A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries DOI Creative Commons

Alice Cervellieri

Energies, Journal Year: 2024, Volume and Issue: 17(23), P. 6107 - 6107

Published: Dec. 4, 2024

The accurate prediction and efficient management of the State Charge (SoC) electric vehicle (EV) batteries are critical challenges in integration vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, potential disruptions power generation. This paper addresses problem optimizing enhance reliability efficiency V2G scheduling MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing Levenberg–Marquardt algorithm varying number hidden neurons achieve better performance; performance was measured by maximum coefficient determination (R2) minimum mean squared error (MSE). Utilizing NASA Prognostics Center Excellence (PCoE) dataset, validate model’s capability accurately predict life cycle EV batteries. Our proposed FFBPN model demonstrates superior compared existing methods from literature, offering significant implications for future system developments. comparison between training, validation, testing phases underscores validity precisely identifies characteristic curves FFBPN, showcasing its profitability, efficiency, production, energy savings, minimize environmental impact.

Language: Английский

A comprehensive review of vehicle-to-grid integration in electric vehicles: Powering the future DOI Creative Commons
Pulkit Kumar, Harpreet Kaur Channi, Raman Kumar

et al.

Energy Conversion and Management X, Journal Year: 2024, Volume and Issue: unknown, P. 100864 - 100864

Published: Dec. 1, 2024

Language: Английский

Citations

8
Investigation on battery and fuel cell electric vehicle-to-grid potential for microgrid frequency regulation DOI

Leonardo Federici,

Laura Tribioli, Raffaello Cozzolino

et al.

International Journal of Hydrogen Energy, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

0
Bidirectional Energy Transfer Between Electric Vehicle, Home, and Critical Load DOI Creative Commons

Ștefan-Andrei Lupu,

Dan Floricău

Energies, Journal Year: 2025, Volume and Issue: 18(9), P. 2167 - 2167

Published: April 23, 2025

In the transition to a sustainable energy system, integration of electric vehicles into residential systems is an innovative solution for increasing resilience and optimizing electricity consumption. This article presents bidirectional AC/DC converter capable charging vehicle battery under normal conditions, while providing power critical consumer in event grid outage. The simulations performed show us functionality this converter, demonstrating its efficiency ensuring continuity supply.

Language: Английский

Citations

0
A Feed-Forward Back-Propagation Neural Network Approach for Integration of Electric Vehicles into Vehicle-to-Grid (V2G) to Predict State of Charge for Lithium-Ion Batteries DOI Creative Commons

Alice Cervellieri

Energies, Journal Year: 2024, Volume and Issue: 17(23), P. 6107 - 6107

Published: Dec. 4, 2024

The accurate prediction and efficient management of the State Charge (SoC) electric vehicle (EV) batteries are critical challenges in integration vehicle-to-grid (V2G) systems within multi-energy microgrid (MMO) models. Inaccurate SoC estimation can lead to inefficiencies, increased costs, potential disruptions power generation. This paper addresses problem optimizing enhance reliability efficiency V2G scheduling MMO coordination. In this work, we develop a Feed-Forward Back-Propagation Network (FFBPN) using MATLAB 2024 software, employing Levenberg–Marquardt algorithm varying number hidden neurons achieve better performance; performance was measured by maximum coefficient determination (R2) minimum mean squared error (MSE). Utilizing NASA Prognostics Center Excellence (PCoE) dataset, validate model’s capability accurately predict life cycle EV batteries. Our proposed FFBPN model demonstrates superior compared existing methods from literature, offering significant implications for future system developments. comparison between training, validation, testing phases underscores validity precisely identifies characteristic curves FFBPN, showcasing its profitability, efficiency, production, energy savings, minimize environmental impact.

Language: Английский

Citations

0