State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks
Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8840240 |
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| author | Li Zhang Min Zheng Dajun Du Yihuan Li Minrui Fei Yuanjun Guo Kang Li |
| author_facet | Li Zhang Min Zheng Dajun Du Yihuan Li Minrui Fei Yuanjun Guo Kang Li |
| author_sort | Li Zhang |
| collection | DOAJ |
| description | Lithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method. |
| format | Article |
| id | doaj-art-eb5eb38d2d104c82a854c967f94eae47 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-eb5eb38d2d104c82a854c967f94eae472025-02-03T06:07:41ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/88402408840240State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural NetworksLi Zhang0Min Zheng1Dajun Du2Yihuan Li3Minrui Fei4Yuanjun Guo5Kang Li6Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaUniversity of Leeds, Leeds LS2 9JT, UKShanghai Key Laboratory of Power Station Automation Technology, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, ChinaShenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, ChinaUniversity of Leeds, Leeds LS2 9JT, UKLithium-ion batteries have been widely used as energy storage systems and in electric vehicles due to their desirable balance of both energy and power densities as well as continual falling price. Accurate estimation of the state-of-charge (SOC) of a battery pack is important in managing the health and safety of battery packs. This paper proposes a compact radial basis function (RBF) neural model to estimate the state-of-charge (SOC) of lithium battery packs. Firstly, a suitable input set strongly correlated with the package SOC is identified from directly measured voltage, current, and temperature signals by a fast recursive algorithm (FRA). Secondly, a RBF neural model for battery pack SOC estimation is constructed using the FRA strategy to prune redundant hidden layer neurons. Then, the particle swarm optimization (PSO) algorithm is used to optimize the kernel parameters. Finally, a conventional RBF neural network model, an improved RBF neural model using the two stage method, and a least squares support vector machine (LSSVM) model are also used to estimate the battery SOC as a comparative study. Simulation results show that generalization error of SOC estimation using the novel RBF neural network model is less than half of that using other methods. Furthermore, the model training time is much less than the LSSVM method and the improved RBF neural model using the two-stage method.http://dx.doi.org/10.1155/2020/8840240 |
| spellingShingle | Li Zhang Min Zheng Dajun Du Yihuan Li Minrui Fei Yuanjun Guo Kang Li State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks Complexity |
| title | State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks |
| title_full | State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks |
| title_fullStr | State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks |
| title_full_unstemmed | State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks |
| title_short | State-of-Charge Estimation of Lithium-Ion Battery Pack Based on Improved RBF Neural Networks |
| title_sort | state of charge estimation of lithium ion battery pack based on improved rbf neural networks |
| url | http://dx.doi.org/10.1155/2020/8840240 |
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