A scenario-based genetic algorithm for controlling supercapacitor aging and degradation in the industry 4.0 era

Electric double layer capacitors (EDLCs) are promising energy storage solutions, yet aging and degradation issues impede reliability and lifespan. The research proposes integrated simulation, modeling, and optimization to actively control EDLC degradation during charge -discharge cycles, mathematically modeling and simulating electrical and aging dynamics. These aging simulations are coupled with a genetic algorithm (GA) optimization routine that identifies the optimal combinations of influential EDLC parameters like internal resistance and capacitance for mitigating deterioration. Equivalent circuit models quantify electrical signatures, as aging factors induce gradual drifts in capacitance and resistance during thousands of simulated operational cycles. These MATLAB simulations effectively capture aging phenomena noted in real EDLCs in terms of measurable capacitance fading and resistance growth trends over continual usage in line with experimental data. The GA optimization subsequently determines optimal charging voltage ranges and achievable reductions in charge/ discharge asymmetry by over 70 % that significantly enhance lifespan trajectories through aging control under similar test conditions. The technique's efficacy is further ascertained through systematic tuning of GA parameters like mutation rates and population sizes using Taguchi experimental models. The findings showcase superior optimization outcomes for larger populations and lower mutation probabilities. The research integrates digital twin for rapid evaluations, addressing reliability challenges via computational aging control. The flexible modeling platform enables customized what -if analyses for EDLC designers, aiding in material, cycling, and duty cycle exploration. This aging mitigation approach offers simulation -driven insights and automated optimization tools to extend the operational duration of high-performance EDLCs.