Synthetic IR data for the Attia et al. (2020) battery dataset

Data was produced to accompany the paper Strange et al. (2020, Nov):
Calum Strange, Shawn Li, Richard Gilchrist and Goncalo dos Reis, 2020 Nov,
'Predicting capacity and internal resistance of Li-ion cells: A machine learning approach with knees and elbows, and synthetic data'
(currently in review)

Data completes the dataset of Attia et. al. (2020) (https://www.nature.com/articles/s41586-020-1994-5), of (A123 APR18650M1A cylindrical cells) cycled in a constant temperature environment under a variety of fast charging protocols, for which IR measurements were not provided.

The predictor model for this data was trained on the dataset of Severson et. al. (2019) (https://www.nature.com/articles/s41560-019-0356-8), which contains data for the same type of battery cells (A123 APR18650M1A cylindrical cells) cycled in the same environment.

Both datasets and corresponding descriptions can be found at https://data.matr.io/1/.

Strange, Calum; dos Reis, Goncalo. (2020). Synthetic IR data for the Attia et al. (2020) battery dataset, [dataset]. University of Edinburgh. School of Mathematics. https://doi.org/10.7488/ds/2957.