Active Cell Equalization for Battery Management Systems: A Comprehensive Review of DC-DC Converter Topologies

1  Introduction

The BMS is indispensable to maintaining the safety, reliability, and optimal performance of battery packs. The BMS functions are shown in Fig. 1. Failures in battery management can result in serious consequences, including premature battery degradation, thermal instability, and even catastrophic events such as fires or explosions. This paper initiates a comprehensive exploration of essential cell equalization methods for the BMS technology, highlighting their significance in advancing and refining battery systems. Despite promising outcomes in controlled testing environments, many existing techniques face limitations when applied in varied and dynamic real-world conditions, highlighting the urgent need for the development of more resilient and adaptive BMS solutions [1].

Figure 1: Functioning of battery management system. Adapted from Ref. [3]. Copyright@2016.

A typical BMS architecture comprises several vital components: battery modules with integrated Cell Supervisory Circuits (CSCs), a central control unit, high-side (HS) contactors, current sensors, and thermal management systems, all of which work collaboratively to ensure effective monitoring and control of battery operations. Calculating important metrics like the State of Charge (SoC) and State of Health (SoH) is essential to this functionality, which informs the vehicle’s operational readiness or State of Function (SoF).

However, lack of uniformity in cell manufacturing & varying load circumstances may result in imbalances among cells, negatively impacting battery life, SoC accuracy, internal resistance, thermal behavior, system efficiency, and overall safety. Precise cell monitoring and effective charge equalization are critical BMS functions that are necessary to prevent system failures and ensure long-term performance [2].

Cell imbalance within battery packs arises from a combination of internal and external factors that affect individual cell characteristics. Internally, variations introduced during manufacturing, such as discrepancies in capacity, internal resistance, and charge-discharge efficiency, can lead to unequal cell behavior. Externally, factors such as non-uniform current distribution in peripheral circuits and thermal inconsistencies across the battery pack further exacerbate the imbalance issues [4].

To address these challenges, cell equalization techniques are broadly categorized based on their energy management approaches: passive and active balancing [5]. Passive methods dissipate surplus energy from overcharged cells through resistive components. Although cost-effective and straightforward, these methods are inherently inefficient because of energy loss as heat. Active balancing, on the other hand, transfers energy from cells with larger charges to those with lower charges, employing sophisticated circuit topologies involving capacitors, inductors, transformers, or power electronic converters [6]. The classification of the cell equalization methods is illustrated in Fig. 2. This study provides a comprehensive review and comparative analysis of various active equalization techniques, highlighting their principles, advantages, and implementation challenges.

Figure 2: Cell equalization methods.

2  Cell Equalization Methods and Analysis

A. Passive Balancing

Shunt resistors are used in passive cell Equalizations redirect surplus charge from cells with higher voltage levels, dissipating this surplus energy as heat to equalize cell voltages. The dissipation rate is directly influenced by the voltage of the cells, with higher voltages resulting in greater energy loss during charging and discharging cycles [7]. This method is generally categorized into fixed and switch-controlled shunt resistor approaches and is most effective during the charging phase, as it lacks the bidirectional energy control required during discharge cycles [8]. Despite its simplicity and low implementation cost, passive balancing faces several critical limitations particularly in high-capacity battery systems used in Electric vehicles (EVs) and Hybrid Electric vehicles (HEVs) including restricted balancing current (typically 0.1–0.5 A), increased power loss, poor thermal efficiency, and inability to function effectively during discharging [8].

In contrast, active cell Equalization offers a more sophisticated and efficient approach by redistributing charges from higher-energy cells to lower-energy counterparts, rather than dissipating them. This dynamic energy transfer significantly improves system efficiency, prolongs battery life, and maintains better voltage consistency across cells, especially under frequent charge-discharge conditions [9]. These advantages do, however, come at the price of more complicated circuits and more expensive implementation. The comparison of passive and active balancers is given in Table 1. It provides distinct advantages, such as improved voltage uniformity, lower energy waste, enhanced overall system performance, and extended battery lifespan making it the go-to option for advanced BMS architectures in high-performance applications. A comparative analysis of various energy flow topologies is presented in Table 2.

