# One-shot battery degradation trajectory prediction with deep learning
# Introduction
The data and code in this repository associated with the paper 'One-shot battery degradation trajectory prediction with deep learning' by W. Li, N. Sengupta, P. Dechent, D. Howey, A. Annaswamy, and D. U. Sauer.
The data and code in this repository associated with the paper ['One-shot battery degradation trajectory prediction with deep learning'](https://authors.elsevier.com/sd/article/S0378-7753(21)00552-8) by W. Li, N. Sengupta, P. Dechent, D. Howey, A. Annaswamy, and D. U. Sauer.
# Raw Experimental Data
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).
