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Review

A Comprehensive Review of the Art of Cell Balancing Techniques and Trade-Offs in Battery Management Systems

by
Adnan Ashraf
1,
Basit Ali
1,*,
Mothanna S. A. Al Sunjury
2 and
Pietro Tricoli
1
1
Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston Campus, Birmingham B15 2TT, UK
2
Technical Engineering College of Mosul, Northern Technical University, Mosul 41002, Iraq
*
Author to whom correspondence should be addressed.
Energies 2025, 18(13), 3321; https://doi.org/10.3390/en18133321
Submission received: 19 May 2025 / Revised: 18 June 2025 / Accepted: 23 June 2025 / Published: 24 June 2025
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Abstract

The battery pack is a critical component of electric vehicles, with lithium-ion cells being a frequently preferred choice. Lithium-ion cells are known for long life, high power and energy density, and are reliable for a broad range of temperatures. However, these batteries have a drawback of over-voltage, under-voltage, thermal runaway, and especially, state of charge or voltage imbalance. Among these, the cell imbalance is particularly important because it causes an uneven power dissipation in each cell, resulting in non-uniform temperature distribution. This uneven temperature distribution negatively affects the lifetime and efficiency of a battery pack. Cell imbalance is mitigated by cell balancing techniques, of which several methods have been presented over the last few years. These methods consider different power electronics circuits and control approaches to optimise cell balancing characteristics. This paper reviews basic to advanced cell balancing techniques and compares their circuit designs, costs, switching stresses, complexity, sizes, and control techniques to highlight the recent trends and future directions. This paper also compares the recent trend of machine learning integration with basic cell balancing topologies and provides a critical analysis of the outcomes.

1. Introduction

In recent years, the rapid production of lithium-ion (Li-ion) batteries and their usage in electric vehicles (EVs) and energy storage systems have brought renewed focus to the issue of cell imbalance [1,2]. Cell imbalance poses a major challenge to the safety, performance, and overall longevity of Li-ion battery systems [3]. The difference in the state of charge (SOC) or voltage of the series-connected cells in a battery pack causes certain cells to be overcharged, while others remain undercharged [4]. Overcharged cells can lead to faster degradation that reduces cell capacity and poses serious safety risks such as thermal runaway and potential cell failure [5]. On the other side, undercharged cells reduce the battery pack’s overall energy storage capacity, resulting in decreased system performance and reduced reliability [6,7,8]. Due to the variations in leakage currents and chemical properties of the battery cells, the voltage or SOC imbalance between the cells increases, which develops after multiple charges and discharges, progressively damages the cells, and shortens battery service life [9]. As is well-known, effective cell balancing is critical for series-connected battery packs. If the cells are not balanced, the voltage differences between cells can lead to further imbalances and the potential failure of the entire battery pack. Thus, cell balancing becomes even more critical to ensure the proper functioning and safety of series-connected Li-ion battery systems [10].
Over the years, numerous review articles on cell balancing techniques have been published. However, there is a need for a structured review article that bridges the fundamental methods with recent advancements in cell balancing topologies for EV applications. For instance, in [11], a comprehensive survey on passive and active balancing methods was conducted. However, the study lacked an in-depth analysis of balancing topologies in the context of emerging EV battery architectures. Another review paper, ref. [12], emphasised the use of advanced control algorithms to enhance balancing accuracy, but did not sufficiently address practical deployment challenges, such as isolation requirements and system scalability. The article [13] reviewed topologies focusing on cost-effectiveness, but their analysis did not adequately address the underlying causes of cell imbalance or the critical role of balancing in maintaining overall battery health. More recently, ref. [14] investigated the incorporation of smart sensing and real-time monitoring to enhance balancing performance. However, their review offered a limited comparison of balancing strategies, particularly in terms of energy transfer methods and control complexity. Furthermore, none of these reviews discussed the incorporation of machine learning (ML) techniques within balancing topologies or provided a critical comparison of their effectiveness.
To bridge these gaps, this paper presents a comprehensive overview of cell balancing techniques from basic to advanced topologies. It also examines the key factors leading to cell imbalance and highlights the importance of balancing. Furthermore, recent developments in balancing strategies for EV applications are discussed, along with a detailed comparison of various topologies based on component count, energy transfer methods, and their respective advantages and limitations. The paper evaluates the functional characteristics of each approach, including control complexity, isolation capabilities, and their suitability for deployment in practical EV battery systems. In addition, it provides a comparison of ML-integrated balancing topologies, providing a critical analysis of their performance and potential.
Section 2 provides a survey of recent trends in the literature on cell balancing techniques in EVs, Section 3 briefly explains the battery management system, an explanation of cell balancing topologies is presented in Section 4, Section 5 describes the comparison between cell balancing techniques, Section 6 discusses emerging trends in machine learning in cell balancing topologies, and Section 7 provides a brief conclusion along with recommendations.

