Electrical Engineering, Год журнала: 2024, Номер 106(5), С. 6649 - 6663
Опубликована: Июнь 12, 2024
Язык: Английский
Optimal Control Applications and Methods, Год журнала: 2024, Номер 45(5), С. 2076 - 2099
Опубликована: Май 2, 2024
Abstract This article proposes a Fire Hawk Optimizer (FHO) technique for photovoltaic fed grid connected wireless electric vehicle battery‐charger. The optimization issues are solved by the FHO across countless endless exploring space. main aim of proposed is to enhance efficiency, reduce energy demand, improve communication amid receiver and transmitter sides (EV) range anxiety. A (PV) panel, an storage unit (ESU), vehicles part topology. Each separately regulated, converter uses voltage‐regulation mechanism guarantee that direct current bus voltage kept in nominal‐level when operating various circumstances. An essential requirement quick commercialization EVs ability charge them. Moreover, charging‐station smartly power event battery empty generation solar array not available. inverter tuned using technique. method done MATLAB software it evaluated their performance. methodology provides higher efficiency 91% than existing techniques.
Язык: Английский
Процитировано
1
Electrical Engineering, Год журнала: 2024, Номер unknown
Опубликована: Окт. 25, 2024
Язык: Английский
Процитировано
0
Optimal Control Applications and Methods, Год журнала: 2024, Номер 45(4), С. 1524 - 1545
Опубликована: Фев. 21, 2024
Abstract Integrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach EV with planning. The technique is joint execution of coati optimization algorithm (COA) cascade‐correlation‐growing deep learning neural network, commonly known as COA‐CCG‐DLNN technique. objective proposed method minimize cost EVs, it forecasts best course action. based on vehicle‐to‐building (V2B), vehicle‐to‐grid (V2G), grid‐to‐vehicle (G2V). COA used CCG‐DLNN predict optimal solution system. executed MATLAB platform compared existing techniques like particle swarm (PSO), heap‐based (HBO), wild horse (WHO). achieves low $1.33 high accuracy 99.5% other techniques. performance metrics include 2996.348 result, 3000.100 mean, 3001.261 worst, standard deviation 1.160348, along median 2998.816, all which outperform methods.
Язык: Английский
Процитировано
0
IETE Journal of Research, Год журнала: 2024, Номер 70(8), С. 6901 - 6912
Опубликована: Март 13, 2024
This article presents a novel approach to enhance the efficiency, control, and power management capabilities of traditional Zeta Converters in renewable energy applications. The proposed particle swarm optimization (PSO)-modified three-port zeta converter (MTPZC) features multiport structure with dedicated input ports for photovoltaic (PV) battery sources, along an output port. integration PSO aims optimize maximum point tracking extraction. Extensive simulations experimental validations showcase MTPZC's superior performance extraction from both PV Battery sources. innovative design positions as promising solution conversion systems contributes advancement electronics by offering improved reliability efficiency. Simulation results, validated MATLAB compared existing methods, demonstrate impressive efficiency 97.8%. MTPZC outperforms approaches, indicating lower losses highlighting its potential practical implementation diverse systems.
Язык: Английский
Процитировано
0
Electrical Engineering, Год журнала: 2024, Номер 106(5), С. 6649 - 6663
Опубликована: Июнь 12, 2024
Язык: Английский
Процитировано
0