Nonlinear Model Predictive Control for Energy-Efficient Thermal Management in Battery Electric Vehicles

This study presents a nonlinear model predictive control (NMPC)-based strategy for thermal management in battery electric vehicles (BEVs), with a particular focus on component cooling. The proposed control framework pursues multiple objectives, maintaining component temperatures within their optimal operating ranges, enhancing the energy efficiency of the thermal management system (TMS), and preserving actuator longevity by enforcing physical constraints. Due to the complex interactions between TMS subsystems and varying thermal dynamics of individual components, a high-fidelity model was developed in MATLAB/Simulink. This model is based on reduced-order physical equations, ensuring a balanced trade-off between accuracy and computational efficiency. To ensure high control performance and stability, the NMPC, which was implemented in MATLAB using the CasADi optimization framework, underwent a comprehensive parameter study. The performance of the NMPC strategy was benchmarked against a conventional rule-based (RB) controller across various driving cycles and ambient conditions. The results demonstrate that the TMS controlled by NMPC achieves more balanced temperature distribution, particularly for the high-voltage battery (HVB), while reducing TMS electrical energy consumption substantially. Furthermore, the predictive approach enables coordinated use of the cooling circuit valves, allowing efficient exploitation of smaller heat storage capacities and cooling potentials. These advantages contribute to improved component protection, extended system longevity, and confirm the controller’s suitability for real-time automotive applications.