Energy-Efficient Battery Thermal Management in Electric Vehicles Using Artificial-Neural-Network-Based Model Predictive Control

This study presents a Model Predictive Control (MPC) strategy for the Battery Thermal Management System (BTMS) in electric vehicles (EVs) to optimize energy efficiency while maintaining battery temperature within the optimal range. Due to the complexity of BTMS dynamics, a high-fidelity model was developed using MATLAB/Simscape (2021a), and an artificial neural network (ANN)-based model was designed to achieve high accuracy with reduced computational load. To mitigate oscillatory control inputs observed in conventional MPC, an infinity-horizon MPC framework was introduced, incorporating a value function that accounts for system behavior beyond the prediction horizon. The proposed controller was evaluated using a simulation environment against a conventional rule-based controller under varying ambient temperatures. Results demonstrated significant energy savings, including a 78.9% reduction in low-temperature conditions, a 36% reduction in moderate temperatures, and a 27.8% reduction in high-temperature environments. Additionally, the controller effectively stabilized actuator operation, improving system longevity. These findings highlight the potential of ANN-assisted MPC for enhancing BTMS performance while minimizing energy consumption in EVs.