Model-Predictive Energy Management for Optimizing the System Efficiency of a Fuel Cell Truck Considering Fuel Cell Degradation
Year: 2026
Type: article
Abstract: In this work a model predictive control (MPC) approach is presented with the aim to optimize the powertrain system efficiency of a fuel cell electric heavy-duty vehicle. The MPC is calculating the power split between the high-voltage (HV) battery and the fuel cell in order to follow the power requirements from the drive cycle. The second objective of the MPC is to extend the lifetime of the fuel cell stack. In order to realize this, the degradation of the fuel cell stack is considered within the cost function.The task of the control system is to adjust the power trajectory of the fuel cell depending on different driving cycles within a specified prediction horizon. The objective is to maintain a target state of charge (SoC) for the high-voltage (HV) traction battery at the end of the time-discrete horizon while optimizing operation in terms of fuel consumption and avoiding excessive degradation of the fuel cell stack, based on the driving profile information. The MPC calculates the control variables based on a discrete-time simplified fuel cell, vehicle, and battery model. To solve the non-linear optimization problem, the open-source software package acados with interfaces to higher programming languages is integrated into MATLAB Simulink to achieve real-time capability without limiting computing power.The MPC is implemented and tested in a model-in-the-loop (MiL) environment. For the VECTO Long Haul drive cycle a reduction of 11.83 % in hydrogen consumption could be achieved, while the life time can be increased up to 42.9 % in comparison to a rule-based strategy.
Source: Energies
DOI: 10.3390/en19215047
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7117569675