## 1 Introduction  
Government regulations worldwide are pushing toward clean, renewable energy to reduce pollution levels and create a sustainable future. In response, the automotive industry has invested heavily in innovative technology such as electrified powertrains. Electrification of powertrains provides attractive benefits over conventional powertrains, including engine downsizing, electric motor assist, and engine start-stop. Similarly, the automation of driving promises safer driving; hence, the automotive industry is embracing connected and automated vehicle (CAV) technology. A CAV uses advanced on-board sensing technology to obtain information about surroundings (commonly referred to as look-ahead data) and make informed decisions about present and future actions. Perception and control algorithms, such as sensor fusion and adaptive cruise control (ACC), leverage this information to make vehicles safer and more efficient. The integration of electrification and driving automation has the potential of extending the benefits of both these technologies. However, the additional degrees of freedom result in a complex system which demands more advanced control methods. To achieve maximum powertrain efficiency, researchers and automotive manufacturers have dedicated significant effort to the development and advancement of energy management strategies (EMS) [1]. For hybrid electric vehicle (HEV) powertrain control, an EMS is a set of algorithms that decide the power split between the internal combustion engine and the EM. The goal of the EMS is to improve fuel economy (FE) and optimize the performance of HEVs [2]. In [3], a discrete dynamic programming (DP) approach is used to optimize torque split, gear shifting, and velocity trajectory for a parallel HEV. A model predictive controller (MPC) is used in [4] to optimize the velocity profile and torque split with a gradient-based optimization. In [5], DP with an underlying adaptive equivalent consumption minimization strategy (ECMS) is used to optimize torque split and velocity trajectory. Although energy management of HEVs and CAVs has been the focus of many recent publications, the use of look-ahead data for optimal energy management remains an open research topic. A key component of a look-ahead EMS is the use of vehicle-to-everything (V2X) communication. The authors in [6; 7] showed how vehicle-to-infrastructure (V2I) and GPS signals can be used as inputs to a perception model to employ an artificial neural network (ANN) to characterize driving patterns in an optimal EMS for a plug-in HEV (PHEV). A traffic model was applied in [8] to predict future vehicle velocity with vehicle-to-vehicle (V2V) and V2I as inputs, and an optimal EMS was derived using [PERSON]'s minimum principle (PMP). In [9], traffic light signal phase and timing (SPaT) was used to give dynamic speed advice to the driver and demonstrated a FE improvement of 12-14%. Although the main focus of recent development in optimal energy management has been on look-ahead data-based optimal control, the key concern stems from the computational requirements of global optimization algorithms. The implementation of global optimization algorithms on microprocessors available on board a typical passenger car is challenging due to various reasons, including the high cost of hardware [1]. Research programs such as FutureTruck, EcoCAR, and NEXTCAR were designed to address these issues. A team of students and faculty at Ohio State University (OSU) implemented ECMS on a model-year 2000 Chevrolet Suburban SUV as part of the FutureTruck 2000 competition and achieved approximately 1.5 times the FE of the production Suburban [10]. More recently, the OSU NEXTCAR team implemented a model predictive control framework on a test vehicle using principles of approximate dynamic programming (ADP) with various look-ahead data sources. Real-world testing was conducted on a test track and resulted in more than 20% FE improvement over the base vehicle control strategy [11].