2. Recent Trends of Cell Balancing Techniques in EVs

Recent research highlights a clear trend in the adoption of various cell balancing techniques within EV batteries and energy storage systems. The decision between passive and active balancing is typically based on trade-offs between cost, complexity, efficiency, and application requirements. Passive balancing is still widely used in many commercial EVs due to its simplicity and dependability; however, hybrid and ML-integrated topologies are gaining attention due to their benefits of maximising battery life and efficiency. The integration of the battery management system (BMS) in practical EV applications is shown in Figure 1, where it communicates with the vehicle control unit (VCU) through protocols such as the Controller Area Network (CAN) and the Local Interconnect Network (LIN). The BMS is directly connected to the battery pack and the thermal management system, continuously monitoring parameters like voltage, current, and temperature. It uses this data for battery state estimations, such as state of charge (SOC) and state of health (SOH). Using these estimations, the BMS carries out cell balancing to ensure uniform charge distribution, enhancing battery efficiency and lifespan while protecting against electrical short circuits. Furthermore, the BMS interacts with the thermal management system to regulate battery temperature and protect against thermal risks. This integrated setup highlights the increasing complexity of modern BMS architectures, which enable advanced cell balancing techniques in EVs. The following Table 1 provides an overview of the types of balancing techniques used in real-world EV applications.

3. Battery Management System

The safety and proper operation of a lithium-ion battery pack made up of series-connected cells requires an advanced battery management system (BMS). The BMS monitors and controls various aspects of the battery, including cell voltages, temperatures, SOC, state-of-health (SOH), safety, data acquisition, and cell balancing, as exhibited in Figure 2 [25,26,27,28,29,30].

3.1. Cell Monitoring

The BMS regularly checks for imbalances, overvoltage, or undervoltage by monitoring the voltage level of each battery cell. Potential performance issues and safety risks can be recognised with the help of this information [31].

3.2. Battery State Estimation

Based on voltage and current measurements as well as other battery properties, the SOC and SOH of the battery are estimated by the BMS. Determining the available capacity can be determined by accurate state estimation [32,33,34].

3.3. Thermal Management

The BMS keeps track of the battery cells’ temperature and activates cooling or heating systems as necessary. Enhancing battery performance, preventing thermal runaway, and extending battery life all depend on maintaining ideal temperature levels [35,36].

3.4. Cell Balancing

The voltage or SOC of each battery cell is balanced by the BMS using cell balancing algorithms. Cell balancing makes certain that all the cells function within desirable ranges, optimising capacity utilisation and extending battery life [37,38,39].

3.5. Safety

The battery is protected by the BMS safety measures against overcharging, overdischarging, short circuits, and high temperatures. It incorporates several protection devices, including cell disconnect switches, fuses, and temperature sensors, to ensure safe battery operation and stop critical breakdowns [40].

3.6. Data Recording and Communication

The battery performance parameters, voltage, current, temperature, and operating conditions are recorded by the BMS. It enables real-time monitoring, remote control, and data exchange between the battery system and external devices or control systems [41].
The key component of the BMS is cell balancing, which plays a key role in battery safety, operation, and longevity. The fowling section provides a detailed consideration of the fundamentals and significance of cell balancing.

