A two-stage eco-cooling control strategy for electric vehicle thermal management system considering multi-source information fusion

Thermal management systems (TMS) of electric vehicles (EV) have a significant impact on cabin thermal comfort and battery life, and few studies have adequately considered it. This study proposes a two-stage eco-cooling (TSEC) control strategy to reduce TMS energy consumption while improving cabin thermal comfort and extending battery life. Multi-source information, such as vehicle speed, weather conditions, passenger charac-teristics, and battery conditions, are all considered. In the first stage, the passenger characteristics and battery conditions are used to calculate the cabin comfort temperature and the battery's optimal operating temperature, respectively. The dynamic programming (DP) algorithm is used to optimize cabin and battery temperature trajectories. Fuzzy PID-based controllers are used in the second stage to achieve the desired temperature. The air conditioning (AC)-cabin thermal model and the battery thermal-electro-aging model have been developed and validated. The proposed TSEC control strategy can improve cabin thermal comfort by automatically adjusting the calculated comfort temperature. Compared with the on-off and PID controllers, the battery life under the TSEC control strategy is improved by 21.48% and 8.55%, respectively, and the energy consumption is reduced by 42.86% and 18.54%, respectively. The proposed control strategy may provide new insight into the TMS of electric vehicles.

Automated summary

This study proposes a two-stage eco-cooling control strategy to reduce energy consumption of thermal management systems (TMS) in electric vehicles (EV) while improving cabin thermal comfort and extending battery life. The strategy considers multiple factors such as vehicle speed, weather conditions, passenger characteristics, and battery conditions. It optimizes cabin and battery temperature trajectories using a dynamic programming algorithm in the first stage, and achieves the desired temperature using fuzzy PID-based controllers in the second stage. Experimental results show that the proposed strategy significantly improves battery life and reduces energy consumption compared to traditional controllers.