Reduced Order Modeling

Master Thesis – Reduced Order Modeling


Developing control system software for the thermal management functionality of a vehicle is becoming more and more complex. This process involves, amongst other things, running many simulations of the complete thermal system of the vehicle in conjunction with the control system prototype software. These simulations are very complex, and the software development could be sped up significantly if the execution time could be improved. The focus of this thesis will be to develop a methodology to replace these simulation models with a machine learning model of acceptable accuracy with a significant improvement in execution time.
Who we are:
The Thermal Management department within Vehicle Technology is responsible for developing, delivering, and maintaining an optimized Cab Climate and Thermal Supply systems for all types of propulsion installations to all truck brands within the Volvo Group. We are responsible for leading the work with strategies and advanced engineering globally. We are located at Gothenburg and Bangalore, and we have close cooperation with the engineering sites located in Greensboro and Lyon. We understand the final customer needs and apply our knowledge to develop technical concepts and solutions that satisfy customer and business needs. The work is based on innovation, shared technology, common architecture and brand uniqueness. We want to make a difference by being there for our customers and by providing uptime and reliable products.
As a master thesis student in the Thermal Management Verification and Validation team you will be a valued contributor to our deliveries and continuous learning. You will be surrounded by a global and diverse team of highly skilled and engaged colleagues who will be interested in the progress of your work and eager to help and support along the way.

Description of tasks and expected outcome

Literature study on what type of machine learning technique would be most appropriate for this type of application
Investigate how to create training data in the best way using GT-Suite
Use the training data to train promising machine learning models, and compare their effectiveness, as well as a comparison to the existing GT-suite simulation model.
Document best practice for data generation, as well as the process for choosing an appropriate machine learning model

Suitable background

This master thesis requires analytical skills and a good understanding of thermodynamics, data handling, as well as machine learning techniques. To be successful in this master thesis project we believe that it is important that you recognize yourself in the following description.
  • Final year student in Master program for Automotive, Physics, Computer Science, Mechatronics or similar
  • Experience with machine learning frameworks like Tensorflow or Pytorch are a merit
  • Programming skills in Python, Matlab or similar
  • Fundamental understanding of thermodynamics
  • Fundamental knowledge of vehicle technology
  • Analytical mindset and problem-solving skills
  • Fluent in English
Thesis level: Master, 30 ECTS credits

Number of students: 2

Start date: Second half of January 2024, or upon agreement

Industrial supervisors: Marcel Aarts and Dongyu Liu

Location: Volvo Lundby site, Gothenburg, Sweden

We look forward to receiving your application!

Kindly note that due to GDPR, we will not accept applications via mail. Please use our career site.

We value your data privacy and therefore do not accept applications via mail.

Who we are and what we believe in
Our focus on Inclusion, Diversity, and Equity allows each of us the opportunity to bring our full authentic self to work and thrive by providing a safe and supportive environment, free of harassment and discrimination. We are committed to removing the barriers to entry, which is why we ask that even if you feel you may not meet every qualification on the job description, please apply and let us decide.

Applying to this job offers you the opportunity to join Volvo Group. Every day, across the globe, our trucks, buses, engines, construction equipment, financial services, and solutions make modern life possible. We are almost 100,000 people empowered to shape the future landscape of efficient, safe and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents with sharp minds and passion across the group’s leading brands and entities.

Group Trucks Technology are seeking talents to help design sustainable transportation solutions for the future. As part of our team, you’ll help us by engineering exciting next-gen technologies and contribute to projects that determine new, sustainable solutions. Bring your love of developing systems, working collaboratively, and your advanced skills to a place where you can make an impact. Join our design shift that leaves society in good shape for the next generation.

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