A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehicles
With the aging of Lithium-ion batteries (LIBs) of electric vehicles in the near future, research on the second use of retired LIBs is becoming more and more critical. The classification method of the retired LIBs is challenging before the second use due to large cell variations. This paper proposes...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2020-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484719313903 |
| _version_ | 1818601767743520768 |
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| author | Ping Zhou Zhonglin He Tingting Han Xiangjun Li Xin Lai Liqin Yan Tiaolin Lv Jingying Xie Yuejiu Zheng |
| author_facet | Ping Zhou Zhonglin He Tingting Han Xiangjun Li Xin Lai Liqin Yan Tiaolin Lv Jingying Xie Yuejiu Zheng |
| author_sort | Ping Zhou |
| collection | DOAJ |
| description | With the aging of Lithium-ion batteries (LIBs) of electric vehicles in the near future, research on the second use of retired LIBs is becoming more and more critical. The classification method of the retired LIBs is challenging before the second use due to large cell variations. This paper proposes a rapid classification method based on battery capacity and internal resistance, because batteries with different capacities and internal resistances have different voltage curves during charge/discharge. First, the piecewise linear fitting method established by the specified tested batteries with capacities and their corresponding characteristic voltages is used to sort the batteries. Then combined with the nonlinear function approximation ability of the radial basis function neural network (RBFNN) model, battery capacity and internal resistance are predicted after the model training. 108 cells are used for the simulation classification with experimental classification performed on 12 cells. The results prove that the classification method is accurate. |
| first_indexed | 2024-12-16T12:56:37Z |
| format | Article |
| id | doaj.art-595ba5a56ee441a483cb69d48292346d |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-16T12:56:37Z |
| publishDate | 2020-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-595ba5a56ee441a483cb69d48292346d2022-12-21T22:31:00ZengElsevierEnergy Reports2352-48472020-11-016672683A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehiclesPing Zhou0Zhonglin He1Tingting Han2Xiangjun Li3Xin Lai4Liqin Yan5Tiaolin Lv6Jingying Xie7Yuejiu Zheng8College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR ChinaShanghai Institute of Space Power-Sources, Shanghai 200245, PR ChinaElectrical Engineering and New Material Department, China Electric Power Research Institute, Beijing 100192, PR ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR ChinaShanghai Institute of Space Power-Sources, Shanghai 200245, PR ChinaShanghai Institute of Space Power-Sources, Shanghai 200245, PR ChinaShanghai Institute of Space Power-Sources, Shanghai 200245, PR ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, PR China; Corresponding author.With the aging of Lithium-ion batteries (LIBs) of electric vehicles in the near future, research on the second use of retired LIBs is becoming more and more critical. The classification method of the retired LIBs is challenging before the second use due to large cell variations. This paper proposes a rapid classification method based on battery capacity and internal resistance, because batteries with different capacities and internal resistances have different voltage curves during charge/discharge. First, the piecewise linear fitting method established by the specified tested batteries with capacities and their corresponding characteristic voltages is used to sort the batteries. Then combined with the nonlinear function approximation ability of the radial basis function neural network (RBFNN) model, battery capacity and internal resistance are predicted after the model training. 108 cells are used for the simulation classification with experimental classification performed on 12 cells. The results prove that the classification method is accurate.http://www.sciencedirect.com/science/article/pii/S2352484719313903Retired lithium-ion batteryRapid classificationCapacity estimationBattery packRBFNN |
| spellingShingle | Ping Zhou Zhonglin He Tingting Han Xiangjun Li Xin Lai Liqin Yan Tiaolin Lv Jingying Xie Yuejiu Zheng A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehicles Energy Reports Retired lithium-ion battery Rapid classification Capacity estimation Battery pack RBFNN |
| title | A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehicles |
| title_full | A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehicles |
| title_fullStr | A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehicles |
| title_full_unstemmed | A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehicles |
| title_short | A rapid classification method of the retired LiCoxNiyMn 1−x−yO2 batteries for electric vehicles |
| title_sort | rapid classification method of the retired licoxniymn 1 x yo2 batteries for electric vehicles |
| topic | Retired lithium-ion battery Rapid classification Capacity estimation Battery pack RBFNN |
| url | http://www.sciencedirect.com/science/article/pii/S2352484719313903 |
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