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Details

Autor(en) / Beteiligte
Titel
Internal resistance mapping preparation to optimize electrode thickness and density using symmetric cell for high-performance lithium-ion batteries and capacitors
Ist Teil von
  • Journal of power sources, 2018-08, Vol.396, p.207-212
Ort / Verlag
Elsevier B.V
Erscheinungsjahr
2018
Link zum Volltext
Quelle
Elsevier ScienceDirect Journals Complete
Beschreibungen/Notizen
  • Methods for characterizing and optimizing the internal resistance of electrodes are crucial for achieving the simultaneous goals of high energy density and high power density in lithium-ion batteries. In this study we propose—and confirm the efficacy of—a method for electrode design optimization based on the construction of an internal resistance map, a visualization tool for minimizing electrode resistance. The construction of the map proceeds by identifying the three primary components of the electrode resistance—charge-transfer resistance, ionic resistance, and contact resistance—and elucidating the dependence of each component on electrode density and thickness. We fabricate electrode sheets of various densities and thicknesses and conduct electrode impedance spectroscopy (EIS) measurements to measure the dependence of internal resistance on density and thickness, which we characterize via empirical formulas incorporated into our internal resistance map. Using our map, we predict that the resistance per unit area of a nickel-cobalt- manganese (NCM) electrode attains its minimum value at thickness 70 μm and density 2.9 g cm−3. We then further use the map to predict variations in IR drop for NCM electrodes of different densities, obtaining results in excellent agreement with experimental measurements. •An internal resistance prediction map was proposed for LIB electrode optimization.•New cell design—the counter-electrode-removable symmetric cell— was also proposed.•Internal resistances were analyzed applying EIS on newly-designed symmetric cells.•Empirical formula between Rct and electrode density was deduced from EIS analysis. .
Sprache
Englisch
Identifikatoren
ISSN: 0378-7753
eISSN: 1873-2755
DOI: 10.1016/j.jpowsour.2018.05.083
Titel-ID: cdi_crossref_primary_10_1016_j_jpowsour_2018_05_083

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