A comparative analysis of ANFIS and fuzzy controllers for a dynamic hybrid model

Transitioning from combustion engines to electric motors is essential to reduce CO₂ emissions and combat climate change. This study presents a dynamic hybrid model combining a fuel cell and battery for electric vehicles, emphasizing simplified parameter extraction from battery datasheets. The model integrates two energy storage systems: batteries for electrochemical storage and hydrogen for chemical storage, converted into electricity via a fuel cell stack. This dual approach enables flexible refueling options with electricity or hydrogen. An air compressor in the proton exchange membrane (PEM) fuel cell stack optimizes performance across varying driving conditions. The research aims to minimize fuel cell consumption and enhance energy storage efficiency using Sim Power Systems software. It employs traditional proportional integral derivative (PID) controllers and advanced optimization techniques, including fuzzy and ANFIS, to achieve optimal power distribution between the fuel cell system (FCS) and the energy secondary source (ESS) for specific road scenarios. The proposed ANFIS-based approach demonstrates superior control in balancing energy efficiency and driving dynamics, surpassing both PID and fuzzy logic controllers in key metrics. This innovative closed-loop control system offers a promising solution for hybrid electric vehicles, ensuring optimal performance and energy management.