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State of charge estimation of Li-ion batteries in electric vehicle based on radial-basis-function neural network |
| Bi Jun (毕军)a b, Shao Sai (邵赛)b, Guan Wei (关伟)b, Wang Lu (王璐 )b |
a School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China;
b MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China |
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Abstract The on-line estimation of the state of charge (SOC) of the batteries is important for reliable running of the pure electric vehicle in practice. Because the nonlinear feature exists in the batteries and the radial-basis-function neural network (RBF NN) has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation. Finally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.
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Received: 22 February 2012
Revised: 21 May 2012
Accepted manuscript online:
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PACS:
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88.85.Hj
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(Electric vehicles (EVs))
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88.80.ff
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(Batteries)
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07.05.Mh
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(Neural networks, fuzzy logic, artificial intelligence)
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| Fund: Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303) and the Beijing Municipal Science & Technology Project, China (Grant No. Z111100064311001). |
Corresponding Authors:
Bi Jun
E-mail: bilinghc@163.com
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Cite this article:
Bi Jun (毕军), Shao Sai (邵赛), Guan Wei (关伟), Wang Lu (王璐 ) State of charge estimation of Li-ion batteries in electric vehicle based on radial-basis-function neural network 2012 Chin. Phys. B 21 118801
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