Master Thesis - Adaptive Equivalent Consumption Minimization Strategy for Fuel Cell Trucks

Job 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. 

Background

In contrast to conventional fossil-fueled vehicles and pure battery electric vehicles, fuel cell hybrid electric vehicles (FCHEVs) provide an excellent balance between energy efficiency and driving performance. With a hybrid powertrain, the wheel power demand can be flexibly allocated between the fuel cell system (FCS) and the energy storage system (ESS), i.e., a battery pack, to achieve improved fuel economy. This additional degree of freedom in power provision, however, requires efficient onboard energy management strategies (EMSs), which largely determine FCHEV performance.

 

The equivalent consumption minimization strategy (ECMS) is a widely used approach for HEV energy management. By introducing a costate variable known as the equivalence factor (EF), ECMS transforms a global energy management problem into a one-step optimal control problem and computes a (quasi-)optimal solution in real time. Accordingly, the EF is a key component of ECMS and strongly influences its decision-making.

 

Because accurate information about future driving conditions is generally unavailable, the optimal EF trajectory over an entire trip cannot be determined in practice. As a result, advanced ECMS variants with online EF adaptation—referred to as adaptive ECMS (A-ECMS)—are needed for real-time FCHEV applications. A fundamental requirement for A-ECMS is to maintain the ESS state of charge (SOC) near a prescribed target while keeping it within upper and lower limits. In addition, EF adaptation tends to stabilize FCS power output, thereby reducing fuel cell cycling cost without compromising FCHEV drivability. Total fuel consumption over a complete trip remains an important performance metric for A-ECMS.

Project Tasks

The research tasks in this thesis project are summarized as follows:

 

·  Conduct a comprehensive literature review and critically assess existing A-ECMS-based energy management strategies (EMSs) for fuel cell hybrid electric vehicles (FCHEVs), with particular emphasis on equivalence factor (EF) adaptation methods.
·  Develop at least one novel real-time EF adaptation method that preserves ESS charge sustainability and effectively reduces fuel cell system (FCS) cycling frequency compared with benchmark EF calculation approaches (e.g., constant EF values and SOC-based tangent functions).
·  Further improve the robustness of the proposed method by ensuring stable EF and FCS power trajectories, even in noisy environments where measurable state variables are corrupted by measurement noise.
·  Implement the final solution on Volvo’s Global Simulation Platform (GSP), evaluate its performance across multiple driving scenarios, and verify its effectiveness and superiority.

 

In summary, this Master’s thesis aims to develop novel real-time EF adaptation methods to enhance ECMS-based energy management for FCHEVs in terms of ESS charge sustainability, FCS power stability, and total fuel consumption. Optimization techniques will be used to balance short-term performance with long-term operating efficiency.

Candidate Profile

This thesis project will be conducted in collaboration with the Software Solutions team at Volvo Group Trucks Technology (GTT) and is recommended for a group of two Master Thesis students. Qualified candidates should possess solid knowledge and strong skills in the following areas:

 

·  Academic background in vehicle engineering, mechanical engineering, automatic control, computer science, applied mathematics, machine learning, or related disciplines
·  Experience in probability theory, optimization algorithms, or stochastic dynamical systems
·  Expertise in model-based design, artificial neural network, or reinforcement learning
·  Proficiency in MATLAB/Simulink
·  Strong interest in energy management strategies for fuel cell hybrid electric vehicles

Start Date & Contact People

Project start:  September 2026  

 

Contact people:   

 

Tong Liu , (Industrial Supervisor), Volvo GTT – Software Solutions, Göteborg, Sweden 

E-mail: tong.liu@volvo.com

Tel: +46 760937205

 

Professor Torsten Wik (Academic Supervisor & Examiner) – Systems and Control - Chalmers University of Technology, Göteborg, Sweden

E-mail: torsten.wik@chalmers.se

Tel: +46 31 7725146

 

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.

Applying to this job offers you the opportunity to join Volvo Group . Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. ​We are passionate about what we do, and we thrive on teamwork. ​We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment. 

 

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. 

Job Category:  Technology Engineering

Organization:  Trucks Technology & Industrial

Travel Required:  No Travel Required

Requisition ID:  31693