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To monitor and guarantee batteries of electric vehicles in normal operation, battery models should be established primarily for the further application in battery management system such as parameter identification and state estimation including state of charge (SOC), state of health (SOH) and so on. In this paper, an improved battery modeling method is proposed which is based on the recursive least square (RLS) algorithm employing an optimized objective function. The proposed modified objective function not only includes the normal sum of voltage error squares between measured voltage and model output voltage but also introduces a new variable representing the sum of first order difference error squares for both kinds of voltages. This specialty can undoubtedly guarantee better agreement for the measured output and the model output. The battery model used in this paper is selected to be the conventional second order equivalent circuit model.

This paper aims at accurately modeling the nonlinear hysteretic relationship between open circuit voltage (OCV) and state of charge (SOC) for LiFePO4 batteries. The OCV-SOC hysteresis model is based on the discrete Preisach approach which divides the Preisach triangle into finite squares. To determine the weight of each square, a linear function system is constructed including a series of linear equations formulated at every sample time. This function system can be solved by computer offline. When applying this approach online, the calculated square weight vector is pre-stored in advance. Then through multiple operations with hysteresis state vector of squares updated online at every sampling time, the SOC considering the influence of OCV-SOC hysteresis is predicted.