2.1 Comparative Analysis of Active Cell Equalization Methods

Figs. 3 and 4 present the analysis of the cell equalization time and efficiency across various DC-DC converters. The radar charts reveal that the Bidirectional Flyback converter requires the shortest balancing time of approximately 1800 s whereas the dual active bridge converter provides nearly 98.5% efficiency.

Figure 3: Cell equalization time of all active cell balancers. Adapted from Ref. [13]. Copyright@2023.

Figure 4: Max. efficiency of DC-DC converter topologies for cell equalization. Adapted from Ref. [13]. Copyright@2023.

2.1.1 DC-DC Converter Topologies

The design and functionality of DC-DC converters are significantly influenced by the directionality of energy transfer. Unidirectional converters facilitate power flow solely from the source to the load, whereas Bidirectional converters are ideal for applications needing dynamic energy exchange, such as battery management systems, because they permit reversible energy transfer. Depending on whether a transformer is present in the circuit, DC-DC converters are further divided into isolated and non-isolated variants. High-frequency transformers are used in isolated converters to create galvanic separation between the input and output, increasing flexibility and safety [14].

Isolated topologies, particularly in active cell Equalization, offer notable advantages including compactness, cost-effectiveness, high balancing speeds, and improved energy efficiency, when compared to conventional capacitor- or transformer-based designs. This review underscores the necessity of optimizing both the conversion efficiency and cost-effectiveness for widespread commercial adoption [14].

To evaluate the energy-handling capabilities of the capacitive and inductive elements within these converter topologies, a quantitative comparison was performed by calculating the total accumulated energy stored in each component. This method provides a robust framework for assessing and optimizing component selection based on the energy demands of an application [15].

For capacitors:

EC=12CVC2(1)

For inductors (magnetic elements):

EL=12LIL2(2)

where, EC and EL are the accumulated energy in the capacitors and inductors respectively C is the capacitance L is the inductance VC is the voltage across the capacitors and IL is the current through the inductors [16].

In order to drastically lower switching losses, DC-DC converters also use soft-switching strategies like zero voltage switching (ZVS) and zero current switching (ZCS). These methods enhance power density and efficiency but introduce added complexity to control and circuit design. For instance, bidirectional Dual Active Bridge (DAB) converters implementing soft switching can achieve efficiencies approaching 95% under optimal conditions [17].

Compare the Factors of Various DC-DC Converters [18]:

Based on the reviewed articles, a qualitative comparison of various DC-DC converters is shown in Figs. 5 and 6.

Figure 5: Non-isolated DC-DC converter topologies. Adapted from Ref. [18]. Copyright@2023. Level: 1 = low, 2 = medium, 3 = high, and 4 = very high.

Figure 6: Isolated DC-DC converter topologies. Adapted from Ref. [18]. Copyright@2023. Level: 1 = low, 2 = medium, 3 = high, and 4 = very high.

Comparison of Various Topologies for DC-DC Converters and Control Techniques for BCE.

A. Buck-Boost Converter

The basic buck-boost converter topology is illustrated in Fig. 7. The converter regulates the output voltage to be either lower or higher than the input voltage. It operates in buck mode when the output is lower than the input, and in boost mode when the output exceeds the input voltage [19].

Figure 7: Buck-boost converter. Adapted from Ref. [19]. Copyright@2024.

The buck-boost converter can increase or decrease the supply voltage,

L=VTsΔIL∗D(3)

where,

L = Inductance

V = The average cell voltage

Ts = Switching period

∆IL = Inductance ripple current

D = Duty cycle.

It has fast balancing with high efficiency but it requires 2n − 2 switches and n − 1 inductors for n cells and also the voltage sensing and control technique is very complex [20].

B. Cuk Converter

The conventional Cuk equalizer topology, shown in Fig. 8, comprises n cells and n − 1 Cuk converter circuits, allowing neighboring cells to transfer energy exclusively. When cells requiring equalization are located far apart, energy must pass sequentially through the intermediate cells, leading to prolonged equalization times and reduced efficiency [21].

Figure 8: Cuk converter. Adapted from Ref. [21]. Copyright@2024.

Instead of using an inductor to transfer energy, the Cuk converter uses a capacitor.

