Performance analysis and multi-objective optimization of refrigerant-based integrated thermal management system for electric vehicles

• Published in: Applied thermal engineering Vol. 244; p. 122707
• Main Authors: Liang, Kunfeng, Zhang, Yunpeng, Wang, Wenbing, Gao, Chunyan, Chen, Haoyuan, Li, Kun, Zhou, Xun, Chen, Bin, Cao, Yong, Wang, Weimin, Yang, Dapeng, Song, Lusheng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2024
• Subjects: Comprehensive performance; Electric vehicle; Multi-objective optimization; Thermal management system; Comprehensive performance; Electric vehicle; Multi-objective optimization; Thermal management system
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2024.122707

•A refrigerant-based efficient ITMS for cabin and battery is developed.•ITMS with switchable working modes and system structures is simulated via AMESim.•An NSGA-II-based algorithm for ITMS multi-objective optimization is established.•ITMS regulation parameters are found through multi-objective optimization.•The optimization results verified under CLTC-P driving cycle. In order to ensure the safety and efficient operation of electric vehicles (EVs), the performance of the thermal management system (TMS) has grown increasingly pivotal. An efficient TMS should be seamlessly integrated with the vehicle to ensure passenger comfort and uphold optimal operating conditions for vehicle components. This study introduces a refrigerant-based integrated thermal management system (ITMS) capable of transitioning between series and parallel configurations in response to varying conditions. Concentrating on summer and winter scenarios, this study utilized AMESim simulations to evaluate the performance of the ITMS when operating in diverse ambient temperatures, following the China Light-Duty Vehicle Test Cycle Passenger (CLTC-P). The results show that the cabin temperature can promptly reach the desired level, ensuring passenger thermal comfort, while maintaining the battery within the optimal temperature range of 25–40 °C. The battery's State of Charge (SOC) experiences a maximum reduction of 21 %. To ensure holistic performance in vehicle operation, we have considered vehicle power, safety, range, and comfort as the multi-objective optimization criteria for the ITMS. We have developed a multi-objective optimization algorithm based on non-dominated sorting genetic algorithm (NSGA-II) to determine the operational optimization and control parameters for the ITMS.