Thesis - Machine learning based testing of battery and fuel cells

Your Responsibilities

Fitting Physical Models to the test measurements of the Batteries or Fuel Cells are a powerful tool in capturing their inner characteristics. However, the fidelity of the physical model is highly dependent on the set of physical phenomena coved by mathematical formalism. Differently, testing based on data-driven models, like artificial neural networks (ANN) does not require the use of prior electrochemical knowledge and their inference relies entirely on the data collected during the testing. Although such data-driven models can be very accurate, they also require a large training dataset and do not generalize well outside the training data domain.

• Development of machine learning methods
• Implementation of the developed methods into AVL testing pipeline of Batteries and Fuel Cells
• Overcoming limitations of sparse and out-of-distribution training datasets

Your Profile

• Ongoing studies in the fields of Computer Science, Telematics, Physics or Electrical Engineering
• Good programming skills in Python or C++
• Knowledge of Machine Learning
• Good knowledge of German and English
• Skills in solving PDE are beneficial
• For this thesis is your presence at our headquarter in Graz required!

We offer

• You can write your thesis independently and receive professional guidance and support from our experienced employees.
• You will have the opportunity to exchange ideas with experts in the company and benefit from their expertise.
• Take the opportunity to immerse yourself in the world of AVL and embed your theoretical knowledge in a practical environment.

The successful completion of the thesis is remunerated with a one-time fee of 3.500,00 EUR before tax.

About AVL

AVL is one of the world's leading mobility technology companies for development, simulation and testing in the automotive industry, and beyond. The company provides concepts, solutions and methodologies in fields like vehicle development and integration, e-mobility, automated and connected mobility (ADAS/AD), and software for a greener, safer, better world of mobility.