Energy optimization in freight train operations: Algorithmic development and testing

Abstract

This research applies multi-objective dynamic programming – specifically, goal programming – solved using a computationally efficient heuristic minimum path-finding algorithm (A*) to improve energy efficiency in freight train operations. The investigation focuses on the U.S. freight network, evaluating the impact of the proposed system on six powertrain technologies, namely diesel, biodiesel, diesel-hybrid, biodiesel-hybrid, hydrogen fuel cell, and battery electric on energy consumption and travel time. The primary findings indicate that when prioritizing energy reduction, diesel and biodiesel hybrids emerge as the most effective, achieving a 47% decrease in energy consumption compared to scenarios without optimization. Hydrogen and battery electric technologies demonstrate a 26% energy saving. In contrast, diesel and biodiesel powertrains show the least improvement, with a 21.5% reduction in energy consumption, accompanied by a 60% increase in travel time for all powertrains except hydrogen, which incurs only a 29% increase. Furthermore, when the multi-objective optimization model incorporates travel time, assigning a 70% weight to energy consumption and a 30% weight to travel time, the results are consistent. In this scenario, diesel and biodiesel hybrids yield an 11% reduction in energy consumption, followed by a 7% reduction for hydrogen fuel cells and a 6% reduction for battery electric trains, with diesel and biodiesel powertrains achieving a 5% reduction. This optimization leads to a mere 7% increase in travel time compared to the non-optimized scenario.