Estimation of State-of-Energy for Electric Vehicles Based on the Identification and Prediction of Driving Condition

被引：0

作者：

Liu W. ^{[1
,2
]}

Wang L. ^{[1
]}

机构：

[1] Key Laboratory of Power Electronics and Electric Drives, Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing

[2] University of Chinese Academy of Sciences, Beijing

来源：

Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2018年 / 33卷 / 01期

关键词：

Electric vehicle model; Identification algorithm; Lithium-ion battery; Prediction algorithm; SOE estimation;

D O I：

10.19595/j.cnki.1000-6753.tces.161325

中图分类号：

学科分类号：

摘要：

State-of-energy (SOE) is an important index of the internal state of electric vehicle traction batteries that determines the range of electric vehicles directly and which is influenced by the driving condition significantly. In order to estimate SOE based on the driving condition, the SOE estimation algorithm, driving condition identification algorithm, driving condition prediction algorithm were studied in this paper. A battery state of residual energy (SOR) estimation algorithm based on battery model was established. A driving condition identification algorithm based on the informational entropy theory was built. A driving condition prediction algorithm was proposed with Markov chain theory. The battery predicted working condition schedule was achieved by modeling the electric vehicle system. In the end, the SOE estimation algorithm based on the identification and prediction of driving condition was achieved. Validation results show that the proposed SOE estimation algorithm was efficient. © 2018, Electrical Technology Press Co. Ltd. All right reserved.

引用

页码：17 / 25

页数：8

共 16 条

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