Model Predictive Control and Adaptive Algorithms for Optimizing Battery Thermal Management and State Estimation in Electric Vehicles

Electric vehicles' (EVs') quick development calls for sophisticated battery management systems (BMS) that can preserve the safety, performance, and health of batteries. The integration of adaptive algorithms and Model Predictive Control (MPC) in Battery Thermal Management Systems (BTMS) and State of Charge (SOC)/State of Health (SOH) estimation procedures is examined in this study. This study suggests a hybrid intelligent control approach to improve efficiency and extend battery longevity by tackling issues including temperature-induced deterioration, non-linear battery behaviour, and imprecise state predictions. The suggested method dramatically enhances thermal control, lowers energy consumption, and boosts battery durability under dynamic driving circumstances, according to simulation and validation findings.