The data and code in this repository associated with the research work at ISEA, RWTH Aachen University in battery degradation prediction.
The raw dataset consists of the data from initial characterization tests (multi-pulse test, capacity test with various C-rates, qOCV test, electrochemical impedance spectroscopy at different temperatures), cycling ageing tests (high-resolution data of current, voltage, capacity, energy and temperature) and regular characterization tests (multi-pulse test, capacity test with various C-rates and qOCV test).
The processed dataset extracted the most important data from the raw dataset and consists of analyzsed data of 48 cells during ageing tests, and the change of cell capaicty, resistances at different frequency domains and temperature during ageing are provided. Furthermore, the matlab codes to extract the metrics from the dataset are provided.
The capacity degradation dataset and resistance increase dataset together with the code regarding preprocessing for deep learning.
The codes for the multi-task learning and single-task learning in battery capacity and power degradation prediction.
Weihan Li weihan.li@isea.rwth-aachen.de