A MIMO–ANFIS-Controlled Solar-Fuel-Cell-Based Switched Capacitor Z-Source Converter for an Off-Board EV Charger

Abstract

The efficiency of a nation’s progress is determined by a variety of factors; however, transportation plays a critical role in boosting progress because it facilitates trade and communication between countries. The majority of transportation is powered by fossil fuels such as gasoline or diesel, which will be depleted in less than 50 years. Another option is to operate transportation systems after replacing conventional vehicles with electric vehicles (EV). Powering these vehicles with green electricity contributes zero carbon emissions from production to the final product. Together with the controller, an efficient charger ensures that the entire system is reliable and stable. The current work focuses on charging an off-board EV from greener energy sources (both a fuel cell and PV array forming a micro-grid) based on their availability via an efficient converter controlled by an adaptive multi-objective controller. A novel multi-output-based adaptive neuro fuzzy inference system (ANFIS) controller for charging the off-board EV at a constant current and voltage for both line and load regulations is proposed, in the current work. A comparison study of grid partitioning and subtractive clustering was conducted in order to select an optimized algorithm for generating FIS. Novelty is achieved by ensuring closed-loop stability is the main aim of the work. The entire work was created with the MATLAB/Simulink software.

Providing a Control System for Charging Electric Vehicles Using ANFIS

“…Also, a comparative analysis is accomplished to adjust the PID controller Control the frequency of the grid, optimize the power fow, and improve the efciency of power plants using particle swarm optimization (PSO) and frefy algorithms to minimize the deviation in frequency. A hierarchical control system is presented in reference [29,30], and all measurements are based on the Clarke transformation. Tis research introduces microgrid control in three levels: primary, secondary, and tertiary.…”

“…Experts have always tried to model the system with the highest possible accuracy. However, without enough knowledge about the system's behaviour, the desired accuracy cannot be obtained, and related problems will arise [26][27][28][29][30][31][32]. ANFIS is a neurofuzzy system based on a combined learning process, which emits the input data to the output space based on fuzzy if-then rules.…”

Photovoltaic Systems Based on Average Current Mode Control: Dynamical Analysis and Chaos Suppression by Using a Non-Adaptive Feedback Outer Loop Controller

“…The obtained behaviors a chaotic behavior to period 1 behavior passing through subharmonic behaviors. Our co trol system consists of four blocks: a block representing the PV system [45][46][47], a blo representing the internal controller that can produce the control voltage con v , a block th In Figure 4, the loop consisting of the internal PWM controller and the photovoltaic system is the one we studied in the first part of this paper. The obtained behaviors are chaotic behavior to period 1 behavior passing through subharmonic behaviors.…”

“…The obtained behaviors are chaotic behavior to period 1 behavior passing through subharmonic behaviors. Our control system consists of four blocks: a block representing the PV system [45][46][47], a block representing the internal controller that can produce the control voltage v con , a block that provides PWM to open and close the control switch SW, and a block representing the external controller. Note that the PV system is modeled by Equation ( 2), the internal controller by Equation ( 5), and the PWM block contains a ramp (modulating signal) of fixed frequency at its comparator.…”

Hybrid Dynamical Modeling and Control of Permanent Magnet Synchronous Motors: Hardware-in-the-Loop Verification

“…In the literature, many control methods for the PMSM have been developed, including scalar control [9][10][11], field-oriented control (FOC) [12][13][14], direct torque control (DTC) [15][16][17][18][19], PI control [20][21][22], and model predictive control (MPC) [23][24][25]. However, all these results are developed for either continuous-time or discrete-time cases but are not adapted for sampled-data implementation.…”