Remaining Capacity Prediction for Automotive HV Batteries

We are looking for a motivated student to conduct their master thesis in li-ion batterie modelling using statistical-/ physical modelling techniques. The automobile industry's transition to electric vehicles is accelerating, with this paradigm shift, the efficiency and reliability challenges for these complex physical systems are increasing. Since the battery is the most important component in an electric vehicle, the battery state of health determination is of high importance. Therefore, the aim of this master's thesis is to evaluate advanced physical-based modeling approaches, employing state-of-the-art analytical methods, for predicting the remaining capacity of li-ion batteries using sparsely resolved fleet data. Literature review on existing analysis techniques for battery aging modeling, sparse model identification for physical systems, and up sampling Implementation of data processing routines and feature engineering algorithms on a large data set Determination of physical principles and models for capacity estimation Definition of baseline for data up sampling Concept and implementation of data up sampling with sparse model identification, validation and evaluation of methods Battery aging modeling: concept and models (2-3 approaches), validation and evaluation of models Comparison with results without up sampling