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Physics-informed neural networks for battery smart modeling and state estimation with electrochemical mechanisms and data-driven intelligence

Kailong Liu Weiming Li Qiao Peng Chunhui Zhao Hongwei Zhou Bin Duan Xiangjun Li Chenghui Zhang

全球能源互联网（英文）2026，Vol.9Issue(4)：639-663,25.

全球能源互联网（英文）2026，Vol.9Issue(4)：639-663,25.DOI:10.1016/j.gloei.2026.07.001

Physics-informed neural networks for battery smart modeling and state estimation with electrochemical mechanisms and data-driven intelligence

Kailong Liu ¹Weiming Li ¹Qiao Peng ²Chunhui Zhao ³Hongwei Zhou ⁴Bin Duan ¹Xiangjun Li ⁵Chenghui Zhang¹

作者信息

1. School of Control Science and Engineering,Shandong University,Jinan 250100,P.R.China
2. Information Technology,Analytics & Operations Group,Queen's University Belfast,Belfast BT71NN,UK
3. State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027,P.R.China
4. TBEA SunOasis Co.,Ltd.,Urumqi 830011,P.R.China
5. China Electric Power Research Institute,Beijing 100192,P.R.China
折叠

摘要

关键词

Physics-informed neural networks/Battery modeling/Battery state estimation/Battery management systems

Key words

Physics-informed neural networks/Battery modeling/Battery state estimation/Battery management systems

引用本文复制引用

Kailong Liu,Weiming Li,Qiao Peng,Chunhui Zhao,Hongwei Zhou,Bin Duan,Xiangjun Li,Chenghui Zhang..Physics-informed neural networks for battery smart modeling and state estimation with electrochemical mechanisms and data-driven intelligence[J].全球能源互联网（英文）,2026,9(4):639-663,25.

基金项目

This work was supported by the National Natural Science Foundation of China under Grant 62373224,and Shenzhen Science and Technology Program under Grant GJHZ20240218113404009,and Natural Science Foundation of Shandong Province under Grant ZR2024JQ021. （）

全球能源互联网（英文）

ISSN：2096-5117

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