Lithium-ion battery simulation optimization and lifetime prediction
Xinqi Xie, Ruixin Yang, Weixiang Shen, Rui Xiong
Abstract
The rapid development of battery technologies necessitates substantial time and effort to conduct experiments and obtain cell characteristics. To address this challenge, this paper develops an electrochemical-thermal coupled model to simulate lithium-ion cells, with model parameters identified by using voltage and temperature as optimization targets. A genetic algorithm is employed for parameter identification and optimization. The model is validated through experiments on various cells under different operating conditions. The results demonstrate that the simulated voltage achieves high accuracy with a root mean square error (RMSE) ranging from 16 to 34 mV. Furthermore, a cell lifetime model is established by incorporating multiple internal degradation mechanisms. Validation results show that the RMSE in lifetime prediction is less than 0.0024. As a result, the developed models exhibit high accuracy in cell performance simulation and lifetime prediction with a certain degree of generalizability, enabling quick adaptation to other cell types while reducing testing costs and shortening the model development cycle.