Energy-Aware Driving Profile of Autonomous Electric Vehicles Considering Regenerative Braking Limitations

Abstract

This paper focuses on finding an optimal energy-aware speed trajectory of an Autonomous Electric Vehicle (AEV) considering regenerative braking capability and its limitations. A position-based Electric Vehicle (EV) energy consumption model is used to emulate vehicle-road operating conditions. It is assumed that the EV is driven in an urban area where the route is only constrained by maximum speed limits and traffic signs. The eco-driving problem is formulated as a Mixed Integer Linear Programming (MILP) problem and is solved for two different case studies to demonstrate the importance of considering regenerative braking in identifying optimal speed trajectory of AEVs. The MILP problem is coded in Python and CPLEX is used as a solver for the optimization problem. The results show a variation in the optimal speed trajectories and confirm that when regenerative braking limitations are considered in the calculations leading to an energy-aware speed trajectory, energy consumption can be reduced. This study sets forth a framework for optimizing the braking profile of an AEV by realistically taking into account the vehicle's regenerative braking limitations which ultimately yields an optimal speed trajectory.