Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects
Automotive industry; Charging (batteries); Deep learning; Digital storage; Electric vehicles; Health; Learning systems; Secondary batteries; And electric vehicle; Charge state; Deep learning; Electric vehicle batteries; Life estimation; Method implementations; Remaining useful lives; State of health...
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2023
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my.uniten.dspace-266652023-05-29T17:36:07Z Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects Hossain Lipu M.S. Ansari S. Miah M.S. Meraj S.T. Hasan K. Shihavuddin A.S.M. Hannan M.A. Muttaqi K.M. Hussain A. 36518949700 57218906707 57226266149 57202610180 57205215021 25960374400 7103014445 55582332500 57208481391 Automotive industry; Charging (batteries); Deep learning; Digital storage; Electric vehicles; Health; Learning systems; Secondary batteries; And electric vehicle; Charge state; Deep learning; Electric vehicle batteries; Life estimation; Method implementations; Remaining useful lives; State of health; States of charges; System methods; Battery management systems State of Charge (SOC), state of health (SOH), and remaining useful life (RUL) are the crucial indexes used in the assessment of electric vehicle (EV) battery management systems (BMS). The performance and efficiency of EVs are subject to the precise estimation of SOC, SOH, and RUL in BMS which enhances the battery reliability, safety, and longevity. However, the estimation of SOC, SOH, and RUL is challenging due to the battery capacity degradation and varying environmental conditions. Recently, deep learning (DL) has received wide attention for battery SOC, SOH, and RUL estimation due to the accessibility of a vast amount of data, large storage volume, and powerful computing processors. Nevertheless, the application of DL in SOC, SOH, and RUL estimation for EVs is still limited. Therefore, the novelty of this paper is to deliver a comprehensive review of DL-enabled SOC, SOH, and RUL estimation for BMS, focusing on methods, implementations, strengths, weaknesses, issues, accuracy, and contributions. Moreover, this study explores the numerous important implementation factors of DL methods concerning data type, features, size, preprocessing, algorithm operation, functions, hyperparameter adjustments, and performance evaluation. Additionally, the review explores various limitations and challenges of DL in BMS related to battery, algorithm, and operational issues. Finally, future opportunities and prospects are delivered that would support the EV engineers and automotive industries to establish an accurate and robust DL-based SOC, SOH, and RUL estimation technique towards smart BMS in future sustainable EV applications. � 2022 Elsevier Ltd Final 2023-05-29T09:36:07Z 2023-05-29T09:36:07Z 2022 Review 10.1016/j.est.2022.105752 2-s2.0-85138478861 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85138478861&doi=10.1016%2fj.est.2022.105752&partnerID=40&md5=845dc6867ce01092b963946aaa457502 https://irepository.uniten.edu.my/handle/123456789/26665 55 105752 Elsevier Ltd Scopus |
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| description |
Automotive industry; Charging (batteries); Deep learning; Digital storage; Electric vehicles; Health; Learning systems; Secondary batteries; And electric vehicle; Charge state; Deep learning; Electric vehicle batteries; Life estimation; Method implementations; Remaining useful lives; State of health; States of charges; System methods; Battery management systems |
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36518949700 |
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36518949700 Hossain Lipu M.S. Ansari S. Miah M.S. Meraj S.T. Hasan K. Shihavuddin A.S.M. Hannan M.A. Muttaqi K.M. Hussain A. |
| format |
Review |
| author |
Hossain Lipu M.S. Ansari S. Miah M.S. Meraj S.T. Hasan K. Shihavuddin A.S.M. Hannan M.A. Muttaqi K.M. Hussain A. |
| spellingShingle |
Hossain Lipu M.S. Ansari S. Miah M.S. Meraj S.T. Hasan K. Shihavuddin A.S.M. Hannan M.A. Muttaqi K.M. Hussain A. Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects |
| author_sort |
Hossain Lipu M.S. |
| title |
Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects |
| title_short |
Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects |
| title_full |
Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects |
| title_fullStr |
Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects |
| title_full_unstemmed |
Deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: Methods, implementations, issues and prospects |
| title_sort |
deep learning enabled state of charge, state of health and remaining useful life estimation for smart battery management system: methods, implementations, issues and prospects |
| publisher |
Elsevier Ltd |
| publishDate |
2023 |
| _version_ |
1806426428230598656 |