Abstract
Electric Vehicle (EV) is an emerging trend in the automobile industry. The critical component of an EV is the battery. For accurate estimation of the state of charge (SOC) and maintaining the battery in operating region a battery management system (BMS) is implemented in EV. In literature various methods hava been proposed for SOC estimation, however, it has disadvantages such as accumulative error problem, high computation cost, complex algorithm. Another limitation is that the impact of factors such as the temperature, internal chemical composition and charging/discharging rate of battery on SOC is not considered. In view of this, the paper proposes a method to analyze the impact of these factors on SOC for its accurate estimation. For highlighting the impact of the temperature on the SOC, the temperature coefficient is proposed in this paper. A state space model of battery is developed by introducing a temperature coefficient in the existing battery model. For increasing the accuracy of estimating the SOC, an extended Kalman filter is used. The proposed model is implemented in the MATLAB environment and results show the impact of temperature on open circuit voltage (OCV) and SOC of the battery.
Published Version
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