A GPU Implementation of a Look-Ahead Optimal Controller for Eco-Driving Based on Dynamic Programming

TL;DR

This paper presents a GPU-accelerated dynamic programming approach for eco-driving in connected hybrid vehicles, achieving over 15% fuel savings and a 90% reduction in computation time.

Contribution

It introduces a parallel GPU implementation of a receding horizon optimal control problem for eco-driving, enabling real-time application with significant efficiency gains.

Findings

Over 15% fuel economy improvement compared to baseline.

GPU parallelization reduces solver time by more than 90%.

Effective real-time control for connected hybrid vehicles.

Abstract

Predictive energy management of Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, has the potential to significantly improve energy savings in real-world driving conditions. In particular, the eco-driving problem seeks to design optimal speed and power usage profiles based upon available information from connectivity and advanced mapping features to minimize the fuel consumption between two designated locations. In this work, the eco-driving problem is formulated as a three-state receding horizon optimal control problem and solved via Dynamic Programming (DP). The optimal solution, in terms of vehicle speed and battery State of Charge (SoC) trajectories, allows a connected and automated hybrid electric vehicle to intelligently pass the signalized intersections and minimize fuel consumption over a prescribed route. To enable real-time implementation, a parallel architecture of DP is proposed for an NVIDIA GPU with CUDA programming. Simulation results indicate that the proposed optimal controller delivers more than 15% fuel economy benefits compared to a baseline control strategy and that the solver time can be reduced by more than 90% by the parallel implementation when compared to a serial implementation.