Thesis - Self-supervised Learning for Battery Health Estimation f/m/d

Thesis - Self-supervised Learning for Battery Health Estimation f/m/d

Join to apply for the Thesis - Self-supervised Learning for Battery Health Estimation f/m/d role at AVL.

Job Description

We are seeking a motivated student to undertake their master thesis in Li-ion battery modeling using advanced machine learning techniques. The focus is on developing methods to estimate battery health and performance in the automotive sector. The goal is to leverage deep neural networks to learn degradation trajectories from existing data, enabling zero-shot learning for state-of-health estimation without requiring the entire operational history. This project aims to address practical challenges in field SOH estimation and provide insights into aging factors.

• Literature review: Identify current state-of-the-art architectures and techniques relevant to the application.
• Data preparation: Use time series analysis and aggregation for feature engineering during charge cycles; select target variables.
• Data segmentation: Prepare datasets from experimental data for model training.
• Comparison study: Establish baseline models (e.g., MLP, RNN, LSTM) for benchmarking.
• Model evaluation: Test trained models on experimental and real-world data.
• Sensitivity analysis: Apply Explainable AI methods to identify key factors influencing model outputs.

Candidate Profile

• BSc in Applied Statistics, Mathematics, Computer Science, Data Science, Automotive, or Electrical Engineering.
• Strong skills in data analysis, deep learning, and time series prediction.
• Proficiency in Python programming.
• Familiarity with statistical methods and transformer models (LLMs).
• Ability to work independently, conduct experiments, and analyze complex data.
• Excellent problem-solving and communication skills.

What We Offer

• Opportunity to write your thesis with professional guidance from experienced staff.
• Engage with experts and gain practical insights in the automotive industry.
• Integrate theoretical knowledge into real-world applications at AVL.

Additional Details

• Seniority level: Internship
• Employment type: Contract
• Job function: Other
• Industry: Motor Vehicle Manufacturing