Machine Learning Based Electrical Vehicle Charging Management under the Optimization of Flyback Converter for Solar Photovoltaic Applications

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 9

Year of Publication : 2023

Authors : P. Dhanalakshmi, U. Janardhan Reddy, M. Ranjit Reddy, G. Ravikanth

10.14445/23488379/IJEEE-V10I9P113

How to Cite?

P. Dhanalakshmi, U. Janardhan Reddy, M. Ranjit Reddy, G. Ravikanth, "Machine Learning Based Electrical Vehicle Charging Management under the Optimization of Flyback Converter for Solar Photovoltaic Applications," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 9, pp. 137-153, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I9P113

Abstract:

Flyback topologies are an exciting study area for photovoltaic applications driven by their efficiency and low complexity. However, the current converter design has its disadvantages of being heavy and inefficient. However, solar Photovoltaic (PV) systems rapidly expand and hold great promise as a sustainable energy option. Because the converter needs a higher voltage than the PV panels can produce, it is common practice to employ a conversion device. The panel's Maximum Power Point (MPP) has been tracked using the “Perturb and Observe” (P&O) technique. Given that the maximum power output of each converter is only 2/N of the total, the buck converter's power capacity can be significantly increased using N-overlapped flyback converters. To design an optimization strategy for cutting down on power loss within a budgetary constraint, we provide a Model Predictive Control (MPC) MPPT technique. The control loop is reduced in length by using the proposed predictive controller for MPP, which begins to predict errors as soon as the flyback DC/DC converter receives the signal from the switch. Calculating the battery's SoC for an electric car is time-consuming and challenging. This study introduces a machine learningbased technique to better manage energy production in remotely controlled and partitioned solar power plants. The suggested method employs a state-of-the-art support vector machine to model and estimate Hybrid Electric Vehicle (HEV) charging demand. Experimental findings with the integrated circuits are presented and analyzed with mathematical operations, specifications, predictions, and simulators of the power circuits portion of the MPPT converter. Effective clamps are added to the circuit to improve performance under test conditions. The efficiency of the flyback converter with the suggested dynamic resistor divider and the concurrent systems is computationally confirmed using a 250W interleaved flyback converter circuitry setup. Regarding PV panels, sustaining a high voltage and generating substantial electricity production, MPPT is significant. Therefore, it is essential to have an endless energy supply, and it is also crucial to reach self-sufficiency in energy demands. The electrical vehicle load of an ensemble can be partially met by its solar system.

Keywords:

MPPT, MPC, Photovoltaic system, Solar Energy, MPP, Flyback converter.

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