@ARTICLE{Abd_El_Baset_Abd_El_Halim_Ahmed_Implications_2024,
 author={Abd El Baset Abd El Halim, Ahmed and Eid Bayoumi, Ehab Hassan and El-Khattam, Walid and Ibrahim, Amr Mohamed},
 volume={72},
 number={3},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e149171},
 howpublished={online},
 year={2024},
 abstract={This article examines in depth the most recent thermal testing techniques for lithium-ion batteries (LIBs). Temperature estimation circuits can be divided into six divisions based on modeling and calculation methods, including electrochemical computational modeling, equivalent electric circuit modeling (EECM), machine learning (ML), digital analysis, direct impedance measurement and magnetic nanoparticles as a base. Complexity, accuracy and computational cost-based EECM circuits are feasible. The accuracy, usability and adaptability of diagrams produced using ML have the potential to be very high. However, none of them can anticipate the low-cost integrated BMS in real time due to their high computational costs. An appropriate solution might be a hybrid strategy that combines EECM and ML.},
 type={Article},
 title={Implications of lithium-ion cell temperature estimation methods for intelligent battery management and fast charging systems},
 URL={http://ochroma.man.poznan.pl/Content/130176/PDF/BPASTS-03846-EA.pdf},
 doi={10.24425/bpasts.2024.149171},
 keywords={BMS, fast charging, lithium-ion batteries, machine learning, thermal testing techniques},
}