by providing the required product lifecycle data [145].
Figure 7 shows the main concept, impact, and stakeholders of DP technology [146,147,148,149,150]. DP can store critical data, including manufacture information, compliance, carbon footprint, SOH, remaining useful life, cycle efficiency, and durability, etc. Typically, a unique identification number and digital signature are employed to protect second-life battery data, which provides a unique and secure solution for different stakeholders, including battery manufacturers, automotive Original Equipment Manufacturers (OEMs), used car buyers, recyclers and remanufacturers.
In [146], a battery digital passport technology was developed to investigate the applicability of a second-life battery. This method can collect and dynamically fill data into a battery passport to determine whether the battery is suitable for reuse at the end of its first useful life. However, this method slightly considers the operation performance of a second-life battery. In [147], a Physical Unclonable Functions (PUF) based certificate method was developed to obtain a trusted product life cycle. This method creates a unique cryptographic key for each second-life battery and issues a certificate, which thus promotes trust in the issued certificate by using a physically unclonable feature. And this method can detect attacks on product lifecycle data and certificates, as well as the introduction of manipulation and counterfeit batteries. However, it is difficult to directly apply for data encryption in BMS. In [148], a DP method was developed to support the practical application of a second-life battery, which labels each battery with production information, carbon footprint, SOH, and remaining useful life (RUL). This method can improve safety and reliability for second-life battery utilization. However, the accuracy of this method for evaluating SOH and RUL still needs to be improved. In [149], a digital twin method for EV battery systems is proposed. This method provides virtual real-time representations of real battery systems (physical twins) to improve the performance and safety of battery systems by applying advanced multi-layer models, artificial intelligence, advanced sensing units, and cloud computing technology. However, this method only applies to the usage environment of some second-life batteries, which lacks an evaluation of their potential applications. In [150], a Radio Frequency Identification (RFID) system is proposed to achieve battery traceability. This method can achieve cyclic battery management and sustainability of a battery digital passport system, and safely collect second-life battery parameters and store them in the database.

6.2. Smart Meter for Second-Life Battery

The data exchange between second-life battery energy storage systems and users is a potential weakness for data security. Smart meter technology ensures the security of data communication channels and the physical security of interactive assets through privacy protection, to improve the security and reliability of energy systems.
In [151], a smart meter-based method was proposed to protect consumer privacy based on virtual energy demand. In a house with a second-life battery-based energy storage system, a smart meter is adopted to measure data on energy loads. This approach can fill security gaps in smart grids, protect consumer privacy, and allow data to be shared. However, the virtual demand data is not the same as the practical demand data. In [152], a second-life battery assisted privacy protection method based on a smart meter is proposed. This method is a non-invasive load monitoring attack method based on time convolutional networks used to identify the usage of electrical equipment according to commonly used load curves. Although privacy protection is well implemented, the cost is increased. In [153], a privacy preserving intelligent billing communication aggregation mechanism was proposed, which is based on differential privacy and re-encryption models. Also, the ElGamal algorithm was adopted to construct a re encryption scheme and differential privacy. This method can effectively enhance data security between a second-life battery system and power supply households. However, this approach lacks testing for other types of privacy attacks, i.e., non-intrusive load monitoring. In [154], it was proposed to measure privacy through its information leakage rate. The information leakage rate represents the average mutual information between the user’s actual energy consumption and the energy requested from the grid, which SM reads and reports to the utility provider (UP).
Smart meter technology can improve data security by protecting the data exchange channel between the second-life battery system and the users. However, the applicability of smart meter technology in different second-life battery systems should be analyzed and improved. Also, cost reduction is an important requirement.

6.3. Internet of Things-Based Protection Method

The Internet of Things (IoTs) technology encrypts second-life battery data through encryption algorithms with a large and secure database for storing and processing data, which can protect the data security of a second-life battery.
In [155], a cloud computing-based data security enhancement method for a second-life battery was developed. This method is based on a base station with powerful computing equipment, which first collects and calculates the received energy storage consumption data, then realizes long-distance data exchange through the base station to ensure security of data transmission. However, it is difficult to control the data outsourced in a public cloud environment. In [156], an energy-saving physical layer security method based on the Internet of Things was proposed, which is based on sensors transmitting energy data from second-life battery to servers. And artificial interference signals are introduced to confuse eavesdroppers. Compared with traditional methods, this approach can significantly improve data confidentiality without requiring any additional power resources. However, there is a lack of performance evaluation for hardware devices, such as Universal Software Radio Peripherals (USRP). A data protection method based on a smart battery was proposed in [157], where a communication method using a power line carrier (PLC) was adopted to measure parameters such as the SOC and SOH of a battery, and accurately evaluate its operation state. In addition, a networked data management framework was established to detect battery states and realize the unified data management, which improves the safety and reliability of an energy storage system. In addition, IoT devices can be optimally designed, which adopts more secure network protocols and strengthens device protection measures to enhance data security such as encryption and authentication.

