State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles

被引:169
作者
Zahid, Taimoor [1 ,2 ]
Xu, Kun [1 ,2 ]
Li, Weimin [1 ,2 ,3 ]
Li, Chenming [4 ]
Li, Hongzhe [5 ]
机构
[1] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
[2] Univ Chinese Acad Sci, Shenzhen Coll Adv Technol, Shenzhen 518055, Peoples R China
[3] Chinese Acad Sci, Jining Inst Adv Technol, Jining 272000, Peoples R China
[4] Chinese Univ Hong Kong, Dept Elect Engn, Shatin, Hong Kong, Peoples R China
[5] China Univ Min & Technol, Sch Informat & Control Engn, Xuzhou, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Battery management system; Electric vehicle; Energy storage; Battery state estimation; State of charge; Machine learning; LITHIUM-ION BATTERIES; OF-CHARGE; OBSERVER DESIGN; NETWORK MODEL; CELL; MANAGEMENT; PARAMETERS; PACK;
D O I
10.1016/j.energy.2018.08.071
中图分类号
O414.1 [热力学];
学科分类号
070201 [理论物理];
摘要
State of charge estimation is one of the most critical factors to solve the key issues of monitoring and safety concerns of an electric vehicle power battery. In this paper, a state of charge estimation approach using subtractive clustering based neuro-fuzzy system is presented and evaluated by the simulation experiments using advanced vehicle simulator in comparison with back propagation neural network and Elman neural networks. Input parameters to model the state of charge estimation approach using subtractive clustering based neuro-fuzzy system are current, temperature, actual power loss, available and requested power, cooling air temperature and battery thermal factor. Data collected from 10 different drive cycles are utilized for the training and testing stages of the state of charge estimation model. Experimental results illustrated that the proposed model exhibits sufficient accuracy and outperforms both neural network and Elman neural network based models. Thus, the proposed model under different drive cycles show remarkable advancement in state of charge estimation with high potential to overcome the drawbacks in traditional methods and therefore provides an alternative approach in state of charge estimation. In addition, a sensitivity analysis is also performed to determine the importance of each input parameter on output i.e. state of charge. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码:871 / 882
页数:12
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