A multi‐model real covariance‐based battery state‐of‐charge fusion estimation method for electric vehicles using ordered weighted averaging operator

Abstract

Uncertainty prediction of lithium-ion battery state-of-charge (SOC) is key for electric vehicle battery management systems. Aiming at the shortcomings of a single equivalent circuit model (ECM) and traditional SOC fusion estimation algorithms, this paper proposes a new multi-model SOC fusion method. First, three sub-models are established. Second, an adaptive extended Kalman filter is applied to each sub-model in parallel to predict the battery terminal voltage and SOC simultaneously. Then, based on the ordered weighted averaging (OWA) operator theory, the real covariance matrix of the output voltage error of each model is obtained, and the weight factor of each sub-model is calculated using this matrix. Finally, the SOC estimation of each model is weighted and synthesized to realize the SOC fusion estimation. The experimental results show that the maximum absolute error of the multi-model SOC fusion estimation based on the OWA operator is close to the optimal value of a single model, whether it is the fusion of three ECMs or two ECMs with a degraded electrochemical model, and the multi-model SOC fusion estimation based on OWA operator has better robustness than the single model SOC estimation.