## 1 Introduction  
Due to global environmental issues, the number of regulations focused on the automotive industry are steadily increasing. The U.S. Corporate Average Fuel Economy (CAFE) standard aims to improve fuel economy by an average of about 5% per year from 2020 to 2025 and achieve an average fleet fuel economy of 46.7 mpg. The EU adopted regulation (EU) 2019/631, which sets targets to reduce CO\({}_{2}\) emissions by 15% by 2025 and 37.5% by 2030 [1]. Among the solutions to environmental regulations are hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). Various approaches have been proposed as energy management strategies (EMSs) to improve the fuel economy of (P)HEVs [2; 3; 4; 5; 6; 7]. Typical approaches include optimization-based solutions that determine the power distribution between the engine and the motor. These include offline optimization algorithms such as linear programming and dynamic programming and online optimization algorithms such as Pontryagin's minimum principle (PMP), model predictive control (MPC), and the equivalent consumption minimization strategy (ECMS) [8]. In addition, with the help of artificial intelligence, learning-based EMSs using machine learning and reinforcement learning techniques have been proposed. [PERSON] et al. [9] proposed a multi-objective EMS based on deep reinforcement learning, which showed improvements from three perspectives: training effort, fuel economy, and battery degradation suppression. The recent attention of the connected and automated vehicle (CAV) industry has led to the development of intelligent transportation system (ITS) technologies. This is expanding the possibilities of improving the EMS performance of connected (P)HEVs using vehicle to everything (V2X) communication [10]. In an effort to obtain high fuel economy, it has been identified that the performance of EMSs can be improved by utilizing various information outside the vehicle, such as road information and traffic information. [PERSON] et al. [11] show that fuel economy is improved by considering the slope of the planned driving route through a terrain preview. [PERSON] et al. [12] proposed an EMS that uses traffic preview information to improve fuel economy by optimizing speed profiles and minimizing fuel consumption. [PERSON] et al. [13] proposed an EMS that utilizes forward driving path information using radar sensors. [PERSON] et al. [14] introduced an eco-driving strategy that considers traffic conditions and traffic lights using V2I communication. They compared the results for fuel economy improvement and waiting time according to market penetration rate (MPR), congestion, vehicle following model, etc. Existing EMSs have mainly presented approaches in fixed driving cycles, such as FTP-75 and HWFET [15; 16; 17; 18]. Recently, EMSs for connected (P)HEVs mostly derive speed and/or SOC profiles based on external information and perform optimal power distribution based on them. [PERSON] et al. in [19] utilize traffic light timetables to optimize speed, and from this, power distribution and gear shift schedules are determined using MPC. [PERSON] et al. [20] used data from navigation to compute a predictive SOC trajectory for the long horizon. This SOC trajectory was then used to optimize the combined vehicle speed and SOC for the short horizon. [PERSON] et al. [21] proposed a connected eco-driving assistance system to optimize the operation of PHEVs by integrating powertrain and ITS technologies. Predicting a vehicle's speed based on upcoming information can reflect the changing driving environment in real time, thus improving the potential for actual vehicle fuel economy. However, connected vehicles are currently difficult to evaluate on the road. This is because there are only a limited number of locations and vehicles with ITS infrastructure and equipment. In our previous work, we developed an integrated driving hardware-in-the-loop (IDHIL) simulator equipped with real V2X communication devices [22].