Particle Dynamics-Based Stochastic Modeling of Carbon Particle Charging in the Flow Capacitor Systems

Abstract

Aqueous electrochemical flow capacitors (EFCs) have demonstrated high-power capabilities and safety at low cost, making them promising energy storage devices for grid applications. A primary performance metric of an EFC is the steady-state electrical current density it can accept or deliver. Performance prediction, design improvements, and up-scaling are areas in which modeling can be useful. In this paper, a novel stochastic superparticle (SP) modeling approach was developed and applied to study the charging of carbon electrodes in the EFC system, using computational superparticles representing real carbon particles. The model estimated the exact values of significant operating parameters of an EFC, such as the number of particles in the flow channel and the number of electrolytic ions per carbon particle. Optimized model parameters were applied to three geometrical designs of an EFC to estimate their performance. The modeling approach allowed study of the charge per carbon particle to form the electric double-layer structure. The linear relationship between the concentration of SPs and the ionic charge was observed when optimized at a constant voltage of 0.75 V. The simulation results are in excellent agreement with experimental data, providing a deep insight into the performance of an EFC and identifying limiting parameters for both engineers and material scientists to consider.

A Feature Engineering-Assisted CM Technology for SMPS Output Aluminium Electrolytic Capacitors (AEC) Considering D-ESR-Q-Z Parameters

“…Recent studies/research have changed the dynamics and approach to maintenance, which involves the integration of diagnosis, fault detection and identification (FDI), and prognosis, and can be termed "conditioned-based maintenance" (CBM), unlike traditional-based maintenance (preventive maintenance). Owing to the scarcity of data, most researchers work mainly with the model-based [6][7][8][9] approach, which requires empirical and analytical models compared to the data-driven approach [10][11][12][13]. In power converters, the most vulnerable components are capacitors and switching devices.…”