Lithium-Ion Battery Model Analysis Based on Levenberg-Marquardt Algorithm

As the application of lithium-ion batteries becomes more widespread, precise estimation of battery state becomes increasingly important. In many energy storage fields, batteries play an important role, and SOC has a significant impact on the battery management system (BMS). In this paper, a model analysis method of lithium-ion battery based on Levenberg-Marquardt algorithm (LMA) is proposed. This method only needs to obtain the voltage and current data of the battery under different working conditions, and then the parameters can be quickly identified and analyzed through the equivalent circuit model, thus improving the computational efficiency. The study is divided into two main stages: the first is to optimize the model parameters using the LMA algorithm, and the second is to verify whether SOC has improved the accuracy through experimental data. In this study, lithium-ion batteries are used as experimental materials. The results show that the proposed LMA identification framework not only has good robustness but also highly matches the experimental data. This algorithm is very effective in improving accuracy and provides great help for the future development of lithium-ion batteries.