Enhancing the Performance Index of Battery Management System Using Nonlinear Approach

Battery technology is an important component of an electric vehicle (EV). The modelling and state estimation of the battery are very relevant in the theoretical and practical operation. In addition to that, it helps to increase the battery life span, maximize its output, and reduces its cost. The state of charge (SOC) is a key parameter in the Battery Management System (BMS) because it helps to operate the battery in safe operating conditions. However, because of strong nonlinear characteristics of batteries, the internal states of the batteries cannot be measured directly. In the literature, various methods for SOC estimation have been developed. Though some of these methods are widely used in industries, some of these methods have their limitations like heavy computation cost, complex behaviour, etc. These methods fail to consider various parameters that affect the SOC of the battery. Thus, to address this issue the paper proposes a method which assists in the accurate estimation of SOC considering the parameters like temperature, aging, and charging-discharging rate of the battery. In this paper, a novel approach for the estimation of SOC of batteries with the modified state-space model using a Nonlinear Observer (NLO) is presented. The state equations derived from the second-order circuit model (Thevenin’s model) is used to simulate a battery’s complex dynamical behaviours. Further, the controller is designed using feedback linearization approach for optimal charging of the battery. The results obtained in MATLAB show a small error in estimated and actual SOC, along with this it highlights the effect of the temperature on SOC.