Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach
Accurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in deep learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning appro...
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| Veröffentlicht in: | IEEE access 2018-01, Vol.6, p.50587-50598 |
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| creator | Ren, Lei Zhao, Li Hong, Sheng Zhao, Shiqiang Wang, Hao Zhang, Lin |
| description | Accurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in deep learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with deep neural network (DNN). First, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model-based DNN is trained for multi-battery remaining cycle life estimation. The proposed approach is applied to the real data set of lithium-ion battery cycle life from NASA, and the experiment results show that the proposed approach can improve the accuracy of RUL prediction. |
| doi_str_mv | 10.1109/ACCESS.2018.2858856 |
| format | Article |
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The advances in deep learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with deep neural network (DNN). First, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model-based DNN is trained for multi-battery remaining cycle life estimation. 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(IEEE) 2018</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c524t-9dd121f1fd6256c8fee69cb8a7f092d21e9fcea4fb0a2ddd247ca7ba74fdaa183</citedby><cites>FETCH-LOGICAL-c524t-9dd121f1fd6256c8fee69cb8a7f092d21e9fcea4fb0a2ddd247ca7ba74fdaa183</cites><orcidid>0000-0001-6346-6930 ; 0000-0002-9219-3756</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8418374$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,777,781,861,2096,27614,27905,27906,54914</link.rule.ids></links><search><creatorcontrib>Ren, Lei</creatorcontrib><creatorcontrib>Zhao, Li</creatorcontrib><creatorcontrib>Hong, Sheng</creatorcontrib><creatorcontrib>Zhao, Shiqiang</creatorcontrib><creatorcontrib>Wang, Hao</creatorcontrib><creatorcontrib>Zhang, Lin</creatorcontrib><title>Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach</title><title>IEEE access</title><addtitle>Access</addtitle><description>Accurate prediction of remaining useful life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. 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| source | IEEE Open Access Journals; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Artificial neural networks Battery cycles Deep learning deep neural network Feature extraction Life prediction Lithium Lithium-ion batteries Lithium-ion battery Machine learning Management systems Prediction models Predictive models Rechargeable batteries remaining useful life RUL prediction model Temperature measurement Useful life Voltage measurement |
| title | Remaining Useful Life Prediction for Lithium-Ion Battery: A Deep Learning Approach |
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