Battery Degradation Dataset (Fixed Current Profiles＆Arbitrary Uses Profiles)

77 nominally identical high-energy 18650 lithium-ion batteries are cycled with fixed or arbitrary uses current profiles. 22 batteries are cycled with fixed current profiles of charge current (1C, 2C, or 3C) and discharge current (1C, 2C, or 3C). 55 batteries are cycled with arbitrary uses profiles of charge current (obeys a uniform distribution among 1C, 2C, or 3C, and changes randomly every 5 cycles) and a specified discharge current (3C).

All the battery degradation tests were carried out by the Advanced Energy Storage and Application (AESA) Group at Beijing Institute of Technology.

If you make use of our data, please cite our dataset directly using its DOI, as well as the following papers:
[1] Lu, J., Xiong, R., Tian, J., Wang, C., Hsu, C. W., Tsou, N. T., Sun, F., & Li, J. (2022). Battery Degradation Prediction Against Uncertain Future Conditions with Recurrent Neural Network Enabled Deep Learning. Energy Storage Materials, 50, 139-151. https://doi.org/10.1016/j.ensm.2022.05.007
[2] Tian, J., Xiong, R., Shen, W., Lu, J., & Yang, X. G. (2021). Deep neural network battery charging curve prediction using 30 points collected in 10 min. Joule, 5(6), 1521-1534. https://doi.org/10.1016/j.joule.2021.05.012

Please also consider citing our papers on these topics, see:
http://en.aesa.net.cn/About.aspx?ClassID=12