Precise equivalent circuit model for Li-ion battery by experimental improvement and parameter optimization

The equivalent circuit model (ECM) is a type of lithium-ion battery model that is widely used in electric vehicle battery management systems (BMS). BMS is an important component that affects the performance of electric vehicles, and accurate battery model is the foundation of BMS. For different usage scenarios, improving the accuracy of battery model in BMS plays a crucial role in improving the energy utilization of electric vehicles and ensuring the safe use of batteries. The accuracy of the battery model is strongly influenced by the accuracy of the battery model parameters, therefore, this study aimed at elucidating on these factors. In this paper, experimental procedures for model parameter identification are designed and optimized by orthogonal analysis in terms of model accuracy, model consistency, and maximum model error. Model parameters that can synthetically balance the three model evaluation indices are finally obtained through experiments. By combining the obtained experimental contents and analysis results, we quantitatively investigated the sensitivity of different model performances to the three model parameters in ECM under different state of charge (SOC) using a combination of polynomial fitting and derivative solving sensitivity by single-factor sensitivity analysis. This study provides a basis for future battery modeling, model error analysis, and model parameter identification. In addition, we propose the optimization parameter as an indicator for optimizing the parameters in the battery model in conjunction with parameter sensitivity to model performance and degree of deviation from standard model parameters. The validation shows that the optimized battery model parameters using this method can improve the model's specific performance.

Automated summary

The study explores the factors affecting the accuracy of the equivalent circuit model in electric vehicle battery management systems (BMS) and designs experimental procedures to identify and optimize the model parameters. By utilizing polynomial fitting and sensitivity analysis, the study investigates the impact of three model parameters on different performance aspects under different state of charge (SOC). This study provides a foundation for battery modeling and model parameter identification, and proposes an optimization parameter as an indicator.