Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning
Predicting the performance of rechargeable batteries in real time is of great importance to battery research and industrial production, and hence has been a long pursuit. Previously, sophisticated apparatus is required to measure indicator properties of performance, while machine learning approaches...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-12-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.201900102 |
| _version_ | 1828484212831813632 |
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| author | Xi Chen Luhan Ye Yichao Wang Xin Li |
| author_facet | Xi Chen Luhan Ye Yichao Wang Xin Li |
| author_sort | Xi Chen |
| collection | DOAJ |
| description | Predicting the performance of rechargeable batteries in real time is of great importance to battery research and industrial production, and hence has been a long pursuit. Previously, sophisticated apparatus is required to measure indicator properties of performance, while machine learning approaches based on feature engineering procedures require a priori expertise that is challenged by the complicated environment of real‐world applications. Here, for a more effective real‐time prediction of battery life and failure, a novel end‐to‐end unsupervised machine learning approach is shown; this approach is free from feature engineering and uses only the raw images of the charge–discharge voltage profiles. This model enables unsupervised real‐time automatic extraction of latent physical factors that control the performance of Na‐ion batteries to classify good or bad cycling performance by using only the voltage profile of the first cycle. This model can also monitor the safety of Li‐metal battery systems by giving warnings when the battery is approaching a failure. With the beyond expert‐level prediction ability, the abovementioned framework can be a promising prototype to further develop and enable high accuracy predictions of battery performance for real‐world applications in the future. |
| first_indexed | 2024-12-11T08:51:52Z |
| format | Article |
| id | doaj.art-b67a3684012b4ddb95eb4ace9966f52f |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-12-11T08:51:52Z |
| publishDate | 2019-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-b67a3684012b4ddb95eb4ace9966f52f2022-12-22T01:13:59ZengWileyAdvanced Intelligent Systems2640-45672019-12-0118n/an/a10.1002/aisy.201900102Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine LearningXi Chen0Luhan Ye1Yichao Wang2Xin Li3John. A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USAJohn. A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USAJohn. A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USAJohn. A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USAPredicting the performance of rechargeable batteries in real time is of great importance to battery research and industrial production, and hence has been a long pursuit. Previously, sophisticated apparatus is required to measure indicator properties of performance, while machine learning approaches based on feature engineering procedures require a priori expertise that is challenged by the complicated environment of real‐world applications. Here, for a more effective real‐time prediction of battery life and failure, a novel end‐to‐end unsupervised machine learning approach is shown; this approach is free from feature engineering and uses only the raw images of the charge–discharge voltage profiles. This model enables unsupervised real‐time automatic extraction of latent physical factors that control the performance of Na‐ion batteries to classify good or bad cycling performance by using only the voltage profile of the first cycle. This model can also monitor the safety of Li‐metal battery systems by giving warnings when the battery is approaching a failure. With the beyond expert‐level prediction ability, the abovementioned framework can be a promising prototype to further develop and enable high accuracy predictions of battery performance for real‐world applications in the future.https://doi.org/10.1002/aisy.201900102battery failure predictionbattery performance predictionmachine learning |
| spellingShingle | Xi Chen Luhan Ye Yichao Wang Xin Li Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning Advanced Intelligent Systems battery failure prediction battery performance prediction machine learning |
| title | Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning |
| title_full | Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning |
| title_fullStr | Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning |
| title_full_unstemmed | Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning |
| title_short | Beyond Expert‐Level Performance Prediction for Rechargeable Batteries by Unsupervised Machine Learning |
| title_sort | beyond expert level performance prediction for rechargeable batteries by unsupervised machine learning |
| topic | battery failure prediction battery performance prediction machine learning |
| url | https://doi.org/10.1002/aisy.201900102 |
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