A Comprehensive Review of Active Cell Balancing and State-of-Charge Estimation Techniques for Lithium-Ion Battery Management Systems in Electric Vehicles

Abstract

Lithium-ion batteries (LIBs) have established themselves as the dominant energy storage technology for electric vehicles (EVs) due to their high specific energy, long service life, and excellent charge retention capabilities. However, as battery packs consist of numerous interconnected cells, inevitable variations in manufacturing, operating temperature, and aging characteristics cause inconsistencies in cell capacity and internal resistance. These differences lead to state-of-charge (SOC) imbalance, which not only limits the overall usable capacity but also poses serious risks to system efficiency, thermal stability, and operational safety. Addressing these issues demands a sophisticated battery management system (BMS) capable of performing accurate SOC estimation and effective cell balancing. This review paper presents a comprehensive assessment of recent advancements in both active and passive cell balancing strategies, emphasizing their control mechanisms, circuit architectures, and suitability for high-power EV applications. Passive balancing methods, although simple and cost-effective, suffer from significant energy dissipation, while modern active balancing topologies—such as switched-capacitor, inductor-based, and multi-stage modular configurations—offer superior energy utilization and faster equalization rates. The discussion extends to stackable integrated circuit (IC) architectures and two-stage converter-based designs that enhance scalability and fault tolerance in large battery systems. In parallel, the paper investigates state-of-the-art SOC estimation algorithms, including model-based approaches (extended and unscented Kalman filters), data-driven machine learning models, and hybrid estimation frameworks that combine algorithmic robustness with adaptive learning capabilities. By synthesizing insights from recent IEEE and peer-reviewed studies, the paper highlights current technological trends and identifies key trade-offs between balancing speed, energy efficiency, system complexity, and cost. The study concludes by proposing a forward-looking perspective on intelligent, data-driven BMS architectures that integrate predictive analytics, real-time diagnostics, and self-optimizing balancing control for next-generation electric mobility solutions.