## 1 Introduction
Many different methods to overcome the challenges of implementing global optimization algorithms are proposed in the literature. In [12], a DP-based EMS is used in a predictive control scheme to feed a conventional cruise controller with new set points on a heavy duty truck. The authors demonstrate real-world fuel consumption reduction for highway driving. In [13], DP optimization is done using cloud computing for optimizing the velocity reference that is tracked by a human driver. The authors of [14] reduced computational cost by separately formulating the car-following control and energy management strategy and then coupling the control laws in a MPC framework. Online implementation was demonstrated in hardware-in-the-loop experiments. In recent studies, the authors in [15] proposed an intelligent ECMS, where a Bayesian regularization neural network was used to predict the near-optimal equivalence factor (EF), and a back propagation neural network was used to forecast the engine on/off state to improve the quality of the EF prediction. In [16], a two-layer strategy was used where the upper layer extracts trip and vehicle information using CAV technology and solves DP on a remote server. The lower level controller was based on Q-learning and was used for real-time implementation of the optimal result obtained from DP. In [17], the authors developed a hybrid deep Q-learning and policy gradient method for vehicles driving along multi-lane urban signalized corridors to learn well-established longitudinal fuel-saving strategies. Numerical experiments demonstrate up to a 46% fuel reduction compared to their baseline model. The authors of [18] proposed a look-ahead horizon-based co-optimization of vehicle dynamics and powertrain operation to minimize energy consumption with the constraint of driving safety to avoid traffic collisions. The optimal solution was derived by PMP and verified in a traffic-in-the-loop simulation environment. Most of the past literature assumes no stochastic uncertainties in the prediction of future events. However, with existing hardware limitations, it is unrealistic to implement the stochastic optimal control methods. The current work proposes a method of using CAV technology to improve the energy efficiency of HEVs with a clear path to on-board implementation. The novelty of the work lies in the approach that combines machine learning, optimal control and traditional ECMS and concurrently optimizes the vehicle speed profile as well as EMS. The proposed approach is computationally efficient and provides better FE gains than ECMS. The primary objective is to quantify the potential for FE improvements using a look-ahead predictive EMS. An approach is outlined for using look-ahead data to obtain a prediction of future vehicle state trajectories (e.g., vehicle speed and battery state of charge) for a single vehicle using a DP-based rollout algorithm. A novel EF adaptation technique for ECMS is introduced, and the effect of traffic uncertainty on FE is measured through statistical methods. In a simulation study, we demonstrate about 10% FE gains over a baseline consisting of a combination of A-ECMS and the intelligent driver model. Recently, an MPC-based approach was shown to provide 20% FE gains over a rule-based EMS [11] but required higher computation time. Considering that the baseline in [11] was inferior to A-ECMS and required more computational resources, the FE gains obtained in this study are significant. The remainder of this paper first outlines the modeling framework and methodology used to formulate the proposed EMS. This is followed by a discussion of simulation results and key findings. Finally, a conclusion of the study is provided.