Analysis of SOH Estimation and RUL Prediction for Lithium Batteries Based on V2G and XGBoost Technology
This paper describes the problem of low accuracy in SOH prediction caused by the difficulty in feature extraction of lithium-ion batteries.A charging and discharging process based on V2G technology and XGBoost based method for SOH prediction and RUL prediction of lithium-ion batteries are proposed.By extracting the filtered peak value and corresponding point voltage from the charging curve as the health factor during the charging process,as well as the time and average voltage attenuation of the periodic discharge voltage reaching its lowest point during the discharge process,as input for the XGBoost model of the health factor during the discharge process,battery SOH prediction is carried out.Combined with SOH estimation values and estimation models,long-term prediction of RUL is achieved.The experimental results show that the improved model has higher estimation accuracy,as well as higher accuracy in SOH estimation and RUI prediction.
万方数据
维普
