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A High Voltage Gain Interleaved Boost Converter for Fuel Cell Based Electric Vehicle Applications Using MATLAB

Roshani D. Borkar, A. P. Thakare

Abstract


Because of the strict regulations of coal and gas emissions, the financial system, electric motors, fuel elements (FCEV), is becoming more popular inside the car enterprise. In this case, the neural network represents the majority of electrical place-checking controller (MPPT) power of 1.26 kW, which modifies the surface membranes of a gas-powered vehicle (PEMFC), which provide the power plant of an electric vehicle, using a DC-to-DC power conversion device. Proposal MPPT radial Basis Community Management neural network (RBFN), use of PEMFC, maximum power point tracking algorithm (MPPT). High switching frequency and high level of DC converted energy, this is important for the continuity of business FCEV. Maximum energy benefits of 3- phase Alternating Current Supply Converter (IBC) are designed for FCEV system. Alternating voltage technique is used for input voltage and electrical voltage in a semiconductor, electrical networks. End- to-end RBFN performance analysis is an FCEV gadget that MPPT-deals with the comparison of fuzzy Logic controllers (FLCs) of the MATLAB / Simulink platform.


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References


Chiu, H. J., & Lin, L. W. (2006). A bidirectional DC–DC converter for fuel cell electric vehicle driving system. IEEE Transactions on Power Electronics, 21(4), 950-958.

Geng, B., Mills, J. K., & Sun, D. (2014). Combined power management/design optimization for a fuel cell/battery plug-in hybrid electric vehicle using multi-objective particle swarm optimization. International journal of automotive technology, 15(4), 645-654.

Hemi, H., Ghouili, J., & Cheriti, A. (2014). A real time fuzzy logic power management strategy for a fuel cell vehicle. Energy conversion and Management, 80, 63-70.

Mebarki, N., Rekioua, T., Mokrani, Z., Rekioua, D., & Bacha, S. (2016). PEM fuel cell/battery storage system supplying electric vehicle. International Journal of Hydrogen Energy, 41(45), 20993-21005.

Abdi, S., Afshar, K., Bigdeli, N., & Ahmadi, S. (2012). A novel approach for robust maximum power point tracking of PEM fuel cell generator using sliding mode control approach. International Journal of Electrochemical Science, 7(1), 4192-4209.

Esram, T., & Chapman, P. L. (2007). Comparison of photovoltaic array maximum power point tracking techniques. IEEE Transactions on energy conversion, 22(2), 439-449.

Saravanan, S., & Babu, N. R. (2016). Maximum power point tracking algorithms for photovoltaic system–A review. Renewable and Sustainable Energy Reviews, 57, 192-204.

Ram, J. P., Rajasekar, N., & Miyatake, M. (2017). Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review. Renewable and Sustainable Energy Reviews, 73, 1138-1159.

Khanh, L. N., Seo, J. J., Kim, Y. S., & Won, D. J. (2010). Power-management strategies for a grid-connected PV-FC hybrid system. IEEE transactions on power delivery, 25(3), 1874-1882.

Khanh, L. N., Seo, J. J., Kim, Y. S., & Won, D. J. (2010). Power-management strategies for a grid-connected PV-FC hybrid system. IEEE transactions on power delivery, 25(3), 1874-1882.


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