Informationen zur Anzeige:
Thesis - Self-supervised Learning for Battery Health Estimation f/m/d
Graz (Österreich)
Aktualität: 07.03.2025
Anzeigeninhalt:
07.03.2025, AVL List GmbH
Graz (Österreich)
Thesis - Self-supervised Learning for Battery Health Estimation f/m/d
Über uns:
AVL is one of the world's leading mobility technology companies for development, simulation and testing in the automotive industry, and beyond. We provide 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.
Aufgaben:
- We are looking for a motivated student to conduct their master thesis in the area of Li-ion batterie modelling using state-of-the-art machine learning modelling techniques. This master thesis focuses on developing advanced techniques to estimate the health estimation of battery health and performance in the automotive industry. By leveraging deep neural network architectures for learning the trajectory of the degradation with existing amount of test data, the aim is to estimate the state-of-health without having the entire history of the battery's operation (zero-shot learning). The thesis will contribute to the overcome practical issues for SOH estimation in-field and will offer valuable insights into understanding the influencing aging factors.
- Literature research: Identify the state-of-art for the specific applications and rank most relevant architectures/techniques
- Data preparation and pre-processing: Utilize time series analysis and aggregation techniques to create a pipeline for feature engineering during charge cycles. Selection of the target variables
- Data segmentation: Prepare sample of data from existing experimental datasets for training the models
- Comparison and ablation study: Establish a set of baseline methods (i.e., MLP, RNN, LSTM) that will be used for comparison purposes
- Final model evaluation: Utilize the trained models for final evaluation in both experimental and real-world data
- Sensitivity analysis: Utilize Explainable-AI methods to pinpoint influencing factors and explain model's outputs
Qualifikationen:
- BSc in domains similar to Applied Statistics/Mathematics, Computer Science, Data Science, Automotive or Electrical Engineering
- Strong background in data analysis, deep learning, and time series prediction
- Proficiency in programming languages such as Python for implementing data analysis algorithms
- Familiarity with statistical methods and transformers (LLMs)
- Ability to work independently, conduct experiments, and analyze complex data sets
- Excellent problem-solving and critical-thinking skills
- Strong communication skills to present findings and recommendations effectively
Wir bieten:
- 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 EUR EUR3,500.00 before tax.
Unser Kontakt:
Find out more: www.avl.com
Weitere Informationen:
At AVL, we foster and celebrate diversity: We recognize that diverse ways of thinking are required to achieve our vision of a greener, safer, and better world of mobility. Different backgrounds, attitudes, interests, and experiences make us successful. As Equal Opportunity Employer we consider all qualified applicants without regard to ethnicity, religion, gender, sexual orientation or disability status.
Fachrichtung
Ingenieurwesen
Informatik, IT
Elektrotechnik
Informatik
Mathe, Naturw., Umwelt
Mathematik
Fahrzeugbau
Art des Angebots
Standorte
