Lithium-Ion Battery Life Prediction Using Deep Transfer Learning
Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/10/12/434 |
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| author | Wen Zhang R. S. B. Pranav Rui Wang Cheonghwan Lee Jie Zeng Migyung Cho Jaesool Shim |
| author_facet | Wen Zhang R. S. B. Pranav Rui Wang Cheonghwan Lee Jie Zeng Migyung Cho Jaesool Shim |
| author_sort | Wen Zhang |
| collection | DOAJ |
| description | Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential to ensure device safety and reliability. Conventional RUL prediction methods typically rely on regression analysis, signal processing, and machine learning techniques to assess battery conditions such as charge/discharge cycles, voltage, temperature, and durability. Although effective, these approaches are constrained by their dependence on large amounts of labeled data and the necessity for complex feature engineering to capture battery physical characteristics. In this study, we propose an approach that employs deep transfer learning to address these limitations. By leveraging pretrained model weights, the proposed method significantly improves the efficiency and accuracy of RUL prediction even under limited training data conditions. Furthermore, we investigate the impact of external environmental factors and physical battery characteristics on RUL prediction precision, thereby contributing to a more robust and reliable prediction framework. |
| format | Article |
| id | doaj-art-c8d875bb67f643bc9b1bb69e59a1b9ee |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-c8d875bb67f643bc9b1bb69e59a1b9ee2024-12-27T14:10:39ZengMDPI AGBatteries2313-01052024-12-01101243410.3390/batteries10120434Lithium-Ion Battery Life Prediction Using Deep Transfer LearningWen Zhang0R. S. B. Pranav1Rui Wang2Cheonghwan Lee3Jie Zeng4Migyung Cho5Jaesool Shim6School of Mechanical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of KoreaSchool of Mechanical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of KoreaSchool of Mechanical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of KoreaKorea Textile Machinery Convergence Research Institute, 27, Sampung-ro, Gyeongsan-si 38542, Republic of KoreaSchool of Mechanical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of KoreaDepartment of Computer & Media Engineering, Tongmyong University, Busan 48520, Republic of KoreaSchool of Mechanical Engineering, Yeungnam University, Gyeongsan-si 38541, Republic of KoreaLithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential to ensure device safety and reliability. Conventional RUL prediction methods typically rely on regression analysis, signal processing, and machine learning techniques to assess battery conditions such as charge/discharge cycles, voltage, temperature, and durability. Although effective, these approaches are constrained by their dependence on large amounts of labeled data and the necessity for complex feature engineering to capture battery physical characteristics. In this study, we propose an approach that employs deep transfer learning to address these limitations. By leveraging pretrained model weights, the proposed method significantly improves the efficiency and accuracy of RUL prediction even under limited training data conditions. Furthermore, we investigate the impact of external environmental factors and physical battery characteristics on RUL prediction precision, thereby contributing to a more robust and reliable prediction framework.https://www.mdpi.com/2313-0105/10/12/434deep transfer learningvgg16lithium-ion batteryremaining useful lifeprediction model |
| spellingShingle | Wen Zhang R. S. B. Pranav Rui Wang Cheonghwan Lee Jie Zeng Migyung Cho Jaesool Shim Lithium-Ion Battery Life Prediction Using Deep Transfer Learning Batteries deep transfer learning vgg16 lithium-ion battery remaining useful life prediction model |
| title | Lithium-Ion Battery Life Prediction Using Deep Transfer Learning |
| title_full | Lithium-Ion Battery Life Prediction Using Deep Transfer Learning |
| title_fullStr | Lithium-Ion Battery Life Prediction Using Deep Transfer Learning |
| title_full_unstemmed | Lithium-Ion Battery Life Prediction Using Deep Transfer Learning |
| title_short | Lithium-Ion Battery Life Prediction Using Deep Transfer Learning |
| title_sort | lithium ion battery life prediction using deep transfer learning |
| topic | deep transfer learning vgg16 lithium-ion battery remaining useful life prediction model |
| url | https://www.mdpi.com/2313-0105/10/12/434 |
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