A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries
The state of charge (SOC) is an important parameter for batteries, especially those for electric vehicles. Since SOC cannot be obtained directly by measurement, SOC estimation methods are required. In this paper, three model-based methods, including the extended particle filter (EPF), cubature parti...
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| Format: | Article |
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MDPI AG
2017-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/10/8/1149 |
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| author | Bizhong Xia Zhen Sun Ruifeng Zhang Deyu Cui Zizhou Lao Wei Wang Wei Sun Yongzhi Lai Mingwang Wang |
| author_facet | Bizhong Xia Zhen Sun Ruifeng Zhang Deyu Cui Zizhou Lao Wei Wang Wei Sun Yongzhi Lai Mingwang Wang |
| author_sort | Bizhong Xia |
| collection | DOAJ |
| description | The state of charge (SOC) is an important parameter for batteries, especially those for electric vehicles. Since SOC cannot be obtained directly by measurement, SOC estimation methods are required. In this paper, three model-based methods, including the extended particle filter (EPF), cubature particle filter (CPF), and unscented particle filter (UPF), are compared in terms of complexity, accuracy, and robustness. The second-order resistor-capacitor (RC) equivalent circuit model is selected as the circuit model of the lithium-ion battery, and the parameters of the model are obtained by off-line identification. Then, the City test is applied to compare the performance of the methods. The experimental results show that the EPF method exhibits low complexity and fast running speed, but poor accuracy and robustness. Compared with the EPF method, the complexity of the CPF and UPF methods is relatively high, but these models offer improved accuracy and robustness. |
| first_indexed | 2024-04-11T18:45:44Z |
| format | Article |
| id | doaj.art-e348f90caa4c439bbb55bf21c46256e5 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:45:44Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e348f90caa4c439bbb55bf21c46256e52022-12-22T04:08:51ZengMDPI AGEnergies1996-10732017-08-01108114910.3390/en10081149en10081149A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion BatteriesBizhong Xia0Zhen Sun1Ruifeng Zhang2Deyu Cui3Zizhou Lao4Wei Wang5Wei Sun6Yongzhi Lai7Mingwang Wang8Graduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaGraduate School at Shenzhen, Tsinghua University, Shenzhen 518055, ChinaSunwoda Electronic Co. Ltd., Shenzhen 518108, ChinaSunwoda Electronic Co. Ltd., Shenzhen 518108, ChinaSunwoda Electronic Co. Ltd., Shenzhen 518108, ChinaSunwoda Electronic Co. Ltd., Shenzhen 518108, ChinaThe state of charge (SOC) is an important parameter for batteries, especially those for electric vehicles. Since SOC cannot be obtained directly by measurement, SOC estimation methods are required. In this paper, three model-based methods, including the extended particle filter (EPF), cubature particle filter (CPF), and unscented particle filter (UPF), are compared in terms of complexity, accuracy, and robustness. The second-order resistor-capacitor (RC) equivalent circuit model is selected as the circuit model of the lithium-ion battery, and the parameters of the model are obtained by off-line identification. Then, the City test is applied to compare the performance of the methods. The experimental results show that the EPF method exhibits low complexity and fast running speed, but poor accuracy and robustness. Compared with the EPF method, the complexity of the CPF and UPF methods is relatively high, but these models offer improved accuracy and robustness.https://www.mdpi.com/1996-1073/10/8/1149state of chargelithium-ion batteryextended particle filter (EPF)cubature particle filter (CPF)unscented particle filter (UPF) |
| spellingShingle | Bizhong Xia Zhen Sun Ruifeng Zhang Deyu Cui Zizhou Lao Wei Wang Wei Sun Yongzhi Lai Mingwang Wang A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries Energies state of charge lithium-ion battery extended particle filter (EPF) cubature particle filter (CPF) unscented particle filter (UPF) |
| title | A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries |
| title_full | A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries |
| title_fullStr | A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries |
| title_full_unstemmed | A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries |
| title_short | A Comparative Study of Three Improved Algorithms Based on Particle Filter Algorithms in SOC Estimation of Lithium Ion Batteries |
| title_sort | comparative study of three improved algorithms based on particle filter algorithms in soc estimation of lithium ion batteries |
| topic | state of charge lithium-ion battery extended particle filter (EPF) cubature particle filter (CPF) unscented particle filter (UPF) |
| url | https://www.mdpi.com/1996-1073/10/8/1149 |
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