Robustness estimation for state-of-charge of a lithium-ion battery based on feature fusion

State of charge (SOC) of the lithium-ion battery stands as one of the crucial parameters of the battery management system. To enhance the robustness and accuracy of data-driven methods for estimating SOC, in this study, a novel framework based on feature selection and closed-loop estimation framework is proposed for estimating the SOC of a battery. Firstly, the maximum mutual information coefficient is used to correlate the data collected from the battery experiments. Secondly, an improved least squares support vector machine model (LSSVM) is proposed for SOC estimation using the sine cosine optimization algorithm for parameter optimization of LSSVM. Furthermore, based on the Sage-Husa adaptive Kalman filter, this study constructed a closed-loop estimation model. Finally, we conduct experiments on batteries with variable temperatures and working conditions, to verify its robustness and generalization. The results indicate that the proposed method has great SOC estimation accuracy compared to the control group. Even under simulated measurement noise conditions, the proposed method achieves the root mean square error 1.28 % and 1.12 % respectively, demonstrating strong robustness.