Implementation of Deep Learning-Based Bi-Directional DC-DC Converter for V2V and V2G Applications—An Experimental Investigation

Mohan Krishna Banda, Sreedhar Madichetty () and Shanthi Kumar Nandavaram Banda
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Mohan Krishna Banda: Department of Electrical and Computer Engineering, Ecole Centrale School of Engineering, Mahindra University, Hyderabad 500043, India
Sreedhar Madichetty: Department of Electrical and Computer Engineering, Ecole Centrale School of Engineering, Mahindra University, Hyderabad 500043, India
Shanthi Kumar Nandavaram Banda: Department of Electrical and Computer Engineering, Ecole Centrale School of Engineering, Mahindra University, Hyderabad 500043, India

Energies, 2023, vol. 16, issue 22, 1-23

Abstract: Growth in renewable energy systems, direct current (DC) microgrids, and the adoption of electric vehicles (EVs) will substantially increase the demand for bi-directional converters. Precise control mechanisms are essential to ensure optimal performance and better efficiency of these converters. This paper proposes a deep neural network (DNN)-based controller designed to precisely control bi-directional converters for vehicle-to-vehicle (V2V) and vehicle-to-grid (V2G) applications. This control technique allows the converter to quickly attain new reference values, enhancing performance and efficiency by significantly reducing the overshoot duration. To train the DNN controller, large synthetic data are used by performing simulations for various sets of conditions, and the results are validated with a hardware setup. The real-time performance of the DNN controller is compared with a conventional proportional–integral (PI)-based controller through simulated results using MATLAB Simulink (version 2023a) and with a real-time setup. The converter attains a new reference of about 975 μ s with the proposed control technique. In contrast, the PI controller takes about 220 ms, which shows that the proposed control technique is far better than the PI controller.

Keywords: non-isolated bi-directional converter (NIBC); V2V charger; deep learning; high voltage; low voltage (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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