Master Thesis Student - Battery Lifetime Analytics
Confirmed on the employer's own hiring board on Oct 19, 2026. First seen by Alion on Oct 13, 2026.
30 hp -Transfer learning for battery lifetime predictions
Thesis work is an excellent way to get closer to TRATONGroup and build relationships for the future. Many of today's employees at TRATONbegan their career with a degree project.
TRATON is a group of strong brands, including Scania, MAN and International, with a shared mission: to create the future of sustainable transport solutions. Development of heavy-dutybattery electric vehicles (BEVs) constitute a core part of this mission.Batteries make up a large fraction of the cost of BEVs, and their capacity and performance (state of health) degradeover time in a usage-dependent way. The residual value of a used battery follows the same trend. Models that estimate, predict,and explain the degradation of vehicle batteries throughout their life cycle are therefore an important success factor for TRATON, both to help our customers get the most out of their electric vehicles and to improve future product designs.Battery degradation is a complex process that is difficult to model from first principles and expensive to characterise experimentally. Practical aging models therefore rely on data-driven approaches that combine domain expertise with data from multiple sources, and the ability to model across battery generations is potentially valuable.
Objective
This thesis project aims to exploremethods to build lifetime models that combine data from vehicles in operation with some different data source, such as labtestsorsimulations, in order to facilitate continuous and cumulative improvements. We favour Bayesian methodsto enable probabilistic forecasts using complex non-linear models.
Job description
The student will implement battery aging modelsthat combines models or datafrom lab tests with operational data, and explore the ability to refine predictions and learningsas operational accumulates over time. The work will be conducted on siteat TRATON R&D in Södertälje.
Education/program/focus
We are looking forastudent in applied mathematics or engineering with knowledge of Bayesian statistics, strong skills in numerical programminganddata analysis using Ror python, and experience with analytical calculations. Knowledge of SQL, time series models and probabilistic programming in Stan or a similar language is valuable, as is a scientific background from undergraduate courses in engineering or physical sciences. Knowledge of electrochemistry and battery technology is helpful but not necessary.
Number of students: one
Start date for the thesis work: January 2027(flexible).
Estimated time required: one semester.
Contact persons and supervisors
Martin Lindén, PhD, development engineer,Battery Lifetime Analytics team, [email protected]
Daniel Carlsson, head of unit Lifetime, Regulations and Safety, [email protected]
Application:
Your application must include a CV, personal letter and transcript of gradesand be submitted via our website.Make sure to high-light relevant skills and course work, including ongoing courses. A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.