The rapid development of intelligent and connected technologies is conducive to the efficient energy utilization of hybrid electric vehicles (HEVs). However, most energy management strategies (EMSs) with optimized, intelligent, and connected functions have not been directly applied to such vehicles because existing technical conditions cannot meet the theoretical requirements of complex EMSs. Therefore, based on the mapping rela-tionship between the information decision-making ability and the energy management effect, this study is the first to propose four development stages of HEV energy management practical application as follows: energy management based on instantaneous driving cycles (Stage 1 or S1); energy management based on forward driving cycle prediction (Stage 2 or S2); energy management based on global driving cycle prediction (Stage 3 or S3); and energy management based on autonomous velocity planning (Stage 4 or S4). The key technologies of each development stage are not independent, i.e., they complement each other in the process of practical application development. Furthermore, realizing energy management practical applications not only requires novel algorithm models but also involves several challenges such as acquiring and processing multi-source in-formation, predicting the vehicle power demand in the spatial-temporal domain during travel, and determining the vehicle control characteristics and ability. Finally, according to the development goals of energy manage-ment, this study proposes an implementation framework for HEV energy management in higher development stages, namely cooperative vehicle-edge-cloud for intelligent energy management, i.e., CVEC-IEM, which ex-ecutes information decision tasks on different computing platforms and realizes interconnection and interaction to provide development directions and goals for the efficient utilization of energy and successful deployment of HEV practical applications.