State of health prediction of lithium-ion batteries based on VMD-HPO-NBEATS model
The State of Health(SOH)of lithium-ion batteries is crucial for maintaining the stability of new energy electric vehicle systems.To improve the accuracy of SOH prediction for lithium batteries,a SOH prediction method based on the Variational Mode Decomposition(VMD)Hunter-Prey Optimization(HPO)Neural Basis Expansion Analysis(NBEATS)neural network is proposed.Firstly,by analyzing the battery aging data,health indicators(HIs)highly related to SOH are extracted and fused;secondly,the fused HIs are decomposed into multiple modal components using the VMD method,and the characteristics and temporal patterns of each modal component are captured using the NBEATS model with HPO hyperparameter optimization.Finally,the SOH prediction of the battery is obtained by summing and reconstructing the predicted values of each component.Ablation experiments on the NASA battery dataset show that compared with the NBEATS,HPO-NBEATS,and VMD-NBEATS models,the VMD-HPO-NBEATS model has an improvement of more than 2%in MAE,RMSE,and r2 evaluation metrics,proving the effectiveness and superiority of the proposed method in SOH prediction.
lithium-ion batterystate of healthNBEATS modelhunter-prey optimizer algorithmvariational mode decomposition
NETL
NSTL
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