# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 5 Conclusions  
Due to the good fuel economy of hybrid vehicles and the versatility of control strategies, hybrid technology is one of the powerful measures needed to achieve peak carbon and carbon neutrality from now to the long term. Hence, extensive attention has been paid to HEVs from both academic and industrial researchers. This paper provides a comprehensive review of various aspects related to HEVs, with a specific focus on their modelling, power structure, and EMS. The aim is to analyze and present the advantages and disadvantages of different EMS categories in HEVs.  
In this paper, we investigate three different categories of EMS in-depth and summarize the findings. We discuss and compare the control structure, innovation, and application environment of each strategy in detail. To illustrate the effectiveness of these strategies, we select the results of several papers that have optimized different objectives such as fuel economy, driving comfort, and battery power maintenance. The modelling of HEVs includes the development of mathematical models that simulate the behavior of different components, such as the engine, electric motor, and battery, under various operating conditions. The power structure of HEVs includes the design and integration of these components, taking into account their individual characteristics and constraints. The EMSof HEVs involves the control and coordination of the power sources, energy storage, and power distribution systems to achieve optimal performance and efficiency. The three categories of EMS we investigate in this paper are rule-based EMS, optimization-based EMS, and artificial intelligence-based EMS. Rule-based EMS utilizes predefined rules to control the operation of the power sources and energy storage system. Optimization-based EMS employs mathematical optimization techniques to determine the optimal power distribution strategy based on predefined objectives and constraints. Artificial intelligence-based EMS uses machine learning algorithms to learn and adapt to the driving conditions and optimize the power distribution strategy in real time. By analyzing and comparing the different EMS categories, we aim to provide a comprehensive understanding of their advantages and disadvantages. Furthermore, by presenting the results achieved in previous studies, we demonstrate the effectiveness of different EMS categories in achieving different objectives. Overall, this paper provides valuable insights for researchers, engineers, and policymakers involved in the development and implementation of HEVs.  
Although many strategies, including ECMS, MPC, and rule-based strategies, have been extensively researched and applied, there are still a number of gaps in the current research on control strategies of HEVs. Future research directions will also be more and more closely integrated with computer technology, which will break through previous limitations and further realize the optimization effect and real-time of EMS.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Where data is unavailable due to privacy or ethical restriction.  
The authors declare no conflict of interest.