文献类型：期刊文献

英文题名：A Physics-Informed Composite Network for Modeling of Electrochemical Process of Large-Scale Lithium-Ion Batteries

作者：Wang, Bing-Chuan[1,2];Ji, Zhen-Dong[1];Wang, Yong[1];Li, Han-Xiong[3];Li, Zhongmei[2]

机构：[1]Cent South Univ, Sch Automation, Changsha 410083, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]City Univ Hong Kong, Dept Syst Engn, Kowloon, Hong Kong 999077, Peoples R China

年份：2025

卷号：21

期号：1

起止页码：287

外文期刊名：IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS

收录：;EI(收录号：20244317261566);WOS:【SCI-EXPANDED(收录号:WOS:001336001800001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62106287,Grant 62476290, and Grant U23A20347, in part by the GRF Project from RGC of Hong Kong under Grant CityU 11206623, in part by the Hunan Provincial Natural Science Foundation under Grant 2024JJ4072,in part by the Fundamental Research Funds for the Central Universities,and in part by the High Performance Computing Center of Central South University.

语种：英文

外文关键词：Modeling; Electrochemical processes; Physics; Computer architecture; Accuracy; Training; Mathematical models; Spatiotemporal phenomena; Lithium-ion batteries; Electrodes; Data; electrochemical process; lithium-ion battery; physics; surrogate model

摘要：Accurately modeling the electrochemical process of large-scale lithium-ion batteries (LLBs), which involves estimating the electrochemical state distributions within the process, is crucial for the design and management of LLBs. A two-dimensional (2-D) physics-based model can describe the electrochemical process of LLBs accurately. However, due to the presence of complex partial differential equations (PDEs), solving the model becomes a challenging task. This article develops a physics-informed composite network (PICN) as a surrogate model of the 2-D physics-based model. Specifically, PICN consists of four deep neural networks (DNNs) to estimate the distributions of four key electrochemical states, respectively. Since the architecture of PICN is inspired by PDE characteristics, it can achieve high accuracies with four lightweight DNNs. Additionally, by incorporating physics and data, PICN achieves accurate estimations using limited data. It can even estimate the electrochemical state distributions that may not be measured directly. Moreover, PICN presents a low-frequency information-based pretraining strategy and a two-stage loss balance strategy to address the convergence failure and loss imbalance that may arise in the training of PICN. PICN is a new attempt to model the electrochemical process of LLBs by integrating physics with data. Extensive experiments show that it is better than state-of-the-art models.