Optimizing thermal management in battery electric vehicle powertrains with energy-efficient model predictive control

Thermal management is critical for energy efficiency and reliability in battery electric vehicle (BEV) powertrains. Conventional rule-based strategies often prioritize thermal safety over efficiency. This study presents a model predictive control (MPC)-based thermal management strategy for a BEV powertrain with a mixed coolant/oil cooling architecture. The MPC framework utilizes a control-oriented model to minimize battery energy consumption while adhering to thermal constraints for the inverter, electric motor, and transmission. Simulations over representative real-world driving cycles show that the MPC-based strategy reduces battery energy consumption by 0.26% in urban driving and 1.06% in highway driving compared to a rule-based benchmark. The thermal durability of the inverter and motor is evaluated, showing that the impact of the MPC-based strategy varies with the driving profile: lifetimes improve under urban driving due to enhanced cooling, but degrade under sustained highway operation as higher temperatures are allowed. For a representative daily round-trip commute, the estimated lifetimes of the inverter (10,100h) and motor (20,100h) nevertheless exceed typical BEV durability requirements. These findings demonstrate that MPC-based thermal management can achieve significant energy savings without compromising component longevity.