More Curve Fitting with Python: Complicated Models with Vastly Varying Parameters

We return to the topic of curve fitting. We will fit a complicated nonlinear differential algebraic equation system to experimental data. This problem is compounded by the fact the parameters we are searching for vary by over 10 orders of magnitude. We can fit such a system in the usual way using the curve_fit or minimize function, but the vast difference in the parameters will cause problems with the algorithms. One needs to rescale these numbers in such a way as to be roughly the same order of magnitude. We will accomplish this (for this case), by coding to fit the log of the parameters instead.

Github: https://github.com/kpmooney/numerical_methods_youtube/blob/master/curve_fitting/battery_model/Curve%20Fitting%20Battery%20Model.ipynb

Paper Referenced in this Video: https://iopscience.iop.org/article/10.1149/1.2422896

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