Abstract: In response to the increasing societal emphasis on environmental performance and the growing demand for energy-efficient transportation methods, hybrid electric vehicles (HEVs) are recognized as a key enabler of future green mobility. The energy management strategy within the powertrain of HEVs is regarded as a pivotal technology to ensure high energy utilization efficiency and low carbon emissions. Especially in the context of the development of intelligence and connectivity, the integration of smart connectivity technologies with energy management strategies has become a prominent focal point in this domain. Hence, this paper undertakes an in-depth analysis and comprehensive survey of the current state and categorization of intelligent and connected vehicles (ICVs) energy management strategies. The objective is to provide insights for further research in the domain of energy management strategies for hybrid electric vehicles in intelligent traffic environments, while also offering guidance for future technological advancements. Firstly, this paper reviews a series of classic energy management strategy methods that have emerged in the field of hybrid electric vehicles in recent years. These methods encompass traditional rule-based and optimization-based strategies, as well as more advanced machine learning-based approaches. Through a comparative analysis of these methods, it is revealed that mainstream research approaches can be categorized based on their primary contributions, including： enhancement of existing algorithms or introduction of novel algorithms; integration and fusion of different algorithms; incorporation of additional environmental information to enhance adaptability to varying operating conditions. These introductions can provide an overall understanding of the research foundation to help researchers understand the evolution and trends in the field. Secondly, this paper provides a detailed overview of the system architecture and operation principles of intelligent-connected cloud control system, emphasizing their critical role in hybrid electric vehicle energy management, and analyzes the combined application of cloud control system and energy management strategy, which provides potential avenues for more intelligent energy management. Subsequently, this paper places particular emphasis on conducting in-depth research into energy management strategies for HEVs within the context of intelligent connected environments, with a specific focus on both single-vehicle and multi-vehicle scenarios, and summarizes the design methodologies found in the literature. Regarding single-vehicle strategies, the paper outlines the classifications of energy management strategies under the context of energy-efficient path planning, energy-efficient speed profile generation, and state-of-charge management. For multi-vehicle scenarios, it investigates how vehicles can cooperatively control energy management strategies to reduce overall energy consumption. This includes energy management strategies based on adaptive cruise control (ACC) in platooning scenarios and predictive cruise control (PCC) in convoy scenarios, as well as energy management strategy planning for homogeneous and heterogeneous vehicle fleets. Finally, this paper identifies some limitations in current research, including practical applications in complex traffic environments and algorithm efficiency. In light of these limitations, future research priorities are proposed to further advance the application of intelligent technologies in hybrid electric vehicles. These areas of focus encompass： adoption of efficient and adaptive optimization algorithms; development of novel solutions for intelligent traffic system deployment; exploration of new methods for multi-vehicle cooperative optimization. In summary, through a comprehensive and in-depth study of intelligent-connected hybrid electric vehicle energy management strategies, this paper provides a holistic understanding of the field. It offers valuable references and guidance for future research and development directions, aiming to achieve more efficient and environmentally-friendly transportation methods. These efforts hold the potential to contribute to the realization of sustainable green mobility in the future, thereby making significant contributions to environmental and societal sustainability.