6.4. Regulations and Rules for Data Safety of a Second-Life Battery

The regulations about data security are important to support second-life battery utilization. In [158], the main regulatory structure for a second-life battery was proposed, including rules and technical standards. The method was systematically evaluated according to a strict protocol, including identifying relevant research, extracting data, and summarizing information evaluation. The urgent need for cooperation among battery holders (government, industry, and academic institutions) is emphasized to develop standards and policies that promote sustainable battery lifecycle management. It is helpful to develop new technical regulations and laws to achieve data security for second-life batteries. However, unifying regulations between countries and regions is challenging, and further research and development are needed in the field of battery recycling technology. In [159], it was proposed to establish a standardized and accessible information repository. The Digital Battery Passport (DBP) aims to enhance the traceability, sustainability, and accountability of the entire electric vehicle battery ecosystem. This innovative approach not only provides solutions to the challenges posed by the lifecycle of electric vehicle batteries, but also aligns with the broader sustainable development goals of the European Union. However, the standardization of safety regulations and rules for second-life battery data poses significant challenges in different countries and regions.

7. Discussion and Future Trends

This article provides a comprehensive overview about potential challenges and solutions of second-life batteries, including second-life battery safety, cell inhomogeneity, system compatibility, and data security. The key technical challenges and potential solutions are discussed as follows.
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Safety Management Technology of a Second-life Battery
The safety of a second-life battery is the primary concern for energy storage applications, which is highly related to the thermal runaway of battery systems. Internal chemical reactions and external physical interactions are the main factors for thermal runaway. The problem of thermal runaway can be mainly addressed through structural optimization, parameter identification, advanced BMS, and AI-based control strategies. Accurate and real-time parameter identification is critical to improve the safety of second-life batteries. AI-based identification methods are promising solutions to handle the nonlinear characteristics of second-life batteries to improve recognition accuracy. Furthermore, the establishment of intelligent risk prediction mechanisms by AI-based methods is an important trend to improve battery safety.
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Cell Inhomogeneity of Second-life Battery Systems
The cell inhomogeneity phenomenon in a second-life battery system can affect the performance of the battery module and weaken its lifespan. An efficient and cost-effective battery balancing strategy is important to mitigate the cell inhomogeneity of second-life batteries. A combination of active balancing and passive balancing is a promising trend to provide an efficient and cost-effective solution for battery cell balancing.
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Combability Issue for Second-life Battery Utilization
The compatibility of second-life batteries in energy systems is an important concern. It is essential to investigate whether electrical dynamic characteristics of second-life batteries can meet the performance requirements for energy storage such as fluctuation suppression and transient power regulation. Therefore, the advanced control strategies such as model predictive control are important to improve the dynamic characteristics of energy storage systems based on second-life batteries. In addition, the compatibility of second-life batteries from different manufacturers with energy management systems is another important concern. The coordinated control and operation strategy for second-life battery systems should be further developed, such as optimizing the control structure and establishing standardized interfaces.
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Data Safety and Protection Strategy of Second-life Batteries
To ensure the data security of a second-life battery, different data protection technologies have been developed, including DP technology, smart meters, and the Internet of Things to ensure data security. DP provides an ID for each second-life life battery, recording factory information, usage information, and various parameters. Smart meters protect the data transmission channel between second-life batteries and users. The Internet of Things uses algorithms to encrypt data and the processes it uses to secure databases. The compatibility and standardization of various data protection methods are urgent to ensure data security and further promote the practical applications of second-life battery energy storage systems.

Author Contributions

Conceptualization, H.S. and Y.W.; investigation, H.S., H.C., Y.W. and X.-E.S.; writing—original draft preparation, H.S. and Y.W.; writing—review and editing, H.S., H.C., Y.W. and X.-E.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Not applicable.

