Battery thermal management system optimization using Deep reinforced learning algorithm

• Published in: Applied thermal engineering Vol. 236; p. 121759
• Main Authors: Cheng, Hangyu, Jung, Seunghun, Kim, Young-Bae
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 05.01.2024
• ISSN: 1359-4311
• DOI: 10.1016/j.applthermaleng.2023.121759

•Phase change material is used for battery thermal managament system.•Deep reinforcement learning is used to the battery thermal arrangement optimization.•Deep reinforcement learning is firsty used to optimize battery perfromance. The temperature of the battery plays a critical role in the working conditions and a suitable temperature environment can help the battery extend its capacity. Air cooling and water cooling are prevalent as positive heat dissipation methods used in research. Phase change material (PCM) has a good heat dissipation capacity because of its latent heat property. Therefore, PCM is chosen as the battery thermal management method in this study. Adding two water channels in the center of the pack strengthens the heat dissipation capacity. Deep reinforcement learning (DRL) is used to explore the multi-optimal solution of the battery thermal arrangement, and its results are compared with the classical methods such as NSGA-ii and MOPSO. Max temperature, temperature difference of the battery, and average temperature of PCM are selected as the optimization targets. A comparison between DRL optimization and the initial system shows that the increased max temperature decreased by 0.33 ℃ (2.1%), and the average temperature of PCM decreased by 0.3 ℃ (2.3%). The temperature difference of DRL optimization decreased by 1.3% compared with that of NSGA-ii and MOPSO. The max temperature decreased by 0.2% and the average temperature of PCM decreased by 0.3% compared with those of NSGA-ii. In summary, DRL has the potential application in battery thermal management system (BTMS) optimization. The most important contribution of this work is utilizing DRL in the optimization process, which shows better results than existing optimization methods.