Stage of Charge Estimation of a Lithium-ion Battery Based on the Interactive Multi-model

Abstract

In order to address issues affecting the estimation accuracy of the state of charge (SOC) in the state of health (SOH) of lithium-ion batteries throughout their whole life cycle, this paper proposed a method to estimate the SOC of lithium-ion batteries based on the Interacting Multiple Model (IMM). By establishing multiple battery models with different degrees of aging in the parallel filtering process of IMM, the likelihood function was used to calculate the model probability of a single model. Moreover, the state estimates of multiple single models were then fused and output, solving the poor SOC estimation accuracy due to battery aging. The method was subsequently verified on multiple sets of randomly aging battery data via experimentation, for which the findings indicated that the proposed method was able to accurately track battery SOC and perform real-time estimation of battery capacity.