Improving Battery Pack SOC Estimation through Multi -Chemistry Hybridization

This paper examines the accuracy with which one can estimate the state of charge (SOC) of a series string of battery cells with distinct chemistries. Accurate SOC estimation is an integral function of any battery management system (BMS): it helps in avoiding cycling a battery beyond its capacity limits, which has the potential to accelerate degradation and failure. Previous work in the literature quantifies limitations in SOC estimation accuracy, and attempts to address them through improved battery modeling, improved estimation algorithms, and the creation of series battery-capacitor packs. However, to the best of the authors' knowledge, this is the first body of work quantifying and demonstrating the degree to which the series hybridization of distinct battery chemistries can help improve SOC estimation accuracy. The paper derives Cramer-Rao bounds for the error variance with which one can estimate SOC, with and without series hybridization. This is followed by a Monte Carlo simulation of SOC estimation for series-connected commercial LiFePO4 (LFP) and LiNiMnCo02 (NMC) battery cells, based on laboratory-characterized models of these cells. Both of the above analytic study and Monte Carlo simulation study show a significant potential for improving battery pack SOC estimation accuracy through series hybridization.