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A combined experimental‐numerical framework for residual energy determination in spent lithium‐ion battery packs
Author(s) -
Garg Akhil,
Yun Liu,
Shaosen Su,
Goyal Ankit,
Niu Xiaodong,
Gao Liang,
Bhalerao Yogesh,
Panda Biranchi
Publication year - 2019
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.4564
Subject(s) - internal resistance , cyclic voltammetry , battery (electricity) , electrochemistry , lithium (medication) , lithium ion battery , work (physics) , electrode , ion , voltage , materials science , residual , dispersion (optics) , analytical chemistry (journal) , scanning electron microscope , nuclear engineering , chemistry , composite material , electrical engineering , thermodynamics , computer science , engineering , mechanical engineering , physics , chromatography , power (physics) , algorithm , endocrinology , optics , medicine , organic chemistry
Summary The present research proposes a combined framework that evaluates remaining capacity, material behavior, ions concentration of remaining metals, and current rate of chemical reactions of spent Li‐ion batteries accurately. Voltage, temperature, internal resistance, and capacity were studied during charging and discharging cycles. Genetic programming was applied on the obtained data to develop a model to predict remaining capacity. The results of experimental work and those estimated from model were found to be correlated, confirming the validation of model. Materials structure and electrochemical behavior of electrodes during cycles were studied by cyclic voltammetry, scanning electron microscopy, and energy dispersion spectrum.