Integrated Renewable Energy System for Electric Vehicles Utilizing a PV-Fed Three-Phase Synchronous Reluctance Motor and High Gain DC-DC Converter
| International Journal of Electrical and Electronics Engineering |
| © 2024 by SSRG - IJEEE Journal |
| Volume 11 Issue 8 |
| Year of Publication : 2024 |
| Authors : P. R. Jeevarathinam, K. Vinoth |
10.14445/23488379/IJEEE-V11I8P125
How to Cite?
P. R. Jeevarathinam, K. Vinoth, "Integrated Renewable Energy System for Electric Vehicles Utilizing a PV-Fed Three-Phase Synchronous Reluctance Motor and High Gain DC-DC Converter," SSRG International Journal of Electrical and Electronics Engineering, vol. 11, no. 8, pp. 275-294, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I8P125
Abstract:
This research proposes an integrated approach to enhance electric vehicle (EV) performance and efficiency using renewable energy. It combines a photovoltaic (PV) array with a High Gain Improved Single-Ended Primary Inductor Converter with Luo topology (Integrated SEPIC-Luo DC-DC converter) to optimize PV system output. An African Buffalo Algorithm (ABA) optimized Artificial Neural Network (ANN) Maximum Power point tracking (MPPT) controller is introduced, which maximizes PV system power output by continuously adapting to environmental changes. Excess energy is stored in a supercapacitor via a bi-directional converter, allowing for rapid storage and release as needed. The EV’s propulsion system features a three-phase synchronous reluctance motor (SyRM) integrated with an (n+1) diode and (n+1) semiconductor converter, with speed regulation by a Hysteresis controller-based Proportional-Integral (PI) controller for precise motor control. The system also supplies excess energy to the grid through a single-phase Voltage Source Inverter (VSI), enabling grid integration and renewable energy injection. This integrated system optimizes PV system output, enhances EV efficiency, and contributes to sustainability and grid stability. The tracking efficiency of the African Buffalo Algorithm-ANN based MPPT is 98.85%, whereas the efficiency of the proposed converter is 90%. The simulation tool MATLAB was used for the whole evaluation.
Keywords:
1ΦVSI, ABA optimized ANN controller, Hysteresis controller-based PI controller, Integrated SEPIC-Luo Converter, Three-Phase SRMN.
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