A Differential Evolution-Based Optimized Fuzzy Logic MPPT Method for Enhancing the Maximum Power Extraction of Proton Exchange Membrane Fuel Cells

Recently, fuel cells (FCs) have found vast employment in several applications. However, unique maximum power point tracking (MPPT) exists for each set of operating condition for the efficient operation of FCs. Therefore, this paper presents a differential evolution optimization algorithm (DEOA)-based optimized fuzzy-logic (OFLC) MPPT method for enhancing the maximum power extraction of FCs. The various settings for the membership functions (MFs) of the input and output variables are optimized in the proposed method. Thence, more degree-of-freedom can be employed for accurate and fast tracking of the optimal power point of the proton exchange membrane FCs (PEMFCs). Whereas, existing MPPT methods in the literature for FC applications suffer from decreased degree-of-freedom for optimizing their performance, and lack of adaptivity, which obstructs their suitability for the wide operating range of FCs. The superiority and performance effectiveness of the proposed OFLC MPPT method have been validated and compared with the most prevalent techniques in the literature. Moreover, the robustness and sensitivity of the proposed OFLC MPPT method have been tested at various step changes in the water content of membrane and various temperature changes. Moreover, the proposed design of the suggested OFLC MPPT is general and it can be implemented on low-cost microcontrollers. The results verify the superior performance of the proposed OFLC MPPT method from the accurate and fast MPPT extraction, smooth output power with low ripple, and simplicity of the design point of views.