A novel dual-scale cell state-of-charge estimation approach for series-connected battery pack used in electric vehicles

Abstract

Accurate estimations of cell state-of-charge for series-connected battery pack are remaining challenge due to the inhabited inconsistency characteristic. This paper tries to make three contributions. (1) A parametric modeling method is proposed for developing model-based SoC estimation approach. Based on the analysis for the mapping relationship between battery parameters and its SoC, a three-dimensional response surface open circuit voltage model is proposed for correcting erroneous SoC estimation. (2) An improved battery model considering model and parameter uncertainties is developed for modeling multiple cells in battery pack. A filtering process for selecting cell having “average capacity” and “average resistance” of battery pack has been developed to build the nominal battery model. Then a bias correction for single cells based on an average cell model is proposed for improving the expansibility of the nominal battery model. (3) A novel model-based dual-scale cell SoC estimator has been proposed. It uses micro and macro time scale to estimate the SoC of the selected cell and unselected cells respectively. Lastly, the proposed approach has been verified by two lithium-ion battery packs. The results show that the maximum estimation errors for cell voltage and SoC are less than 30 mV and 1% respectively against uncertain diving cycles and battery packs.