2025-10-02 –, Coffee room
Language: English
A supercapacitor is a complex charge storing device which relies on multiple physical phenomena, namely electrical double layer formation and pseudocapacitance, that are to this day not so well understood. When theory stagnates, so does innovation. Where trial and error give results, it could give faster results if the trial could be guided by knowledge. Part of the process in trying to understand how a supercapacitor works is finding an equivalent circuit model, acting as a sort of a rigorous guide through uncharted territory using charted territory in the form of already known circuit elements which describe, to some extent, the physical phenomena behind a supercapacitor. Therefore, this is what the presented work is concerned with, using Julia to find a circuit model from acquired supercapacitor data.
Current equivalent circuit model discovery algorithms require the user to input a plausible equivalent circuit based on the measured data they have, which is Bode and Nyquist plots from electrochemical impedance spectroscopy (EIS) measurements, and the software begins an optimization process of fitting the data with the theoretical model corresponding to the equivalent circuit candidate until there is a close enough match between them. This method leaves a lot to be desired, for if this particular circuit could fit the data, so could other a million mathematically equivalent circuits, therefore making certainty over the result quite impossible. What the algorithm developed by me plans to do is gather as much information as possible from the data itself first, automatically, and through that information have the said algorithm suggest some possible circuit configurations that fit the physical equations. Therefore, a main stage of parameter discovery and one of parameter discrimination would be introduced for a better accuracy for the results. Afterwards, an optimization stage will use the discovered parameters and the theoretical model for the circuit configuration in order to fit it to the existing data. Having some initial values at the start of optimization will guarantee the model will be more likely to converge to the actual circuit values that would accurately model the respective supercapacitor measured. This work proposes on improving on the circuit model discovery package I have previously developed which currently models part of the supercapacitor’s behaviour using Optimization.jl (https://github.com/Ir1n-a/NyquistCircuitModeling.jl).
I am a PhD student in physics, working as a researcher with my main specialty being supercapacitors and electric measurements. I got into Julia because I believe it's the right programming language for the scope of my research, and, well, I also like it more than other programming languages, so there's that.