Improved adaptive fusion parameter identification and chaotic gravitational search-Kalman particle filtering method for state-of-energy accurate estimation of lithium-ion batteries

State-of-energy (SOE) is an important parameter in the battery management system, which determines the current maximum possible range of electric vehicles. In this study, an improved chaotic gravitational search-Kalman particle filtering method for SOE estimation of lithium-ion batteries based on adaptive fusion dual-factor parameter identification is proposed. Firstly, the adaptive forgetting factor-limited memory recursive extended least squares algorithm is designed by integrating the forgetting factor and the memory length factor to improve the accuracy and generalization ability of online parameter identification. Secondly, to address the problem of particle degradation and loss of diversity, this study introduces the square root cubature Kalman filtering and the chaotic gravitational search algorithm to improve the accuracy and stability of particle filtering. Finally, a chaotic gravitational search-square root cubature Kalman particle filtering model is constructed to effectively improve the estimation performance of SOE. The experimental results under complex working conditions show that the mean absolute error of the parameter identification method proposed in this study is between 0.56 % and 0.68 %, and the root mean square error of the proposed estimation method for SOE remains between 1.04 % and 1.17 %, indicating that the method proposed in this study has high robustness and accuracy.