| Issue |
E3S Web Conf.
Volume 616, 2025
2nd International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2025)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 10 | |
| Section | Renewable Energy | |
| DOI | https://doi.org/10.1051/e3sconf/202561601004 | |
| Published online | 24 February 2025 | |
ANN-Based Design of a Hybrid Renewable Energy System for Hybrid Electric Vehicle Applications
1 Department of Electrical and Electronics Engineering Methodist College of Engineering and Techonlogy, Abids, Hyderabad, Telangana, India
2 Departmentment of CSE Methodist College of Engineering and Techonlogy, Abids, Hyderabad, Telangana, India
3 Department of Electrical Engineering, University College of Engineering (A), Osmania University, India
* Corresponding author: babu.ramesh444@gmail.com
This article examines energy management strategies for hybrid power systems, leveraging an Artificial Neural Network (ANN) to optimize power flow based on real-time needs. The ANN controller ensures maximum power point tracking (MPPT) from renewable sources like photovoltaics (PV), wind turbines (WTs), and fuel cells (FCs), utilizing DC-DC converters. By regulating power flow and dampening fluctuations, the ANN-based method is tested on hybrid setups with PV panels, WTs, and FCs. Simulations in MATLAB/Simulink show that ANN outperforms Fuzzy Logic in MPPT efficiency, particularly for standalone and grid applications at variable loads, enhancing renewable energy reliability.
© The Authors, published by EDP Sciences, 2025
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.