CITATIONS.bib

@article{Feng2020,
title = {A reliable approach of differentiating discrete sampled-data for battery diagnosis},
journal = {eTransportation},
volume = {3},
pages = {100051},
year = {2020},
issn = {2590-1168},
doi = {https://doi.org/10.1016/j.etran.2020.100051},
url = {https://www.sciencedirect.com/science/article/pii/S2590116820300084},
author = {Xuning Feng and Yu Merla and Caihao Weng and Minggao Ouyang and Xiangming He and Bor Yann Liaw and Shriram Santhanagopalan and Xuemin Li and Ping Liu and Languang Lu and Xuebing Han and Dongsheng Ren and Yu Wang and Ruihe Li and Changyong Jin and Peng Huang and Mengchao Yi and Li Wang and Yan Zhao and Yatish Patel and Gregory Offer},
keywords = {Battery aging, Lithium-ion batteries, Energy storage, Differential analysis, Incremental capacity analysis, Differential thermal voltammetry},
abstract = {Over the past decade, major progress in diagnosis of battery degradation has had a substantial effect on the development of electric vehicles. However, despite recent advances, most studies suffer from fatal flaws in how the data are processed caused by discrete sampling levels and associated noise, requiring smoothing algorithms that are not reliable or reproducible. We report the realization of an accurate and reproducible approach, as “Level Evaluation ANalysis” or LEAN method, to diagnose the battery degradation based on counting the number of points at each sampling level, of which the accuracy and reproducibility is proven by mathematical arguments. Its reliability is verified to be consistent with previously published data from four laboratories around the world. The simple code, exact fitting, consistent outcome, computational availability and reliability make the LEAN method promising for vehicular application in both the big data analysis on the cloud and the online battery monitoring, supporting the intelligent management of power sources for autonomous vehicles.}
}

@misc{Kirkaldy_batteryDAT_2023,
  author = {Niall Kirkaldy},
  title = {batteryDAT},
  year = {2023},
  version = {1.0.0},
  doi = {10.5281/zenodo.1234},
  url = {https://github.com/ImperialCollegeLondon/batteryDAT},
  note = {Released on 2023-09-28}
}

@article{Chen2020,
  author = {Chen, Chang-Hui and Brosa Planella, Ferran and O'Regan, Kieran and Gastol, Dominika and Widanage, W. Dhammika and Kendrick, Emma},
  title = {{Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models}},
  journal = {Journal of The Electrochemical Society},
  volume = {167},
  number = {8},
  pages = {080534},
  year = {2020},
  publisher = {The Electrochemical Society},
  doi = {10.1149/1945-7111/ab9050},
}