An Approach for Fast-Charging Lithium-Ion Batteries State of Health Prediction Based on Model-Data Fusion

Abstract

Abstract Fast charging has become the norm for various electronic products. The research on the state of health prediction of fast-charging lithium-ion batteries deserves more attention. In this paper, a model-data fusion state of health prediction method which can reflect the degradation mechanism of fast-charging battery is proposed. First, based on the Arrhenius model, the log-power function (LP) model and log-linear (LL) model related to the fast-charging rate are established. Second, combined with Gaussian process regression prediction, a particle filter is used to update the parameters of models in real-time. Compared with the single Gaussian process regression, the average root-mean-square error of LP and LL is reduced by 71.56% and 69.11%, respectively. Finally, the sensitivity and superiority of the two models are analyzed by using Sobol method, Akaike and Bayesian information criterion. The results show that the two models are more suitable for fast-charging lithium batteries than the traditional Arrhenius model, and LP model is better than LL model.