Enhancing PEM fuel cell efficiency with flying squirrel search optimization and Cuckoo Search MPPT techniques in dynamically operating environments.

This study looks into how to make proton exchange membrane (PEM) fuel cells work more efficiently in environments that change over time using new Maximum Power Point Tracking (MPPT) methods. We evaluate the efficacy of Flying Squirrel Search Optimization (FSSO) and Cuckoo Search (CS) algorithms in adapting to varying conditions, including fluctuations in pressure and temperature. Through meticulous simulations and analyses, the study explores the collaborative integration of these techniques with boost converters to enhance reliability and productivity. It was found that FSSO consistently works better than CS, achieving an average increase of 12.5% in power extraction from PEM fuel cells in a variety of operational situations. Additionally, FSSO exhibits superior adaptability and convergence speed, achieving the maximum power point (MPP) 25% faster than CS. These findings underscore the substantial potential of FSSO as a robust and efficient MPPT method for optimizing PEM fuel cell systems. The study contributes quantitative insights into advancing green energy solutions and suggests avenues for future exploration of hybrid optimization methods.

Advanced efficient energy management strategy based on state machine control for multi-sources PV-PEMFC-batteries system.

This article offers a PV-PEMFC-batteries energy management strategy (EMS) that aims to meet the following goals: keep the DC link steady at the standard value, increase battery lifespan, and meet power demand. The suggested multi-source renewable system (MSRS) is made to meet load demand while using extra power to fill batteries. The major energy source for the MSRS is photovoltaic, and fuzzy logic MPPT is used to guarantee that the PV operates at optimal efficiency under a variety of irradiation conditions. The suggested state machine control consists of 15 steps. It prioritizes the proton exchange membrane fuel cell (PEMFC) as a secondary source for charging the battery when power is abundant and the state of charge (SOC) is low. The MSRS is made feasible by meticulously coordinating control and power management. The MSRS is made achievable by carefully orchestrated control and electricity management. The efficacy of the proposed system was evaluated under different solar irradiance and load conditions. The study demonstrates that implementing the SMC led to an average improvement of 2.3% in the overall efficiency of the system when compared to conventional control techniques. The maximum efficiency was observed when the system was operating under high load conditions, specifically when the state of charge (SOC) was greater than the maximum state of charge (SOCmax). The average efficiency achieved under these conditions was 97.2%. In addition, the MSRS successfully maintained power supply to the load for long durations, achieving an average sustained power of 96.5% over a period of 7.5 s. The validity of the modeling and management techniques mentioned in this study are confirmed by simulation results utilizing the MATLAB/Simulink (version: 2016, link: https://in.mathworks.com/products/simulink.html ) software tools. These findings show that the proposed SMC is effective at managing energy resources in MSRS, resulting in improved system efficiency and reliability.