Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm
Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/3/75 |
| _version_ | 1797239023280848896 |
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| author | Yuqi Dong Kexin Chen Guiling Zhang Ran Li |
| author_facet | Yuqi Dong Kexin Chen Guiling Zhang Ran Li |
| author_sort | Yuqi Dong |
| collection | DOAJ |
| description | Conducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation. |
| first_indexed | 2024-04-24T17:44:56Z |
| format | Article |
| id | doaj.art-52a0016e58654255b7a698cf3e45cb95 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-04-24T17:44:56Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-52a0016e58654255b7a698cf3e45cb952024-03-27T14:08:33ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-02-011537510.3390/wevj15030075Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning AlgorithmYuqi Dong0Kexin Chen1Guiling Zhang2Ran Li3School of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Materials Science and Chemical Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaSchool of Electrical and Electronic Engineering, Harbin University of Science and Technology, Harbin 150080, ChinaConducting online estimation studies of the SOH of lithium-ion batteries is indispensable for extending the cycle life of energy storage batteries. Data-driven methods are efficient, accurate, and do not depend on accurate battery models, which is an important direction for battery state estimation research. However, the relationships between variables in lithium-ion battery datasets are mostly nonlinear, and a single data-driven algorithm is susceptible to a weak generalization ability affected by the dataset itself. Meanwhile, most of the related studies on battery health estimation are offline estimation, and the inability for online estimation is also a problem to be solved. In this study, an integrated learning method based on a stacking algorithm is proposed. In this study, the end voltage and discharge temperature were selected as the characteristics based on the sample data of NASA batteries, and the B0005 battery was used as the training set. After training on the dataset and parameter optimization using a Bayesian algorithm, the trained model was used to predict the SOH of B0007 and B0018 models. After comparative analysis, it was found that the prediction results obtained based on the proposed model not only have high accuracy and a short running time, but also have a strong generalization ability, which has a great potential to achieve online estimation.https://www.mdpi.com/2032-6653/15/3/75BMSensemble learningSOHBayesian optimization |
| spellingShingle | Yuqi Dong Kexin Chen Guiling Zhang Ran Li Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm World Electric Vehicle Journal BMS ensemble learning SOH Bayesian optimization |
| title | Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm |
| title_full | Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm |
| title_fullStr | Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm |
| title_full_unstemmed | Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm |
| title_short | Joint Estimation of State of Charge and State of Health of Lithium-Ion Batteries Based on Stacking Machine Learning Algorithm |
| title_sort | joint estimation of state of charge and state of health of lithium ion batteries based on stacking machine learning algorithm |
| topic | BMS ensemble learning SOH Bayesian optimization |
| url | https://www.mdpi.com/2032-6653/15/3/75 |
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