L=DVinΔLiFs(4)

C=DIDCΔVcFs(5)

Here, the duty ratio is denoted by D, the input voltage by Vin, the input inductance current ripple ∆Li, switching frequency by Fs, the output current by IDC, and the capacitor voltage ripple by ∆Vc. This configuration offers low ripple in both input and output currents, and capacitors that are more cost-effective than inductors for energy storage are preferred. However, the use of numerous components increases the overall volume of the system [22].

C. Resonant Converter

In order to facilitate energy transfer during the balancing process, two nearby battery cells are connected to a quasi-resonant converter. The switching devices are controlled by pulse width modulation (PWM) signals, which allow energy to be transferred between nearby cells [23].

Resonant converters, a class of DC-DC converters, employ a resonant tank circuit to enable soft switching either zero voltage switching (ZVS) or zero current switching (ZCS) which significantly reduces switching losses and improves efficiency. Compared with conventional converters, resonant converters offer several advantages, including reduced higher-order harmonic ripple content, smaller output filter requirements, and enhanced efficiency. However, they also present certain limitations, such as the introduction of non-linear behavior owing to the resonant tank circuit and the necessity of frequency modulation for control [24].

A bidirectional DC-DC converter with quasi-resonant zero-current switching (QRZCS) was suggested by a reviewer [25] as a component of a cell voltage balancing control strategy in Fig. 9. This reduces switching losses and enhances efficiency. Nevertheless, this approach faces several challenges, including complex circuit design, a high component count, reduced balancing time, and inefficiencies arising from MOSFETs that do not switch precisely at the zero-current point.

Figure 9: QRZCS battery equalizer from Ref. [26]. Copyright@2011.

Resonant converters use a tank circuit to reduce switching losses.

Lr=1(4π2CrFs2)(6)

Lm=V0D4VinFs(7)

Cr=1(2πFs)2Lr(8)

where Lr is the resonance inductance, Lm is magnetizing inductance, Cr the resonance capacitance, Fs the resonance frequency, V0 the output voltage, D the duty ratio, and Vin the input voltage. This topology effectively reduces switching losses, thereby enhancing efficiency; however, it also significantly increases design complexity and implementation costs [27].

D. Push-Pull Converter

The push-pull converter configuration is shown in Fig. 10. This strategy has been found to be a promising one for lithium-ion battery packs’ active cell Equalization. However, its practical implementation presents several challenges, including issues related to scalability, increased design complexity, and the necessity for validation within real-time operating environments [28].

Figure 10: Cell equalization circuit using push-pull converter. From Ref. [28] Copyright@2021.

The push-pull topology acts as an isolated converter, separating the source and the load from galvanic contact. To guarantee optimum performance, the design procedure includes exact calculations of capacitance and inductance values,

L=V0(1−D)7ΔLi(9)

C=1−2D32FS2xViΔV0(10)

where V0 is the output voltage, D is the duty ratio, ΔLi is the input inductance current ripple, Vi is the input voltage, FS is the switching frequency, and ΔV0 is the output voltage ripple. It provides galvanic isolations and supports cell-cell (CTC), CTP (cell-pack) and PTC (pack-cell) energy transfer however; its efficiency is poor compared to other topologies [29].

E. Dual Active Bridge Converter (DAB)

In Fig. 11, the DAB converter is described. A bidirectional DC-DC topology is widely employed in battery balancing systems. It comprises two H-bridge circuits interconnected through a high-leakage inductance transformer, which enables galvanic isolation and efficient power transfer. Each H-bridge operates with a fixed 50% duty cycle and the energy transfer is controlled via phase shifting between the transformer’s primary and secondary sides. It can be controlled by two methods: the PWM method and the phase shift method [30]. Both the boost (cell-to-pack) and buck (pack-to-cell) operating modes are supported by the DAB converter, making it highly versatile for bidirectional energy flow in advanced battery management applications [31].

Figure 11: DAB converter-based battery balancing topology.

It offers galvanic isolation and excellent power transfer capabilities.

C=1−2DFS2xV0ΔV0(11)

where D is the duty ratio, V0 is the output voltage, FS is the resonance frequency, and ΔV0 is the output voltage ripple. The DAB converter supports a broad voltage range, functioning effectively in both buck and boost modes. However, its implementation entails high design complexity, significant costs, and increased system weight [31]. As illustrated in the figure, the first H-bridge operates in buck mode (pack-cell), while the second H-bridge functions in boost mode (cell-pack). This topology provides quick and effective balancing, making it especially appropriate for battery packs with many cells. However, it is hampered by its large physical size and high implementation expenses [32].

