Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model
Lithium plating in lithium-ion batteries (LIBs) is one of the main causes of safety accidents in electric vehicles (EVs). The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. Therefore, this paper proposes an intelligent li...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-02-01
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| Series: | Green Energy and Intelligent Transportation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773153724000197 |
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| author | Guangying Zhu Jianguo Chen Xuyang Liu Tao Sun Xin Lai Yuejiu Zheng Yue Guo Rohit Bhagat |
| author_facet | Guangying Zhu Jianguo Chen Xuyang Liu Tao Sun Xin Lai Yuejiu Zheng Yue Guo Rohit Bhagat |
| author_sort | Guangying Zhu |
| collection | DOAJ |
| description | Lithium plating in lithium-ion batteries (LIBs) is one of the main causes of safety accidents in electric vehicles (EVs). The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. Therefore, this paper proposes an intelligent lithium plating detection and early warning method for LIBs based on the random forest model. This method can accurately detect lithium plating during the charging process of LIBs, and play an early warning role according to the detection results. First, pulse charging experiments of LIBs, including normal and lithium plating charging tests, were completed and validated using in situ characterization methods. Second, the normalized internal resistance from the pulse charging test is used to detect lithium plating in LIBs. Third, a lithium plating feature extraction method is proposed to address the lack of useful lithium plating information for LIBs during the charging process. Finally, the Random Forest machine learning technique is used to classify and predict the lithium plating of LIBs. The model validation results show that the detection accuracy of lithium plating is greater than 97.2%. This is of significance for the study of intelligent lithium plating detection algorithms for LIBs. |
| format | Article |
| id | doaj-art-c38f584135394ba797f0f0c97fe6de5d |
| institution | Kabale University |
| issn | 2773-1537 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Green Energy and Intelligent Transportation |
| spelling | doaj-art-c38f584135394ba797f0f0c97fe6de5d2025-01-09T06:17:06ZengElsevierGreen Energy and Intelligent Transportation2773-15372025-02-0141100167Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest modelGuangying Zhu0Jianguo Chen1Xuyang Liu2Tao Sun3Xin Lai4Yuejiu Zheng5Yue Guo6Rohit Bhagat7School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; Corresponding author.School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, ChinaCentre for E-Mobility and Clean Growth Research, Coventry University, Coventry, CV1 5FB, United KingdomCentre for E-Mobility and Clean Growth Research, Coventry University, Coventry, CV1 5FB, United KingdomLithium plating in lithium-ion batteries (LIBs) is one of the main causes of safety accidents in electric vehicles (EVs). The study of intelligent machine learning-based lithium plating detection and warning algorithms for LIBs is of great importance. Therefore, this paper proposes an intelligent lithium plating detection and early warning method for LIBs based on the random forest model. This method can accurately detect lithium plating during the charging process of LIBs, and play an early warning role according to the detection results. First, pulse charging experiments of LIBs, including normal and lithium plating charging tests, were completed and validated using in situ characterization methods. Second, the normalized internal resistance from the pulse charging test is used to detect lithium plating in LIBs. Third, a lithium plating feature extraction method is proposed to address the lack of useful lithium plating information for LIBs during the charging process. Finally, the Random Forest machine learning technique is used to classify and predict the lithium plating of LIBs. The model validation results show that the detection accuracy of lithium plating is greater than 97.2%. This is of significance for the study of intelligent lithium plating detection algorithms for LIBs.http://www.sciencedirect.com/science/article/pii/S2773153724000197Lithium-ion batteryLithium plating detectionFeature extractionRandom forest algorithm |
| spellingShingle | Guangying Zhu Jianguo Chen Xuyang Liu Tao Sun Xin Lai Yuejiu Zheng Yue Guo Rohit Bhagat Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model Green Energy and Intelligent Transportation Lithium-ion battery Lithium plating detection Feature extraction Random forest algorithm |
| title | Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model |
| title_full | Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model |
| title_fullStr | Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model |
| title_full_unstemmed | Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model |
| title_short | Intelligent lithium plating detection and prediction method for Li-ion batteries based on random forest model |
| title_sort | intelligent lithium plating detection and prediction method for li ion batteries based on random forest model |
| topic | Lithium-ion battery Lithium plating detection Feature extraction Random forest algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2773153724000197 |
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