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.

混合动力电动汽车 (HEV) 被定义为具有两个或多个动力源的车辆，混合动力电动汽车是具有代表性的环保车辆，因为它可以使用每个动力源高效运行，并且只需要一个小尺寸的电源。然而，如果没有有效的能源管理系统 (EMS)，就不可能开发出高效的 HEV，精心设计的 EMS 在 HEV 中至关重要，因为它们需要管理两个电源。受此启发，人们不断努力研究和制定合适的能源管理策略，以开发高效的 HEV。过去，HEV 的许多能源管理策略都是基于最优控制理论开发的。最近，基于机器学习和人工智能（AI）领域的突破，各种机器学习技术已应用于HEV EMS开发。机器学习是一个研究领域，它允许计算机执行由数据而非显式编程引导的任意任务。根据训练数据的结构方式，机器学习可以分为监督学习、强化学习（半监督学习）和无监督学习。在这项研究中，我们研究了使用每个类别的机器学习技术来制定 HEV 能源管理策略的案例和研究。机器学习是一个研究领域，它允许计算机执行由数据而非显式编程引导的任意任务。根据训练数据的结构方式，机器学习可以分为监督学习、强化学习（半监督学习）和无监督学习。在这项研究中，我们研究了使用每个类别的机器学习技术来制定 HEV 能源管理策略的案例和研究。机器学习是一个研究领域，它允许计算机执行由数据而非显式编程引导的任意任务。根据训练数据的结构方式，机器学习可以分为监督学习、强化学习（半监督学习）和无监督学习。在这项研究中，我们研究了使用每个类别的机器学习技术来制定 HEV 能源管理策略的案例和研究。