Battery SOH Prediction Based on Features Polynomial and Improved Whale Algorithm
Lithium battery is the core equipment of new energy ship system,and accurate prediction of its state of health(SOH)is conducive to system energy management and safe operation of the ship.In order to improve the prediction accuracy of battery SOH,a prediction method combining multiple health features(MHF)fusion and improved whale optimization algorithm(IWOA)is proposed.Based on the traditional support vector regression(SVR)as the prediction method,four typical health features(HF)are selected by the Pearson analysis method,and a polynomial model is constructed by using the weighted method to fuse multiple HF.Considering the influence of the weight coefficient of the feature and the penalty coefficient C of SVR,the kernel parameter δ and the maximum error ε on the prediction accuracy,the IWOA is used to jointly optimize the weight coefficient and three hyperparameters in the model.The simulation results show that the proposed MHF-IWOA-SVR method has higher prediction accuracy and better fit,and the prediction error is basically kept within±0.5％.
health features(HF)support vector regressionimproved whale optimization algorithm(IWOA)state of health(SOH)
万方数据
维普
