Thesis - Physics-Informed Machine Learning for Reduced-Order Battery Modelling in Embedded BMS
Scania GroupRekommenderat
Sammanfattning
This master's thesis opportunity focuses on developing physics-informed machine learning models for battery management systems in heavy-duty electric vehicles. The work involves analyzing electrochemical battery data, creating machine learning baselines, and investigating advanced modeling techniques. The position is based in Södertälje, Sweden, and offers a chance to collaborate with Scania's R&D team, enhancing future career prospects.Jobbet i korthet
Arbetstid
heltid/deltid
Det här erbjuder vi
Opportunity to build relationships with Scania for future career prospects.Experience in advanced machine learning and embedded systems.Collaboration with a leading R&D team in the automotive industry.
Ansök senast: Öppet tillsvidare
Publicerad: 2026-11-11
Beskrivning
30 hp - Physics-Informed Machine Learning for Reduced-Order Battery Modelling in Embedded BMS
Introduction
Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project.
TRATON R&D develops core technologies for the global brands Scania, MAN, and International. This master's thesis offers an opportunity to work with advanced machine learning, physics-based modelling, and embedded systems for heavy-duty electric vehicles.
Background
Advanced electrochemical battery models, such as the Doyle-Fuller-Newman (DFN) model, provide detailed information about internal battery behaviour. However, their computational complexity limits their direct application in production Battery Management Systems (BMS).
Physics-Informed Neural Networks (PINNs) offer a promising approach to developing computationally efficient models by combining physical equations with machine learning. Their accuracy, generalization, and suitability for embedded applications require further investigation.
Objective
Develop and evaluate physics-informed machine learning approaches for computationally efficient electrochemical battery modelling.
The thesis will investigate whether incorporating physical knowledge improves prediction accuracy, robustness, and generalization compared with conventional machine learning, while assessing the feasibility of embedded BMS implementation.
Job description
The thesis work will include the following tasks:
1. Analyze synthetic data generated from electrochemical battery models, including selected internal states and operating conditions.
2. Develop a conventional machine learning baseline for approximating battery dynamics.
3. Investigate physics-informed approaches, including PINNs, using governing equations and physical constraints.
4. Compare approaches in terms of accuracy, robustness, generalization, and computational complexity.
5. Evaluate the most promising approach for reduced-order modelling and embedded BMS feasibility.
Education/program/focus
Master's student (civilingenjör) in computer science, machine learning, applied mathematics, engineering physics, or a related field.
Good knowledge of Python and machine learning is important. Experience with PyTorch or TensorFlow is an advantage. Knowledge of scientific machine learning, numerical optimization, PINNs, or embedded systems is beneficial.
Number of students: 1
Start date for the thesis work: To be agreed
Estimated time required: 20 weeks
Contact persons and supervisors
Madeleine Ekström Unit Head madeleine.ekstrom@scania.com
Björn Bökelund Software Architect bjorn.bokelund@scania.com
Prem Balaji Chinnadurai Industrial PhD prem.chinnadurai@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.
Requisition ID: 35090
Number of Openings: 1.0
Part-time / Full-time: Full-time
Permanent / Temporary: Temporary
Country / Region: SE
Location(s):
Södertälje, SE, 151 38
Required Travel: 0%
Workplace: Hybrid
Introduction
Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project.
TRATON R&D develops core technologies for the global brands Scania, MAN, and International. This master's thesis offers an opportunity to work with advanced machine learning, physics-based modelling, and embedded systems for heavy-duty electric vehicles.
Background
Advanced electrochemical battery models, such as the Doyle-Fuller-Newman (DFN) model, provide detailed information about internal battery behaviour. However, their computational complexity limits their direct application in production Battery Management Systems (BMS).
Physics-Informed Neural Networks (PINNs) offer a promising approach to developing computationally efficient models by combining physical equations with machine learning. Their accuracy, generalization, and suitability for embedded applications require further investigation.
Objective
Develop and evaluate physics-informed machine learning approaches for computationally efficient electrochemical battery modelling.
The thesis will investigate whether incorporating physical knowledge improves prediction accuracy, robustness, and generalization compared with conventional machine learning, while assessing the feasibility of embedded BMS implementation.
Job description
The thesis work will include the following tasks:
1. Analyze synthetic data generated from electrochemical battery models, including selected internal states and operating conditions.
2. Develop a conventional machine learning baseline for approximating battery dynamics.
3. Investigate physics-informed approaches, including PINNs, using governing equations and physical constraints.
4. Compare approaches in terms of accuracy, robustness, generalization, and computational complexity.
5. Evaluate the most promising approach for reduced-order modelling and embedded BMS feasibility.
Education/program/focus
Master's student (civilingenjör) in computer science, machine learning, applied mathematics, engineering physics, or a related field.
Good knowledge of Python and machine learning is important. Experience with PyTorch or TensorFlow is an advantage. Knowledge of scientific machine learning, numerical optimization, PINNs, or embedded systems is beneficial.
Number of students: 1
Start date for the thesis work: To be agreed
Estimated time required: 20 weeks
Contact persons and supervisors
Madeleine Ekström Unit Head madeleine.ekstrom@scania.com
Björn Bökelund Software Architect bjorn.bokelund@scania.com
Prem Balaji Chinnadurai Industrial PhD prem.chinnadurai@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.
Requisition ID: 35090
Number of Openings: 1.0
Part-time / Full-time: Full-time
Permanent / Temporary: Temporary
Country / Region: SE
Location(s):
Södertälje, SE, 151 38
Required Travel: 0%
Workplace: Hybrid
Ansök till tjänsten
Thesis - Physics-Informed Machine Learning for Reduced-Order Battery Modelling in Embedded BMS
Rekommenderat
OM FÖRETAGET
Scania Group
