A fuzzy-predictive current control with real-time hardware for PEM fuel cell systems

This research study presents the application of the FC-PCC (Fuzzy Logic Predictive Current Control) algorithm in the context of maximum power point tracking (MPPT) for a proton exchange membrane fuel cell system employing a three-level boost converter (TLBC). The proposed approach involves the integration of an intelligent fuzzy controller with a predictive current control strategy in order to improve the performance of MPP tracking. Initially, the utilization of fuzzy logic involves the utilization of data values obtained from the PEMFC. The maximum point (P-I) of the PEMFC polarization curve is determined, followed by the selection of the reference current. A predictive current control technique employs the reference current to ensure the voltage balance of the output capacitor in the three-level converter. The hardware-in-the-loop system utilizes a real-time and high-speed simulator, specifically the PLECS RT Box 1, to obtain the findings. The computational cost of the overall system is rather low, making it feasible to construct using PLECS RT Box 1. The new MPPT algorithm quickly finds the maximum power point (MPP) and balances the voltage of capacitors in a number of different proton exchange membrane fuel cells. The suggested MPPT technique has been verified to demonstrate rapid tracking of the maximum power point (MPP) location, as well as precise balancing of capacitor voltage and robustness to environmental variations. This approach was tested and found to outperform conventional MPPT methods like Perturb and Observe (P&O) and Incremental Conductance (IC) in terms of tracking duration, precision, and voltage balancing, achieving a 15% reduction in tracking duration, a 5% deviation from the MPP value for voltage, and superior stability under changing temperature and pressure.