Electric Vehicle Battery Thermal and Cabin Climate Management Based on Model Predictive Control
Yuanzhi Liu, Jie Zhang
Abstract
Abstract Energy management plays a critical role in electric vehicle (EV) operations. To improve EV energy efficiency, this paper proposes an effective model predictive control (MPC)-based energy management strategy to simultaneously control the battery thermal management system (BTMS) and the cabin air conditioning (AC) system. We aim to improve the overall energy efficiency and battery cycle-life, while retaining soft constraints from both BTMS and AC systems. The MPC-based strategy is implemented by optimizing the battery operations and discharging schedules to avoid a peak load and by directly utilizing the regenerative power instead of recharging the battery. Compared with the benchmark system without any control coordination between BTMS and AC, the proposed MPC-based energy management has shown a 4.3% reduction in the recharging energy and a 6.5% improvement for the overall energy consumption. Overall, the MPC-based energy management is a promising solution to enhance the battery efficiency for EVs.