Correlation between the model accuracy and model-based SOC estimation. (20th February 2017)

• Record Type: Journal Article
• Authors: Wang, Qianqian; Wang, Jiao; Zhao, Pengju; Kang, Jianqiang; Yan, Few; Du, Changqing
• Is Part Of: Electrochimica acta. Volume 228(2017)
• Page Start: 146
• Page End: 159
• Publication Date: 2017-02-20
• Subjects: Lithium-ion battery -- Battery models -- SOC estimation -- Correlation -- Regression; Electrochemistry -- Periodicals; Electrochemistry, Industrial -- Periodicals; 541.37
• DOI: 10.1016/j.electacta.2017.01.057
• Languages: English
• ISSNs: 0013-4686

Abstract

Abstract: State-of-charge (SOC) estimation is a core technology for battery management systems. Considerable progress has been achieved in the study of SOC estimation algorithms, especially the algorithm on the basis of Kalman filter to meet the increasing demand of model-based battery management systems. The Kalman filter weakens the influence of white noise and initial error during SOC estimation but cannot eliminate the existing error of the battery model itself. As such, the accuracy of SOC estimation is directly related to the accuracy of the battery model. Thus far, the quantitative relationship between model accuracy and model-based SOC estimation remains unknown. This study summarizes three equivalent circuit lithium-ion battery models, namely, Thevenin, PNGV, and DP models. The model parameters are identified through hybrid pulse power characterization test. The three models are evaluated, and SOC estimation conducted by EKF-Ah method under three operating conditions are quantitatively studied. The regression and correlation of the standard deviation and normalized RMSE are studied and compared between the model error and the SOC estimation error. These parameters exhibit a strong linear relationship. Results indicate that the model accuracy affects the SOC estimation accuracy mainly in two ways: dispersion of the frequency distribution of the error and the overall level of the error. On the basis of the relationship between model error and SOC estimation error, our study provides a strategy for selecting a suitable cell model to meet the requirements of SOC precision using Kalman filter.