Review Article

State of the Art and Future Research Needs for Multiscale Analysis of Li-Ion Cells

[+] Author and Article Information
K. Shah

Department of Mechanical and Aerospace Engineering,
The University of Texas at Arlington,
Arlington, TX 76019

N. Balsara, M. Chintapalli

Department of Chemical and Biomolecular Engineering,
University of California at Berkeley,
Berkeley, CA 94720

S. Banerjee

School of Mechanical and Materials Engineering,
Washington State University,
Pullman, WA 99164

A. P. Cocco, W. K. S. Chiu

Department of Mechanical Engineering,
University of Connecticut,
Storrs, CT 06269

I. Lahiri

Department of Metallurgical and Materials Engineering,
Indian Institute of Technology Roorkee,
Roorkee 247667, India

S. Martha

Department of Chemistry,
Indian Institute of Technology Hyderabad,
Hyderabad 502285, India

A. Mistry, P. P. Mukherjee

Department of Mechanical Engineering,
Texas A&M University,
College Station, TX 77843

V. Ramadesigan, S. Mitra

Department of Energy Science and Engineering,
Indian Institute of Technology Bombay,
Mumbai 400076, India

C. S. Sharma

Department of Chemical Engineering,
Indian Institute of Technology Hyderabad,
Hyderabad 502285, India

V. R. Subramanian

Department of Chemical Engineering,
University of Washington,
Seattle, WA 98105

A. Jain

Department of Mechanical and Aerospace Engineering,
The University of Texas at Arlington,
Arlington, TX 76019
e-mail: jaina@uta.edu

1Corresponding author.

Manuscript received February 6, 2017; final manuscript received March 24, 2017; published online May 16, 2017. Assoc. Editor: Kevin Huang.

J. Electrochem. En. Conv. Stor. 14(2), 020801 (May 16, 2017) (17 pages) Paper No: JEECS-17-1017; doi: 10.1115/1.4036456 History: Received February 06, 2017; Revised March 24, 2017

The performance, safety, and reliability of Li-ion batteries are determined by a complex set of multiphysics, multiscale phenomena that must be holistically studied and optimized. This paper provides a summary of the state of the art in a variety of research fields related to Li-ion battery materials, processes, and systems. The material presented here is based on a series of discussions at a recently concluded bilateral workshop in which researchers and students from India and the U.S. participated. It is expected that this summary will help understand the complex nature of Li-ion batteries and help highlight the critical directions for future research.

Copyright © 2017 by ASME
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Fig. 1

(a) Yearwise publication trend and (b) demographic distribution of Li-ion battery research

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Fig. 2

Relationship between ionic conductivity, σ, and salt concentration, r, in SEO/LiTFSI and PEO/LiTFSI mixtures at 90 °C. The molecular weights of the polystyrene and poly(ethylene oxide) blocks are given in kilogram mole in parentheses. Ionic conductivity in PEO exhibits a simple maximum, whereas ionic conductivity in SEO has two local maxima.

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Fig. 3

(a) Grain size, Lr, expressed as a number of lamellae per grain, is plotted as a function of salt concentration, r. Grain size decreases as salt concentration increases, in SEO/LiTFSI of two different molecular weights. (b) Conductivity of SEO/LiTFSI normalized by the conductivity and volume fraction of PEO/LiTFSI, σn, is plotted as a function of Lr. The marker shows the concomitant changes in r. Larger markers represent higher values of r. Normalized conductivity decreases as grain size increases.

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Fig. 4

(a) Grain size measured by small angle X-ray scattering, L, is plotted as a function of temperature during the anneal. (b) Grain size measured by DPLS, w, is plotted as a function of temperature during the anneal. The data in (a) show an increase in grain size upon heating, and the data in (b) show a decrease in grain size.

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Fig. 5.

