A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique

Abstract

In order to maximize the capacity/energy utilization and guarantee safe and reliable operation of battery packs used in electric vehicles, an accurate cell state-of-charge (SoC) estimator is an essential part. This paper tries to add three contributions to the existing literature. (1) An integrated battery system identification method for model order determination and parameter identification is proposed. In addition to being able to identify the model parameters, it can also locate an optimal balance between model complexity and prediction precision. (2) A radial basis function (RBF) neural network based uncertainty quantification algorithm has been proposed for constructing response surface approximate model (RSAM) of model bias function. Based on the RSAM, the average pack model can be applied to every single cell in battery pack and realize accurate terminal voltage prediction. (3) A systematic SoC estimation framework for multi-cell series-connected battery pack of electric vehicles using bias correction technique has been proposed. Finally, three cases with twelve lithium-ion polymer battery (LiPB) cells series-connected battery pack are used to verify and evaluate the proposed framework. The result indicates that with the proposed systematic estimation framework the maximum absolute SoC estimation error of all cells in the battery pack are less than 2%.