Health Prediction of Lithium-ion Batteries by combining with Empirical Mode Decomposition and PF-GPR Algorithm

A new model based on empirical mode decomposition (EMD) and particle filter-gaussian progress regression (PF-GPR) algorithm is developed for estimating state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries (LIBs). The capacity degradation process of LIBs is investigated and reveals that the new model is convenient and accurate for estimating SOH and RUL of LIBs. The relative error of SOH prediction is less than 1.5%, while the maximum absolute error of RUL prediction is one cycle. Compared with other models reported in previous works, present model shows smaller absolute error and root mean square error (RMSE). In addition, the EMD-PF-GPR fusion model based on combined kernel function and mahalanobis distance possesses a good generalization ability and ability of learning from local variations. The work provides a new method to accurately predict health of LIBs.