An Empirical-Data Hybrid Driven Approach for Remaining Useful Life prediction of lithium-ion batteries considering capacity diving

Considering the variabilities among each cell especially during the battery accelerated decay period, the parameterized empirical model is doubtful for predicting the Lithium-ion (Li-ion) battery Remaining Useful Life (RUL). Thus, an Empirical-Data Hybrid Driven Approach (EDHDA) is proposed to utilize both the prior knowledge and the historical dataset for the lifetime prediction of the Li-ion battery under capacity diving conditions. A polynomial-based model is firstly proposed to provide the basic accuracy for the EDHDA. Meanwhile, an improved Gaussian Process Regression (GPR) with a partial charging voltage profile is designed to make full use of the operational dataset. The EDHDA is then established with a dual Particle Filter (PF) framework combining the advantages of the above two methods. In this way, accurate estimations of the current capacity can be obtained by fusing the two models, even under capacity diving conditions. The parameters of the empirical model can also be updated according to the fused capacity to obtain accurate RUL predictions with uncertainty levels. Experimental results show that the proposed EDHDA has a high RUL prediction accuracy under capacity diving even with limited data.