Battery SOH Prediction Based on Support Vector Regression Optimized by Genetic Algorithm
The current available capacity of the battery is difficult to obtain,and the health status of the battery is difficult to estimate accurately during the operation of the vehicle.Therefore,this paper proposed to use the parking and charging segment data of the vehicle to correct the battery capacity obtained by ampere-hour integration method through box diagram and Kalman filter algorithm.The support vector regression model was constructed for battery degradation prediction.The effective model input parameters were determined by Pearson correlation analysis.The model parameters were optimized by genetic algorithm.Results show that the fitting accuracy of the optimized model reaches 88％,which is 12％higher than that before optimization,can accurately predict the SOH of vehicle battery.
万方数据
维普