Control Methods of Dual active bridge converter [33]

Single phase shift (SPS) modulation incorporates a single degree of freedom (D0), whereas both Dual phase shift (DPS) and Extended phase shift (EPS) extend this by utilizing two degrees of freedom (D0 and D1). By contrast, TPS uniquely introduces a third level of control, employing three degrees of freedom (D0, D1, and D2) [34].

a. Single phase shift control (SPS):

In the SPS modulation technique, the gate pulses of the waveforms are maintained as square waves, as shown in Fig. 12. A deliberate phase shift is introduced between these waveforms, which effectively governs the direction and magnitude of power transfer through H-bridge converters [35].

Figure 12: Gate pulses of SPS mode. From Ref. [26]. Copyright@2017.

As shown in Fig. 12, S1 and S3, Q5 and Q7 are 180° phase shifted. The Power flow equation under SPS control is,

P=VpVs2πfswLsin⁡(δ)(12)

where,

Vp = the output rms voltage of the primary bridge (H1)

Vs = the output rms voltage of secondary voltage (H2)

L= leakage inductance of primary plus secondary leakage inductance referred to primary side

fsw = switching frequency

δ = phase shift.

As illustrated in the above Fig. 13, the primary voltage Vpri leads the secondary voltage Vsec (exhibiting a positive phase shift ϕ), indicating that the DAB’s primary side is receiving power from the secondary side [35].

Figure 13: Voltage waveforms in SPS mode. From Ref. [26]. Copyright@2017.

b. Dual phase shift control (DPS):

The phase difference between S1 and S3 corresponds to D1. Similarly, the phase shift D0 is applied between S1 and Q5, as shown in Fig. 14, while S1 and S7 exhibit a lag of D0 + D1 rad. By adding an equal phase mismatch between the legs of each H-bridge (D1), Vpri and Vsec will be generated with three voltage levels (+Vdcx, 0, and −Vdcx), which contributes to increasing the efficiency of the HF transformer and lowering the THD.

Figure 14: Gate pulses of DPS mode. From Ref. [35]. Copyright@2017.

Power flow equation under DPS control [35],

P=NVpVs2πfswL[D2(1−D2)D122](13)

Vp = the output rms voltage of the primary bridge (H1)

Vs = the output rms voltage of secondary voltage (H2)

L = Leakage inductance of primary plus secondary leakage inductance referred to primary side

fsw = switching frequency

Fig. 15 illustrates the Ipri and Isec waveforms resulting from the application of the DPS modulation to the DAB converter. In the DPS control although the peak values of Ipri and Isec are approximately twice as high, the reactive power and stress on the semiconductors are significantly reduced.

Figure 15: Primary and secondary waveforms in DPS mode. From Ref. [35]. Copyright@2017.

c. Extended Phase Shift (EPS) control:

Under EPS control, switching occurs between the diagonal switches of each bridge (e.g., S1 and S4 or Q1 and Q4), referred to as the inner phase shift (δin). Additionally, a separate phase shift, known as the outer phase shift (δout), is introduced between the two bridges, corresponding to Vp and Vs [36].

In Fig. 16 shows that the phase shift D1 is applied between S1 and S3 and S5 is D0 radian ahead of S1 but S7 is phase shifted by D0 + 180° from S1. Therefore, S5 and S7 were both out of phase.

Figure 16: Gate pulses of EPS mode. From Ref. [36]. Copyright@2017.

Power flow equation under EPS control [36],

P=nVsVp4Lfsw[δin(1−δin−2δ)+2δ(1−δ)](14)

Vp = output RMS voltage of the primary bridge (H1)

Vs = output RMS voltage of secondary voltage (H2)

L = Inductance

fsw = Switching frequency

δin = Inner phase shift

From Figs. 13 and 17, It can be concluded that the harmonic content in a Dual Active Bridge (DAB) operating under single phase shift (SPS) control (15.06% THD) is higher than that under extended phase shift (EPS) control (12.90% THD) [36].

Figure 17: Primary and secondary voltage & current waveforms in EPS mode. From Ref. [36]. Copyright@2024.

