Master Thesis: Road Section Weather and Vehicle Energy Consumption Modeling

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. 

What we are looking for:

•    Two motivated students with a strong background in modeling, signal processing, and machine learning.
•    Interest in vehicle dynamics and simulation is a benefit.
•    Excellent analytical skills and a proactive approach to problem-solving.

 

What you will gain:

•    Hands-on experience working on cutting-edge vehicle energy efficiency solutions.
•    Close collaboration with Volvo GTT experts and academic guidance from Chalmers University of Technology.
•    An opportunity to contribute to real-world applications that can influence future vehicle design and operation.

About the project:

Are you interested in vehicle dynamics, signal processing, and machine learning? Do you want to contribute to developing models that improve energy efficiency and range estimation for heavy vehicles? Volvo Group Trucks Technology offers an exciting Master’s thesis opportunity starting in January 2026, in close collaboration with Chalmers University of Technology.

 

Modeling vehicle energy consumption is crucial for several reasons. Accurate models can be used for model-based predictive cruise controllers to minimize fuel and energy consumption, as well as for functions like energy-efficient route planning, vehicle range estimation, and charge planning.

 

Vehicle energy consumption is affected by weather conditions. Cold temperatures, heavy rain, and strong headwinds all substantially increase energy consumption compared to nominal conditions, such as 20°C ambient temperature, dry conditions, and no wind. The main cause of this increase is the rise in motion resistance, including air drag and rolling resistance. Head (and cross-) winds increase air drag, while heavy rain and low ambient temperatures increase both air drag and rolling resistance.

 

While models exist describing how weather affects air drag and rolling resistance, they are often developed in controlled environments, such as wind tunnel tests and test rigs. These models do not fully capture all aspects of weather effects in real traffic. For example, wind conditions are very local but only measured at a few roadside spots. Interpolating between these spots is difficult because it depends on local geography, vegetation, and buildings. Additionally, traffic on the roads disturbs the wind, which can significantly impact air drag.

Focus of the Thesis:

The work aims to investigate the differences between actual road weather affecting vehicle energy consumption in real traffic and the more general weather data available from external sources. This includes differences in what can be measured directly by the vehicle and more difficult-to-measure factors, such as local turbulence created by other vehicles.

Objectives of the Master Thesis:

•    To investigate correlations between weather data measured by a vehicle on specific road segments and weather data from sources such as SMHI, Trafikverket, and Klimator, and to build models for this.
•    To investigate how surrounding traffic affects wind speed measured by the vehicle and possibly develop simplified models for this.
•    To investigate whether surrounding traffic also affects air drag in ways that are not captured by the wind sensor.
•    To model, or at least suggest methods to adapt, vehicle energy consumption predictions on different road segments based on available weather nowcast/forecasts.
•    The ultimate goal is to predict vehicle energy consumption over a specific road segment with high accuracy, given general weather and traffic information.

Ready for the next challenge?

Start date: January 2026
Location: Volvo GTT, in collaboration with Chalmers University of Technology

 

Interested?
Apply via our website or in the link on this page! For questions – contact our Thesis Supervisor Mikael Askerdal at mikael.askerdal@volvo.com.

Last application date: 9 November, 2025

 

Join us in shaping the future of sustainable transportation!

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. 

 

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.

Job Category:  Technology Engineering

Organization:  Group Trucks Technology

Travel Required:  No Travel Required

Requisition ID:  25991