面向提升能效与电池寿命的网联自动驾驶电动汽车综合功率与热管理

Abstract Effective power and thermal management in Connected and Automated Electric Vehicles presents significant challenges due to the multi-timescale dynamics of vehicle longitudinal motion and battery thermal systems, as well as the intricate trade-offs among energy efficiency, battery degradation, and driving safety. This paper proposes an integrated power and thermal management (IPTM) strategy based on the multi-horizon model predictive control framework, specifically designed to overcome these challenges and enable real-time implementation. The proposed IPTM can leverage real-time information, such as ambient temperature, road slope, and preceding vehicle speed, to proactively optimize battery temperature and vehicle speed, ensuring efficient performance under varying driving conditions. The results demonstrate that the proposed strategy delivers significant improvements, including reductions of 14.22 % in cooling energy, 8.26 % in traction energy, 22.00 % in battery degradation, and 36.57 % in battery degradation inconsistency across cells compared to the benchmark. Furthermore, the mean computation time per step is 0.34 s shorter than the sampling time, ensuring feasibility for real-time application. Sensitivity analyses of key parameters—such as weighting factors, sampling time, and prediction horizons further underscore the robustness of the IPTM strategy and reinforce its potential for practical deployment.