| A REVIEW OF OPTIMAL ENERGY MANAGEMENT STRATEGIES USING MACHINE LEARNING TECHNIQUES FOR HYBRID ELECTRIC VEHICLES |
| Changhee Song1, Kiyoung Kim1, Donghwan Sung1, Kyunghyun Kim1, Hyunjun Yang1, Heeyun Lee1, Gu Young Cho2, Suk Won Cha1 |
1Seoul National University
2Dankook University |
|
|
|
|
|
|
|
| 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. |
| Key Words:
Hybrid electric vehicle, Energy management strategy, Machine learning, Artificial intelligence, Optimal control theory |
|
|
|
Corresponding Author.
Suk Won Cha , Email. 
Preview
Full text via DOI
Download Citation 
Print 