4. Cell Balancing Topologies

The fundamental principles of cell balancing revolve around identifying and focusing on cell imbalances [42]. The cause of voltage or SOC imbalance includes differences in each cell capacity, internal resistance, self-discharge rates, and ageing characteristics. Even though the Li-ion cells are produced in the same manufacturing environment, these imbalances are still present, and grow over time due to changes in cell usage and ageing [43,44,45]. Cell imbalances can have several negative consequences on battery performance, including shorter cycle life, increased self-discharge rates, rapid capacity dissipation, poorer energy efficiency, and reduced overall capacity. The battery pack’s internal voltage fluctuations brought on by unbalanced cells may also result in performance problems or safety risks [46].
Cell imbalances can cause some cells to exceed their capacity thresholds earlier than others, reducing the battery’s overall capacity. The battery’s effective runtime is increased, and maximum capacity utilisation is ensured by balancing the cells’ voltage levels [47]. Due to imbalances, overcharging or overdischarging a particular cell can hasten cell deterioration, resulting in capacity fade and a shorter battery life. Techniques for cell balancing help to keep cells within their ideal voltage and SOC ranges, preventing capacity loss and extending the battery’s total life [48]. Cell imbalances may result in voltage oscillations within the battery pack, which may compromise either performance or safety. The likelihood of thermal runaway, overvoltage, or undervoltage conditions that could jeopardise battery safety is reduced by balancing the cells [49]. By reducing the energy losses caused by overcharged or undercharged cells, cell balancing allows for efficient energy utilisation. Through this optimisation, the battery system’s energy efficiency is increased, resulting in improved overall performance and longer hours of operation.
The various methods utilised in EV applications to achieve cell balancing in Li-ion battery systems are shown in Figure 3. Cell-balancing techniques can be roughly categorised as passive or active [50].

4.1. Passive Cell Balancing

The additional energy from a high-voltage cell is removed using passive components, like resistors in a passive cell balancing. These resistors are either fixedly connected to the battery or connected in switched mode. Fixed shunting resistors and switching shunting resistors are the two divisions of passive cell balancing. The resistor entirely dissipates the excess energy from the high-voltage battery, which results in heating issues [51,52,53,54,55].

4.1.1. Fixed Shunting Resistor

A resistor is fixed in parallel with each cell in a collection of series-connected cells. This concept is very basic and straightforward, and is implemented using MATLAB Simulink (version 2020) in [56]. The extra energy is passed to parallel-connected resistors and dissipated as heat. The loss of energy and the production of heat are the main problems of using this topology. The balancing current is also limited according to the value of the resistors. The balancing currents are proportional to the voltages at the cell terminals, making it possible to utilise this method to balance the energy levels of cells without using any controlling actions. Figure 4 shows the fixed shunting resistor topology in which the resistors (R1, R2, …, Rn) relate to each cell (B1, B2, …, Bn).

4.1.2. Switched Shunting Resistor

A passive cell balancing system in which a control strategy is introduced using switches connected between parallel resistors and individual cells [57]. The control algorithm for switches can be implemented in a way that only turns on the specific switch connected with the highest voltage of the cell. It still transfers extra energy to resistors and is dissipated as heat.
A classic circuit diagram for switched shunting resistor topology is shown in Figure 5, where switches (S1, S2, …, Sn) are connected in series with shunting resistors (R1, R2, …, Rn) and resistors are connected in parallel with cells (B1, B2, …, Bn). The simulation of the switched shunting resistor topology is presented in [56,58], and hardware implementation can be seen in [58].

4.2. Active Cell Balancing

The four major categories of active cell balancing designs are capacitor-based, inductor-/transformer-based, converter-based, and bypass. In an active cell, energy is transferred from a high-voltage cell to a low-voltage cell via balanced capacitors or transformers. Various kinds of converters can also be used to transfer the surplus energy from a high-voltage cell to a low-voltage cell so that the battery voltage is balanced. As indicated, the bypass cell topology’s operating concept differs from that of the other active cell balancing topology. According to the control logic design for cell balancing, it bypasses high-energy cells instead of transferring energy from one cell to another [59,60,61,62,63,64,65].

4.2.1. Single Inductor

A single inductor incorporates control switches for energy transfer between cells in a battery pack. This topology combines the simplicity and cost-effectiveness of single inductor balancing with the control capabilities of active balancing techniques [66]. Figure 6 illustrates the circuit diagram for the single-inductor approach. The inductor is introduced in parallel with control switches, such as MOSFETS in [57]. By selectively connecting and disconnecting the inductor from different cells, these switches enable control over the energy transfer process. The control switch is opened to isolate the inductor from that cell once the voltage levels are equal. This allows selective balancing of cells, which is beneficial when just a certain number of cells need to be balanced [67].

4.2.2. Coupled Inductor

The cell balancing method that promotes energy transmission between cells in a battery pack by coupling inductors and control switches, as seen in Figure 7. By utilising the mutual inductance between the coupled inductors, this topology aims to increase the effectiveness and efficiency of cell balancing [68]. The coupled inductors are made up of two or more inductors having a common magnetic flux, which enables energy transmission between them [69,70,71].

