Thesis work: 30 hp - Bayesian modelling of battery lifetime

Introduction
Thesis work is an excellent way to get closer to Traton Group and build relationships for the future. Many of today's employees began their Scania career with their degree project.

Background 
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 duty battery 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) degrade over 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 data from multiple sources with domain expertise.

Objective
This thesis project aims to explore the use Bayesian methods, in particular probabilistic programming languages such as Stan and related packages, to make probabilistic forecasts about battery state of health in different usage scenarios.

Job description
The student will develop and implement a battery aging model in a Bayesian setting based on existing models and data, and explore questions related to statistical and computational performance. The work will be conducted mainly at Traton R&D in Södertälje.

Education/program/focus
We are looking for students in applied mathematics or engineering programmes with knowledge of Bayesian statistics, strong skills in numerical programming, data analysis using R or python, and experience with analytical calculations. Knowledge of probabilistic programming in Stan or a similar language is valuable, as is experience with Markov Chain Monte Carlo methods and some scientific background from for example undergraduate courses in engineering or physical sciences. Knowledge of electrochemistry and battery technology is helpful but not necessary.

Number of students: 1

Start date for the thesis work: January 2026 (flexible)

Estimated time required: one semester.

Contact persons and supervisors
Martin Lindén, PhD, development engineer, Battery Lifetime Analytics team, martin.linden@scania.com

Daniel Carlsson, head of unit Lifetime, Regulations and Safety, daniel_x.carlsson@scania.com

Application:
Your application must include a CV, personal letter and transcript of grades.

A background check might be conducted for this position. We are conducting interviews continuously and may close the recruitment earlier than the date specified.