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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
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| Series: | Batteries |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2313-0105/10/12/434 |
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| Summary: | 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. |
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| ISSN: | 2313-0105 |