ANN-Based Design of a Hybrid Renewable Energy System for Hybrid Electric Vehicle Applications
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 (WT...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/16/e3sconf_icregcsd2025_01004.pdf |
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| Summary: | 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. |
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| ISSN: | 2267-1242 |