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Published in:
19-05-2024 | Original Paper
Optimal synergetic control using chaotic dragon fly-based MPPT with cascaded ANFIS method for ultrafast electric vehicle charging by hybrid RES systems
Authors: Nilam Patil, Rajin M. Linus
Published in: Electrical Engineering | Issue 6/2024
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Abstract
The increasing adoption of electric vehicles (EVs) has heightened the demand for efficient and fast charging solutions. Integrating clean energy sources like photovoltaic (PV) and wind energy into EV charging infrastructure can significantly reduce carbon emissions and enhance sustainability. This article introduces an optimal synergetic control system using a chaotic dragonfly-based MPPT method and a cascaded ANFIS approach for ultrafast EV charging. The proposed system combines PV and wind energy systems to harness green energy sources efficiently, facilitating rapid EV charging. The chaotic dragonfly-based MPPT controller enhances the efficiency of wind turbine systems by precisely tracking the maximum power point (MPP) of the turbine generator, especially at low and variable wind speeds. Additionally, the modified high gain Zeta-KY (MHG-ZKY) converter is implemented to maximize the PV output voltage with high efficiency and minimized energy losses. The cascaded ANFIS controller further enhances the performance, stability, and efficiency of the DC-DC converter, enabling quick system performance. The proposed system is validated through MATLAB simulations and FPGA-based experimental setups, demonstrating superior efficiency, reduced THD, and rapid tracking efficiency compared to traditional methods. This innovative approach presents a promising solution for sustainable and efficient ultrafast EV charging, contributing to the global efforts towards cleaner transportation ecosystems.
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Abstract
This work provides a synergetic control approach for ultra-battery charging using a hybrid photovoltaic (PV) system and a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS). The developed system aims to maximize the utilization of renewable energy sources and improve the charging efficiency of electric vehicle (EV) battery. To achieve ultrafast battery charging, an innovative chaotic dragon fly optimization-based maximum power point (Chaotic DFO-MPPT) along with cascaded adaptive neuro-fuzzy inference system (ANFIS) control strategy is developed based on the power generation and load demand. These control algorithms optimizes the power flow among the PV array, DFIG wind turbine and the EV battery system to ensure efficient battery charging while maintaining grid stability. The purpose of using a chaotic DFO-based MPPT for a DFIG-based WECS is to track the power output and efficiency of the wind turbine, while ensuring the power stability and quality of the grid. Proportional integral (PI) controller is employed to govern the voltage of rectifier. Besides, the Modified High Gain Zeta-KY converter is employed to strengthen the poor output voltage of PV panel with maximum efficiency and reduced current ripples. With the application of cascaded ANFIS controller, the converter effectiveness enlarged with constant DC link voltage. The DC link voltage is transmitted to an isolated battery converter for EV battery charging. The surplus power produced from hybrid renewable energy sources is fed into the grid and used throughout high-demand periods. For controlling the inverter, a PI controller is used, supplemented by a cascaded ANFIS for increased grid-side control. In conclusion, the hybrid PV-DFIG WES with an optimized control strategy offers a promising solution for ultrafast EV battery charging with a maximum tracking efficiency of 98.72%. Finally, the entire proposed system validated through MATLAB/Simulink Platform to verify its effectiveness and reliability.
