FPGA based design and implementation of power conditioning unit for fuel cell powered vehicle using adaptive vector reference control method
The output of renewable energy is the power of low voltage and high current rated designs. Due to the output voltage being too low it does not meet the required maximum voltage load requirements. Therefore, in order to meet the high voltage load, the required limit of the power converter (DC-DC) is...
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Veröffentlicht in: | Microprocessors and microsystems 2020-09, Vol.77, p.103120, Article 103120 |
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Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | The output of renewable energy is the power of low voltage and high current rated designs. Due to the output voltage being too low it does not meet the required maximum voltage load requirements. Therefore, in order to meet the high voltage load, the required limit of the power converter (DC-DC) is used to increase the voltage to the maximum. . The DC output voltage from a renewable energy source is given as input to a DC-DC converter, and the output generated from the converter is used to drive a load. KY boost converter is one of the recently developed DC-DC converters to reduce output voltage ripple. It is suitable for operation in equipment to below low ripple conditions. The disadvantage of this converter is that the boost voltage is a very low design parameter. To obtain the desired boosted voltage, and also to reduce the output voltage ripple, an optimized algorithm is used. Compared with the existing Drosophila optimization technology, the proposed Adaptive Vector Reference Control (AVRC) Method has higher convergence characteristics and lower output ripple. The simulation results are verified by MATLAB simulation with hardware results. The hardware is developed using the Xilinx FPGAs SPARTAN 6A controller, which simplifies the XC3S500E development board prototype in addition to providing additional flexibility for further improvements. The results clearly show improved performance and validate the model. |
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ISSN: | 0141-9331 1872-9436 |
DOI: | 10.1016/j.micpro.2020.103120 |