Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:<scp>Hybrid</scp>approach

Gopal, Soundra Devi; Jawahar, Rahila; Athmanathan, Radhika; Pandi, Meenalochini

doi:10.1002/oca.3066

Cited by 6 publications

(2 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The CCG‐DLNN technique is similar to the CCNN approach but adds additional hidden layers within the layers of input and output that contain multiple neurons. In turn, each iteration either adds a neuron onto the most recent a novel hidden layer or a hidden layer is added and a new neuron 39–41 . Figure 4 displays the structure of CCG‐DLNN.…”

Section: Energy Management Of Ev Charging Using Coa‐ccg‐dlnn Approachmentioning

confidence: 99%

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Rahila,

Soundra Devi,

Radhika

et al. 2024

Optim Control Appl Methods

View full text Add to dashboard Cite

Integrating network expansion planning into electric vehicle (EV) smart charging solutions involves designing scalable infrastructure to accommodate the growing demand for electric mobility while considering grid capacity and energy distribution efficiency. This paper proposes a hybrid approach for EV smart charging with network expansion planning. The hybrid technique is the joint execution of the coati optimization algorithm (COA) and cascade‐correlation‐growing deep learning neural network, commonly known as the COA‐CCG‐DLNN technique. The objective of the proposed method is to minimize the cost of charging EVs, and it forecasts the best course of action. EV charging with network expansion is based on vehicle‐to‐building (V2B), vehicle‐to‐grid (V2G), and grid‐to‐vehicle (G2V). The COA approach is used to minimize the cost of EV charging and the CCG‐DLNN approach is used to predict the optimal solution for the system. The proposed method is executed on the MATLAB platform and is compared with existing techniques like particle swarm optimization (PSO), heap‐based optimization (HBO), and wild horse optimization (WHO). The proposed method achieves a low cost of $1.33 and a high accuracy of 99.5% compared with other existing techniques. The performance metrics for the proposed method include 2996.348 as the best result, 3000.100 as the mean, 3001.261 as the worst, and a standard deviation of 1.160348, along with a median of 2998.816, all of which outperform other existing methods.

show abstract

Section: Energy Management Of Ev Charging Using Coa‐ccg‐dlnn Approachmentioning

confidence: 99%

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Rahila,

Soundra Devi,

Radhika

et al. 2024

Optim Control Appl Methods

View full text Add to dashboard Cite

show abstract

“…[ 35 ] The energy flow that must occur in both directions to meet the demands of the power train and to recharge the energy sources, especially during braking periods, and the exchanges between the two energy sources, is made possible by this converter. [ 36,37 ] The equation uses the current smoothing inductor to stand in for the first part of the chopper model. The ability of this element to retain and release magnetic energy makes it unique.…”

Section: Configuration Of Ultrafast Cs Of Solar‐fed Ev Storage Systemmentioning

confidence: 99%

Optimizing Cost and Emission Reduction in Photovoltaic–Battery‐Energy‐Storage‐System‐Integrated Electric Vehicle Charging Stations: An Efficient Hybrid Approach

Kirubadevi,

Sathesh Kumar,

et al. 2024

Energy Tech

View full text Add to dashboard Cite

In this article, an optimal photovoltaic (PV) and battery energy storage system with hybrid approach design for electric vehicle charging stations (EVCS) is proposed. The hybrid approach combines the use of polar transformer networks (PTNs) and the puzzle optimization algorithm (POA); hence it is called as POA–PTN approach. The main objective is to lessen the charging station cost and pollutant emissions. The proposed method is minimizing the pollutant emissions and the annual cost of PV with EVCS, which is done by POA method and predicts the optimal solution is done by PTN approach. The proposed method is executed in the MATLAB platform, and compared with existing methods, including heap‐based optimization, particle swarm optimization, and wild horse optimizer approaches. The proposed technique demonstrates superior performance, achieving the lowest net present cost of €1.45 × 106 and emissions of 2.25 × 108 g year−1 with a shorter computing time of 13.04 s compared to the other methods.

show abstract

An efficient hybrid approach based design of photovoltaic fed grid integrated wireless electric vehicle battery charger

Kumar,

Rahul,

Kumar

et al. 2024

Optim Control Appl Methods

View full text Add to dashboard Cite

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 a countless endless exploring space. The main aim of the proposed technique is to enhance the efficiency, reduce energy demand, improve communication amid the receiver and transmitter sides of electric vehicle (EV) and reduce range anxiety. A photovoltaic (PV) panel, an energy storage unit (ESU), and electric vehicles are part of the proposed topology. Each unit is separately regulated, and the converter of energy storage unit uses a voltage‐regulation mechanism to guarantee that the direct current bus voltage is kept in nominal‐level when operating in various circumstances. An essential requirement for the quick commercialization of EVs is the ability to charge them. Moreover, the charging‐station smartly uses grid power in the event that the battery storage is empty and the generation of solar photovoltaic array is not available. The inverter is tuned using the proposed technique. The FHO method is done in MATLAB software and it evaluated their performance. The proposed methodology provides higher efficiency of 91% than the existing techniques.

show abstract

Power electronics converters for an electric vehicle fast charging station based energy storage system and renewable energy sources:Hybridapproach

Cited by 6 publications

References 66 publications

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Electric vehicle smart charging with network expansion planning using hybrid COA‐CCG‐DLNN approach

Optimizing Cost and Emission Reduction in Photovoltaic–Battery‐Energy‐Storage‐System‐Integrated Electric Vehicle Charging Stations: An Efficient Hybrid Approach

An efficient hybrid approach based design of photovoltaic fed grid integrated wireless electric vehicle battery charger

Contact Info

Product

Resources

About