Cover Image

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...

Full description

Saved in:
Bibliographic Details
Main Authors: 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.
Other Authors: 36518949700
Format: Review
Published: Elsevier Ltd 2023
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-26665
record_format dspace
spelling 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
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
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
author2 36518949700
author_facet 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
score 13.222552

Similar Items