4.2.3. Single Transformer

This is a unidirectional energy transfer cell balancing topology in which the charge is transferred from a strong cell to the pack and then from the battery pack to a weak cell. In this configuration, the charge is distributed throughout the cells using just one transformer, so it has low magnetic losses. However, it only transfers charge from or to one cell at a time, which may reduce overall balancing speed. Single-winding transfer consists of a transformer with only one primary winding and one secondary winding for n number of cells, as shown in Figure 8 and presented in [72].

4.2.4. Multi-Winding Transformer (Flyback Structure)

The battery pack terminals are connected to the primary side, while individual battery cells are connected to the secondary windings through diodes, as shown in Figure 9. The polarity of the primary and secondary windings is opposite to each other. When the main switch is closed, then the energy will be saved in the primary winding of the transformer as a magnetic flux. The stored energy cannot be transferred to the secondary due to the diode’s reverse bias. The energy will be transferred to the secondary windings in the next mode when the switch is turned off [73,74,75].

4.2.5. Multiple Transformers

Using multiple transformers enables energy transfer, and voltage equalising between the cells in a pack is shown in Figure 10. Because each transformer is dedicated to a particular cell, efficient cell balancing is achievable [76,77]. When balancing cells, using several transformers has several benefits. Each cell has a unique channel for transferring energy, enabling accurate control and effective energy transmission. The simultaneous operation of several transformers allows for the simultaneous balancing of numerous cells, shortening the overall balancing time.

4.2.6. Single Switched Capacitor

A cell balancing technique that utilises a single capacitor with a series-connected equivalent series resistor (ESR) connected in parallel at the two ends of a series-connected battery pack to facilitate energy transfer and voltage equalisation among cells in a battery pack. The capacitor stores any additional energy from the strong cell and transfers it to the weak cell to balance the voltages between the cells [78]. Compared to other balancing circuits, the usage of a single switched capacitor circuit simplifies the balancing system, as seen in Figure 11.

4.2.7. Multiple Switched Capacitor

The single-tiered switched capacitor method, as depicted in Figure 12, uses a layer of capacitors to move energy from the strong voltage cell to the weak voltage cell by repeatedly connecting each capacitor to two neighbouring cells. The same PWM signal is applied to all even switches, its counter PWM signal is applied to all odd switches, and all switches toggle on and off at the same frequency; this is implemented in [79,80,81].

4.2.8. Buck–Boost Converter

Each cell is linked to a distinct buck–boost converter during cell balancing. Bidirectional energy transfer is made possible by buck–boost converters, allowing energy to move across cells with various voltage levels [82,83]. Figure 13 depicts the circuit layout of a common buck–boost converter balancing technique, as presented in [68,82].

4.2.9. Quasi-Resonant Converter

In quasi-resonant converter-based cell balancing, a separate converter is connected between each pair of cells, as shown in Figure 14. The converter works in a resonant mode to transfer energy between cells, which reduces switching losses and boosts overall effectiveness by operating at resonant frequencies, as presented in [20,82].

4.2.10. Full-Bridge Converter

A power electronic converter configuration that permits bidirectional energy flow and effective energy transfer is the full-bridge converter, sometimes referred to as an H-bridge. According to this configuration, each battery cell is coupled to a separate full-bridge converter. However, it will increase the circuit’s complexity, size, and cost. This topology is preferred for module-level cell balancing, and its main flaws are its relatively expensive and complicated control. The full-bridge converter in Figure 15, which is utilised in modular design for bidirectional charge equalisation, is made up of a switch-bridged network running parallel to the cell module, as presented in [84,85].

4.2.11. Cell Bypass

In the bypass cell balancing topology, as shown in Figure 16, each cell or module in the battery pack is connected to a pair of switches, presented in [86,87,88,89]. One switch is placed to operate in series with the other cells, and another switch is utilised to disconnect the series connection of the cells for a period set by the controller. Throughout the cell balancing process, the switches are managed based on the voltage levels of the individual cells. The maximum voltage cell in the pack is bypassed while it is charging, and the minimum voltage cell is bypassed while the discharge process is in progress.

5. Comparison of Cell Balancing Topologies

The selection of an appropriate cell balancing topology is critical to the performance, efficiency, and reliability of BMS, particularly in EV and energy storage applications. While numerous topologies have been proposed and implemented, their practical effectiveness varies significantly depending on factors such as energy transfer mechanisms, circuit complexity, balancing speed, and system-level requirements. This section presents a structured and in-depth comparison of cell balancing topologies, divided into two focused subsections. The first subsection analyses each topology based on energy transfer method, hardware complexity, and associated advantages and disadvantages. The second subsection evaluates key features, control complexity, isolation, and suitability for EV applications.

