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

The results shown in this paper extend our research group’s previous work, which presents the theoretically achievable hydrogen engine-out NOeo x (H2-NOeo x ) Pareto front of a hydrogen hybrid electric vehicle (H2-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 H2-HEV with explicit consideration of the H2-NOeo x 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 NOeo x . In a case study, it is shown that MPC is able to satisfy user-defined NOeo x 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 NOeo x target on four realistic routes for passenger cars.