Master Thesis | Li-ion Cell modeling - methods for online cell ageing diagnosis

The opportunity

The increased global average temperature and extreme weather events raise the need for immediate actions to combat climate change. Polestar is committed to driving change towards a climate-neutral future with sustainable electric mobility. However, the speed and convenience of charging at a public station remain as challenges that warrant further investigation.

The battery is a complex core component in a modern EV, especially when high performance is combined with a strive to reduce carbon footprint to zero. Moreover, battery technology is evolving fast, and hence tools and methods need continuous improvements to enable full utilization at each development step. Advanced simulation models, both physics-based as well as empirical or data-driven models are needed to optimize battery pack design, dimensioning and control.

This project specifically aims to investigate non-destructive characterization of Li-ion cell ageing. By using models of the electrodes in conjunction with data from lab cell testing, or, possibly, vehicle data, it is possible to predict not only the amount of degradation, but how the degradation happens in the cell; different ageing modes such as loss of lithium inventory, active material loss, impedance growth and Li-plating can be predicted using this approach. This decomposition also allows for the development of more accurate life models of Li-ion cells, which can be based on machine learning techniques. These are all topics within the scope of this thesis work.

This Master thesis project will take place during 2025, within our Cell, Module & EPS CAE team in Propulsion department at Arendal office in Gothenburg, Sweden.

The responsibilities 

• Develop and optimize code to derive electrode potentials from full cell data

• Investigate extensions of the model and sensitivity of the full cell models

• Investigate its application to semi-empirical life models

• Participate in half-cell characterization

• Validate the approach by experimental cell testing

The ideal candidate
We are looking forward to reading your application with the following requirements:

• M.Sc. in Electrical Engineering, Engineering Physics, Chemistry, Applied Mathematics, Computer Science or similar

• Good knowledge in Python, specifically using numpy, scipy, and similar scientific libraries. Experience in optimizing code for speed is an advantage

• Interest and skills in experimental work and measurement techniques

• Completed courses and good knowledge in electrical circuit theory and programming
• A background with electrochemistry/battery technology and experience with advanced modelling tools and familiarity with machine learning and computational science in general is an advantage

• Analytical and independent

The process

If the above matches your ambitions, be sure to apply! Applications will be processed continuously and interviews will be held on an ongoing basis. Please note that applications via email will not be accepted.