Mandatory Internship in Hybrid Modeling of High-Voltage Traction Batteries Using Physics-Informed Neural Networks
Mandatory Internship in Hybrid Modeling of High-Voltage Traction Batteries Using Physics-Informed Neural Networks
Employer Active
Job Alert
You will be updated with latest job alerts via email
Share
Job Alert
You will be updated with latest job alerts via email
Not Disclosed
Salary Not Disclosed
1 Vacancy
Job Description
- During your internship you will conduct a literature review of classical electrochemical battery models physicsinformed neural networks and hybrid modeling techniques applied to highvoltage EV traction batteries.
- You will investigate and compare methods of incorporating physical laws into neural networks evaluating their strengths and weaknesses to provide a comprehensive overview.
- Furthermore you will develop a hybrid model that integrates electrochemical physicsbased models with datadriven methods to address limitations of common electrochemical models.
- You will implement the most promising physicsinformed neural network approaches using Python and suitable deep learning frameworks. You will also train and calibrate the implemented models using laboratory and realworld battery telemetry data performing hyperparameter tuning and rigorous validation including sensitivity analysis to assess reliability and generalizability.
- Additionally you will analyze and optimize model performance focusing on accuracy robustness and computational efficiency.
- Finally you will compile research findings into a clear and concise internship report or master thesis detailing the hybrid modeling approach implemented models and performance results.
Qualifications :
- Education: Master studies in the field of Physics (Electro)Chemistry Applied Mathematics Computer Science Electrical Engineering or comparable with good grades
- Experience and Knowledge: proficient in Python and MATLAB; familiar with deep learning frameworks (e.g. TensorFlow PyTorch); knowledge in electrochemical battery models machine learning data analysis and computational modeling
- Personality and Working Practice: independent and analytical working style
- Enthusiasm: for Electromobility LithiumIon Battery Technology and Programming
- Languages: very good in English
Additional Information :
Start: according to prior agreement
Duration: 6 months (confirmation of mandatory internship required)
We offer you
- 35 hours/week with flextime
- a permanent contact person who will accompany you during your internship
- a modern working environment as well as mobile working by arrangement
- the opportunity to become part of our student network Stuttgart
- discounts in our company restaurants
Requirement for this internship is the enrollment at university. Please attach your CV transcript of records enrollment certificate examination regulations and if indicated a valid work and residence permit.
Diversity and inclusion are not just trends for us but are firmly anchored in our corporate culture. Therefore we welcome all applications regardless of gender age disability religion ethnic origin or sexual identity.
Need further information about the job
Christoph Krner (Functional Department)
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
