Master Thesis - Activity recognition and ML models for auxiliary energy consumption

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:
The Virtual Product Development and Digital Services (VPD & DS) team within Volvo GTT is focused on delivering high-quality products based on data-driven approaches and fact-based decision-making. The team aims to improve the quality of the products, reduce development costs, and enhance customer experience through its connected services.

A key feature in the digital service for both sales and customers is the capability to forecast energy consumption for our heavy-duty vehicles. Timeseries data collected onboard vehicles via sensors can be used to extract meaningful embeddings. These embeddings can then be applied for downstream tasks, such as building data-driven models for predicting auxiliary energy consumption, or activity recognition related to vehicle operations. Incorporating these insights will enhance the accuracy of total energy consumption predictions for heavy-duty vehicles.

We believe you:
•    Are a student studying an MSc program in computer science, machine learning, or equivalent.
•    Feel confident in programming in Python and/or Pyspark, preferably with some experience in relevant frameworks such as Pytorch.
•    Have strong knowledge in deep learning methods and transformer models.
•    Having worked with streaming data, time-series data, understanding vehicle usage and energy consumption is a merit.
•    Having worked with time-series foundation models and representation learning is a merit.

Thesis Description:
As a master thesis student, you will be working with real-world time-series data from heavy-duty battery electric vehicles (BEVs), gathered from both testing and customer trucks. The focus is to investigate time-series embedding methods and analyze vehicle operation data for activity recognition. You will also develop machine learning models to predict auxiliary energy consumption based on the identified activities. Several embedding models/methods will be explored, evaluated, and compared. You will work closely with the Advanced Analytics Team, and have the opportunity to collaborate with other domain experts as well as various stakeholders in different tech streams. Furthermore, you will be part of a highly motivated team working with data-driven methods for predictive maintenance, increasing the uptime of heavy-duty vehicles.

In this thesis titled, “Activity recognition and ML models for auxiliary energy consumption of HD BEVs”, you will be working with multi-variate time-series data, collected with on-board sensors from heavy-duty vehicles. You will create compact and structured data representations by applying dimensionality reduction, feature extraction, and representation learning techniques. Additionally, you will apply transfer learning techniques and leveraged aggregated data to build scalable, generalized models across larger vehicle populations.

Furthermore, machine learning models are built for forecasting auxiliary energy consumption based on the identified activity. The performance and modularity of the code should also be a major focus of this thesis. As a stretch scope, you will explore the time-series foundation models. Generation of synthetic data is an extended task as well.

Objectives and learning outcome:
•    Literature survey including review of related machine learning methods, in particular for applications addressing time-series data.
•    Develop and implement the ML models for time-series activity recognition and energy consumption.
•    Compare and evaluate ML models.
•    Write a Master Thesis report and present the results at the company.
•    Upon successful completion of this work, a feasibility study for further research on this domain is desirable.

Duration:
•    The duration of the Thesis is 20 weeks (Approx.).
•    The thesis starts in January 2026 or earlier if possible.
•    30 ECTS (academic credits) if in agreement with your thesis advisor at the University.
•    This thesis is suitable for 1-2 students.

Last application date is 27of November. 

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.