Master Thesis - Anomaly detection in time series for heavy-duty BEVs
Göteborg, SE, 417 15
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 dedicated to delivering high-quality products through data-driven approaches and fact-based decision-making. The team aims to enhance the quality of the products, reduce development costs, and improve customer experience through its connected services.
Ensuring operational safety is critical for commercial heavy-duty vehicles. The objective of this project is to develop anomaly detection methods for multi-variate time series data, collected from on-board sensors in battery electric vehicles (BEVs). Beyond safety, implementing cost-efficient and reliable maintenance strategies are particularly essential for operating commercial vehicle fleets, where operational availability and total maintenance cost directly impact profitability. By applying anomaly detection methods to identify early signs of degradation or failure in vehicle components, the project aims to develop a system that facilitates proactive maintenance interventions, reducing downtime and maintenance expenses.
We believe you:
• Are currently pursuing a Master’s degree (MSc) in Computer Science, Data Science, Machine Learning, or another related engineering discipline.
• Have strong knowledge in anomaly detection and representation learning using deep learning-based methods. Experiences in causal inference, contrastive learning, and explainable AI (XAI) methods is a merit.
• Have hands-on experience working with multi-variate time-series dataset.
• Are proficient in programming with Python, preferably with experiences in relevant libraries and frameworks for anomaly detection and deep learning, such as PyOD, PyTorch, Keras, or Tensorflow.
Thesis Description:
As a MSc thesis student, you will work with real-world time-series data collected from heavy-duty battery electric vehicles (BEVs), including both test vehicles and customer fleets, to develop and evaluate anomaly detection algorithms. You will work closely with the Advanced Analytics Team and collaborate with domain experts in different tech streams and areas. In addition, you will be part of a highly motivated team working with data-driven methods for predictive maintenance, with the goal of enhancing vehicle reliability and maximizing the operational uptime of heavy-duty BEVs.
The aim of this project is to explore and develop context aware and explainable anomaly detection algorithms for monitoring critical components (and their efficiencies) in energy storage systems, such as battery modules and service boxes, enabling early identification of abnormal behaviours that could lead to unexpected downtime. A promising research direction involves investigating representation learning techniques (such as contrastive learning, continual learning, federated learning etc.) to capture and encode key characteristics of time series data for anomaly detection. For instance, many state-of-the-art time methods utilize autoencoders, using learned embeddings in the latent features or reconstruction errors to compute anomaly scores. In addition, methods that are inherently explainable (e.g. causal graph/relations learned via causal inferences), that can be learned in an incremental setting (e.g. decision tree-based approaches) or computationally efficient (e.g. echo state network via reservoir computing), are of great interest as well. The developed approach will be evaluated on a real-world dataset collected from commercial BEVs.
Objectives and learning outcome:
• Literature survey including review of related machine learning methods, e.g. contextual (and explainable) anomaly detection, representation learning, and time-series embedding methods etc.
• Develop anomaly detection and representation learning methods.
• Test and evaluate proposed approaches and compare it with state-of-the-art methods on an EVs dataset.
• 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 in the University.
• This thesis is suitable for 1-2 students.
• On-site presence is preferred and appreciated, as it is easier to collaborate with our teams, as well as getting access to various resources.
Last application date is the 27yh of November.
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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.