可解释的支持人工智能的自适应模糊MPPT和双面光伏和电池驱动电动汽车充电系统的能源管理
Vineet Kumar Tiwari1, Awadhesh Kumar1, Shekhar Yadav1
1Department of Electrical Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, India.
Scientific reports
|January 7, 2026
概括
本研究介绍了一种先进的太阳能电动电动汽车充电系统,使用可解释的人工智能支持的模糊逻辑来追踪最大功率点,以及一套能效电力流动的能源管理系统. 该系统确保可靠的电动汽车充电,提高了效率和电网支持.
科学领域:
- 可再生能源系统可再生能源系统
- 在工程领域的人工智能.
- 电动汽车技术 电动汽车技术
背景情况:
- 广泛采用电动汽车 (EV) 需要可持续和高效的充电基础设施.
- 双面光伏 (PV) 面板与电池储能系统 (BESS) 结合,为不间断的电动汽车充电提供了一个解决方案.
- 集成先进的控制策略对于优化混合太阳能驱动电动汽车充电系统至关重要.
研究的目的:
- 开发和评估一个可解释的人工智能 (XAI) 支持的适应性模糊最大功率点跟踪 (MPPT) 控制器,用于双面太阳能驱动的电动汽车充电系统.
- 实施基于规则的层次能源管理系统 (EMS) 来调节混合系统中的电力流量.
- 在各种环境条件下评估系统的性能,效率和电网支持能力.
主要方法:
- 设计了一种10千瓦双面太阳能驱动的电动汽车充电系统,采用20千瓦时的LiFePO4电池组和10千瓦双向逆变器.
- 使用RETScreen Expert软件获取实时太阳辐射和大气数据.
- 一个XAI-Fuzzy控制器和一个基于等级规则的EMS被开发和模拟使用MATLAB/Simulink.
主要成果:
- 在部分遮阳下,XAI-Fuzzy控制器实现了80.9%的跟踪效率,比Perturb and Observe (P&O) 算法的效率高4.5%.
- 电气管理系统有效地管理了五种操作模式的电力流量,保持了电网电力质量,总波扭曲率 (THD) 为2.40% (低于IEEE 519标准).
- 可解释性指标显示了高保真度 (0.96) 和一致性 (0.91),证实了可解释的控制器行为.
结论:
- 拟议的混合系统提高了整体效率,并为电动汽车充电提供了可靠的电网支持.
- XAI-Fuzzy MPPT控制器和EMS为可持续的电动汽车充电基础设施提供了一个强大的解决方案.
- 该系统的性能通过在各种环境条件下的模拟进行验证,确认其实际适用性.
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