Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation
Abstract In order to guarantee the durability and security of electric vehicles (EV), the ageing estimation of lithium‐ion batteries (LIBs) is of great practical significance. Lithium inventory is an important indicator for assessing the LIB ageing process. Incremental capacity (IC), particle swarm...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Energy Systems Integration |
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| Online Access: | https://doi.org/10.1049/esi2.12163 |
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| _version_ | 1832582961855201280 |
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| author | Xingbo Zhang Kui Chen Zhou Long Yang Luo Yang Li Jiamin Zhu Kai Liu Guoqiang Gao Guangning Wu |
| author_facet | Xingbo Zhang Kui Chen Zhou Long Yang Luo Yang Li Jiamin Zhu Kai Liu Guoqiang Gao Guangning Wu |
| author_sort | Xingbo Zhang |
| collection | DOAJ |
| description | Abstract In order to guarantee the durability and security of electric vehicles (EV), the ageing estimation of lithium‐ion batteries (LIBs) is of great practical significance. Lithium inventory is an important indicator for assessing the LIB ageing process. Incremental capacity (IC), particle swarm optimisation (PSO) and support vector machine (SVM) are proposed to estimate the LIBs lithium inventory. Firstly, the IC curve that reflect the electrochemical reaction is analysed, and the middle peak of IC curve that characterises the material phase transition point is selected to represent the LIB lithium inventory. IC curve is smoothed by the Savitzky–Golay method to eliminate noise. Three features of the charging voltage curve are selected as the LIB health feature, and the correlation between three features and the lithium inventory is analysed by using the grey relation analysis method. Then, the mapping relationship between the lithium inventory and three health features is established based on SVM. PSO is used to optimise SVM kernel and penalty parameters to improve the precision of LIBs lithium inventory estimation. Finally, the proposed method is verified by three ageing experiments of LIBs. The results show that the proposed method can precisely estimate the lithium inventory of different LIBs. |
| format | Article |
| id | doaj-art-913781d745394280837f4c06011a8fa9 |
| institution | Kabale University |
| issn | 2516-8401 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Energy Systems Integration |
| spelling | doaj-art-913781d745394280837f4c06011a8fa92025-01-29T05:18:54ZengWileyIET Energy Systems Integration2516-84012024-12-016S176577510.1049/esi2.12163Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisationXingbo Zhang0Kui Chen1Zhou Long2Yang Luo3Yang Li4Jiamin Zhu5Kai Liu6Guoqiang Gao7Guangning Wu8Southwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaSouthwest Jiaotong University Chengdu Sichuan ChinaAbstract In order to guarantee the durability and security of electric vehicles (EV), the ageing estimation of lithium‐ion batteries (LIBs) is of great practical significance. Lithium inventory is an important indicator for assessing the LIB ageing process. Incremental capacity (IC), particle swarm optimisation (PSO) and support vector machine (SVM) are proposed to estimate the LIBs lithium inventory. Firstly, the IC curve that reflect the electrochemical reaction is analysed, and the middle peak of IC curve that characterises the material phase transition point is selected to represent the LIB lithium inventory. IC curve is smoothed by the Savitzky–Golay method to eliminate noise. Three features of the charging voltage curve are selected as the LIB health feature, and the correlation between three features and the lithium inventory is analysed by using the grey relation analysis method. Then, the mapping relationship between the lithium inventory and three health features is established based on SVM. PSO is used to optimise SVM kernel and penalty parameters to improve the precision of LIBs lithium inventory estimation. Finally, the proposed method is verified by three ageing experiments of LIBs. The results show that the proposed method can precisely estimate the lithium inventory of different LIBs.https://doi.org/10.1049/esi2.12163battery storage plantsparticle swarm optimisation |
| spellingShingle | Xingbo Zhang Kui Chen Zhou Long Yang Luo Yang Li Jiamin Zhu Kai Liu Guoqiang Gao Guangning Wu Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation IET Energy Systems Integration battery storage plants particle swarm optimisation |
| title | Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation |
| title_full | Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation |
| title_fullStr | Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation |
| title_full_unstemmed | Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation |
| title_short | Lithium inventory estimation of battery using incremental capacity analysis, support vector machine, particle swarm optimisation |
| title_sort | lithium inventory estimation of battery using incremental capacity analysis support vector machine particle swarm optimisation |
| topic | battery storage plants particle swarm optimisation |
| url | https://doi.org/10.1049/esi2.12163 |
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