PreprintArticleVersion 1This version is not peer-reviewed
A Feed-Forward Back Propagation Neural Network Approach For Integration Of Ev Into Vehicle-To-Grid (V2g) To Predict State Of Charge Lithium-Ion Batteries
Version 1
: Received: 14 October 2024 / Approved: 15 October 2024 / Online: 16 October 2024 (03:42:38 CEST)
How to cite:
Cervellieri, A. A Feed-Forward Back Propagation Neural Network Approach For Integration Of Ev Into Vehicle-To-Grid (V2g) To Predict State Of Charge Lithium-Ion Batteries. Preprints2024, 2024101213. https://doi.org/10.20944/preprints202410.1213.v1
Cervellieri, A. A Feed-Forward Back Propagation Neural Network Approach For Integration Of Ev Into Vehicle-To-Grid (V2g) To Predict State Of Charge Lithium-Ion Batteries. Preprints 2024, 2024101213. https://doi.org/10.20944/preprints202410.1213.v1
Cervellieri, A. A Feed-Forward Back Propagation Neural Network Approach For Integration Of Ev Into Vehicle-To-Grid (V2g) To Predict State Of Charge Lithium-Ion Batteries. Preprints2024, 2024101213. https://doi.org/10.20944/preprints202410.1213.v1
APA Style
Cervellieri, A. (2024). A Feed-Forward Back Propagation Neural Network Approach For Integration Of Ev Into Vehicle-To-Grid (V2g) To Predict State Of Charge Lithium-Ion Batteries. Preprints. https://doi.org/10.20944/preprints202410.1213.v1
Chicago/Turabian Style
Cervellieri, A. 2024 "A Feed-Forward Back Propagation Neural Network Approach For Integration Of Ev Into Vehicle-To-Grid (V2g) To Predict State Of Charge Lithium-Ion Batteries" Preprints. https://doi.org/10.20944/preprints202410.1213.v1
Abstract
The incorporation of electric vehicles (EVs) with vehicle-to-grid (V2G) offers the development of multi-energy microgrid (MMOs) models. However, MMOs have among their goals to decrease electricity costs by coordinating with vehicle-to-grid (V2G) scheduling. Controlling V2G models poses uncertainties about the voltages developed and creates the need for power generation disruptions. In this work, a Feed-Forward-Back-Propagation Network (FFBPN) is developed with Matlab software, based on the Levenberg-Marquardt algorithm by varying the number of hidden neurons to realize better performance thanks to the value of the Maximum Coefficient of De-termination (R2) and the Minimum Mean Squared Error (MSE). In this article, the dataset from NASA PCoE has been used to make proper consideration of the model that best calculates the life cycle of the battery, demonstrating relevant implications for future development of this type of system based on FFBPN for the integration of EV into V2G. The proposed FFBPN demonstrates better performance than other methods taken from the literature. In conclusion, the comparison between the phases of training, validation and test are used to completely represent the validity of the proposed model and precisely to identify the characteristics curves of FFBPN for future ap-plications highlighting its performance to create profitability, efficiency, production, energy saving and minimum environmental impact.
Keywords
Vehicle-to-grid; Lithium-ion batteries; electric vehicles; multi-energy microgrid models
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
