Multiphysics-informed DeepONet of a lithium-ion battery to predict thermal runaway



Published Sep 4, 2023
Jinho Jeong Eunji Kwak Jun-Hyeong Kim Ki-Yong Oh


This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) to predict the thermal runaway of lithium-ion batteries (LIBs) under a variety of thermal operational and abuse conditions. Specifically, this study aims to address the functional mapping from a heating curve to predict the evolution of the temperature of a LIB and dimensionless concentration of dominant components of the LIB including an anode, cathode, electrolyte, and solid electrolyte interphase. The proposed method has two key characteristics. First, the MPI-DeepONet is supervised by using ordinary and partial differential equations, which govern highly complex and nonlinear phenomena of thermal runaway of a LIB, including the chemical reaction degradation of the dominant four components and thermodynamics. This feature enables to train of the proposed neural network with a small amount of data available, suggesting that the proposed neural network is accurate and robust even though the proposed method is trained even with limited data. Second, the proposed neural network is trained with the data that is generated from high-fidelity finite element analysis under a variety of thermal operational and abuse conditions because measurements for the thermal runaway of a LIB are limitedly available. Hence, the MPIDeepONet does not require actual measurements, which is extremely difficult in field experiments. Finally, the accuracy and robustness of the proposed architecture are verified through actual measurements and other scenarios, which are different from the data trained. The analysis of results reveals that the MPI-DeepONet secures higher accuracy and robustness than purely data-driven DeepONet. The proposed surrogate model, which is faster than existing surrogate models, suggesting that this model contributes to developing a digital twin model of a LIB, which can be deployed on a battery thermal management system and provides sufficient information for effective power and energy management.  

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digital twin, physics-informed, governing equation, thermal runaway, lithium-ion battery

Wang, S., Wang, H., & Perdikaris, P. “Improved architectures and training algorithms for deep operator networks.” Journal of Scientific Computing, 92(2), 35. (2022).

Kwak, E., Kim, J. H., Hong, S. H., & Oh, K. Y. “Detailed modeling investigation of thermal runaway pathways of a lithium iron phosphate battery.” International Journal of Energy Research, 46(2), 1146-1167. (2022).
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