Remaining useful life prediction of lithium-ion batteries
SOH prediction has been a popular topic of discussion and research in recent years, with many new developments around Machine Learning and Artificial Intelligence. Leveraging Machine Learning techniques requires large datasets. As such, this project does not only aim to highlight the developments in...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2022
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/163604 |
| _version_ | 1826124375400644608 |
|---|---|
| author | Er, Harrick Yue Hui |
| author2 | Xu Yan |
| author_facet | Xu Yan Er, Harrick Yue Hui |
| author_sort | Er, Harrick Yue Hui |
| collection | NTU |
| description | SOH prediction has been a popular topic of discussion and research in recent years, with many new developments around Machine Learning and Artificial Intelligence. Leveraging Machine Learning techniques requires large datasets. As such, this project does not only aim to highlight the developments in Machine Learning that has not yet been implemented to current conventional Recurrent Neural Networks, but also participate in a battery aging experiment to generate an aging dataset based off both dynamic and static load profiles. This poses a huge hurdle in Machine Learning techniques, as datasets are scarce and severely time-consuming to procure.
In this project, many current methods of SOH prediction and RUL prediction are discussed in this project, and SOH prediction models are trained to predict the RUL of a battery cell, using different Machine Learning techniques. Such techniques include the Regression model and the Long Short-Term Memory model, an improvement over the convention Recurrent Neural Network model. |
| first_indexed | 2024-10-01T06:19:36Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/163604 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T06:19:36Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1636042023-07-07T19:08:00Z Remaining useful life prediction of lithium-ion batteries Er, Harrick Yue Hui Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering SOH prediction has been a popular topic of discussion and research in recent years, with many new developments around Machine Learning and Artificial Intelligence. Leveraging Machine Learning techniques requires large datasets. As such, this project does not only aim to highlight the developments in Machine Learning that has not yet been implemented to current conventional Recurrent Neural Networks, but also participate in a battery aging experiment to generate an aging dataset based off both dynamic and static load profiles. This poses a huge hurdle in Machine Learning techniques, as datasets are scarce and severely time-consuming to procure. In this project, many current methods of SOH prediction and RUL prediction are discussed in this project, and SOH prediction models are trained to predict the RUL of a battery cell, using different Machine Learning techniques. Such techniques include the Regression model and the Long Short-Term Memory model, an improvement over the convention Recurrent Neural Network model. Bachelor of Engineering (Electrical and Electronic Engineering) 2022-12-12T07:32:43Z 2022-12-12T07:32:43Z 2022 Final Year Project (FYP) Er, H. Y. H. (2022). Remaining useful life prediction of lithium-ion batteries. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163604 https://hdl.handle.net/10356/163604 en A1219-212 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Er, Harrick Yue Hui Remaining useful life prediction of lithium-ion batteries |
| title | Remaining useful life prediction of lithium-ion batteries |
| title_full | Remaining useful life prediction of lithium-ion batteries |
| title_fullStr | Remaining useful life prediction of lithium-ion batteries |
| title_full_unstemmed | Remaining useful life prediction of lithium-ion batteries |
| title_short | Remaining useful life prediction of lithium-ion batteries |
| title_sort | remaining useful life prediction of lithium ion batteries |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/163604 |
| work_keys_str_mv | AT erharrickyuehui remainingusefullifepredictionoflithiumionbatteries |