A Review of Optimal Energy Management Strategies Using Machine Learning Techniques for Hybrid Electric Vehicles

A hybrid electric vehicle (HEV) is defined as a vehicle that has two or more power sources, the hybrid electric vehicle is a representative eco-friendly vehicle because it can operate efficiently with each power source and requires only a small sized electric power source. However, it is not possibl...

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Published in	International journal of automotive technology Vol. 22; no. 5; pp. 1437 - 1452
Main Authors	Song, Changhee, Kim, Kiyoung, Sung, Donghwan, Kim, Kyunghyun, Yang, Hyunjun, Lee, Heeyun, Cho, Gu Young, Cha, Suk Won
Format	Journal Article
Language	English
Published	Seoul The Korean Society of Automotive Engineers 01.10.2021 Springer Nature B.V 한국자동차공학회
Subjects	Artificial intelligence Automotive Engineering Control theory Electric vehicles Energy management Engineering Hybrid electric vehicles Machine learning Optimal control Power sources 자동차공학 Optimal control theory Energy management strategy Hybrid electric vehicle Artificial intelligence Machine learning
Online Access	Get full text
ISSN	1229-9138 1976-3832
DOI	10.1007/s12239-021-0125-0

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Abstract	A hybrid electric vehicle (HEV) is defined as a vehicle that has two or more power sources, the hybrid electric vehicle is a representative eco-friendly vehicle because it can operate efficiently with each power source and requires only a small sized electric power source. However, it is not possible to develop high efficiency HEVs without an effective energy management system (EMS), a well-designed EMS is vital in HEVs because they need to manage two power sources. Motivated by this, there are continuing efforts being made to research and establish suitable energy management strategies in order to develop high efficiency HEVs. In the past, many energy management strategies for HEVs were developed based on optimal control theory. Recently, various kinds of machine learning technologies have been applied to HEV EMS development based on breakthroughs in the fields of machine learning and artificial intelligence (AI). Machine learning is a field of research that allows computers to perform arbitrary tasks guided by data rather than explicit programming. Machine learning can be classified into supervised learning, reinforcement learning (semi-supervised learning), and unsupervised learning depending on how the training data is structured. In this study, we look at cases and studies in which machine learning techniques from each category were used to develop HEV energy management strategies.
AbstractList	A hybrid electric vehicle (HEV) is defined as a vehicle that has two or more power sources, the hybrid electric vehicle is a representative eco-friendly vehicle because it can operate efficiently with each power source and requires only a small sized electric power source. However, it is not possible to develop high efficiency HEVs without an effective energy management system (EMS), a well-designed EMS is vital in HEVs because they need to manage two power sources. Motivated by this, there are continuing efforts being made to research and establish suitable energy management strategies in order to develop high efficiency HEVs. In the past, many energy management strategies for HEVs were developed based on optimal control theory. Recently, various kinds of machine learning technologies have been applied to HEV EMS development based on breakthroughs in the fields of machine learning and artificial intelligence (AI). Machine learning is a field of research that allows computers to perform arbitrary tasks guided by data rather than explicit programming. Machine learning can be classified into supervised learning, reinforcement learning (semi-supervised learning), and unsupervised learning depending on how the training data is structured. In this study, we look at cases and studies in which machine learning techniques from each category were used to develop HEV energy management strategies. A hybrid electric vehicle (HEV) is defined as a vehicle that has two or more power sources, the hybrid electric vehicle is a representative eco-friendly vehicle because it can operate efficiently with each power source and requires only a small sized electric power source. However, it is not possible to develop high efficiency HEVs without an effective energy management system (EMS), a well-designed EMS is vital in HEVs because they need to manage two power sources. Motivated by this, there are continuing efforts being made to research and establish suitable energy management strategies in order to develop high efficiency HEVs. In the past, many energy management strategies for HEVs were developed based on optimal control theory. Recently, various kinds of machine learning technologies have been applied to HEV EMS development based on breakthroughs in the fields of machine learning and artificial intelligence (AI). Machine learning is a field of research that allows computers to perform arbitrary tasks guided by data rather than explicit programming. Machine learning can be classified into supervised learning, reinforcement learning (semi-supervised learning), and unsupervised learning depending on how the training data is structured. In this study, we look at cases and studies in which machine learning techniques from each category were used to develop HEV energy management strategies. KCI Citation Count: 12
Author	Yang, Hyunjun Kim, Kiyoung Song, Changhee Sung, Donghwan Kim, Kyunghyun Cho, Gu Young Cha, Suk Won Lee, Heeyun
Author_xml	– sequence: 1 givenname: Changhee surname: Song fullname: Song, Changhee organization: Department of Mechanical Engineering, Seoul National University – sequence: 2 givenname: Kiyoung surname: Kim fullname: Kim, Kiyoung organization: Department of Mechanical Engineering, Seoul National University – sequence: 3 givenname: Donghwan surname: Sung fullname: Sung, Donghwan organization: Department of Mechanical Engineering, Seoul National University – sequence: 4 givenname: Kyunghyun surname: Kim fullname: Kim, Kyunghyun organization: Department of Mechanical Engineering, Seoul National University – sequence: 5 givenname: Hyunjun surname: Yang fullname: Yang, Hyunjun organization: Department of Mechanical Engineering, Seoul National University – sequence: 6 givenname: Heeyun surname: Lee fullname: Lee, Heeyun organization: Department of Mechanical Engineering, Seoul National University – sequence: 7 givenname: Gu Young surname: Cho fullname: Cho, Gu Young organization: Department of Mechanical Engineering, Dankook University – sequence: 8 givenname: Suk Won surname: Cha fullname: Cha, Suk Won email: swcha@snu.ac.kr organization: Department of Mechanical Engineering, Seoul National University, Institute of Advanced Machines and Design, Seoul National University
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Keywords	Optimal control theory Energy management strategy Hybrid electric vehicle Artificial intelligence Machine learning
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Snippet	A hybrid electric vehicle (HEV) is defined as a vehicle that has two or more power sources, the hybrid electric vehicle is a representative eco-friendly...
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SubjectTerms	Artificial intelligence Automotive Engineering Control theory Electric vehicles Energy management Engineering Hybrid electric vehicles Machine learning Optimal control Power sources 자동차공학
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Title	A Review of Optimal Energy Management Strategies Using Machine Learning Techniques for Hybrid Electric Vehicles
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