• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

一种基于灰狼优化算法-自适应神经模糊推理系统（GWO-ANFIS）控制器的用于并网电动汽车充电站的稳健最大功率点跟踪（MPPT）框架。

A robust MPPT framework based on GWO-ANFIS controller for grid-tied EV charging stations.

作者信息

Mazumdar Debabrata, Biswas Pabitra Kumar, Sain Chiranjit, Ahmad Furkan, Al-Fagih Luluwah

机构信息

Department of Electrical Engineering, National Institute of Technology Mizoram, Aizawl, 796012, India.

Department of Electrical Engineering, Ghani Khan Choudhury Institute of Engineering & Technology, Malda, India.

出版信息

Sci Rep. 2024 Dec 28;14(1):30955. doi: 10.1038/s41598-024-81937-3.

DOI:10.1038/s41598-024-81937-3
PMID:39730598
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11680877/
Abstract

As electric vehicles gain popularity, there has been a lot of interest in supporting their continued development with the aim of enhancing their dependability, environmental advantages, and charging efficiency. The scheduling of navigation and charging for electric vehicles is among the most well-known research topics. For optimal navigation and charging scheduling, the coupled network state between the transportation and power networks must be met; moreover, the scheduling outcomes might significantly impact these networks. To address climate challenges, relying only on fossil fuel-based infrastructure for electric car charging is insufficient. Consequently, Multi-Energy Integrated EV charging stations have emerged as a workable solution that seamlessly integrates grid power, renewable energy sources-particularly solar energy-and EV charging needs. The enhanced grey wolf optimised (GWO) ANFIS controller for Maximum Power Point Tracking (MPPT), standby battery systems, solar power, neural network-integrated grids, and sophisticated control algorithms like PID controller are all proposed in this article as energy-efficient charging terminals for electric vehicles. Moreover, authors had considered four conditional case study and with the help of MATLAB/Simulink 2018a software, the design is thoroughly examined and assessed, providing a viable route for an efficient and sustainable EV charging infrastructure.

摘要

随着电动汽车越来越受欢迎，人们对支持其持续发展产生了浓厚兴趣，目的是提高其可靠性、环境优势和充电效率。电动汽车的导航和充电调度是最著名的研究课题之一。为了实现最佳的导航和充电调度，必须满足交通网络和电力网络之间的耦合网络状态；此外，调度结果可能会对这些网络产生重大影响。为应对气候挑战，仅依靠基于化石燃料的基础设施为电动汽车充电是不够的。因此，多能源集成电动汽车充电站已成为一种可行的解决方案，它将电网电力、可再生能源（特别是太阳能）和电动汽车充电需求无缝集成。本文提出了用于最大功率点跟踪（MPPT）的增强型灰狼优化（GWO）自适应神经模糊推理系统（ANFIS）控制器、备用电池系统、太阳能、神经网络集成电网以及诸如PID控制器等先进控制算法，作为电动汽车的节能充电终端。此外，作者考虑了四个条件案例研究，并借助MATLAB/Simulink 2018a软件对设计进行了全面检查和评估，为高效且可持续的电动汽车充电基础设施提供了一条可行途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/1e106aa1335a/41598_2024_81937_Fig23_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/be97953341f4/41598_2024_81937_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/fff9bcbf8bfc/41598_2024_81937_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/dc5224ea152f/41598_2024_81937_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/9b9e2b797f3d/41598_2024_81937_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/394fed1427b4/41598_2024_81937_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/049d1f8b66b4/41598_2024_81937_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/dde0e7e07454/41598_2024_81937_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/9da6a4cc4636/41598_2024_81937_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/e2c9b3b10ac5/41598_2024_81937_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/acf861115ccb/41598_2024_81937_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/1d78127904df/41598_2024_81937_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/5c10aa6ecc26/41598_2024_81937_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/dffaac88f9f2/41598_2024_81937_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/4fd622a81693/41598_2024_81937_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/c88f28477de6/41598_2024_81937_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/d467f5051133/41598_2024_81937_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/c5df6cf58b2b/41598_2024_81937_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/3ed0d76e6e91/41598_2024_81937_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/23457d384b06/41598_2024_81937_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/44ab0e32a65b/41598_2024_81937_Fig20_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/a18cc4d62082/41598_2024_81937_Fig21_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/117eb82c246f/41598_2024_81937_Fig22_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/1e106aa1335a/41598_2024_81937_Fig23_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/be97953341f4/41598_2024_81937_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/fff9bcbf8bfc/41598_2024_81937_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/dc5224ea152f/41598_2024_81937_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/9b9e2b797f3d/41598_2024_81937_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/394fed1427b4/41598_2024_81937_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/049d1f8b66b4/41598_2024_81937_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/dde0e7e07454/41598_2024_81937_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/9da6a4cc4636/41598_2024_81937_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/e2c9b3b10ac5/41598_2024_81937_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/acf861115ccb/41598_2024_81937_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/1d78127904df/41598_2024_81937_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/5c10aa6ecc26/41598_2024_81937_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/dffaac88f9f2/41598_2024_81937_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/4fd622a81693/41598_2024_81937_Fig14_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/c88f28477de6/41598_2024_81937_Fig15_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/d467f5051133/41598_2024_81937_Fig16_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/c5df6cf58b2b/41598_2024_81937_Fig17_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/3ed0d76e6e91/41598_2024_81937_Fig18_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/23457d384b06/41598_2024_81937_Fig19_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/44ab0e32a65b/41598_2024_81937_Fig20_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/a18cc4d62082/41598_2024_81937_Fig21_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/117eb82c246f/41598_2024_81937_Fig22_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/22e8/11680877/1e106aa1335a/41598_2024_81937_Fig23_HTML.jpg

