Integrated MPPT and bidirectional DC DC converter with reduced switch multilevel inverters for electric vehicles applications.

The necessity for a clean and sustainable Renewable Energy Source (RES) is fueled by the intensifying environmental issue and steady decline of fossil resources. Additionally, expanding use of Electric Vehicles (EVs) across the globe is a result of rising carbon emissions and oil consumption. PV powered EV charging system has the ability to substantially reduce greenhouse emissions when compared with conventional sources-based EV charging system. However, existing PV based EV charging systems lack efficient approaches for adapting optimally to varying environmental conditions. Moreover, the power conversion efficiency may not be optimized leading to lower energy output. Hence, in this work, a Single Ended Primary Inductance Converter (SEPIC) Integrated Isolated Flyback Converter (SIIFC) and Machine Learning Radial Basis Function Neural Network Maximum Power Point Tracking (ML RBFNN MPPT) are used to maximize PV power extraction. EV motor and the grid are powered by a reduced switch 31 level inverter and a 1 Voltage Source Inverter (VSI). In order to effectively synchronize the grid voltage and guarantee that the EV motor runs at the desired speed, an adaptive proportional integral (PI) controller is used. For validating the effectiveness of proposed PV based EV charging station, MATLAB simulations and experimental validations are used. Experimental results demonstrate that the proposed SIIFC and RBFNN MPPT offer an efficiency of 95.4% and 96% respectively. Moreover, the proposed 31-level inverter design increases the reliability and reduces the THD to 2.16%.