Last date to apply:

Master Thesis: Battery Field Data & Diagnostics

We are now looking for master students within data science, AI or a related field that are interested in doing their thesis project at our Battery Competence Center – Advanced Development Department at Husqvarna Group!

Background:

Husqvarna Group has a large installed base of battery-powered products. Significant amounts of battery data are generated through field operation Today this data is fragmented across multiple systems, teams and divisions and is not systematically analyzed. As a result, there is limited visibility into how batteries are really used by customers and how they behave towards end of life.

Turning this raw data into actionable knowledge is key to improving battery quality, reliability and customer experience. Insights on errors and real use-case behavior directly support better designs, diagnostics and battery algorithms. In addition, dealerships are a key touchpoint with the end customer: they handle service, repairs and warranty cases and are therefore both a valuable source of field information and a natural receiver of data-driven insights.

The objective

To address this, the Battery Competence Center (HGBCC) has launched the Battery Field Data & Diagnostics initiative aiming to establish a structured battery data and analytics framework

• Analyze existing data: explore available battery datasets with focus on:
  • Errors: fault events, error codes and failure records, including common, early-life and recurring failures.
  • Use-case behavior: real customer usage, charging and discharging behavior and operating conditions.
• Classify the data and errors: identify trends and groups (e.g. failure categories, usage profiles, operating clusters) using statistical and machine learning methods.
• Share findings with stakeholders: communicate results to BCC R&D and the product groups to improve current tasks, battery diagnostics, algorithms and future designs.
• Enable dealership collaboration: evaluate how dealerships can benefit from battery data (e.g. faster fault diagnosis, better customer support and warranty handling).

Your skills and background

• Master student in Data Science, AI/Machine Learning, Applied Mathematics/Statistics, Electrical Engineering or another relevant field.
• Good programming skills in Python (e.g. pandas, scikit-learn) and SQL; experience with Databricks or Power BI is a plus.
• Interest in Li-ion batteries and battery management systems (BMS); prior battery knowledge is a plus but not required.
• Strong communication skills in English and the ability to present results to both technical and non-technical audiences.

The thesis will include work such as

• Literature review on battery field-data analytics, fault diagnostics and usage profiling methods.
• Mapping of available battery datasets, assessment of data quality, and data cleaning and preparation.
• Exploratory analysis of battery errors and use-case behavior.
• Classification and clustering of errors and usage patterns to identify trends, groups and root-cause indicators.
• Development of dashboards or visualizations (e.g. Databricks, Power BI or Python) to present the findings.
• Workshops and interviews with BCC R&D and product groups to validate findings and capture needs.
• Proposal of a structure and process for data collaboration with dealerships.
• Documentation and a final presentation of results, in line with the requirements of the university.

How to apply  

Please send in your application with CV and cover letter as soon as possible.

Start date of the thesis project is beginning of 2027.

For questions regarding the thesis project please contact:  
Matthias Heim, [email protected]

Read about Husqvarna Group here:  
https://www.husqvarnagroup.com/   

Husqvarna Group is a world-leading producer of outdoor power products for garden, park and forest care. Products include chainsaws, trimmers, robotic lawn mowers and ride-on lawn mowers. The Group is also the European leader in garden watering products and a world leader in cutting equipment and diamond tools for the construction and stone industries.