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

• 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
• ISSN: 1229-9138; 1976-3832
• DOI: 10.1007/s12239-021-0125-0

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.