Despite its advantages, the DAB converter presents several practical control challenges [37] are,

•   Dead-Time Effect: Excessive dead time can lead to voltage overshoots and associated switching losses.

•   Phase Drift: Discrepancies between theoretical and actual phase shift may arise owing to parasitic voltage drops across power devices, component tolerances, and switching delays during Zero Voltage Switching (ZVS) transitions. High inductor current levels can reduce the duration of ZVS transitions, exacerbating this issue.

•   DC Magnetic Flux Bias: In steady state, a DC bias may develop owing to mismatched circuit parameters such as gate-drive inconsistencies, varying turn-on/off delays, or unequal on-state resistances among switching devices. This bias can be mitigated by introducing DC-blocking capacitors in series with transformer windings. Transient DC bias may also occur because of temporary volt-second imbalances during phase shift updates, which can be controlled by limiting the peak inductor current.

F. Flyback Converter

As shown in Fig. 18 the bi-directional flyback converter supports bidirectional operation, functioning in both forward and reverse modes transferring energy from individual cells to the battery pack in the forward mode, and from the pack to individual cells in reverse mode [22]. The design outlined in the reviewed literature enables autonomous charge equalization without relying on dedicated cell voltage-sensing modules, thereby simplifying the system architecture and lowering the overall cost. The reported efficiencies for the charging and discharging processes are approximately 80.5% and 82.7%, respectively [38].

Figure 18: Bi-directional flyback schematic of the equalizer circuit among n cells. From Ref. [38]. Copyright@2015.

The flyback converter is an isolated version of the buck-boost converter.

C=DRFS×V0ΔV0(15)

where D is the duty ratio, V0 is the output voltage, FS is the resonance frequency, and ∆V0 is the output voltage ripple and R is load resistance. Compared with to the Dual Active Bridge (DAB) topology, this configuration utilizes fewer components and supports both cell-pack (CTP) and pack-cell (PTC) energy transfer modes. Although the overall design complexity is reduced, the inclusion of an isolation transformer leads to an increase in circuit size and contributes to higher implementation costs [38] which a shown in Fig. 19 and Tables 3 and 4.

Figure 19: Analysis of cell equalization adapted from Ref. [18]. Copyright@2023.

2.2 Critical Analysis and Research Implications

Current literature often treats converter topologies as modular performance units, focusing primarily on metrics such as efficiency, balancing time, and component count. However, this isolated approach neglects broader system-level impacts derived from the fundamental physics of energy transfer, control variances, and battery electrochemical behaviour.

A. Limitations of Efficiency-Focused Evaluation

While topologies like the Dual Active Bridge (DAB) report peak efficiencies exceeding 95% under steady-state conditions, these figures may be misleading in practical applications. Real-world equalization occurs during transient, partial-load, and asymmetric voltage states where parasitic effects become dominant. Under small voltage mismatches, the DAB converter experiences high reactive current, which reduces effective efficiency and increases semiconductor stress. Consequently, the energy overhead per unit of transferred charge serves as a more accurate metric than peak efficiency.

In scenarios with light loads or small voltage mismatches—common during the final stages of balancing the DAB converter experiences disproportionately high reactive current, which reduces effective efficiency and increases stress on semiconductors. Therefore, the energy overhead per unit of transferred charge, rather than peak efficiency, becomes a more relevant metric. This insight challenges the notion that higher-rated converters are always better for cell balancing tasks.

B. Control Complexity as an Underlying Performance Limitation

Advanced balancing systems typically assume ideal sensing and precise timing. In practice, aging and thermal fluctuations lead to errors in State-of-Charge (SoC) estimation, which can cause incorrect energy redistribution and exacerbate imbalances. Furthermore, sophisticated modulation techniques like Dual Phase Shift (DPS) and Extended Phase Shift (EPS) create complex control spaces. Factors such as parameter drift, digital delays, and dead-time effects can destabilize the control loop. Thus, control robustness—rather than switching strategy alone—is the primary determinant of system performance.

C. Scalability and Interconnection Trade-offs

Scalability is often highlighted as a significant benefit of active balancing, yet it presents considerable architectural challenges. Cell-to-cell (CTC) topologies scale linearly in terms of component count, leading to more failure points and increased wiring complexity in large battery packs. Cell-to-pack-to-cell (CTPTC) architectures reduce interconnections but concentrate power flow through shared magnetic components, creating thermal bottlenecks and single-point failure risks.

