Energy management of slow and fast charge electric vehicles (EV) in smart parking lot using NBO-THDCNN algorithm

B. Devi () and S. Edwin Jose ()
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B. Devi: Rajas Institute of Technology
S. Edwin Jose: P.S.R. Engineering College

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 8, No 83, 20047-20070

Abstract: Abstract The proposed approach is used in this study to control the energy of fast and slow charging of Electric Vehicles in a reconstructed smart parking lot. The purpose of this article is to reduce overall power loss, and fuel, operation, and maintenance costs, as well as environmental costs. Electric vehicle is introduced as both a load and source. This framework uses Real-time Periodic Planning, Day Ahead and Real-Time Intra Period Planning (RTIP) to arrange rigid loads, slowly charging cars, and quickly charging vehicles.By then the proposed technique is executed on the MATLAB platform and then the results show that the proposed method reduces the rapid and slow charging. The NBO-THDCNN approach optimizes rapid and slow charging for EVs in smart parking lots. Initial EV energy level is 1000 kWh, slow charge demand starts at 100 kWh, and transformer free capacity begins at 900 kWh. Pricing varies: 70% level at 0.03 kWh, 20% level at 0.02 kWh, and 10% level at 0.03 kWh. Peak power reaches 400 kW. EV count peaks at 16, and energy levels fluctuate: 50 kWh at 1 h, 240 kWh at 3–6 h, and 1 kWh at 8 h. FCV charge peaks at 110 kWh. These results highlight NBO-THDCNN's effectiveness in managing EV energy.

Keywords: Electric vehicles; Energy storage systems; Smart parking lot; Tree hierarchical deep convolutional neural network; Namib beetle optimization (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10668-024-05241-w

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