Version 1
: Received: 27 April 2023 / Approved: 28 April 2023 / Online: 28 April 2023 (12:54:21 CEST)
How to cite:
Madichetty, S.; Banda, M.K.; Nandavaram Banda, S.K. Implementation of Deep Learning based Bi-Directional DC-DC Converter for V2V and V2G applications in Microgrid - An Experimental Investigation. Preprints2023, 2023041182. https://doi.org/10.20944/preprints202304.1182.v1
Madichetty, S.; Banda, M.K.; Nandavaram Banda, S.K. Implementation of Deep Learning based Bi-Directional DC-DC Converter for V2V and V2G applications in Microgrid - An Experimental Investigation. Preprints 2023, 2023041182. https://doi.org/10.20944/preprints202304.1182.v1
Madichetty, S.; Banda, M.K.; Nandavaram Banda, S.K. Implementation of Deep Learning based Bi-Directional DC-DC Converter for V2V and V2G applications in Microgrid - An Experimental Investigation. Preprints2023, 2023041182. https://doi.org/10.20944/preprints202304.1182.v1
APA Style
Madichetty, S., Banda, M.K., & Nandavaram Banda, S.K. (2023). Implementation of Deep Learning based Bi-Directional DC-DC Converter for V2V and V2G applications in Microgrid - An Experimental Investigation. Preprints. https://doi.org/10.20944/preprints202304.1182.v1
Chicago/Turabian Style
Madichetty, S., Mohan Krishna Banda and Shanthi Kumar Nandavaram Banda. 2023 "Implementation of Deep Learning based Bi-Directional DC-DC Converter for V2V and V2G applications in Microgrid - An Experimental Investigation" Preprints. https://doi.org/10.20944/preprints202304.1182.v1
Abstract
This paper presents a proposal for a non-isolated bidirectional converter (NIBC) controlled by a deep neural network (DNN) to enable vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) charging, which contributes to the development of more efficient and sustainable transportation and energy systems. The DNN controller manages power flow in both directions, making it possible to charge electric vehicles (EVs) and discharge power from EVs to the grid with improved efficiency and performance compared to traditional control methods. The non-isolated topology used in this proposal offers several benefits, including reduced cost, smaller size, and higher efficiency. To train the DNN controller, a large dataset of simulations was used, and the results were validated with a hardware setup. The real-time performance of the DNN controller was compared to a proportional-integral (PI) based controller through simulated results. The findings of the study show that the DNN controller outperforms traditional PI controllers.
Keywords
Non-isolated bi-directional converter (NIBC) 2; V2V charger 3; Deep learning 4; High voltage 5; Low voltage
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.
