Project - Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis

Here are the datasets for our publication entitled "Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis" published in Nature Communications. 

The object of this experiment is the 18650 nickel-cobalt-manganese (NCM) lithium-ion battery manufactured by "LISHEN". The chemical composition is LiNi0.5Co0.2Mn0.3O2. The nominal capacity of the battery is 2000 mAh, and the nominal voltage is 3.6 V. The charging cut-off voltage and discharging cut-off voltage are 4.2 V and 2.5 V, respectively. The whole experiment was conducted at room temperature.  A total of 55 batteries were included in this experiment, conducted under 6 different charging and discharging strategies. The charging and discharging platform is ACTS-5V10A-GGS-D, and the sampling frequency for all data is 1Hz.

Other details can be found in "Data Introduction.pdf" file.

The Python Code for reading and preprocessing this dataset is available at: https://github.com/wang-fujin/Battery-dataset-preprocessing-code-library

Summary of articles using the this dataset: https://github.com/wang-fujin/XJTU-Battery-Dataset-Papers-Summary

If you find this data helpful, please consider citing our paper:

Wang, F., Zhai, Z., Zhao, Z. et al. Physics-informed neural network for lithium-ion battery degradation stable modeling and prognosis. Nat Commun 15, 4332 (2024). https://doi.org/10.1038/s41467-024-48779-z