A Review of Bidirectional DC-DC,DC-AC Converters for V2G and G2V Applications

Chaitanya, M.Krishna; Vinayak, A.Sai; Pavan, B.Jithendra Ganga; Lakshmi, N.Naga; Sandeep, K.

doi:10.1109/piecon56912.2023.10085877

Cited by 3 publications

(1 citation statement)

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“…These applications necessitate the inclusion of bidirectional converters. Various types of bi-directional converters are available for the readily accessible transfer charge between two vehicles, forming the basis for creating a V2V charging system [7]. One prominent example is the non-isolated bi-directional converter (NIBC), known for its minimal energy losses, impressive efficiency, and lesser components, rendering it particularly well suited for low-voltage-level applications in comparison to other non-isolated and isolated converter topologies [8].…”

Section: Motivationmentioning

confidence: 99%

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

Banda,

Madichetty,

Nandavaram Banda

2023

Energies

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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 μs 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.

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Section: Motivationmentioning

confidence: 99%