Perceiving the Battery Multi-Electrochemical States in Real-time Based on Model-Informed Neural Network

Ying Zhang; Qinghao Zhang; Bin Duan; Pingwei Gu; Changlong Li; Chenghui Zhang

doi:10.1109/JAS.2026.125945

Volume 13 Issue 5

May 2026

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 19.2, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2026 > 13(5): 1041-1053

Y. Zhang, Q. Zhang, B. Duan, P. Gu, C. Li, and C. Zhang, “Perceiving the battery multi-electrochemical states in real-time based on model-informed neural network,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 5, pp. 1041–1053, May 2026. doi: 10.1109/JAS.2026.125945

Citation:

Y. Zhang, Q. Zhang, B. Duan, P. Gu, C. Li, and C. Zhang, “Perceiving the battery multi-electrochemical states in real-time based on model-informed neural network,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 5, pp. 1041–1053, May 2026. doi: 10.1109/JAS.2026.125945

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Perceiving the Battery Multi-Electrochemical States in Real-time Based on Model-Informed Neural Network

doi: 10.1109/JAS.2026.125945

Funds: This paper was supported by the National Key Research and Development Program of China (2022YFF0712700), the National Natural Science Foundation of China (62333013), and the National Science Foundation for Young Scholars (52507227)

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Author Bio:
Ying Zhang received the M.S. degree in control theory and control engineering from the School of Control Science and Engineering, Shandong University, in 2021. She is currently pursuing the Ph.D. degree in control theory and control Engineering with the School of Control Science and Engineering, Shandong University. Her research interests include battery modeling and management of batteries

Qinghao Zhang (Student Member, IEEE) received the B.Eng. degree and the Ph.D. degree in electrical engineering in 2019 and 2024 from the Department of Electrical Engineering, Tsinghua University, where he is currently working as a "Shuimu" Assistant Researcher. His current research interests include condition monitoring, intelligent sensing, and wireless communication. Dr. Zhang has published more than 30 papers, and he was a recipient of three Best Paper Awards from the IEEE Industrial Electronics Society

Bin Duan (Senior Member, IEEE) received the B.S. degree in automation and the Ph.D. degree in control theory and control engineering from Shandong University in 2005 and 2010, respectively. He is currently a Professor with the School of Control Science and Engineering, Shandong University. His current research interests include power electronics, battery technology, and modeling and optimal control of complex nonlinear systems

Pingwei Gu received the M.S. in control theory and control engineering and Ph.D. degree in energy and power from the School of Control Science and Engineering, Shandong University in 2020 and 2025. He is currently a Post-Doctoral Research Fellow with the School of Control Science and Engineering, Shandong University. His research interests include modeling and state estimation of lithium-ion batteries

Changlong Li received the Ph.D. degree in electrical engineering from Shandong University in 2022. He is currently an Associate Research Fellow with the School of Control Science and Engineering, Shandong University. His research interests include modeling and management of lithium-ion batteries in electric vehicles and energy storage systems

Chenghui Zhang (Fellow, IEEE) received the B.S. degree in automation engineering, and the Ph.D. degree in control theory and operational research from Shandong University in 1985 and 2001, respectively. In 1988, he joined Shandong University, where he is currently a Professor and the Dean of the School of Control Science and Engineering. His research interests include optimal control of engineering, power electronics and motor drives, and energy-saving techniques
Corresponding author: Qinghao Zhang, e-mail: zhangqh15@tsinghua.org.cn; Bin Duan, e-mail: duanbin@sdu.edu.cn
Received Date: 2025-08-15
Revised Date: 2025-11-11
Accepted Date: 2026-01-28

Abstract

Abstract

Accurate estimation of electrochemical states serves as a pathway to observe internal battery behaviors, effectively bridging the gap between micro mechanism and macro performance and enabling more precise control in an advanced battery management system. Yet conventional pseudo-two-dimensional (P2D) physics methods suffer from high computational complexity and limit their online application. Thus, we develop a model-informed neural network (MINN) framework that synergistically combines deep learning with a physics-based model to accurately monitor the battery electrochemical state (such as lithium-ion concentration, plating potential). Firstly, the MINN model is constructed with the innovative loss term containing experimentally measurable parameters and governing physical laws. Secondly, a composite framework based on a convolutional neural network (CNN) architecture is integrated to automatically extract features and enforce spatial boundary conditions, which significantly reduces the number of boundary loss terms that need to be solved and alleviates the complexity of the training process. After training, the MINN model can achieve an accurate estimation of internal states and even their spatiotemporal distributions that cannot be directly measured based on limited observable data and physical laws. At last, by incorporating dynamic current input, the well-trained basic model exhibits strong robustness and can be directly transferred to other cycling protocols with high accuracy, requiring no further retraining. MINN is a novel and promising framework to realize online and accurate micro electrochemical states monitoring, achieving at least 776 times speedup compared with the P2D model. As an innovative artificial intelligence assisted modeling for electrochemical systems, this framework enables root-cause analysis of battery behavior and failure modes, while empowering the management system with more reliable and trustworthy decision-making capabilities.
- Electrochemical state estimation,
- lithium-ion battery,
- model-informed neural network (MINN),
- real-time application

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References(34)

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