2.3 Control Strategies

The input variable directly influences the accuracy and speed of the balancing process [49]. The various control strategies are shown in Fig. 20.

Figure 20: Classification of control strategies by control variables. Adapted from rom Ref. [18].

a. Voltage-Based Control: This is the most straightforward implementation, utilizing terminal voltage differences to trigger balancing. However, it is prone to inaccuracies due to internal resistance and the flat OCV curves characteristic of chemistries like LiFePO4. Errors in internal resistance measurement, often caused by battery aging, can lead to decreased capacity utilization [50]. This is known as Voltage Drop or Polarization, calculated as,

Vterminal=Vocv±(I×Rin)(16)

b. State-of-Charge (SoC)-Based Control: SoC-based control is considered the most effective indicator for balancing, as it manages energy based on the actual chemical state of the cell. This approach can reduce equalization time by up to 50% compared to voltage-based methods [51].

The methods for estimating SoC in batteries could be categorized into traditional, filter-based, observer-based, and data-driven approaches. Conventional methods include Look-up Table Methods [52] and Open Circuit Voltage (OCV). Filter-based methods [53] encompass the Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF). Observer-based methods [54] feature the Luenberger Observer (LO), Sliding Mode Observer (SMO), and H-infinity Observer (HIO). Data-driven methods include Artificial Neural Networks (ANN), SVM, Fuzzy Logic (FL), and Deep Learning (DL) [55].

While simple, look-up tables and ampere-hour integration struggle with accuracy and sensor drift in practical settings. In contrast, filtering and observation methods improve estimation reliability and noise rejection [56], though they require labour-intensive battery parameterization [57]. Machine learning offers a third path, identifying battery states without detailed physical models [58]. However, these data-driven strategies are prone to fitting errors based on training data quality and present significant computational challenges for on board deployment.

While machine learning and adaptive filters (EKF, UKF) offer improved reliability, their computational intensity often limits them to controlled environments. Consequently, direct measurement and bookkeeping remain the dominant methods in the commercial EV market.

As shown in Tables 5 and 6, the comparison of SOC estimation techniques reveals that most current methods suffer from drawbacks such as programming complexity, high memory demand, or susceptibility to sensor errors. While academic research continues to seek more robust solutions, these advanced algorithms are often difficult to implement outside of controlled environments. For this reason, conventional direct measurement and bookkeeping methods continue to dominate the commercial EV market [59,60].

3  Conclusions

This review confirms that the BMS is vital for the safety and efficiency of high-capacity battery packs in electric vehicles. While passive balancing is economically viable for low-power applications, active equalization, specifically via DAB converters, provides the efficiency and bidirectional capability required for modern energy storage. Future research must prioritize the integration of wide bandgap semiconductors (SiC and GaN) and AI-assisted control strategies to enhance dynamic performance and reliability under diverse operating conditions. Ultimately, the transition toward scalable and adaptive BMS architectures remains essential for meeting the demands of the evolving EV landscape.

Acknowledgement: The author would like to express sincere gratitude to Dr. Jalpa Thakkar, Supervisor, for her valuable guidance, insightful suggestions, and continuous support throughout the course of this research work. The author is also thankful to Prof. Shrikant J. Wagh, Provost, for his constant motivation and for providing the necessary resources and an encouraging research environment. We acknowledge all the researchers and authors whose published works have been referenced in this review, as their contributions have provided in-depth knowledge and a strong foundation for understanding the research area.

Funding Statement: The authors received no specific funding for this study.

Author Contributions: Conceptualization, literature survey, analysis, and manuscript preparation were performed by Jigneshkumar Joshi. Jalpa Thakkar contributed to supervision, critical review, and refinement of the manuscript. All authors reviewed and approved the final version of the manuscript.

Availability of Data and Materials: The findings of this study are available within the article and its supplementary materials. As this is a comprehensive review paper, all technical data, converter parameters, and performance metrics analyzed were derived from the published literature cited in the References section.

Ethics Approval: Not applicable.

Conflicts of Interest: The authors declare no conflicts of interest.

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