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作 者:Yiheng Pang Yun Wang Zhiqiang Niu
机构地区:[1]Renewable Energy Resources Lab(RERL),Department of Mechanical and Aerospace Engineering,University of California,Irvine,CA,92697-3975,United States [2]Department of Aeronautical and Automotive Engineering,Loughborough University,United Kingdom
出 处:《Energy and AI》2024年第4期151-159,共9页能源与人工智能(英文)
摘 要:Fast growing demands for electric vehicles require better longevity,safety and reliability for next-generation high-energy battery technologies.A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics.Nowadays,Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance,health and safety,but is hurdled by a scarcity of data.To mitigate this issue,this study presents one of the first studies for data development through both experimental studies and three-dimensional(3-D)multi-physics modeling to underpin a deep learning framework with indepth examination for battery performance and thermal risk prediction.Specifically,PartⅠfocused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model ac-curacy by two steps:firstly,we validated the multi-physics model against two commercial Lithium-ion batteries,i.e.,Panasonic NCR18650B and 18650BD;Then,the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model,such as voltage evolution and maximum local temperature(hot spot).The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory(CNN-LSTM)in partⅡ.
关 键 词:Li-ion batteries Data development Multi-physics modeling Hot spot Machine learning
分 类 号:TM9[电气工程—电力电子与电力传动]
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