基于遗传算法优化支持向量回归的电池SOH预测

Battery SOH Prediction Based on Support Vector Regression Optimized by Genetic Algorithm

针对实车运行过程中电池当前可用容量难获取、电池健康状态评估不准确的问题,提出利用车辆的停车充电片段数据,通过箱型图及卡尔曼滤波算法对安时积分法计算所得的电池容量进行修正,构建支持向量回归模型用于电池衰减预测,通过皮尔森相关性分析确定有效的模型输入参数,结合遗传算法优化模型参数。结果表明:优化后模型的拟合优度可达88%,相较于优化前提高了12%,可以实现电池健康状态的准确预测。

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.