Abstract
Accurately estimating the online state-of-charge (SOC) of the battery is one of the crucial issues of the battery management system. In this paper, the gas–liquid dynamics (GLD) battery model with direct temperature input is selected to model Li(NiMnCo)O2 battery. The extended Kalman Filter (EKF) algorithm is elaborated to couple the offline model and online model to achieve the goal of quickly eliminating initial errors in the online SOC estimation. An implementation of the hybrid pulse power characterization test is performed to identify the offline parameters and determine the open-circuit voltage vs. SOC curve. Apart from the standard cycles including Constant Current cycle, Federal Urban Driving Schedule cycle, Urban Dynamometer Driving Schedule cycle and Dynamic Stress Test cycle, a combined cycle is constructed for experimental validation. Furthermore, the study of the effect of sampling time on estimation accuracy and the robustness analysis of the initial value are carried out. The results demonstrate that the proposed method realizes the accurate estimation of SOC with a maximum mean absolute error at 0.50% in five working conditions and shows strong robustness against the sparse sampling and input error.
Highlights
With the intensification of the energy crisis and environmental pollution, the research of electric vehicles (EVs) has become a strategic project to hasten progress toward sustainable development throughout the world [1]
Xiong et al [25] proposed an adaptive extended Kalman filter (AEKF) to overcome the drawback that traditional KF is too dependent on the good estimation of the process and measurement noise matrix Q and R
The increase of the sampling would lead to the gradual growth of MAE, the MAEs under Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS) and combined condition only increase within 0.05% when the sampling time increases to 20 s and even the MAE under Constant Current (CC) drops during this period
Summary
With the intensification of the energy crisis and environmental pollution, the research of electric vehicles (EVs) has become a strategic project to hasten progress toward sustainable development throughout the world [1]. Xiong et al [25] proposed an adaptive extended Kalman filter (AEKF) to overcome the drawback that traditional KF is too dependent on the good estimation of the process and measurement noise matrix Q and R This method avoids that uncertainty of the initial noise information results in the degrading the estimation performance but it lacks analysis for cell parameter variances under different temperatures. The above facts mean that there may be a variety of working conditions in one discharge cycle corresponding to the battery of an electric vehicle in real situations To address this issue, Du et al [30] collected the data from the actual driving condition to verify their SOC estimation. It is important to ensure the accurate estimation of SOC under the condition of sparse sampling, that is, increasing the sampling period to reduce costs and achieve practical applications
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