Abstract
To optimise the parameters of electrical elements
contained in an equivalent circuit for electrochemical
impedance spectroscopy, we proposed a simple, intuitive
and universal tree structure code (TSC) to encode an
arbitrary complex circuit, then designed a genetic
algorithm for parameter optimisation (GAPO) to work
with the TSC and estimate the parameter values of
electrical elements. The GAPO uses a novel crossover
operator that performs by the non-convex linear
combination of multiple parents and sets up a crossover
subspace to enhance the global search. We first
examined the effects of some key control parameters in
the GAPO on the optimization process by selecting a
relatively complex equivalent circuit to generate
simulated data and comparing the parameters obtained by
GAPO with the original values. Secondly, to examine the
effectiveness and robustness of GAPO, we chose a set of
simulated data generated by a relatively simple
circuit, three sets of real impedance data on modified
gold electrodes and a set of real impedance data on the
anode of lithium-ion battery to run the GAPO and
compared their calculated results with those obtained
by complex nonlinear least square method (CNLS)
supported by LEVM software. Finally, we compared the
effects of five representative weighting strategies on
the GAPO based on a set of simulated data generated by
a relatively complicated circuit but with up to 10%
Gaussian noise and the set of real impedance data on
the anode of lithium-ion battery. All of these
experimental results show that the GAPO works more
quickly, efficiently and stably than CNLS when
optimising the element parameters. We also found that
appropriate weighting strategies can help reduce the
effects of experimental errors on GAPO, but the effects
really depend on the nature of the specific impedance
data.
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