posted on 2017-03-06, 12:31authored byStephen Fletcher, Iain Kirkpatrick, Rod DringRod Dring, Robert Puttock, Rob Thring, Simon Howroyd
Supercapacitors are an emerging technology with applications in pulse power, motive power, and energy storage. However, their carbon electrodes show a variety of non-ideal behaviours that have so far eluded explanation. These include Voltage Decay after charging, Voltage Rebound after discharging, and Dispersed Kinetics at long times. In the present work, we establish that a vertical ladder network of RC components can reproduce all these puzzling phenomena. Both software and hardware realizations of the network are described.
In general, porous carbon electrodes contain random distributions of resistance R and capacitance C, with a wider spread of log R values than log C values. To understand what this implies, a simplified model is developed in which log R is treated as a Gaussian random variable while log C is treated as a constant. From this model, a new family of equivalent circuits is developed in which the continuous distribution of log R values is replaced by a discrete set of log R values drawn from a geometric series. We call these Pascal Equivalent Circuits. Their behaviour is shown to resemble closely that of real supercapacitors. The results confirm that distributions of RC time constants dominate the behaviour of real supercapacitors.
Funding
This work was sponsored by the EPSRC (UK) Grant Number EP/M009394/1, “Electrochemical Vehicle Advanced Technology” (ELEVATE).
History
School
Science
Department
Chemistry
Published in
Journal of Power Sources
Volume
345
Pages
247 - 253
Citation
FLETCHER, S. ... et al, 2017. The modelling of carbon-based supercapacitors: distributions of time constants and Pascal Equivalent Circuits. Journal of Power Sources, 345, pp. 247 - 253.
This work is made available according to the conditions of the Creative Commons Attribution 4.0 International (CC BY 4.0) licence. Full details of this licence are available at: http://creativecommons.org/licenses/ by/4.0/
Acceptance date
2017-02-04
Publication date
2017-02-11
Notes
This is an Open Access article published by Elsevier and distributed under the terms of the Creative Commons Attribution Licence, CC BY 4.0, https://creativecommons.org/licenses/by/4.0/