相似文献

1
A robust MPPT framework based on GWO-ANFIS controller for grid-tied EV charging stations.一种基于灰狼优化算法-自适应神经模糊推理系统（GWO-ANFIS）控制器的用于并网电动汽车充电站的稳健最大功率点跟踪（MPPT）框架。
Sci Rep. 2024 Dec 28;14(1):30955. doi: 10.1038/s41598-024-81937-3.
2
Hybrid ANN-GWO MPPT with MPC-based inverter control for efficient EV charging under partial shading conditions.基于混合人工神经网络-灰狼优化算法的最大功率点跟踪与基于模型预测控制的逆变器控制，用于部分阴影条件下的高效电动汽车充电。
Sci Prog. 2025 Apr-Jun;108(2):368504251331835. doi: 10.1177/00368504251331835. Epub 2025 Apr 1.
3
Integration of renewable energy sources using multiport converters for ultra-fast charging stations for electric vehicles: An overview.用于电动汽车超快充电站的多端口转换器集成可再生能源：综述。
Heliyon. 2024 Aug 3;10(15):e35782. doi: 10.1016/j.heliyon.2024.e35782. eCollection 2024 Aug 15.
4
Design and simulation of 4 kW solar power-based hybrid EV charging station.基于4千瓦太阳能的混合动力电动汽车充电站的设计与仿真
Sci Rep. 2024 Mar 27;14(1):7336. doi: 10.1038/s41598-024-56833-5.
5
A novel strategy towards efficient and reliable electric vehicle charging for the realisation of a true sustainable transportation landscape.一种实现真正可持续交通格局的高效可靠电动汽车充电新策略。
Sci Rep. 2024 Feb 8;14(1):3261. doi: 10.1038/s41598-024-53214-w.
6
Optimizing quasi-dynamic wireless charging for urban electric buses: A two-scenario mathematical framework with grid and PV-battery systems.优化城市电动公交车的准动态无线充电：一种包含电网和光伏电池系统的双场景数学框架。
Heliyon. 2024 Jul 23;10(15):e34857. doi: 10.1016/j.heliyon.2024.e34857. eCollection 2024 Aug 15.
7
Optimal power generation of proton exchange membrane fuel cell using ANFIS based MPPT algorithm.基于自适应神经模糊推理系统（ANFIS）的最大功率点跟踪（MPPT）算法实现质子交换膜燃料电池的最优发电
Sci Rep. 2024 Nov 2;14(1):26455. doi: 10.1038/s41598-024-77696-w.
8
Optimal scheduling of solar powered EV charging stations in a radial distribution system using opposition-based competitive swarm optimization.基于对立竞争群体优化的辐射状配电系统中太阳能电动汽车充电站的优化调度
Sci Rep. 2025 Feb 10;15(1):4880. doi: 10.1038/s41598-025-88758-y.
9
A Novel dPdI feedback based control scheme using GWO tuned PID controller for efficient MPPT of PEM fuel cell.基于新型 dPdI 反馈的控制方案，使用灰狼优化整定 PID 控制器实现质子交换膜燃料电池的高效最大功率点跟踪。
ISA Trans. 2019 Oct;93:312-324. doi: 10.1016/j.isatra.2019.02.038. Epub 2019 Mar 7.
10
Multi-objective optimization framework for electric vehicle charging and discharging scheduling in distribution networks using the red deer algorithm.基于红鹿算法的配电网电动汽车充放电调度多目标优化框架
Sci Rep. 2025 Apr 17;15(1):13343. doi: 10.1038/s41598-025-97473-7.

本文引用的文献

1
Code and data from an ADALINE network trained with the RTRL and LMS algorithms for an MPPT controller in a photovoltaic system.
Data Brief. 2020 Sep 8;32:106296. doi: 10.1016/j.dib.2020.106296. eCollection 2020 Oct.
2
Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems.一种用于光伏系统的新型先进变步长增量电导最大功率点跟踪算法的仿真与实验设计
ISA Trans. 2016 May;62:30-8. doi: 10.1016/j.isatra.2015.08.006. Epub 2015 Aug 31.