A comprehensive equivalent circuit model of Li-ion batteries for SOC estimation in electric vehicles based on parametric sensitivity analysis

Abstract

On-board estimation of battery state of charge (SOC) plays a critical role in various functionalities performed by battery management systems (BMS) applicable to electric vehicles (EVs). The traditional approach of SOC estimation uses offline identification of battery model parameters as a function of SOC. It requires an update of SOC-dependent parameters in EVs run-time. Since battery dynamics or model parameters change as a function of state of health (SOH), identifying and updating these parameters online is a crucial challenge. Researchers have recently presented many techniques of online state estimation, but they are unsuitable for deployment due to constraints from the embedded point of view. This article presents a detailed investigation and analysis of battery model parameter sensitivity concerning the entire range of SOC and over the life cycle, followed by simplified model-based SOC estimation. First, the second-order equivalent circuit model with hysteresis is developed and validated. The sensitivity of model parameters is investigated using a state-of-the-art one-factor-at-a-time (OFAT) approach to classify parameters as high and low sensitive and to propose a simplified model considering the compromise between accuracy and embedded computations. The extended Kalman filter-based SOC estimation at different SOHs is designed. In the case of lithium-ion NCA battery, the proposed simplified model yields maximum SOC error of 2%, 1.47%, and 3.27% at SOH levels 92.12%, 89.36%, and 85.96%, respectively. Similarly, for lithium-ion LFP battery, the proposed simplified model yields a maximum SOC error of 1.5% when SOH is 100%, which demonstrates how a simplified model provides satisfactory results compared to traditional methods and is suitable for embedded deployment due to reduced computations in run-time.