Sensitivity Analysis-Driven Parameter Optimization Identification for High-precision Electrochemical Model of Lithium Ion Batteries

Lithium-ion batteries are widely employed in the new energy field with the advantages of high energy density and long cycle life. High-precision battery models are extremely important for the study of battery management and degradation mechanisms. The accuracy of the parameters is a key factor affecting the precision of the model. However, improving the precision has some difficulties due to the complex and numerous electrochemical parameters, especially under the large rate charging condition. In this paper, we analyze the sensitivity of the parameters of the electrochemical model (ECM) in different SOC intervals and dynamic working conditions. The sensitivity parameters that can improve the precision of the model are found and re-identified in SOC intervals. The results of model validation show that the improved model has higher precision under different conditions.