5.1. Comparison Based on Component Count, Energy Transfer Method, and Pros/Cons

Cell balancing topologies in Table 2, data collected from [86,90,91,92,93,94,95,96,97,98], compare the number of components, the energy transfer technique, benefits, and drawbacks. Passive methods require minimal components and are simple and cost-effective but suffer from high power losses due to energy dissipation as heat. Inductor-based methods have moderate component usage with relatively low switching stress but become bulky, and complex control is required. Transformer-based topologies offer isolation and modularity with cell-to-pack and pack-to-cell transfer, but significantly increase component count, control complexity, and magnetisation losses. Capacitor-based topologies are more compact and easier to implement but suffer from limited balancing speed and high switch stress in larger systems. Converter-based topologies demonstrate better energy transfer efficiency due to their control flexibility and their capability of using multiple energy transfer methods, but are penalised by their high component count, larger volume, and bulky and demanding complex thermal and control requirements. The bypass topology shows a very low component count but high flexibility in DC link behaviour and switching stress.

5.2. Comparison Based on Key Features and Application Suitability

Table 3 summarises the key features of various cell balancing topologies, highlighting their trade-offs in control complexity, scalability, isolation, switching loss, and suitability for EV applications. Passive balancing methods are the simplest to implement, with minimal control requirements, making them suitable for low-cost, small-scale systems, but they suffer from poor scalability and thermal losses. Inductor and capacitor methods offer moderate balancing performance with reasonable control demands, but lack in electrical isolation. In contrast, transformer-based topologies support electrical isolation and modular architecture, but are generally too complex and bulky for practical EV use due to high component count and losses. Converter-based topologies, though offering flexibility, efficiency, and high balancing speed, face challenges with control complexity, thermal management, and size, limiting their current application mostly to research. Lastly, bypass circuits are simple and suitable for modular packs but add complexity to inverter control and charging mechanisms. Moreover, switching losses vary significantly among these topologies, primarily influenced by the number of switches involved. Full-bridge converter configurations tend to have the highest switching losses due to their higher number of switching devices, whereas the multi-winding transformer method achieves the lowest switching losses, typically requiring only a single switch. The data in Table 3 is critically analysed from the literature [4,6,7,14,43,49,99] and identifies the key features of cell balancing topologies.

6. Emerging Trends of Machine Learning-Based Cell Balancing Techniques

Recent advancements in BMS have increasingly focused on the integration of machine learning (ML) into cell balancing techniques to overcome the limitations of conventional topologies. Traditional passive and active balancing methods often rely on pre-defined rules and cannot adapt to real-time operational inconsistency or battery ageing. To address these challenges, researchers have proposed hybrid architectures that combine established topologies, such as switched capacitor, inductor-based, or dissipative methods, with intelligent ML models that enable adaptive control, fault prediction, and performance optimisation. Recent literature demonstrates the growing role of supervised and reinforcement learning algorithms in predicting cell imbalances, estimating SOC and SOH, and dynamically adjusting balancing control algorithms.
To evaluate the effectiveness of machine learning-enhanced balancing techniques, recent studies have been critically reviewed and compared. Table 4 highlights the key components of each proposed system.
The comparison of recent ML-based cell balancing strategies highlights clear advancements in efficiency, adaptability, and predictive accuracy. DQN with buck–boost converters enables adaptive balancing with low cumulative penalties under varying conditions. Neural networks like LSTM and BPNN significantly reduce power loss and balancing time. MARL with TRPO shows benefits of improving SOC variance and extending driving range. However, MKNA focused on better thermal distribution, but increased balancing time, showing the gap for testing other ML-based approaches for bypass topology.

