Energy Management of Hydrogen Hybrid Electric Vehicles—Online-Capable Control

Abstract

The results shown in this paper extend our research group’s previous work, which presents the theoretically achievable hydrogen engine-out NO​xeo (H​2-NO​xeo) Pareto front of a hydrogen hybrid electric vehicle (H​2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H​2-HEV with explicit consideration of the H​2-NO​xeo trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing NO​xeo. In a case study, it is shown that MPC is able to satisfy user-defined NO​xeo limits over the course of various driving missions. Moreover, a comparison with the optimal Pareto front highlights MPC’s ability to achieve close-to-optimal fuel performance for any desired cumulated NO​xeo target on four realistic routes for passenger cars.