STUDY OF OPTIMAL VELOCITY TRAJECTORY FOR REAL-TIME PREDICTIVE CONTROL OF A MULTI-MODE PHEV

This report presents the development of two algorithms that uses available velocity bounds and powertrain information to generate an optimal velocity trajectory over a prediction horizon for a multi-mode plug-in hybrid electric vehicle. The objective of first optimization problem is to reduce dynamic losses, and required tractive force, while completing trip distance with a given travel time. Sequential Quadratic Programming (SQP) method is employed for this nonlinearly constrained optimization problem. This development illustrates the benefits of optimal velocity trajectories. Validation is completed using 2nd generation GM Volt model in Autonomie. The objective of second optimization problem is to generate velocity trajectory within a prediction horizon to reduce tractive force while monitoring the overall travel time required for the trip. The defined optimization problem is solved incorporating distance-based traffic dynamics and road conditions as compared to time-based optimization in first method. The developed algorithm reduces energy consumption by avoiding wasteful driving maneuvers and utilizes the opportunities for regeneration. The algorithm is implemented by ACADO toolkit for real-time execution. The algorithm is validated using 2nd generation Volt powertrain model developed at MTU