EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach.
Saved in:
| Title: | EV charging and fuel cell vehicle refuelling with distributed energy resources using hybrid approach. |
|---|---|
| Authors: | Senthilkumar, M.1 (AUTHOR) senthilkumar@psgitech.ac.in, Prabhu, Sandeep2 (AUTHOR) sprabhu@sidtm.edu.in, Arun Kumar, U.3 (AUTHOR) arun.udayakumarn@gmail.com, Krishnakumar, R.4 (AUTHOR) krishnakumar.ramasamy@srec.ac.in |
| Source: | Environment, Development & Sustainability. Feb2026, Vol. 28 Issue 2, p4309-4331. 23p. |
| Subject Terms: | *Energy storage, *Fuel cells, Distributed resources (Electric utilities), Optimization algorithms, Electric vehicle charging stations, Metaheuristic algorithms, Artificial neural networks, Cost control |
| Abstract: | This manuscript proposes a hybrid technique for Electric Vehicle (EV) charging and Fuel Cell vehicle refuelling with distributed energy resources. The proposed hybrid approach, known as the BWO-CCG-DLNN method, combines the Beluga Whale Optimization (BWO) algorithm with the Cascade-Correlation Growing Deep Learning Neural Network (CCG-DLNN). The primary goal of the proposed strategy is to reduce reliance on the utility grid while simultaneously reducing the overall cost of distributed energy resources by using battery storage for peak shaving. The EV charging's cost is reduced using the proposed BWO approach, and the ideal outcome of the system is predicted using the CCG-DLNN approach. The proposed strategy is implemented into use on the MATLAB platform, and it is contrasted with current strategys, including the Cuckoo Search Algorithm Color Harmony Algorithm, and Particle Swarm Optimization, The proposed method demonstrates the lowest mean (1.0936) and median (1.0158), indicating its effectiveness. The standard deviation (0.1505) suggests relatively consistent results. The proposed method shows better result when compared to other methods. When compared to other existing approaches, the proposed approach has a high efficiency of 98% and a low cost of 200 ($/kW). [ABSTRACT FROM AUTHOR] |
| Copyright of Environment, Development & Sustainability is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | This manuscript proposes a hybrid technique for Electric Vehicle (EV) charging and Fuel Cell vehicle refuelling with distributed energy resources. The proposed hybrid approach, known as the BWO-CCG-DLNN method, combines the Beluga Whale Optimization (BWO) algorithm with the Cascade-Correlation Growing Deep Learning Neural Network (CCG-DLNN). The primary goal of the proposed strategy is to reduce reliance on the utility grid while simultaneously reducing the overall cost of distributed energy resources by using battery storage for peak shaving. The EV charging's cost is reduced using the proposed BWO approach, and the ideal outcome of the system is predicted using the CCG-DLNN approach. The proposed strategy is implemented into use on the MATLAB platform, and it is contrasted with current strategys, including the Cuckoo Search Algorithm Color Harmony Algorithm, and Particle Swarm Optimization, The proposed method demonstrates the lowest mean (1.0936) and median (1.0158), indicating its effectiveness. The standard deviation (0.1505) suggests relatively consistent results. The proposed method shows better result when compared to other methods. When compared to other existing approaches, the proposed approach has a high efficiency of 98% and a low cost of 200 ($/kW). [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 1387585X |
| DOI: | 10.1007/s10668-024-05138-8 |