7. Conclusions and Recommendations

This review has examined the structure of cell balancing techniques, with a particular focus on their relevance to EV battery systems. By covering both traditional and advanced topologies, we highlighted the strengths and limitations of various approaches in terms of control requirements, hardware complexity, energy transfer method, and feasibility in EV applications. Despite the advancement in balancing techniques, the literature indicates that commercially available EVs still mostly rely on passive balancing due to its simplicity, cost-effectiveness, and ease of control. However, with the increasing demand for compact and lightweight systems, capacitive-based balancing is gaining popularity. However, its limited balancing speed remains a key drawback. The comparative analysis of integrating ML techniques with traditional balancing circuits highlights significant enhancement in the efficiency, adaptability, and reliability of BMSs. It also points to the need for further exploration of generalisation, training data dependency, and real-time implementation challenges. Comparative analysis revealed that no single topology offers a universal solution, and trade-offs must be made based on system-level priorities such as cost, control complexity, space, weight, balancing speed, and thermal management. For instance, as the EV industry shifts toward fast charging, there is a growing need for balancing methods capable of operating efficiently within reduced charging times. In this context, converter-based topologies are emerging as promising solutions, offering faster energy transfer rates and greater control flexibility. These features make them more compatible with the dynamic requirements of modern EV battery systems.
This review aims to inform and guide future research efforts toward overcoming the limitations of existing topologies, ultimately contributing to the development of more advanced BMSs. Future research should focus on developing hybrid balancing systems that combine the strengths of multiple topologies to optimise performance across a range of operating conditions. Additionally, integrating machine learning into balancing strategies holds significant potential for enabling real-time optimisation, further improving efficiency, battery longevity, and system reliability. Moreover, an in-depth analysis of balancing efficiency and associated power losses is essential for fully understanding system performance and guiding the design of more effective balancing solutions.

