Master Thesis: Analys of Model Predictive Control for Fuel Cell E-Vehicles

Company description: Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future. If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match. Job description: Background The transition to low-carbon transport depends on wider access to renewable energy and technologies that use it efficiently. When supplied with hydrogen produced from renewable energy, fuel cell hybrid electric vehicles offer a potential route to reducing emissions in demanding transport applications. Fuel cell hybrid electric vehicles (FCHEVs) integrate a fuel cell system (FCS) and an energy storage system (ESS) to meet vehicle power demand. Effective energy management is pivotal to limiting hydrogen consumption while maintaining reliable operation. Model predictive control (MPC) supports this task by using a powertrain model to predict system behavior over a finite horizon and optimize power allocation subject to operational constraints. Repeating the optimization as new information becomes available allows the controller to respond to disturbances and prediction errors. Applying MPC in an FCHEV also requires consideration of vehicle performance and component durability. Power delivery must remain responsive to changing driving demands, while operating patterns that accelerate fuel cell and battery degradation should be limited. This thesis will examine an existing MPC-based controller, investigate improvements to its performance and robustness, and prepare it for software-in-the-loop (SIL) simulations. Thesis Tasks The students will: Analyze the existing MPC-based controller and compare its performance with other energy management strategies using vehicle simulations. Conduct a sensitivity analysis and tune controller parameters across representative operating conditions. Evaluate robustness toward disturbances, prediction errors, and model uncertainty. Investigate the trade-off between computational complexity and controller performance, particularly under real-time operating requirements. Implement and evaluate the improved controller on Volvo’s Global Simulation Platform (GSP) and develop a software-in-the-loop (SIL) model. The project aims to improve the existing controller and clarify the trade-offs among hydrogen consumption, vehicle performance, component durability, and computational demands. Candidate Profile The thesis will be conducted in collaboration with the Software Solutions team at Volvo Group Trucks Technology and is intended for two Master’s students. Candidates should have: A background in vehicle engineering, mechanical engineering, automatic control, mechatronics, applied mathematics, embedded systems, or related discipline A strong foundation in optimal control theory and methods Experience in dynamic system modeling and embedded system design Proficiency in MATLAB/Simulink Project start: January 2027 Contact: Atefeh Esmaeilzadehrostam, Industrial Supervisor, Volvo TTI – Software Solutions, Göteborg, Sweden Email: atefeh.esmaeilzadehrostam@consultant.volvo.com We value your data privacy and therefore do not accept applications via mail. Who we are and what we believe in We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities. Trucks Technology & Industrial Division hire team players who are ready to create real customer impact. Our decentralized teams work close to our customers, with speed and autonomy, to build what they truly need. Join us to collaborate on innovative, sustainable technologies that redefine how we design, build, and deliver value. Bring your curiosity, your expertise, and your collaborative energy, and together, we’ll turn bold ideas into tangible solutions for our customers and contribute to a more sustainable tomorrow.