A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm
To ensure the safe operation of lithium-ion batteries, it is crucial to accurately predict their state of health (SOH) and remaining useful life (RUL). Addressing the issue of high costs and time consumption due to the reliance on large amounts of labeled data in existing models, this paper proposes...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Energy and AI |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546824001241 |
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| author | Xiaoyu Li Mohan Lyv Xiao Gao Kuo Li Yanli Zhu |
| author_facet | Xiaoyu Li Mohan Lyv Xiao Gao Kuo Li Yanli Zhu |
| author_sort | Xiaoyu Li |
| collection | DOAJ |
| description | To ensure the safe operation of lithium-ion batteries, it is crucial to accurately predict their state of health (SOH) and remaining useful life (RUL). Addressing the issue of high costs and time consumption due to the reliance on large amounts of labeled data in existing models, this paper proposes a co-estimation framework that combines semi-supervised learning (SSL) with long short-term memory networks (LSTM), effectively utilizing unlabeled data. By selecting the most strongly correlated battery health features and constructing a degradation model using a hybrid dataset, the need for labeling is reduced. The verification results indicate SOH estimated error is reduced to 4 % and the maximum root mean square error (RMSE) is 1.58 %. When utilizing 75 % SOH as the end-of-life criterion for battery cycle life, the mean absolute error (MAE) of the RUL predictions for the two tested batteries are 2.5281 and 0.0562 cycles, respectively. The results prove the framework enables accurate prediction and has wide practicability and universal applicability. |
| format | Article |
| id | doaj-art-5177b9f3a26f4ad4b78de8f5ce973f1f |
| institution | Kabale University |
| issn | 2666-5468 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj-art-5177b9f3a26f4ad4b78de8f5ce973f1f2025-01-27T04:22:19ZengElsevierEnergy and AI2666-54682025-01-0119100458A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithmXiaoyu Li0Mohan Lyv1Xiao Gao2Kuo Li3Yanli Zhu4School of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, ChinaSchool of Mechanical Engineering, Hebei University of Technology, Tianjin, 300130, ChinaState Key Laboratory of Explosion Science and Technology, Beijing Institute of Technology, Beijing 100081, China; Corresponding author.To ensure the safe operation of lithium-ion batteries, it is crucial to accurately predict their state of health (SOH) and remaining useful life (RUL). Addressing the issue of high costs and time consumption due to the reliance on large amounts of labeled data in existing models, this paper proposes a co-estimation framework that combines semi-supervised learning (SSL) with long short-term memory networks (LSTM), effectively utilizing unlabeled data. By selecting the most strongly correlated battery health features and constructing a degradation model using a hybrid dataset, the need for labeling is reduced. The verification results indicate SOH estimated error is reduced to 4 % and the maximum root mean square error (RMSE) is 1.58 %. When utilizing 75 % SOH as the end-of-life criterion for battery cycle life, the mean absolute error (MAE) of the RUL predictions for the two tested batteries are 2.5281 and 0.0562 cycles, respectively. The results prove the framework enables accurate prediction and has wide practicability and universal applicability.http://www.sciencedirect.com/science/article/pii/S2666546824001241State of healthRemaining useful lifeSemi-supervised learning methodLong short-term memory |
| spellingShingle | Xiaoyu Li Mohan Lyv Xiao Gao Kuo Li Yanli Zhu A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm Energy and AI State of health Remaining useful life Semi-supervised learning method Long short-term memory |
| title | A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm |
| title_full | A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm |
| title_fullStr | A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm |
| title_full_unstemmed | A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm |
| title_short | A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm |
| title_sort | co estimation framework of state of health and remaining useful life for lithium ion batteries using the semi supervised learning algorithm |
| topic | State of health Remaining useful life Semi-supervised learning method Long short-term memory |
| url | http://www.sciencedirect.com/science/article/pii/S2666546824001241 |
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