Finite element analysis of electric double layer capacitors using a transient
nonlinear Nernst-Planck-Poisson (NPP) model and Nernst-Planck-Poisson-modified
Stern layer (NPPMS) model are presented in 1D and 2D. The NPP model provided
unrealistic ion concentrations for high electrode surface potential. The NPPMS model
uses a modified Stern layer to account for finite ion size, resulting in realistic ion
concentrations even at high surface potential.
The finite element solution algorithm uses the Newton-Raphson method to solve
the nonlinear problem and the alpha family approximation for time integration to solve
the NPP and NPPMS models for transient cases. Cubic Hermite elements are used for
interfacing the modified Stern and diffuse layers in 1D while serendipity elements are
used for the same in 2D. Effects of the surface potential and bulk molarity on the electric potential and ion
concentrations are studied. The ability of the models to predict energy storage capacity is
investigated and the predicted solutions from the 1D NPP and NPPMS models are
compared for various cases. It is observed that NPPMS model provided realistic and
correct results for low and high values of surface potential.
Furthermore, the 1D NPPMS model is extended into 2D. The pore structure on
the electrode surface, the electrode surface area and its geometry are important factors in
determining the performance of the electric double layer capacitor. Thus 2D models
containing a porous electrode are modeled and analyzed for understanding of the
behavior of the electric double layer capacitor. The effect of pore radius and pore depth
on the predicted electric potential, ion concentrations, surface charge density, surface
energy density, and charging time are discussed using the 2D Nernst-Planck-Poissonmodified
Stern layer (NPPMS) model.
Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/4703 |
Date | 25 April 2007 |
Creators | Lim, Jong Il |
Contributors | Whitcomb, John D. |
Publisher | Texas A&M University |
Source Sets | Texas A and M University |
Language | en_US |
Detected Language | English |
Type | Book, Thesis, Electronic Dissertation, text |
Format | 764527 bytes, electronic, application/pdf, born digital |
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