Online State-of-Charge Estimation Based on the Gas–Liquid Dynamics Model for Li(NiMnCo)O 2 Battery

Haobin Jiang, Xijia Chen, Yifu Liu, Qian Zhao, Huanhuan Li and Biao Chen
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Haobin Jiang: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Xijia Chen: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Yifu Liu: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Qian Zhao: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Huanhuan Li: Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China
Biao Chen: School of Automotive and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China

Energies, 2021, vol. 14, issue 2, 1-19

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)O 2 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.

Keywords: state-of-charge estimation; gas–liquid dynamics model; online parameter identification; lithium-ion battery (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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