eprintid: 19410
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/94/10
datestamp: 2024-06-04 14:11:52
lastmod: 2024-06-04 14:11:52
status_changed: 2024-06-04 14:05:39
type: article
metadata_visibility: show
creators_name: Hossain Lipu, M.S.
creators_name: Karim, T.F.
creators_name: Ansari, S.
creators_name: Miah, M.S.
creators_name: Rahman, M.S.
creators_name: Meraj, S.T.
creators_name: Elavarasan, R.M.
creators_name: Vijayaraghavan, R.R.
title: Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities
ispublished: pub
keywords: Charging (batteries); Deep learning; Electric vehicles; Learning systems; Real time systems; Secondary batteries, Automotive battery; Charge state; Deep learning; Energy; Learning approach; Real- time; State of energy; State of health; States of charges; System state, Battery management systems
note: cited By 11
abstract: Real-time battery SOX estimation including the state of charge (SOC), state of energy (SOE), and state of health (SOH) is the crucial evaluation indicator to assess the performance of automotive battery management systems (BMSs). Recently, intelligent models in terms of deep learning (DL) have received massive attention in electric vehicle (EV) BMS applications due to their improved generalization performance and strong computation capability to work under different conditions. However, estimation of accurate and robust SOC, SOH, and SOE in real-time is challenging since they are internal battery parameters and depend on the battery�s materials, chemical reactions, and aging as well as environmental temperature settings. Therefore, the goal of this review is to present a comprehensive explanation of various DL approaches for battery SOX estimation, highlighting features, configurations, datasets, battery chemistries, targets, results, and contributions. Various DL methods are critically discussed, outlining advantages, disadvantages, and research gaps. In addition, various open challenges, issues, and concerns are investigated to identify existing concerns, limitations, and challenges. Finally, future suggestions and guidelines are delivered toward accurate and robust SOX estimation for sustainable operation and management in EV operation. © 2022 by the authors.
date: 2023
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85145663367&doi=10.3390%2fen16010023&partnerID=40&md5=b1270d6918eedd0bac5be0c3072ecff4
id_number: 10.3390/en16010023
full_text_status: none
publication: Energies
volume: 16
number: 1
refereed: TRUE
issn: 19961073
citation:   Hossain Lipu, M.S. and Karim, T.F. and Ansari, S. and Miah, M.S. and Rahman, M.S. and Meraj, S.T. and Elavarasan, R.M. and Vijayaraghavan, R.R.  (2023) Intelligent SOX Estimation for Automotive Battery Management Systems: State-of-the-Art Deep Learning Approaches, Open Issues, and Future Research Opportunities.  Energies, 16 (1).   ISSN 19961073