Minimum Energy Speed Planning for Autonomous Electric Vehicles Using Maximum Current Curve

One often overlooked aspect of calculating minimum energy path for autonomous electric vehicles (AEVs) is consideration of electric motor's physical limitations. This aspect becomes significantly complex when considering other constraints during the speed planning stage. One such limitation is the electric motor’s ability to extract maximum energy during regenerative braking (RB). This paper incorporates maximum current curve (MCC) and other motor limitations for solving optimum speed planning of AEVs using mixed-integer linear programming (MILP). The proposed approach not only determines the most suitable speed profile for a predefined route but also streamlines the integration of RB in obtaining optimal speed profile. This involves operating the motor based on its MCC and analyzing its impact on selecting different speed trajectories. The proposed method is compared to a base case model that does not incorporate MCC into its MILP formulation. The results are validated through MATLAB/Simulink simulation for a route under study and it is shown that the proposed method consumes 2.3% less energy throughout the route when compared to the base case scenario.