Abstract
Fuel cell has been considered as one of the optimistic renewable power technologies for the automotive applications. The output power of a fuel cell is immensely dependent on cell temperature and membrane water content. Hence, a maximum power point tracking controller is essentially required to extract the optimum power from the fuel cell stack. In this paper, an adaptive neuro-fuzzy inference system based maximum power point tracking controller is presented for 1.26 kW proton exchange membrane fuel cell system used in electric vehicle applications. In order to extract the optimum power, a high step-up boost converter is connected between the fuel cell and the BLDC motor. The duty cycle of the converter is controlled by using ANFIS reference model, so that the maximum power is delivered to the BLDC motor. The performance of the proposed controller is tested under normal operating conditions and also for sudden variations in the cell temperatures of the fuel cell. In addition to this, to analyze the effectiveness and tracking behaviour of the proposed controller, the results were compared with those obtained using the fuzzy logic controller. Compared to the fuzzy logic controller, the proposed ANFIS controller has increased the average DC link power by 1.95% and the average time taken to reach the maximum power point is reduced by 17.74%.
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