Azimuthally averaged scattering intensity, C0(q), as a function of scattering vector, q, for a sample probed with two different wavelengths, 473 nm and 640 nm. The scattering intensity decays more slowly for samples with smaller grains. A larger average grain size is observed using the 473 nm light source.

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Fig. 6.

Electrochemical characteristics of MWCNT-based anode: (a) charge–discharge performance in first two cycles, (b) rate capability, (c) stability, and (d) Coulombic efficiency of the MWCNTs grown directly on copper current collector. (Reprinted with permission from Lahiri et al. [33]. Copyright 2010 by American Chemical Society.)

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Fig. 7

Effect of three-dimensional electrode design on the capacity of CNT-based Li-ion battery anode (MWCNTs/2D Cu: MWCNTs on copper foil; MWCNTs/3D Cu: MWCNTs on copper foam; and a-Si/MWCNTs/3D Cu: amorphous Si-coated MWCNTs on copper foam). (Reprinted with permission from Kang et al. [41]. Copyright 2012 by Elsevier.)

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Fig. 8

Cycling behavior at C/10 rates of charge–discharge for the (a) 1 h and (b) 3 h lipon-coated Li-rich NMC composite electrodes (as indicated) in the potential range 2.0–4.9 V, in ethylene carbonate-dimethyl carbonate (EC-DMC) 1:1/LiPF6 1.2 M solutions at 25 °C. Cycling protocol was constant current–constant voltage providing potentiostatic steps at desired higher cut-off potential until the current reaches value of C/50. The lower cut-off potential was 2.0 V versus Li/Li+. The discharge profiles for the 1 h and 3 h lipon-coated LMR-NMC composite electrodes move to lower voltage (2.8 V) plateaus showing significant loss of energy. (Reprinted with permission from Martha et al. [49]. Copyright 2013 by Royal Society of Chemistry.)

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Fig. 9

Voltage profiles for the LMR-NMC and doped cathodes during first cycle. Doped cathodes deliver 20% more capacity than pristine LMR-NMC cathodes and have high plateau voltage.

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Fig. 10

Three-dimensional imaging of lithium cobalt oxide cathode material using X-ray tomography. The colors (online) represent lithium cobalt oxide particles that have been individually surface-meshed. (Reprinted with permission from Roberts et al. [63]. Copyright 2016 by ASME.)

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Fig. 11

Wide range of physical phenomena dictates different computation demands [89]

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Fig. 12

The schematic, adopted from Banerjee and coworkers [128], depicts atomistic modeling based screening of ionic liquid electrolytes based on their oxidation and reduction potentials

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Fig. 13

Illustration of microstructure–performance interplay in typical Li-ion battery electrodes. (a) Effect of packing order of LiFePO4 on cell performance [159]. (Reproduced with permission from Smith et al. [159]. Copyright 2012 by Royal Society of Chemistry.) The plots (b) and (c) display the variation in microstructural properties as different electrode realization are obtained by keeping the same porosity and different active material weight percentage. Corresponding microstructures are presented in (e) through (g). Subfigure (d) relates the electrochemical performance of Li | NMC half cells with these different porous cathode structures. For lower rates of operation (≤1 C), higher active material leads to better performance but at higher rates (e.g., 3C), higher active material at the expense of conductive additive leads to limitations arising from electron conduction.

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Fig. 14

Effect of typical Li-ion battery electrode microstructures on the impedance and thermal response [161,164]. (a) and (b) Variation in active material mean particle size and size distribution significantly affects the interfacial resistance and impedance response. (c) Electrode porosity plays an important role in electrolyte phase resistance and hence cell temperature rise due to Joule heating. (a) Effect of active material size distribution on EIS (reproduced with permission from Cho et al. [161], copyright 2015 by PCCP, Royal Society of Chemistry), (b) effect of mean particle size on EIS (reproduced with permission from Chen and Mukherjee [164], copyright 2015 by Royal Society of Chemistry), and (c) effect of electrode microstructure on performance and temperature rise.




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