Lithium-ion battery multi-scale modeling coupled with simplified electrochemical model and kinetic Monte Carlo model

Hanqing Yu; Lisheng Zhang; Wentao Wang; Kaiyi Yang; Zhengjie Zhang; Xiang Liang; Siyan Chen; Shichun Yang; Junfu Li; Xinhua Liu

doi:10.1016/j.isci.2023.107661

Lithium-ion battery multi-scale modeling coupled with simplified electrochemical model and kinetic Monte Carlo model

iScience. 2023 Aug 17;26(9):107661. doi: 10.1016/j.isci.2023.107661. eCollection 2023 Sep 15.

Authors

Hanqing Yu¹, Lisheng Zhang¹, Wentao Wang¹, Kaiyi Yang¹, Zhengjie Zhang¹, Xiang Liang¹, Siyan Chen², Shichun Yang¹, Junfu Li³, Xinhua Liu^{1

4}

Affiliations

¹ School of Transportation Science and Engineering, Beihang University, Beijing 102206, China.
² College of Automotive Engineering, Jilin University, Changchun 130022, China.
³ School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, China.
⁴ Dyson School of Design Engineering, Imperial College London, London SW7 2AZ, UK.

Abstract

The multi-scale modeling of lithium-ion battery (LIB) is difficult and necessary due to its complexity. However, it is difficult to capture the aging behavior of batteries, and the coupling mechanism between multiple scales is still incomplete. In this paper, a simplified electrochemical model (SEM) and a kinetic Monte Carlo (KMC)-based solid electrolyte interphase (SEI) film growth model are used to study the multi-scale characteristics of LIBs. The single-particle SEM (SP-SEM) is described for macro scale, and a simple and self-consistent multi-particle SEM (MP-SEM) is developed. Then, the KMC-based SEI model is established for micro-scale molecular evolution. And, the two models are coupled to construct the full-cycle multi-scale model. After modeling, validation is performed by using a commercial 18650-type LIB. Finally, the effect of parameters on the SEI model is studied, including qualitative trend analysis and quantitative sensitivity analysis. The growth of SEI film with different particle sizes is studied by MP-SEM coupling simulation.

Keywords: Artificial intelligence; Electrochemistry; Machine learning.