基于机器学习的MPPT集成与二级双延伸直流-直流转换器,用于连接到电网的光伏驱动BLDC电动汽车
D Karthikeyan1, Vinod Kumar Shukla2, K Rajesh3
1Department of Information Technology, Post-Doctoral fellow School of Engineering, Architecture and Interior Design, Amity University Dubai, UAE and Associate professor, Department of Electrical and Electronics Engineering, SRM Institute of science and Technology, Kattankulathur, Chennai, 603203, India. karthikeyand992@gmail.com.
Scientific reports
|March 1, 2026
概括
本研究介绍了一种由太阳能供电的电动汽车 (EV) 的新型转换器和机器学习控制器. 该系统提高了效率,并减少了波扭曲,以实现更清洁的交通.
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
- 人工智能的人工智能
背景情况:
- 越来越多的环境问题需要无排放的运输解决方案,如电动汽车 (EV).
- 无刷直流 (BLDC) 电机对于电动汽车的效率至关重要,它需要来自可再生能源 (RES) 的稳定电力,例如太阳能光伏 (PV) 系统.
- 太阳能光伏系统经常面临的挑战是 DC 输出电压不足以满足负载需求.
研究的目的:
- 为太阳能电动汽车提出一种新的直流-直流转换器和一种基于机器学习 (ML) 的高级最大功率点跟踪 (MPPT) 控制器.
- 为了提高光伏系统的输出电压,并确保最大限度的功率提取.
- 提高电动汽车动力系统的整体性能和能源管理.
主要方法:
- 一个正方形双延伸 (QDE) DC-DC 转换器被设计用于提高光伏电压,降低压力.
- 为MPPT开发了一种海取食优化的辐射偏差功能神经网络 (STFO-RBFNN).
- 在MATLAB/Simulink中模拟了一个集成光伏,电网,电池存储,双向转换器和三相电压源逆变器 (VSI) 的系统.
主要成果:
- 拟议的QDE转换器改善了电压增益和降低了电压应力.
- 该STFO-RBFNN控制器有效地追踪了来自光伏系统的最大功率.
- 综合系统显著提高了性能,实现了95.43%的转换器效率和1.14%的总波扭曲 (THD).
结论:
- 新型转换器和基于ML的MPPT控制器为太阳能电动汽车提供了强大的解决方案.
- 该系统通过补充来源提供高效的能源转换和灵活的能源管理.
- 拟议的方法代表了优化电动汽车性能和可靠性的重大进步,以实现可持续的运输.
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