Online battery health diagnosis for electric vehicles based on DTW-XGBoost
With the rapid development of electric vehicles, electric vehicle battery health diagnosis has become a hot issue. In order to realize online battery health diagnosis, an online battery health diagnosis platform based on DTW-XGBoost was proposed. The feature extraction method of multi-source data fu...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722018509 |
| _version_ | 1797950706508890112 |
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| author | Na Yan Yan-Bing Yao Zeng-Dong Jia Lei Liu Cui-Ting Dai Zhi-Gao Li Zong-Hui Zhang Wei Li Lei Wang Peng-Fei Wang |
| author_facet | Na Yan Yan-Bing Yao Zeng-Dong Jia Lei Liu Cui-Ting Dai Zhi-Gao Li Zong-Hui Zhang Wei Li Lei Wang Peng-Fei Wang |
| author_sort | Na Yan |
| collection | DOAJ |
| description | With the rapid development of electric vehicles, electric vehicle battery health diagnosis has become a hot issue. In order to realize online battery health diagnosis, an online battery health diagnosis platform based on DTW-XGBoost was proposed. The feature extraction method of multi-source data fusion based on clustering was adopted. DTW clustering was used to perform data aggregation and feature extraction for real-time battery data during charging process, and XGBoost algorithm was used to establish SOH prediction model. Build an online battery health diagnosis platform including acquisition and control module, modeling and analysis module and application service module by using cloud platform to improve charging operation and maintenance management level. |
| first_indexed | 2024-04-10T22:19:24Z |
| format | Article |
| id | doaj.art-077949f4876f4237ad9f458b53acffa8 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-10T22:19:24Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-077949f4876f4237ad9f458b53acffa82023-01-18T04:31:35ZengElsevierEnergy Reports2352-48472022-11-018121128Online battery health diagnosis for electric vehicles based on DTW-XGBoostNa Yan0Yan-Bing Yao1Zeng-Dong Jia2Lei Liu3Cui-Ting Dai4Zhi-Gao Li5Zong-Hui Zhang6Wei Li7Lei Wang8Peng-Fei Wang9Corresponding author.; Shandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaShandong Luruan Digital Technology Co.,LTD. Smart Energy Branch, Jinan, 250000, ChinaWith the rapid development of electric vehicles, electric vehicle battery health diagnosis has become a hot issue. In order to realize online battery health diagnosis, an online battery health diagnosis platform based on DTW-XGBoost was proposed. The feature extraction method of multi-source data fusion based on clustering was adopted. DTW clustering was used to perform data aggregation and feature extraction for real-time battery data during charging process, and XGBoost algorithm was used to establish SOH prediction model. Build an online battery health diagnosis platform including acquisition and control module, modeling and analysis module and application service module by using cloud platform to improve charging operation and maintenance management level.http://www.sciencedirect.com/science/article/pii/S2352484722018509Electric vehicleBattery health diagnosisFeature extraction |
| spellingShingle | Na Yan Yan-Bing Yao Zeng-Dong Jia Lei Liu Cui-Ting Dai Zhi-Gao Li Zong-Hui Zhang Wei Li Lei Wang Peng-Fei Wang Online battery health diagnosis for electric vehicles based on DTW-XGBoost Energy Reports Electric vehicle Battery health diagnosis Feature extraction |
| title | Online battery health diagnosis for electric vehicles based on DTW-XGBoost |
| title_full | Online battery health diagnosis for electric vehicles based on DTW-XGBoost |
| title_fullStr | Online battery health diagnosis for electric vehicles based on DTW-XGBoost |
| title_full_unstemmed | Online battery health diagnosis for electric vehicles based on DTW-XGBoost |
| title_short | Online battery health diagnosis for electric vehicles based on DTW-XGBoost |
| title_sort | online battery health diagnosis for electric vehicles based on dtw xgboost |
| topic | Electric vehicle Battery health diagnosis Feature extraction |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722018509 |
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