Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML
Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/13/4594 |
| _version_ | 1797480291266199552 |
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| author | Chenqiang Luo Zhendong Zhang Dongdong Qiao Xin Lai Yongying Li Shunli Wang |
| author_facet | Chenqiang Luo Zhendong Zhang Dongdong Qiao Xin Lai Yongying Li Shunli Wang |
| author_sort | Chenqiang Luo |
| collection | DOAJ |
| description | Accurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the feature extraction related to battery lifespan. Some studies focus on the features only in the battery constant current (CC) charging phase, regardless of the joint impact including the constant voltage (CV) charging phase on the battery aging, which can lead to estimation deviation. In this study, we analyze the features of the CC and CV phases using the optimized incremental capacity (IC) curve, showing the strong relevance between the IC curve in the CC phase as well as charging capacity in the CV phase and battery lifespan. Then, the life prediction model based on automated machine learning (AutoML) is established, which can automatically generate a suitable pipeline with less human intervention, overcoming the problem of redundant model information and high computational cost. The proposed method is verified on NASA’s LIBs cycle life datasets, with the MAE increased by 52.8% and RMSE increased by 48.3% compared to other methods using the same datasets and training method, accomplishing an obvious enhancement in online life prediction with small-scale datasets. |
| first_indexed | 2024-03-09T21:57:56Z |
| format | Article |
| id | doaj.art-ae40ca86983542ceb3f921c729f06e3b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T21:57:56Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ae40ca86983542ceb3f921c729f06e3b2023-11-23T19:54:16ZengMDPI AGEnergies1996-10732022-06-011513459410.3390/en15134594Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoMLChenqiang Luo0Zhendong Zhang1Dongdong Qiao2Xin Lai3Yongying Li4Shunli Wang5College of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaCollege of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaAccurate online capacity estimation and life prediction of lithium-ion batteries (LIBs) are crucial to large-scale commercial use for electric vehicles. The data-driven method lately has drawn great attention in this field due to efficient machine learning, but it remains an ongoing challenge in the feature extraction related to battery lifespan. Some studies focus on the features only in the battery constant current (CC) charging phase, regardless of the joint impact including the constant voltage (CV) charging phase on the battery aging, which can lead to estimation deviation. In this study, we analyze the features of the CC and CV phases using the optimized incremental capacity (IC) curve, showing the strong relevance between the IC curve in the CC phase as well as charging capacity in the CV phase and battery lifespan. Then, the life prediction model based on automated machine learning (AutoML) is established, which can automatically generate a suitable pipeline with less human intervention, overcoming the problem of redundant model information and high computational cost. The proposed method is verified on NASA’s LIBs cycle life datasets, with the MAE increased by 52.8% and RMSE increased by 48.3% compared to other methods using the same datasets and training method, accomplishing an obvious enhancement in online life prediction with small-scale datasets.https://www.mdpi.com/1996-1073/15/13/4594lithium-ion batteryincremental capacityautomated machine learninglife prediction |
| spellingShingle | Chenqiang Luo Zhendong Zhang Dongdong Qiao Xin Lai Yongying Li Shunli Wang Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML Energies lithium-ion battery incremental capacity automated machine learning life prediction |
| title | Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML |
| title_full | Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML |
| title_fullStr | Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML |
| title_full_unstemmed | Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML |
| title_short | Life Prediction under Charging Process of Lithium-Ion Batteries Based on AutoML |
| title_sort | life prediction under charging process of lithium ion batteries based on automl |
| topic | lithium-ion battery incremental capacity automated machine learning life prediction |
| url | https://www.mdpi.com/1996-1073/15/13/4594 |
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