一个基于GWO-ANFIS控制器的强大的MPPT框架,用于网联电动汽车充电站
Debabrata Mazumdar1, Pabitra Kumar Biswas1, Chiranjit Sain2
1Department of Electrical Engineering, National Institute of Technology Mizoram, Aizawl, 796012, India.
Scientific reports
|December 27, 2024
概括
本研究介绍了一种增强的灰狼优化ANFIS控制器,用于多能集成电动汽车充电站. 这种方法优化了充电时间表的可靠性,效率和可持续性.
科学领域:
- 电气工程 电气工程
- 可再生能源系统可再生能源系统
- 运输系统 运输系统
背景情况:
- 电动汽车 (EV) 越来越受欢迎,需要在充电基础设施方面取得进展.
- 目前依赖化石燃料的充电解决方案不足以实现气候目标.
- 整合可再生能源对于可持续的电动汽车充电至关重要.
研究的目的:
- 为电动汽车提出一个节能充电终端.
- 提高电动汽车的可靠性,环境优势和充电效率.
- 为解决电动汽车充电的运输和电力网络之间的整合挑战.
主要方法:
- 开发了一种增强的灰狼优化 (GWO) 适应性神经模糊推理系统 (ANFIS) 控制器.
- 集成最大功率点跟踪 (MPPT),备用电池系统和太阳能.
- 使用神经网络集成网和比例整合导数 (PID) 控制器.
- 使用MATLAB/Simulink 2018a软件进行模拟和评估,并提供四个案例研究.
主要成果:
- 为高效和可持续的电动汽车充电基础设施展示一个可行的路线.
- 验证拟议控制器在管理多种能源方面的有效性.
- 在各种条件情景下评估系统的性能.
- 证实了电动汽车充电效率和可靠性的提高.
结论:
- 拟议的多能集成电动汽车充电站提供了一个可持续的解决方案.
- 增强的GWO-ANFIS控制器有效地管理可再生能源整合和电动汽车充电.
- 开发的系统为未来的电动汽车充电基础设施提供了一个有希望的框架.
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