Lithium Battery SOC Prediction Based on mproved BP eural etwork Algorithm

Aiming at the problems of low prediction accuracy and many internal empirical parameters in the current online prediction methods of lithium-ion battery SOC, a BP neural network prediction algorithm considering the degree of battery degradation is proposed. With current, voltage, temperature as input variables, battery SOC as output variables, genetic algorithm is used to optimize the weights and thresholds of BP neural network, and the measured experimental data are normalized and imported into the neural network model for training and testing. The results show that the improved algorithm can effectively improve the SOC prediction accuracy, and has good effectiveness and robustness. The prediction error between the prediction result and the actual value is less than 1%, which meets the technical requirement of 5% SOC prediction error.