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

doi:10.1016/j.cjche.2025.05.045

Chinese Journal of Chemical Engineering ›› 2025, Vol. 88 ›› Issue (12): 188-197.DOI: 10.1016/j.cjche.2025.05.045

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

Gaoyang Li¹, Xiaoyu Guo¹, Yongshuai Li³, Jialong Huang¹, Zhirui Wang³, Yizheng Ma¹, Litao Zhu⁴, Hui Pan¹, Feng Shao², Hao Ling³, Yulin Min¹

1. Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China;
2. School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
3. State Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China;
4. College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China

Received:2025-02-13 Revised:2025-04-30 Accepted:2025-05-07 Online:2025-08-22 Published:2026-02-09
Contact: Hui Pan,E-mail:fiona_panhui@shiep.edu.cn;Feng Shao,E-mail:shaofeng0824@sjtu.edu.cn;Hao Ling,E-mail:linghao@ecust.edu.cn;Yulin Min,E-mail:minyulin@shiep.edu.cn
Supported by:
This work is supported by National Natural Science Foundation of China (22478239), Science and Technology Commission of Shanghai Municipality (19DZ2271100) and National Natural Science Foundation of China (22208208).

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

Gaoyang Li¹, Xiaoyu Guo¹, Yongshuai Li³, Jialong Huang¹, Zhirui Wang³, Yizheng Ma¹, Litao Zhu⁴, Hui Pan¹, Feng Shao², Hao Ling³, Yulin Min¹

1. Shanghai Key Laboratory of Materials Protection and Advanced Materials in Electric Power, Shanghai University of Electric Power, Shanghai 200090, China;
2. School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
3. State Key Laboratory of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China;
4. College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China

通讯作者: Hui Pan,E-mail:fiona_panhui@shiep.edu.cn;Feng Shao,E-mail:shaofeng0824@sjtu.edu.cn;Hao Ling,E-mail:linghao@ecust.edu.cn;Yulin Min,E-mail:minyulin@shiep.edu.cn
基金资助:
This work is supported by National Natural Science Foundation of China (22478239), Science and Technology Commission of Shanghai Municipality (19DZ2271100) and National Natural Science Foundation of China (22208208).

Abstract

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.

Key words: Internal state parameters of batteries, P2D model, Parameter identification, Deep neural network (DNN), Data-driven evaluation method

摘要： 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.

关键词: Internal state parameters of batteries, P2D model, Parameter identification, Deep neural network (DNN), Data-driven evaluation method

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.

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

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

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