Control Strategy of the Vehicle Thermal Management System for Battery Electric Vehicles Considering Energy Consumption Optimization

The energy consumed by thermal management systems strongly affects the driving range of battery electric vehicles. This study develops an integrated control strategy that couples the Sparrow Search Algorithm (SSA) with Nonlinear Model Predictive Control (NMPC) to simultaneously reduce energy consumption and satisfy cabin comfort and battery safety requirements. A multi-loop coupled, heat pump based integrated thermal management model is constructed, including a compressor, heat exchangers, expansion valves, and an electro thermal battery sub model. Bench and vehicle level tests confirm that the model predicts refrigerant mass flow rate and heating capacity with mean relative errors of 4.76 % and 4.30 %, respectively. The SSA is used to tune the NMPC weighting parameters offline, minimizing the mean absolute errors of the cabin temperature, battery temperature, and total system energy consumption. The resulting SSA NMPC strategy is evaluated under NEDC and CLTC P driving cycles. Under the NEDC cycle, the strategy limits cabin temperature overshoot to 0.35°C and battery temperature fluctuation to 0.26°C, while achieving a 6.31 % energy saving under high speed cruising. The proposed framework focuses on cabin and battery thermal regulation and considers motor waste heat recovery. These results demonstrate that the SSA NMPC approach can improve thermal management performance under the investigated operating conditions.