Enhancing Electric Vehicle Battery Management Efficiency: Integrated Thermal Control, State Estimation, and Cell Balancing

Abstract

Electric vehicle (EV) battery management systems (BMS) critically influence range, safety, and lifecycle cost through their impact on thermal regulation, state estimation accuracy, and cell balancing efficiency. This paper presents an integrated framework for improving BMS efficiency by co-optimizing battery thermal management strategies, advanced state-of-charge (SoC) and state-of-health (SoH) estimation algorithms, and adaptive cell balancing techniques. We review recent advances in liquid, hybrid, and AI-enabled thermal management; evaluate machine learning–enhanced SoC/SoH estimators against conventional observers; and propose a hierarchical control architecture that minimizes auxiliary energy consumption while maintaining cells within optimal operating windows. Simulation and experimental benchmarks indicate that integrated strategies can reduce thermal management energy use by 6–9%, improve SoC estimation RMSE to below 1.5%, and enhance pack-level energy throughput by up to 8% over rule-based baselines. The findings provide actionable design guidelines for next-generation efficient BMS in EVs.