## 1 Introduction
Optimization is also one of the effective ways; several typical HEV models were compared using rule-based control methods [28], and BSFC maps were drawn and used to compare [29]. [PERSON] et al. [30] provided detailed information about the control strategy of a real vehicle, Hyundali Ioniq Hybrid, which is particularly useful for engineers to properly investigate algorithm innovation and optimization, while driving-mode switching optimization is the key factor to determine smooth operation as well as HEV performance according to [PERSON] et al. [31]. In addition, according to different power transmission and distribution methods, HEVs can be divided into parallel and series types [32; 33; 34]. [PERSON] et al. [35] used MATLAB/Simulink to conduct optimization analysis with different algorithms for parallel and series HEVs. The results showed that the fuel economy efficiency and regenerative braking energy recovery of parallel HEVs are better than those of series HEVs. The ICE and EM of a parallel system are connected to the transmission shaft through a power integration device or a transmission device. The vehicle can be driven by the ICE and the EM simultaneously or independently. Similarly, [PERSON] et al. [36] investigated series-parallel HEVs controlled by a novel energy management algorithm; the characteristics of the method were investigated, and then simulation was carried out. The research indicated significant improvement in fuel economy and energy conversion. In addition, simulation based on a complex electromechanical system with a planetary gearbox was conducted by [PERSON] [37]. Two cases in this study were compared in terms of differently developed mathematical models. Several kinds of HEV configuration and control techniques were discussed by [PERSON] et al. [38]. The pros and cons of each kind were analyzed to find the most suitable energy control strategy. In the same way, [PERSON] et al. [39] introduced and classified various types of energy management strategies based on different key factors, such as the mechanism and components of HEVs and external power charging. When the ICE was operated in the optimal operating range for an extended period under different conditions through proper dynamic programming [40] and a power-management strategy [41], the fuel economy of parallel hybrid vehicles was exceptional. [PERSON] et al. [42] proposed a novel method approaching energy management while taking into account battery performance. A fuzzy logic controller and MATLAB/Simulink were applied for simulation purposes, and then the results were compared to verify the efficiency of the two intelligent control methods mentioned. [PERSON] et al. [43] successfully reduced fuel consumption and maintained the ICE and battery's SOC in the optimal working range based on an optimized K-means clustering algorithm. Recently, control strategies for grid distribution for electric vehicles or plug-in HEV charging have also been taken into account. [PERSON] et al. [44; 45] introduced this topic using a fuzzy theory dealing with several charging management issues, such as distance to the charging station and battery charging delay time, and achieved positive results. During the driving process, if the control system of the HEV fails to switch the power mode smoothly, the driving would be delayed and the passengers would feel uncomfortable. A complete set of corresponding energy control rules and management control strategies for parallel HEVs according to driving power requirements can improve this issue [46; 47]. Therefore, this study uses MATLAB/Simulink to create a computer simulation program and verified the energy management control strategy of the system based on driver demand. First, an HEV energy management control strategy is established by dynamic equations. Then, the model parameters according to the test data and the response model comparison are adjusted. After that, the energy management control strategy is used to control the power of the distribution system.