Master Thesis Reliability Analysis and Uncertainty Quantification Using Generative AI for Battery Diagnosis within Automotive Onboard Power Supply Systems
Master Thesis Reliability Analysis and Uncertainty Quantification Using Generative AI for Battery Diagnosis within Automotive Onboard Power Supply Systems
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
Job Description
With the emerging technologies like autonomous driving and xbywire systems the vehicles onboard power supply system also known as the powernet is subject to stringent safety requirements. Failure of the powernet leads immediately to the loss of all the safetyrelated functions such as braking steering autonomous driving features etc. Among all the powernet components special attention shall be paid on batteries due to their complex electrochemical nature. Accurate battery diagnosis is essential to predict the performance of batteries. Safety validation is therefore required to guarantee that the prediction deviations is acceptably low under all realworld conditions. To ensure the robustness of battery diagnosis under realworld conditions uncertainty quantification (UQ) is necessary to assess the prediction deviations. This thesis aims to develop a probabilistic surrogate model using generative AI models (e.g. Conditional Generative Adversarial Networks (cGAN) Conditional Normalizing Flows (cNF) to quantify and distinguish different sources of uncertainty. Additionally the study will explore strategies to estimate failure probabilities with a limited amount of data while maintaining high accuracy. The research questions are: how to quantify and separate epistemic and aleatory uncertainties which generative AI models are best suited for capturing aleatory uncertainties how to evaluate and calibrate the reliability of the predicted uncertainty how does the amount of available data impact the accuracy of uncertainty quantification how to estimate failure probability effectively with a small data set.
- As part of your Master thesis you will conduct an extensive review of the stateoftheart methodologies for uncertainty quantification (UQ) based on generative AI.
- You will prepare synthetic data for study.
- Furthermore you will implement and compare different UQ models in terms of their ability to accurately capture predictive uncertainty and to distinguish between epistemic and aleatory uncertainty.
- In addition you will improve the prediction interval calibration to ensure a robust and reliable uncertainty estimate.
- Last but not least you will develop an approach to estimate the probability of battery diagnostic failure with minimal data using probabilistic surrogate models.
Qualifications :
- Education: Master studies in any field
- Experience and Knowledge: background in Machine Learning Deep Learning Reliability or Uncertainty quantification etc.; experience with PyTorch and deep generative models (cGANs cNF etc.; knowledge of battery diagnostics or battery systems is a plus
- Personality and Working Practice: you are a selfmotivated and proactive person who is able to work independently
- Languages: very good communication skills in written and spoken German or English
Additional Information :
Start: according to prior agreement
Duration: 3 6 months
Requirement for this thesis is the enrollment at university. Please attach your CV transcript of records 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
Zhiyi Xu (Functional Department)
49 2
#LIDNI
Remote Work :
No
Employment Type :
Fulltime
Employment Type
Full-time
