We offer the following research topic
Thesis - Statistical and ML-based Lifetime Prediction for Automotive HV Batteries
Master Thesis
TASK:
- Research on the state of the art survival analysis algorithms
- Examination of possible influencing patterns and failure effects which can be used to predict the remaining battery lifetime
- Implementation of data processing routines and feature engineering algorithms
- Development of a survival model to predict the remaining lifetime of the individual battery
- Possibility to combine weibull and proportional hazard models (e.g., cox)
- Investigation and implementation of proper performance metrics to assess the quality of the developed models
- Investigation and implementation of a suitable model validation approach
- Assessment of the trade-off between the amount of data, model performance and computational time
REQUIREMENTS:
- Attended lectures on the topic of survival analysis
- Proven experience in applying data science methods
- Strong proficiency in Python
- Highly developed quality awareness
- Strong attention to details
- For this thesis your presence at our headquarter in Graz is necessary!
PREFERRED FIELD OF STUDY:
- Applied Statistics
- Computer Science
- Technological Mathematics
The successful completion of the thesis is remunerated with a one-time fee of EUR €3,300.00 before tax.
You don't want to write your final thesis just for the books, then explore the mobility of the future together with us! Maybe you will be a part of it soon!
Graz, AT, 8020
Job Segment:
Computer Science, Engineer, Automotive, Technology, Research, Engineering