Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data

IF 15 1区 工程技术 Q1 ENERGY & FUELS
Jinho Jeong , Eunji Kwak , Jun-hyeong Kim , Ki-Yong Oh
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引用次数: 0

Abstract

A surrogate model that predicts thermal runaway (TR) of lithium-ion batteries (LIBs) fast and accurately is essential, yet complex phenomena of TR present significant challenges to achieving adequate performance in both aspects, particularly as traditional finite element models (FEMs) incur significant time and cost. This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) with encoders to address these issues. This proposed neural network aims to predict TR under various thermal abuse conditions, offering a fast and accurate TR prediction surrogate model. In this study, MPI-DeepONet with encoders is trained with virtual data from a multiphysics FEM to overcome the scarcity of actual TR data. The architecture of DeepONet solves interpolation and extrapolation problems, allowing predictions across multiple thermal abuse conditions once trained. The neural network is further enhanced by the supervision of energy balance and chemical reaction equations, ensuring accurate and robust predictions despite limited data by effectively capturing the complex phenomena of TR. Quantitative analysis, compared against actual experiments and ablation studies, confirms the effectiveness of the proposed neural network. Notably, MPI-DeepONet achieves a mean RMSE of 13.2 °C for temperature predictions in the test set, significantly outperforming the 25.4 °C RMSE of purely data-driven DeepONet. This improvement highlights the importance of integrating multiphysics constraints into the neural network. The generality of the proposed neural network is further evidenced by accurate TR prediction in both LFP and NMC cells. The features deployed on the proposed neural network enable real-time quantification of internal temperature distribution and dimensionless concentration of the key components in LIBs, which are challenging to measure directly, achieving speeds at least 10,000 times faster than FEM. The proposed neural network provides comprehensive information for advanced battery management systems to ensure safety and reliability in LIBs, accelerating the digital twin of electric transportation systems through artificial intelligence transformation.

通过虚拟数据的多物理信息 DeepONet 预测锂离子电池的热失控现象
快速准确地预测锂离子电池(LIB)热失控(TR)的替代模型至关重要,然而复杂的热失控现象给实现这两方面的充分性能带来了巨大挑战,尤其是传统的有限元模型(FEM)需要耗费大量的时间和成本。本研究提出了一种带有编码器的多物理信息深度算子网络(MPI-DeepONet)来解决这些问题。该建议的神经网络旨在预测各种热滥用条件下的 TR,提供快速准确的 TR 预测替代模型。在本研究中,带有编码器的 MPI-DeepONet 使用多物理场有限元的虚拟数据进行训练,以克服实际 TR 数据稀缺的问题。DeepONet 的结构可以解决内插法和外推法问题,一旦训练完成,就可以对多种热滥用条件进行预测。能量平衡和化学反应方程的监督进一步增强了神经网络,通过有效捕捉 TR 的复杂现象,确保在数据有限的情况下仍能进行准确、稳健的预测。根据实际实验和烧蚀研究进行的定量分析证实了所建议的神经网络的有效性。值得注意的是,MPI-DeepONet 对测试集中温度预测的平均 RMSE 为 13.2 °C,明显优于纯数据驱动 DeepONet 的 25.4 °C。这一改进凸显了将多物理约束整合到神经网络中的重要性。对 LFP 和 NMC 电池的 TR 预测准确,进一步证明了所提出的神经网络的通用性。所提出的神经网络所具有的特征能够实时量化锂电池中关键成分的内部温度分布和无量纲浓度,而直接测量这些成分是具有挑战性的,其速度比有限元分析至少快 10,000 倍。拟议的神经网络为先进的电池管理系统提供了全面的信息,以确保锂电池组的安全性和可靠性,通过人工智能转型加速电动交通系统的数字孪生。
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来源期刊
Etransportation
Etransportation Engineering-Automotive Engineering
CiteScore
19.80
自引率
12.60%
发文量
57
审稿时长
39 days
期刊介绍: eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation. The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment. Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.
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