Useful life prediction for lithium-ion batteries based on COA-LSTM and VMD
Degradation of battery packs in electric vehicles is inevitable during their operational lifetime,making the estimated remaining useful life(RUL)a critical indicator of battery performance.This study proposes an optimized long short-term memory(LSTM)network-based RUL prediction model for EV lithium-ion batteries using the coyote optimization algorithm(COA).First,this study examines the capacity degradation characteristics of lithium-ion batteries.Indirect health indicators were extracted from the charge and discharge curves of the batteries,including constant current charging and discharging intervals and constant voltage holding time intervals.The correlations of these indicators were examined using the Pearson approach.Then,variational mode decomposition(VMD)was applied to decompose the health indicators into modal components.The LSTM model was used to predict the RUL of the battery pack.To address the issue of inaccurate LSTM model parameters affecting RUL prediction accuracy,COA was used to optimize these parameters and enhance the predictive capabilities of the model.The proposed method was validated using publicly available datasets from the NASA research center and compared with LSTM,VMD-LSTM,Gaussian process regression,and backpropagation neural network models.The experimental results indicate that the proposed approach can achieve RUL prediction errors of within 2%,demonstrating its ability to accurately predict RUL.
万方数据
维普
