Statistical analysis and Monte-Carlo simulation of printed supercapacitors for energy storage systems

Abstract

This study presents a comprehensive statistical analysis of experimental parameters for 12 printed supercapacitors (SCs) using previously proposed equivalent circuit models (ECMs). Statistical distributions and descriptive statistics, including mean, P-value, and standard deviation (std), are reported indicating a normal distribution for various SC parameters. A statistical method is introduced to determine the maximum potential std in capacitance of multiple SCs within an energy storage module, ensuring voltage limits are not exceeded. A linear relationship is discovered between the applied voltage on the module comprising three SCs in series and the maximum potential std of capacitance, ensuring safe operation. Additionally, a statistical method predicts the energy window range of the SC module after operating an IC chip, enabling better decision-making and system management. Monte-Carlo (MC) simulations predict the long-term charge and discharge performance of individual SCs and the series-connected modules. Results indicate that as long as the parameters’ std remains below a defined threshold, charging behavior remains consistent. The MC simulations provide insight into voltage window ranges after 31 days of self-discharge, aiding in performance prediction and risk assessment. The statistical study approach empowers researchers in the field of printed SC energy storage, supporting performance evaluation, design validation, and evidence-based decision-making.