Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data
Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and co...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-09-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/15/9/553 |
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| author | Natthida Sukkam Tossapon Katongtung Pana Suttakul Yuttana Mona Witsarut Achariyaviriya Korrakot Yaibuathet Tippayawong Nakorn Tippayawong |
| author_facet | Natthida Sukkam Tossapon Katongtung Pana Suttakul Yuttana Mona Witsarut Achariyaviriya Korrakot Yaibuathet Tippayawong Nakorn Tippayawong |
| author_sort | Natthida Sukkam |
| collection | DOAJ |
| description | Electric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system. |
| format | Article |
| id | doaj-art-b7572850a32d43b8ac11116a45d5e8e6 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-b7572850a32d43b8ac11116a45d5e8e62025-08-20T01:55:33ZengMDPI AGInformation2078-24892024-09-0115955310.3390/info15090553Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving DataNatthida Sukkam0Tossapon Katongtung1Pana Suttakul2Yuttana Mona3Witsarut Achariyaviriya4Korrakot Yaibuathet Tippayawong5Nakorn Tippayawong6Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Industrial Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandDepartment of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, ThailandElectric vehicles (EVs) are alternatives to traditional combustion engine-powered vehicles. This work focuses on a thermal management system for battery EVs using liquid cooling and a machine learning (ML) model to predict their thermal-related health. Real-world data of EV operation, battery and cooling conditions were collected. Key influencing factors on the thermal-related health of batteries were identified. The ML model’s effectiveness was evaluated against experimental test data. The ML model proved effective in predicting and analyzing battery thermal health, suggesting its potential for use with the thermal management system.https://www.mdpi.com/2078-2489/15/9/553artificial neural networkbattery thermal managementclean energymultilayer perceptron |
| spellingShingle | Natthida Sukkam Tossapon Katongtung Pana Suttakul Yuttana Mona Witsarut Achariyaviriya Korrakot Yaibuathet Tippayawong Nakorn Tippayawong Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data Information artificial neural network battery thermal management clean energy multilayer perceptron |
| title | Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data |
| title_full | Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data |
| title_fullStr | Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data |
| title_full_unstemmed | Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data |
| title_short | Machine Learning Prediction of a Battery’s Thermal-Related Health Factor in a Battery Electric Vehicle Using Real-World Driving Data |
| title_sort | machine learning prediction of a battery s thermal related health factor in a battery electric vehicle using real world driving data |
| topic | artificial neural network battery thermal management clean energy multilayer perceptron |
| url | https://www.mdpi.com/2078-2489/15/9/553 |
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