Author Contributions

This document is the result of a collaboration between the authors. Conceptualisation, A.A., B.A., and P.T.; methodology, A.A., and B.A.; validation, B.A., M.S.A.A.S., and A.A.; formal analysis, B.A., M.S.A.A.S., and A.A.; investigation, B.A., M.S.A.A.S., and A.A.; writing—original draft preparation, A.A., and B.A.; writing—review and editing, B.A., A.A., and P.T.; visualisation, B.A., M.S.A.A.S., and A.A.; supervision, P.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was fully waived by the editorial office of MDPI.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. BMS integration in practical EV applications.
Figure 1. BMS integration in practical EV applications.
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Figure 2. Battery management system functions.
Figure 2. Battery management system functions.
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Figure 3. Comprehensive overview of cell balancing topologies in BMS.
Figure 3. Comprehensive overview of cell balancing topologies in BMS.
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Figure 4. Fixed shunting resistor diagram.
Figure 4. Fixed shunting resistor diagram.
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Figure 5. Switched shunting resistor diagram.
Figure 5. Switched shunting resistor diagram.
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Figure 6. Single-inductor cell balancing topology.
Figure 6. Single-inductor cell balancing topology.
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Figure 7. Coupled inductor cell balancing topology.
Figure 7. Coupled inductor cell balancing topology.
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Figure 8. Single-transformer cell balancing topology.
Figure 8. Single-transformer cell balancing topology.
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Figure 9. Multi-winding transformer (flyback structure) cell balancing topology.
Figure 9. Multi-winding transformer (flyback structure) cell balancing topology.
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Figure 10. Multiple-transformer cell balancing topology.
Figure 10. Multiple-transformer cell balancing topology.
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Figure 11. Single-switched capacitor cell balancing topology.
Figure 11. Single-switched capacitor cell balancing topology.
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Figure 12. Multiple switched capacitor cell balancing topology.
Figure 12. Multiple switched capacitor cell balancing topology.
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Figure 13. Buck–boost converter cell balancing topology.
Figure 13. Buck–boost converter cell balancing topology.
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Figure 14. Quasi-resonant converter cell balancing topology.
Figure 14. Quasi-resonant converter cell balancing topology.
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Figure 15. Full-bridge converter cell balancing topology.
Figure 15. Full-bridge converter cell balancing topology.
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Figure 16. Bypass cell balancing topology.
Figure 16. Bypass cell balancing topology.
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Table 1. Types of cell balancing techniques in real-world EV applications.
Table 1. Types of cell balancing techniques in real-world EV applications.
Refs.TopologiesMethodDescription and Application
[15,16]Switched shunt resistorHeat dissipationUses resistors to dissipate extra energy as heat from high-energy cells.
Common in commercial EVs due to simplicity and low cost.
[17,18]Capacitor-basedCell-to-cellCapacitors are used to transfer energy between adjacent cells.
Used in some low-power applications (e-bikes and e-scooters) with advanced BMS.
[19,20]Inductor-basedAny Cell to any cellUses inductors or transformers to transfer charge between any two cells.
NenPower has recently introduced an inductor-based active balancing topology for EVs.
[21,22]Converter-basedPack-to-cell or cell-to-packUses bidirectional DC–DC converters to move energy between cells or modules. Emerging in modular or large energy storage systems, but still in the prototype stage.
[23,24]HybridCombinedCombines passive and active topologies. It also integrates ML with basic topologies to enhance efficiency and performance. However, they did not find any commercial deployment.
Table 2. Comparison of component count, energy transfer method, and pros/cons of cell balancing topologies.
Table 2. Comparison of component count, energy transfer method, and pros/cons of cell balancing topologies.
TopologiesComponentsMethodAdvantages/Disadvantages
RLTCDS
Passive
Fixed shunting resistorn00000Cell to heatsmall size, no control, low cost/high power loss, heat management required, overcharge and discharge
Switched shunting resistorn0000nCell to heatsmall size, simple control, low cost, low switching stress/high power loss, heat management required
Active
Single-inductor01002n2nC2P, P2C, C2C, C2P2Clow switching stress/complex control, large size, high cost
Coupled inductor0n−10002n−2C2Clow switching stress, moderate cost/complex control, large size
Single switched capacitor10010n + 5C2P, P2C, C2C, C2P2Clow switching stress, moderate control, low cost, small size/slow balancing speed
Multiple switched capacitorsn−100n−102nC2Chigh balancing speed, small size, moderate cost /high switch count, complex control, increased switching stress
Single transformer00101n + 6C2P, P2C, C2P2Cmoderate size, moderate cost, low switching stress/complex control, magnetisation loss
Multi-winding transformer0010n1C2P, P2C, C2P2Csmall size, moderate control, low cost, low switching stress/magnetisation loss
Multiple transformer00n0nnC2P, P2C, C2P2Ceasy modularised, low switching stress/high cost, complex control, large size, magnetisation loss
Single switched capacitor10010n + 5C2P, P2C, C2C, C2P2Cmoderate control, low cost, low switch voltage stress/slow balancing speed
Multiple switched capacitorsn−100n−102nC2Chigh balancing speed/complex control, high cost, high switch stress
Buck–boost converter0n0n02nC2Clow switching stress, high balancing speed/high cost, complex control, large size
Quasi-resonant converter02n−20n−102n−2C2Clow switching stress, medium balancing speed/high cost, complex control, large size
Full-bridge converter000n04nC2Clow switching stress, medium balancing speed/high cost, complex control, large size
Bypass000002nBypass, no energy transferhigh balancing speed, variable DC link, small size, moderate cost/high switching stress