Acknowledgments

We would like to acknowledge Jinfeng Li about her valuable suggestions during manuscript revision.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Diagram of the lifetime of an EV battery.
Figure 1. Diagram of the lifetime of an EV battery.
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Figure 2. The potential application of second-life batteries in future power grids.
Figure 2. The potential application of second-life batteries in future power grids.
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Figure 3. The phenomena, mechanisms, and control methods for battery safety [9,10,11,12,13,14,15,16].
Figure 3. The phenomena, mechanisms, and control methods for battery safety [9,10,11,12,13,14,15,16].
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Figure 4. SOC inconsistency of different cells in a battery module.
Figure 4. SOC inconsistency of different cells in a battery module.
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Figure 5. A diagram of battery balancing methods. (a) Active balancing method. (b) Passive balancing method.
Figure 5. A diagram of battery balancing methods. (a) Active balancing method. (b) Passive balancing method.
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Figure 6. The potential compatibility problem for second-life battery utilization in energy storage systems.
Figure 6. The potential compatibility problem for second-life battery utilization in energy storage systems.
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Figure 7. The concept, impact and stakeholders of DP technology.
Figure 7. The concept, impact and stakeholders of DP technology.
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Table 1. The potential applications of second life batteries of EV.
Table 1. The potential applications of second life batteries of EV.
ApplicationOperatorsAdvantageLiterature
Renewable energy systemWind power generation, photovoltaic power generationPower fluctuation smoothing, energy storage, energy recovery, energy balance, reduction of environmental impact, improved sustainability, stability improvement[20,25,36,37,38,39,40,41]
Commercial charging stationElectricity tradingEnergy storage balance, improved energy efficiency, and reduced environmental impact [42,43,44]
Back-up power supplyMicrogrid, UPS uninterruptible power supplyHigh capacity and reliability, mobility, environmental protection, economic efficiency[45,46,47]
Grid regulation and supportGrid operatorFrequency regulation, energy storage, power smoothing, grid flexibility[23,48,49,50,51]
Table 2. The main parameters, identification methods and impacts for second-life batteries.
Table 2. The main parameters, identification methods and impacts for second-life batteries.
ParametersIdentification MethodsImpacts
State of change (SOC)Recursive least squares method [26]
Adaptive neural network [30]
Online fitting algorithm [31]
BMS (non-invasive measurement) [37]		Identify abnormal battery cells
Open circuit voltage (OCV)Recursive least squares method [26]
Kalman filter [29]
State of health (SOH)Regeneration estimation method [22]
Online fitting algorithm [31]
Pulse testing [33]
BMS (non-invasive measurement) [37]		Delaying the aging of battery
Remaining useful life (RUL)OPTICS algorithm [24]
Recursive least squares method [26]
BMS (non-invasive measurement) [37]
Series resistor
Charge transfer resistance
Electric double-layer capacitors		Pseudo random noise method [28]Balanced charging and discharging state
Cell temperature differenceNonlinear parameter identification [27]
BMS (non-invasive measurement) [34]
BMS (Passive thermal management) [36]
BMS (air-cooled) [35]
Intelligent control of charging and discharging [39]
artificial neural network [40]		Reduction of battery temperature
Load voltageNonlinear parameter identification [27]
Extended Kalman Filter [32]
BMS (non-invasive measurement) [34]
Intelligent control algorithm [41]
Load currentExtended Kalman Filter [32]
BMS (non-invasive measurement) [34]
Intelligent control algorithm [41]
Table 3. The methods and impacts of active balancing strategies and passive balancing strategies.
Table 3. The methods and impacts of active balancing strategies and passive balancing strategies.
Balancing StrategyMethodImpactAdvantage
Active Balancingactive dynamic balance strategy [105]
optimization of battery pack cooling [106]
distributed Kalman filter [107]
dynamic reconstruction of second-life battery [108]
dual-level passive charger [109]
non-contact battery capacity detection [97]
combining a charger and a battery equalizer [110]
a battery management controller [89]
series current sharing [111,112]
bidirectional flyback DC/DC converter [113]
integrated cascaded bidirectional DC-DC converter [114]
bidirectional flyback converter [115]
ECRC converter [116]
integrated cascaded multiport converter [117]
ICB converter [118]		Realize cell homogeneity through energy conversion between battery cellsHigh balance efficiency, fast balance speed, and high energy utilization efficiency
Passive Balancingdynamic resistance equalizer [119]
time-lag BMS [120]
multi-layer SOH balance control [121]
virtual resistance control [122]
battery balance system [123]
passive balancing circuit [124]
adaptive passive battery balancing [125]
passive battery balancing [126]
switch resistance [130]
multi module collaborative balancing system [128]
BMS based on machine learning [129]
capacitor based shuttle battery balancing circuit [130]		Realize cell homogeneity by changing the energy level of individual battery cellsMature technology, low cost, and reliable methods
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Song, H.; Chen, H.; Wang, Y.; Sun, X.-E. An Overview About Second-Life Battery Utilization for Energy Storage: Key Challenges and Solutions. Energies 2024, 17, 6163. https://doi.org/10.3390/en17236163

AMA Style

Song H, Chen H, Wang Y, Sun X-E. An Overview About Second-Life Battery Utilization for Energy Storage: Key Challenges and Solutions. Energies. 2024; 17(23):6163. https://doi.org/10.3390/en17236163

Chicago/Turabian Style

Song, Hua, Huaizhi Chen, Yanbo Wang, and Xiang-E Sun. 2024. "An Overview About Second-Life Battery Utilization for Energy Storage: Key Challenges and Solutions" Energies 17, no. 23: 6163. https://doi.org/10.3390/en17236163

APA Style

Song, H., Chen, H., Wang, Y., & Sun, X. -E. (2024). An Overview About Second-Life Battery Utilization for Energy Storage: Key Challenges and Solutions. Energies, 17(23), 6163. https://doi.org/10.3390/en17236163

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