Research on Prediction of State of Charge of Lithium-ion Battery Based on Natural Selection Optimized PSO-SVM Algorithm

Abstract

The state of charge (SOC) of lithium batteries is one of the important performance parameters of electric vehicles, and accurate real-time estimation of SOC can ensure the safe operation of electric vehicles. The traditional particle swarm optimization support vector machine algorithm is effective in predicting small samples. However, as the number of samples increases, there are problems in the prediction of lithium battery SOC of abnormal divergence in the later stage and unstable overall estimation results. To solve the above problems, this paper proposes a support vector machine model based on the natural selection method to improve the particle swarm optimization algorithm to realize the state-of-charge prediction of lithium batteries. The results of the simulation and test demonstrate that the method proposed in this paper can reduce the average relative error of prediction from 2.4% to 1.38%. The algorithm can improve the reliability and stability of the estimation results, and effectively guarantee the safe operation of electric vehicles.