A Study on Fast Charging and Thermal Management Integration in Lithium-Ion Battery Management Systems for Electric Vehicles

Rapid charging of lithium-ion batteries is essential for wider electric vehicle (EV) adoption but carries trade-offs in thermal stress, accelerated degradation, and increased safety risk. This study examines integrated strategies for fast charging and thermal management within Battery Management Systems (BMS) to optimize charging speed while preserving battery health and safety. We propose and evaluate a control-oriented framework combining charge scheduling and active thermal control, emphasizing model predictive control (MPC) and real-time temperature feedback. Using a physics-informed, control-oriented battery and thermal model, we simulate four charging strategies: baseline moderate charging (0.5C), fast charging (2C) without thermal management, fast charging with conventional active cooling, and fast charging with MPC-based thermal-aware current scheduling. Key performance metrics include time to 80% state of charge (SoC), peak and average cell temperature during charge, and an estimated capacity-fade proxy over 1000 cycles. Results show that integrated MPC thermal-aware control reduces peak cell temperature by ~6–12°C compared with fast charging without thermal control, achieves fast-charge targets within a comparable timeframe, and projects substantially lower estimated capacity fade. Active cooling alone reduces temperature rise but is less effective than closed-loop MPC in minimizing hotspots and accounting for SoH-based constraints. We also discuss early-warning indicators and cell-level safety measures that complement control strategies to mitigate thermal runaway risk. The findings support the thesis that integrating intelligent charge control with effective thermal management within BMS architecture enables much faster charging with acceptable longevity and safety trade-offs — a critical advance for next-generation EV systems. Recommendations include combining MPC-based current scheduling, targeted active cooling, and multi-level early-warning sens