Lithium-ion battery simulation optimization and lifetime prediction

The rapid development of battery technologies requires 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 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. The 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.