R = resister, L = inductor, T = transformer, C = capacitor, D = diode, S = switch, C2P = cell-to-pack, P2C = pack-to-cell, C2C = cell-to-cell, C2P2C = cell-to-pack and pack-to-cell.
Table 3. Comparison of key features and application suitability of cell balancing topologies.
Table 3. Comparison of key features and application suitability of cell balancing topologies.
TopologiesKey FeaturesControl ComplexitySwitching LossIsolationRemarks on Suitability for the Application
Fixed shunting resistorContinuous dissipationNo controlNoneNoSuitable for small-scale, low-cost applications (e-bikes, power tools)
Switched shunting resistorControlled dissipationVery LowLowNoCommonly used in commercial EVs and small-scale energy storage systems
Single inductorSingle cell balancing at onceLowHighNoSuitable for modular battery packs (advanced BMS for EV and energy storage)
Coupled inductorMultiple cells balancing, which increases balancing speedMediumHighPartialSuitable for modular battery packs (advanced BMS for EV and energy storage) It allows multiple energy transfers for faster balancing speed
Single Switched CapacitorSingle cell balancing at onceLowModerateNoSuitable for EV battery packs due to its compact design
Multiple Switched CapacitorsMultiple cells balancing, which increases balancing speedMediumHighNoEV battery packs are suitable due to their compact design. They allow multiple energy transfers for faster balancing speed
Single transformerProvide isolation with single-cell energy transfer MediumModerateYesNot suitable for EV application due to high component count, and extremely complex control, magnetisation, and switching losses.
Multi-winding transformerMultiple cells balancing with isolationHighVery LowYesNot suitable for EV application due to high component count, and extremely complex control, magnetisation, and switching losses.
Multiple transformerMultiple cells balancing with modular isolationHighLowYesNot suitable for EV application due to high component count, and extremely complex control, magnetisation, and switching losses
Buck–boost converterBidirectional energy transfer to provide faster balancingHighHighPartialConverter-based topologies face numerous challenges in terms of control complexity, large size, and thermal management
Quasi-resonant converterSoft switching for low electromagnetic interferenceVery HighHighPartialConverter-based topologies face numerous challenges in terms of control complexity, large size, and thermal management
Full-bridge converterBidirectional balancing for modular BMS for high power Very HighVery HighYesConverter-based topologies face numerous challenges in terms of control complexity, large size, and thermal management
BypassBypass a weak capacity cell or module for balancinglowHighNoSuitable for EV application, but it increases control complexity for the inverter and charger
Table 4. Comparison of recent machine learning-based cell balancing techniques.
Table 4. Comparison of recent machine learning-based cell balancing techniques.
Ref.Basic Cell Balancing TopologyML TechniqueKey BenefitsQuantitative Metrics
[100]Buck–boost converterDeep Q-NetworkAdvanced control algorithms, achieves faster balancingCell balancing is achieved from ±10% in capacity with a low penalty of −1.6127 on average over the 1000 episodes
[101]Passive switched resistorBack-propagation neural network (BPNN), radial basis neural network (RBNN), and long short-term memory (LSTM)Estimating optimum resistor values to reduce power loss, achieving faster balancing, and reducing temperatureConventional balancing time is reduced from 60 min to 30 min. power loss is reduced from 1.1 Wh to 0.4 Wh.
LSTM is better with mean absolute error, MAE = 0.066.
BPNN is better the RBNN with MAE = 0.1276
[102]DC-DC converterMulti-agent reinforcement learning (MARL) training with the trust region policy optimisation (TRPO) algorithmMaximises pack capacity while minimising SOC variationsAverage SOC variance is reduced by 61.20%. Usable capacity is increased from 3298 mAh to 4203 mAh, which increases the driving range by 8 miles.
[103]Buck–boost converterMarkov decision process (MDP) and deep reinforcement learning (RL)Optimal energy management control is achieved with RL. Presents a model-free energy management strategySOC imbalance is reduced from 50% to approximately 0%. Coulomb counting technique to estimate SOC.
[104]active cell balancingMachine learning models, including Predictive Analytic Recurrent Neural Networks (PA-RNN), Deep-Q Networks (DQN), Amortised-Q Networks (AQN), Adaptive Neural Networks (ADNN), and Automotive Controllers (AC)Improved balancing efficiency, response time, and thermal stability. PA-RNN used for SOC error minimisation, DQN for adaptive control, AQN for quick decision, ADNN for accuracy, and AC for enhanced BMS efficiency.SOC errors show ADNN (−1.04%), achieved the lowest average error compared with PA-RNN (−1.15%), DQN (−3.15%), AQN (−3.25%), and AC (−2.65%).
[105]Bypass topologyMachine learning, multi-dimensional K-nearest control algorithm (MKNA)Improved cell balancing time, reduced temperature stress on specific cells by spreading the temperature effect on others.The temperature spreading comparison of the MKNA method shows better performance instead of SOC sorting and provides peaks. However, for 25 series cells with an initial maximum 20% imbalance, MKNA balances the cells in 2000 sec, which is a higher time than SOC sorting, which is 1600 sec.
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Ashraf, A.; Ali, B.; Al Sunjury, M.S.A.; Tricoli, P. A Comprehensive Review of the Art of Cell Balancing Techniques and Trade-Offs in Battery Management Systems. Energies 2025, 18, 3321. https://doi.org/10.3390/en18133321

AMA Style

Ashraf A, Ali B, Al Sunjury MSA, Tricoli P. A Comprehensive Review of the Art of Cell Balancing Techniques and Trade-Offs in Battery Management Systems. Energies. 2025; 18(13):3321. https://doi.org/10.3390/en18133321

Chicago/Turabian Style

Ashraf, Adnan, Basit Ali, Mothanna S. A. Al Sunjury, and Pietro Tricoli. 2025. "A Comprehensive Review of the Art of Cell Balancing Techniques and Trade-Offs in Battery Management Systems" Energies 18, no. 13: 3321. https://doi.org/10.3390/en18133321

APA Style

Ashraf, A., Ali, B., Al Sunjury, M. S. A., & Tricoli, P. (2025). A Comprehensive Review of the Art of Cell Balancing Techniques and Trade-Offs in Battery Management Systems. Energies, 18(13), 3321. https://doi.org/10.3390/en18133321

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