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

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