A data-driven identification method for reaction rate constant and diffusion coefficient in the P2D model

Gaoyang Li; Xiaoyu Guo; Yongshuai Li; Jialong Huang; Zhirui Wang; Yizheng Ma; Litao Zhu; Hui Pan; Feng Shao; Hao Ling; Yulin Min

doi:10.1016/j.cjche.2025.05.045

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A data-driven identification method for reaction rate constant and diffusion coefficient in the P2D model

Updated：2026-02-12

- A data-driven identification method for reaction rate constant and diffusion coefficient in the P2D model
- Chinese Journal of Chemical Engineering Vol. 88, Issue 12, Pages: 188-197(2025)
- Affiliations：
  
  1. Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power,Shanghai,China,200090
  2. State Key Laboratory of Chemical Engineering, East China University of Science and Technology,Shanghai,China,200237
  3. College of Smart Energy, Shanghai Jiao Tong University,Shanghai,China,200240
  4. School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University,Shanghai,China,200240
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2025.05.045
  CLC：
- Published：2025
- Accepted：
Scan QR Code
Gaoyang Li, Xiaoyu Guo, Yongshuai Li, Jialong Huang, Zhirui Wang, Yizheng Ma, Litao Zhu, Hui Pan, Feng Shao, Hao Ling, Yulin Min. A data-driven identification method for reaction rate constant and diffusion coefficient in the P2D model[J]. Chinese Journal of Chemical Engineering, 2025, 88(12): 188-197.
DOI：

Gaoyang Li, Xiaoyu Guo, Yongshuai Li, Jialong Huang, Zhirui Wang, Yizheng Ma, Litao Zhu, Hui Pan, Feng Shao, Hao Ling, Yulin Min. A data-driven identification method for reaction rate constant and diffusion coefficient in the P2D model[J]. Chinese Journal of Chemical Engineering, 2025, 88(12): 188-197. DOI： 10.1016/j.cjche.2025.05.045.

摘要

Abstract

To ensure the safe operation of batteries

accurately obtaining key internal state parameters is essential. However

traditional parameter measurement methods either require opening the battery or long-term measurements

which are impractical. Therefore

the fixed values are commonly used for these parameters in electrochemical models and have significant limitations. To overcome these limitations

this paper proposes a deep neural network (DNN) based data-driven evaluation method to determine model parameters. By coupling an improved one-dimensional isothermal pseudo-two-dimensional (P2D) model with DNN

this study identified concentration-dependent parameters through detailed discharge curve analysis. The results show that the data-driven method can effectively obtain the change trend of concentration-dependent parameters through the charge and discharge curve

and the method can be extended to different battery systems in different discharge rates and aging applications. This work is expected to provide new parameter selection insights for data-driven battery prediction and monitoring models.

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