Performance Assessment of Heuristic Genetic Algorithm (HGA) for Electrochemical Impedance Spectroscopy Parameter Estimation

Abstract

Due to the importance of cutting-edge nanomaterials applications in energy generation and storage devices, electrochemical impedance spectroscopy (EIS) has been adopted to fully understand the electronic and chemical reactions occurring inside these emerging technologies. Electronic behavior can be correlated with electrochemical properties such as electron transfer resistance, rate of mass diffusion, and the number of electrons in the electrochemical reaction. Although there is a lot of information about the electronic diagrams and methods for parameter estimation, some readers have difficulty analyzing and interpreting EIS curves. Thus, this work proposed using a heuristic approach and genetic algorithms to successfully estimate the resistance and capacitance value of a previously defined circuit model. To assess the potential of the genetic algorithm in electrochemical parameters estimation, we carried out practical measurements with known elements, and then the experimental and theoretical values were compared. Furthermore, the versatility and effectiveness of the algorithm were validated by determining the parameters in an Li-ion battery. The results revealed that the heuristic genetic algorithm (HGA) is a powerful tool for EIS parameters estimation because it can handle large below and upper limits with more pragmatic results in a shorter computational time.