Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning
Abstract Real‐time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery‐powered devices. In this study, a methodology to predict the entire constant‐current cycling curve with limited input information that can be colle...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-09-01
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| Series: | Advanced Science |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/advs.202301737 |
| _version_ | 1797684953772720128 |
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| author | Laisuo Su Shuyan Zhang Alan J. H. McGaughey B. Reeja‐Jayan Arumugam Manthiram |
| author_facet | Laisuo Su Shuyan Zhang Alan J. H. McGaughey B. Reeja‐Jayan Arumugam Manthiram |
| author_sort | Laisuo Su |
| collection | DOAJ |
| description | Abstract Real‐time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery‐powered devices. In this study, a methodology to predict the entire constant‐current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO2‐based batteries at a constant C‐rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO2‐based) using open‐access datasets. The prediction error of the charge curves for the LiCoO2‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications. |
| first_indexed | 2024-03-12T00:37:20Z |
| format | Article |
| id | doaj.art-9a91907bc98443fca3185c36e98b052c |
| institution | Directory Open Access Journal |
| issn | 2198-3844 |
| language | English |
| last_indexed | 2024-03-12T00:37:20Z |
| publishDate | 2023-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Science |
| spelling | doaj.art-9a91907bc98443fca3185c36e98b052c2023-09-15T09:28:59ZengWileyAdvanced Science2198-38442023-09-011026n/an/a10.1002/advs.202301737Battery Charge Curve Prediction via Feature Extraction and Supervised Machine LearningLaisuo Su0Shuyan Zhang1Alan J. H. McGaughey2B. Reeja‐Jayan3Arumugam Manthiram4Materials Science and Engineering Program & Texas Materials Institute The University of Texas at Austin AustinTX78712‐1591USADepartment of Mechanical Engineering Carnegie Mellon University PittsburghPA15213USADepartment of Mechanical Engineering Carnegie Mellon University PittsburghPA15213USADepartment of Mechanical Engineering Carnegie Mellon University PittsburghPA15213USAMaterials Science and Engineering Program & Texas Materials Institute The University of Texas at Austin AustinTX78712‐1591USAAbstract Real‐time onboard state monitoring and estimation of a battery over its lifetime is indispensable for the safe and durable operation of battery‐powered devices. In this study, a methodology to predict the entire constant‐current cycling curve with limited input information that can be collected in a short period of time is developed. A total of 10 066 charge curves of LiNiO2‐based batteries at a constant C‐rate are collected. With the combination of a feature extraction step and a multiple linear regression step, the method can accurately predict an entire battery charge curve with an error of < 2% using only 10% of the charge curve as the input information. The method is further validated across other battery chemistries (LiCoO2‐based) using open‐access datasets. The prediction error of the charge curves for the LiCoO2‐based battery is around 2% with only 5% of the charge curve as the input information, indicating the generalization of the developed methodology for predicting battery cycling curves. The developed method paves the way for fast onboard health status monitoring and estimation for batteries during practical applications.https://doi.org/10.1002/advs.202301737batteriescharge curvesfeature extractionpredictionmachine learning |
| spellingShingle | Laisuo Su Shuyan Zhang Alan J. H. McGaughey B. Reeja‐Jayan Arumugam Manthiram Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning Advanced Science batteries charge curves feature extraction prediction machine learning |
| title | Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning |
| title_full | Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning |
| title_fullStr | Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning |
| title_full_unstemmed | Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning |
| title_short | Battery Charge Curve Prediction via Feature Extraction and Supervised Machine Learning |
| title_sort | battery charge curve prediction via feature extraction and supervised machine learning |
| topic | batteries charge curves feature extraction prediction machine learning |
| url | https://doi.org/10.1002/advs.202301737 |
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