Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development

• Data development for machine learning of Li-ion battery by three-dimensional multiphysics model. • Battery performance and maximum temperature (hot spot) data are obtained. • Data consider various thermal conductivities, heat transfer coefficients, and ambient temperatures. • Modeling data are favorably compared with two commercial Li-ion batteries. • Data will be applied for training convolutional neural network and long short-term memory in part II. 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 in-depth examination for battery performance and thermal risk prediction. Specifically, Part I focused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model accuracy 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 part II.