Thesis Work - Model Predictive Control of Fuel Cell system

Background of thesis project

Fuel Cell technology is a promising solution to improve our current electromobility systems.
In fact, the usage of hydrogen as an energy vector can possibly increase the range and downsize the energy storage system.
Control and optimization of the whole system, propulsion as well as cooling system, becomes crucial to increase efficiency and meet the requirements.

Suitable background

Strong background in:
  • Automatic control
  • Dynamic modeling
  • Optimization
  • You should be able to use Matlab/Simulink and Python is a plus.
  • It’s desirable to have notions about thermal engineering

Description of thesis work


The goal of the thesis is to design and implement a controller, possibly a Model Predictive Controller (MPC), that is able to increase the efficiency of a target system equipped with energy storage system, fuel cell, hydrogen storage and cooling system. Most of the simulation models are available and usable to support the control design.

First step

Literature study to benchmark the different solutions in terms of fuel cell vehicles (or systems in general) control leading to global optimization.

Second step

Linearize the models, identify an objective function and design the controller.

Third step

Verify the results and identify possible improvements.

Thesis Level: Master (30 ECTS points)

Language: English

Starting date: January 2023

Number of students: 1-2


Francesco Galuppo, Senior CAE Engineer, Volvo Penta, +46765534247
Josef Backhans, CAE Simulation Manager, Volvo Penta, +46765534372

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