An intelligent fusion estimation method for state of charge estimation of lithium-ion batteries

The state of charge estimation of lithium-ion batteries in electric vehicles is critical in battery management systems. Using a single model and algorithm for state of charge estimation has some limitations, therefore fusion algorithms are investigated by researchers. However, among many fusion methods, there are no basis for judgement concerning which models and algorithms to be chosen for fusion in different discharge intervals in order to obtain the desired results. To address this problem, this study proposes a three-interval fusion method for state of charge estimation based on fitness values. Firstly, three different types of equivalent circuit models and six filtering algorithms are built in this study. Next, the whole state of charge is divided into three intervals in discharging process and the estimation error characteristics of each interval are extracted. Then, assessment of model-algorithm fits through fitness values. Finally, the combinations of better fitness values in each interval are selected and the three-interval fusion method for estimation of state of charge is achieved by adaptive weighted average. The results show that the maximum absolute error of the three-interval fusion method is less than 0.5 % and the accuracy is improved by 40 % compared to other fusion methods.