Master thesis: Identifying physicochemical battery parameters with Full-time

Are you passionate about the future of energy storage? In this master thesis, you’ll dive into the world of lithium-ion batteries, focusing on further developing advanced electrochemical impedance models and leveraging machine learning (ML) to optimize parameter estimation. Your work will push the boundaries of existing battery models by creating more accurate, efficient approaches that can predict and map battery behavior with greater precision. Using both simulation and experimental data, you’ll develop integrated approaches combining physics-based modeling with machine learning techniques. You’ll collaborate closely with experts, gain valuable experience in data analysis and model development, and contribute to breakthrough innovations in battery science.

Job Description

• Further Develop Electrochemical Impedance Models: Refine physics-based models to simulate the behavior and aging of lithium-ion batteries, focusing on impedance responses under various conditions.
• Implement Identification Algorithms: Evaluate and apply machine learning techniques (e.g., regression, neural networks) to estimate key battery parameters from experimental impedance data, improving model accuracy and computational efficiency.
• Conduct Data Analysis: Analyze experimental data to identify patterns and correlations that can inform the development of more precise battery models.
• Validate, Optimize, and Integrate Models: Validate and optimize the developed models through experimental testing and simulations, integrating machine learning with physics-based modeling to enhance performance and ensure reliable predictions for battery management systems (BMS).

Your Profile

• Study field: You bring a strong academic record with good grades in a relevant field, such as Computer Science, Electrical Engineering, Mathematics, Mechanical Engineering, Robotics, or related disciplines.
• Experience: You have first, practical experience with lithium-ion batteries
• Focus: In your studies you have acquired a background in numerical simulation and ML. You have excellent coding skills in Python.
• As a plus: You already have first experience with battery testing hardware, data analysis, or embedded software engineering in C.
• Personality: Strong problem-solving abilities and out-of-the-box thinking
• Way of work: Ability to work in a fast-paced environment and meet deadlines
• Language skills: You fluently speak English. German is a plus.

Please attach the following documents to your application:

• CV in English
• Certificate of enrollment at university
• Excerpt of the study regulations for the thesis (if applicable)
• Latest grades transcript (not older than 6 months)
• High school report

Further links:

• Find out what we are looking for in your CV
• Find out how the student application process works with us
• Discover our student website

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