## 5 Conclusions  
The objective of this study is to conduct a comprehensive review of HEVs and their EMS classification, while emphasizing the merits of GAs in optimizing these systems. Section 2 presents a detailed classification of HEVs based on hybridization degrees and power-source arrangements, encompassing series, parallel, and Power-Split configurations. Each configuration operates on distinct working principles and is applicable in different scenarios, endowing HEVs with a high level of flexibility and adaptability across various driving environments. Section 3 analyzes the EMS, which serves as the core of hybrid technology and is directly associated with energy consumption, emissions, and vehicle performance. Currently, EMS is predominantly categorized into rule-based strategies and optimization-based driving strategies. While the rule-based strategy is simple and straightforward to implement, it lacks global optimization capabilities; in contrast, the optimization-based driving strategy exhibits superior performance but suffers from higher computational complexity and inadequate robustness. Section 4 focuses on the advantages of GAs in optimizing the EMS of HEVs. As a global optimization algorithm, the GA is capable of handling multiple objectives and constraints simultaneously to prevent getting trapped in local optimal solutions. By simulating natural selection and genetic mechanisms, a GA can identify the global optimal solution within the complex parameter space, thereby significantly enhancing the performance of the EMS. Moreover, the adaptability and flexibility of GAs allow them to dynamically adapt to the driving environment and achieve real-time optimization. Consequently, in this study, GAs are chosen as the core method for optimizing the energy management of HEVs, primarily due to their unique advantages in addressing multi-objective, discrete-variable optimization problems. When compared with algorithms like particle swarm optimization (PSO) and ant colony optimization (ACO), the binary coding feature of GAs is more appropriate for handling discrete control variables in HEVs. Furthermore, its population mechanism and crossover operator design can effectively preserve the diversity of solutions while ensuring the global search capability. Nevertheless, GAs still exhibit limitations in the energy management applications of HEVs. Firstly, with respect to computational efficiency, GA is required to evaluate each individual within the population, and the prolonged computation time under complex operating conditions poses a challenge for real-time control. Secondly, GAs demonstrate high parameter sensitivity, and key parameters, such as the crossover rate and mutation rate, require specific tuning for different driving scenarios (e.g., urban roads and highways), with the optimal parameters varying significantly. Additionally, in high-dimensional optimization problems, GAs may encounter the phenomenon of premature convergence, and their optimization effect is inferior to that of hybrid intelligent algorithms. To overcome these limitations, future research endeavors will concentrate on the following directions: (1) Developing a hybrid optimization framework that integrates the global search capability of GAs and the rapid convergence characteristics of PSO, and designing novel GA-PSO hybrid algorithms to enhance real-time performance. (2) Investigating the parameter adaptive mechanism and employing online learning techniques to achieve dynamic matching between GA parameters and driving conditions. (3) Exploring the parallel computation architecture and reducing the number of parameters and driving scenarios through GPU acceleration and other techniques to decrease the iterative computation time. (4) Combining deep reinforcement learning and utilizing the GA optimization results as the initial strategy to further enhance the control accuracy.