SOC estimation based on data driven exteaded Kalman filter algorithm for power battery of electric vehicle and plug-in electric vehicle

Abstract

State of charge (SOC) estimation has always been a hot topic in the field of both power battery and new energy vehicle (electric vehicle (EV), plug-in electric vehicle (PHEV) and so on). In this work, aiming at the contradiction problem between the exact requirements of EKF (extended Kalman filter) algorithm for the battery model and the dynamic requirements of battery mode in life cycle or a charge and discharge period, a completely data-driven SOC estimation algorithm based on EKF algorithm is proposed. The innovation of this algorithm lies in that the EKF algorithm is used to get the SOC accurate estimate of the power battery online with using the observable voltage and current data information of the power battery and without knowing the internal parameter variation of the power battery. Through the combination of data-based and model-based SOC estimation method, the new method can avoid high accumulated error of traditional data-driven SOC algorithms and high dependence on battery model of most of the existing model-based SOC estimation methods, and is more suitable for the life cycle SOC estimation of the power battery operating in a complex and ever-changing environment (such as in an EV or PHEV). A series of simulation experiments illustrate better robustness and practicability of the proposed algorithm.

Keywords

state of charge / extended Kalman filter / autoregressive model / power battery

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