Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data

This repo includes "Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data" article's code and data. The details of the folders are as follows:

• lithium-ion-impedance-dataset: Empirical Impedance data sets
• dataProcessing&visualization: Code for data visualization and preprocessing
• model-Zphz: Machine Learning Models and Code for Zphz
• model-Idc: Machine Learning Models and Code for Idc
• finalComparison: ML generated Zphz and Idc

Cite: Temiz, S., Kurban, H., Erol, S., & Dalkilic, M. M. (2022). Regeneration of Lithium-ion battery impedance using a novel machine learning framework and minimal empirical data. Journal of Energy Storage, 52, 105022.

Link: https://www.sciencedirect.com/science/article/abs/pii/S2352152X22010246?via%3Dihub

In additon:

• LiCoO2_battery_impedance_model: Matlab script for regression of relevant equivalent circuit parameters

Cite: Erol, S., & Orazem, M. E. (2015). The influence of anomalous diffusion on the impedance response of LiCoO2|C batteries. Journal of Power Sources, 293, 57-64.

Link: https://www.sciencedirect.com/science/article/pii/S0378775315009283