Energy-Efficient Balancing Control in Battery Management

Tan, Ji Kang (2026) Energy-Efficient Balancing Control in Battery Management. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

The rapid growth of electric vehicles and renewable energy storage has made battery management systems (BMS) increasingly important for ensuring the safety, efficiency and longevity of lithium-ion battery packs. A major challenge in BMS design is cell imbalance, which can result from manufacturing differences, uneven operating conditions and cell aging. Conventional passive and active balancing approaches often lead to wasted energy, excess heat or accelerated degradation when aging effects are overlooked. To address these issues, this study introduces an advanced balancing strategy that combines optimal active balancing with aging-aware control using a bidirectional flyback converter. The system was developed and tested in MATLAB/Simulink using a four-cell lithium-ion battery pack model. State of charge (SOC) was tracked through coulomb counting, while state of health (SOH) was estimated using a capacity-based method enhanced by an extended Kalman filter (EKF). The balancing algorithm identified source and destination cells, enforced temperature and current limits and dynamically adjusted balancing actions based on each cell’s SOH and temperature. To prevent current reversal, an issue observed at lower switching frequencies, an additional diode was added to the circuit, improving stability and reducing energy losses. Simulation results showed that, compared with both unbalanced and conventionally balanced systems, the proposed method maintained safe operating conditions, limited thermal stress and slowed SOH degradation. Although the study was constrained to small SOC differences and a four-cell configuration due to computational and hardware limitations, the results demonstrate that the proposed approach meets the objectives of safe operation, energy efficiency, and aging-aware balancing. This research contributes to the development of next-generation BMS by presenting a framework that integrates safety margins and aging effects directly into active balancing strategies. Future work should focus on building hardware prototypes, scaling the method to larger battery packs and improving real-time SOH estimation to confirm the practicality of the approach in real-world applications