Performance Evaluation of an Eco-Driving Controller for Fuel Cell Electric Trucks in Real-World Driving Conditions

Range anxiety in current battery electric vehicles is a challenging problem, especially for commercial vehicles with heavy payloads. Therefore, the development of electrified propulsion systems with multiple power sources, such as fuel cells, is an active area of research. Optimal speed planning and energy management, referred to as eco-driving, can substantially reduce the energy consumption of commercial vehicles, regardless of the powertrain architecture. Eco-driving controllers can leverage look-ahead route information such as road grade, speed limits, and signalized intersections to perform velocity profile smoothing, resulting in reduced energy consumption. This study presents a comprehensive analysis of the performance of an eco-driving controller for fuel cell electric trucks in a real-world scenario, considering a route from a distribution center to the associated supermarket. The eco-driving strategy hereby proposed is based on an Approximate Dynamic Programming framework that uses a long-term route optimization to then solve a short-term model predictive control problem. Model-in-the-Loop simulations are performed using a high-fidelity plant model of a fuel cell electric truck. The objective of this work is to assess the performance of the eco-driving controller by conducting large-scale simulations that consider variability in the traffic conditions. The results obtained by the proposed eco-driving controller are finally compared against an ideal benchmark strategy referred to as wait-and-see controller, to quantify the energy benefits using the Probability Density Function. The estimated hydrogen consumption slightly increases by approximately 1.3% when comparing the proposed eco-driving controller with the wait-and-see benchmark. On the other hand, travel time is minimally impacted with a mean difference of few seconds, however the changes in formulation allow the proposed controller to be implemented in real-world and to obtain realistic results. The obtained results confirm the quality of the proposed eco-driving controller, robust to sudden changes in exogenous inputs.