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Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches

Hybrid Electric Vehicles (HEVs) have emerged as a viable option for reducing pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of HEVs heavily relies on the energy management strategies (EMSs) employed, as it directly impacts vehicle fuel consumption. Developi...

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Main Authors: Jui, Julakha Jahan, Mohd Ashraf, Ahmad, Molla, Md Mamun, Muhammad Ikram, Mohd Rashid
Format: Article
Language:English
Published: Elsevier B.V. 2024
Subjects:
T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/41245/1/Optimal%20energy%20management%20strategies%20for%20hybrid%20electric%20vehicles.pdf
http://umpir.ump.edu.my/id/eprint/41245/
https://doi.org/10.1016/j.jer.2024.01.016
https://doi.org/10.1016/j.jer.2024.01.016
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spelling my.ump.umpir.412452024-06-10T02:59:06Z http://umpir.ump.edu.my/id/eprint/41245/ Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches Jui, Julakha Jahan Mohd Ashraf, Ahmad Molla, Md Mamun Muhammad Ikram, Mohd Rashid T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Hybrid Electric Vehicles (HEVs) have emerged as a viable option for reducing pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of HEVs heavily relies on the energy management strategies (EMSs) employed, as it directly impacts vehicle fuel consumption. Developing suitable EMSs for HEVs poses a challenge, as the goal is to maximize fuel economy yet optimize vehicle performance. EMSs algorithms are critical in determining power distribution between the engine and motor in HEVs. Traditionally, EMSs for HEVs have been developed based on optimal control theory. However, in recent years, a rising number of people have been interested in utilizing machine-learning techniques to enhance EMSs performance. This article presents a current analysis of various EMSs proposed in the literature. It highlights the shift towards integrating machine learning and artificial intelligence (AI) breakthroughs in EMSs development. The study examines numerous case studies, and research works employing machine learning techniques across different categories to develop energy management strategies for HEVs. By leveraging advancements in machine learning and AI, researchers have explored innovative approaches to optimize HEVs’ performance and fuel economy. Key conclusions from our investigation show that machine learning has made a substantial contribution to solving the complex problems associated with HEV energy management. We emphasize how machine learning algorithms may be adjusted to dynamic operating environments, how well they can identify intricate patterns in hybrid electric vehicle systems, and how well they can manage non-linear behaviors. Elsevier B.V. 2024-01-22 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/41245/1/Optimal%20energy%20management%20strategies%20for%20hybrid%20electric%20vehicles.pdf Jui, Julakha Jahan and Mohd Ashraf, Ahmad and Molla, Md Mamun and Muhammad Ikram, Mohd Rashid (2024) Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches. Journal of Engineering Research (Kuwait). pp. 1-14. ISSN 2307-1877. (In Press / Online First) (In Press / Online First) https://doi.org/10.1016/j.jer.2024.01.016 https://doi.org/10.1016/j.jer.2024.01.016
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle T Technology (General)
TK Electrical engineering. Electronics Nuclear engineering
Jui, Julakha Jahan
Mohd Ashraf, Ahmad
Molla, Md Mamun
Muhammad Ikram, Mohd Rashid
Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches
description Hybrid Electric Vehicles (HEVs) have emerged as a viable option for reducing pollution and attaining fuel savings in addition to reducing emissions. The effectiveness of HEVs heavily relies on the energy management strategies (EMSs) employed, as it directly impacts vehicle fuel consumption. Developing suitable EMSs for HEVs poses a challenge, as the goal is to maximize fuel economy yet optimize vehicle performance. EMSs algorithms are critical in determining power distribution between the engine and motor in HEVs. Traditionally, EMSs for HEVs have been developed based on optimal control theory. However, in recent years, a rising number of people have been interested in utilizing machine-learning techniques to enhance EMSs performance. This article presents a current analysis of various EMSs proposed in the literature. It highlights the shift towards integrating machine learning and artificial intelligence (AI) breakthroughs in EMSs development. The study examines numerous case studies, and research works employing machine learning techniques across different categories to develop energy management strategies for HEVs. By leveraging advancements in machine learning and AI, researchers have explored innovative approaches to optimize HEVs’ performance and fuel economy. Key conclusions from our investigation show that machine learning has made a substantial contribution to solving the complex problems associated with HEV energy management. We emphasize how machine learning algorithms may be adjusted to dynamic operating environments, how well they can identify intricate patterns in hybrid electric vehicle systems, and how well they can manage non-linear behaviors.
format Article
author Jui, Julakha Jahan
Mohd Ashraf, Ahmad
Molla, Md Mamun
Muhammad Ikram, Mohd Rashid
author_facet Jui, Julakha Jahan
Mohd Ashraf, Ahmad
Molla, Md Mamun
Muhammad Ikram, Mohd Rashid
author_sort Jui, Julakha Jahan
title Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches
title_short Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches
title_full Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches
title_fullStr Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches
title_full_unstemmed Optimal energy management strategies for hybrid electric vehicles : A recent survey of machine learning approaches
title_sort optimal energy management strategies for hybrid electric vehicles : a recent survey of machine learning approaches
publisher Elsevier B.V.
publishDate 2024
url http://umpir.ump.edu.my/id/eprint/41245/1/Optimal%20energy%20management%20strategies%20for%20hybrid%20electric%20vehicles.pdf
http://umpir.ump.edu.my/id/eprint/41245/
https://doi.org/10.1016/j.jer.2024.01.016
https://doi.org/10.1016/j.jer.2024.01.016
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score 13.2324915

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