Mechanism and Data-Driven Fusion SOC Estimation

Aijun Tian, Weidong Xue, Chen Zhou, Yongquan Zhang and Haiying Dong ()
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Aijun Tian: Lanzhou Haihong Technology Co., Ltd., Lanzhou 730050, China
Weidong Xue: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Chen Zhou: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yongquan Zhang: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Haiying Dong: College of New Energy and Power Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China

Energies, 2024, vol. 17, issue 19, 1-16

Abstract: An accurate assessment of the state of charge (SOC) of electric vehicle batteries is critical for implementing frequency regulation and peak shaving. This study proposes mechanism- and data-driven SOC fusion calculation methods. First, a second-order Thevenin battery model is developed to obtain the physical parameters of the battery. Second, data from the Thevenin battery model and data from four standard cycling conditions in the electric vehicle industry are added to the dataset of the feed-forward neural network data-driven model to construct the test and training sets of the data-driven model. Finally, the error of the mechanism and data-driven fusion modeling method is quantitatively analyzed by comparing the estimation error of the method for the battery SOC at different temperatures with the accuracy of the data-driven SOC estimation method. The simulation results show that the root mean square error, the mean age absolute error, and the maximum error of mechanism and data-driven method for the estimation error of battery SOC are lower than those of the data-driven method by 0.9%, 0.65%, and 1.3%, respectively. The results show that the mechanism and data-driven fusion SOC estimation method has better generalization performance and higher SOC estimation accuracy.

Keywords: SOC estimation; data driven; mechanism; dataset training (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: 2024
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