Hence, it is imperative to benefit from and improve upon eco-driving methods that are capable of precisely predicting vehicle energy use.  
Different eco-driving methods have been proposed and evaluated for conventional internal combustion engine (ICE) vehicles that are operated autonomously [6; 7; 8]. Dynamic programming (DP) is a global optimization method that has been frequently used in the literature for optimizing the fuel consumption of ICE vehicles. This technique has also been utilized for reducing the energy use of vehicles with different electric drive powertrain configurations [9; 10; 11; 12; 13]. In [9], a parallel framework using DP is presented for an eco-driving problem on a predefined route in which the battery state of charge (SOC) and vehicle speed are key decision factors, leading to an optimized speed. In [10], a spatial domain of DP is introduced for a plug-in hybrid electric vehicle (PHEV) powertrain with the aim of finding the optimal power split between the ICE and the electric motor. In this study, initial speed, route length, acceleration, road grade, wind, and payload are considered critical elements affecting eco-driving. In [11], DP is used to identify the optimum charging and discharging pattern of a hybrid electric vehicle (HEV) based on a predefined route. The route is split into different segments with predefined properties, and it is assumed that in each segment, the total consumed energy of the vehicle is dependent upon the SOC. The authors of [12] use safety control and speed planning to form a two-level receding horizon control architecture for calculating the energy consumption of a PHEV. In this architecture, the outer layer controls the vehicle speed while the inner layer is responsible for making decisions to ensure compliance with traffic restrictions. In [13], model predictive control (MPC) is used to find optimum fuel economy for an HEV by taking advantage of a two-level MPC approach to further simplify the problem.  
In the majority of previous studies, regenerative braking (RB) and its practical limitations were either overlooked or oversimplified. Energy harvesting capability is an important part of eco-driving in EVs which should accurately be considered during the RB process. Since the decision-making process in AEVs is achieved without driver interference, more flexibility is attained in terms of the duration and extent of applying RB while calculating the desired speed profile. This paper contributes to obtaining the optimum speed profile for AEVs with the aim of minimizing trip-level energy needs and sets forth a framework for integrating realistic limitations of RB into the calculation of the optimum speed trajectory for the eco-driving problem. The mixed-integer linear programming (MILP) method is selected for calculating the objective function due to the fact that DP-based methods are computationally expensive, especially as the size of the problem increases. In the authors' previous work [14], the effect of low-speed operation as a static constraint on the RB process was investigated. In this study, a dynamic constraint which provides a much more realistic representation of RB operation at low speeds is examined and integrated into the decision-making process to realistically capture the real-life behavior of an electric motor during RB at low speeds and to further maximize energy harvesting capability of AEVs during the route-planning stage.  
The remainder of this study is organized as follows. In Section 2, practical RB limitations are presented, and the significance of including these limitations in the process of identifying an optimal speed profile is outlined. The eco-driving formulation as MILP is discussed in Section 3. In Section 4, verification steps for the proposed approach are detailed and in Section 5, two case studies are simulated based on the physical limitations of RB, followed by a discussion of the results for each scenario. Finally, conclusions are presented in Section 6.