Data-driven battery health monitoring
With the development of machine learning technology, data-driven methods are widely applied in researching complex systerms. The extreme learning machine (ELM) is one of the most advanced data-driven methods nowadays because of its high accuracy and efficiency. Besides, as the key factors in electri...
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| 格式: | Thesis-Master by Coursework |
| 語言: | English |
| 出版: |
Nanyang Technological University
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/143507 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | With the development of machine learning technology, data-driven methods are widely applied in researching complex systerms. The extreme learning machine (ELM) is one of the most advanced data-driven methods nowadays because of its high accuracy and efficiency. Besides, as the key factors in electric vehicles, the battery degradation is hard to model and estimate in real application because the battery is a complicated system. Thus, this paper uses ELM to solve the battery health monitoring problem. |
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