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Illustration of experimental, machine learning, and characterization methods for study of performance of Li‐ion batteries
Author(s) -
Garg Akhil,
Singh Surinder,
Li Wei,
Gao Liang,
Cui Xujian,
Wang ChinTsan,
Peng Xiongbin,
Rajasekar Natarajan
Publication year - 2020
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.5159
Subject(s) - battery (electricity) , voltage , sensitivity (control systems) , depth of discharge , parametric statistics , characterization (materials science) , electrical engineering , ion , work (physics) , fault (geology) , battery capacity , materials science , computer science , automotive engineering , engineering , chemistry , electronic engineering , mechanical engineering , nanotechnology , power (physics) , physics , organic chemistry , statistics , mathematics , quantum mechanics , seismology , geology
Summary The development of fault diagnosis of Li‐ion batteries used in electric vehicles is vital. In this perspective, the present work conducted a comprehensive study for the evaluation of coupled and interactive influence of charging ratio, number of cycles, and voltage on the discharge capacity of Li‐ion batteries to predict the life of battery. The charging‐discharging experimental tests on Li‐ion batteries have been performed. The data such as charging ratio, number of cycles, voltage, and discharge capacity of Li‐ion batteries are measured. Machine learning approach of neural networks is then applied on the obtained data to compute the effects, normal distribution, parametric analysis, and sensitivity analysis of the input parameters on the capacity of battery. It can be noticed that discharge capacity increased with an increase in full voltage. Further, it has been observed from the sensitivity analysis that the full voltage is most relevant parameters to the capacity of the battery. Additionally, scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) of the electrodes before and after experiments have been performed, to investigate the elemental dissolution due to the charging/discharging cycles. The findings and analysis from the proposed study shall facilitate experts in making decisions on the remaining life and charging capacity of the battery.