基于在线自适应神经模糊的燃料电池混合动力系统能量管理方案

M. Basit, R. Badar
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引用次数: 2

摘要

混合动力系统由于其低燃料消耗和更高的效率而被广泛使用。在这种情况下,燃料电池与传统电源的集成成为一个有趣的解决方案。选择燃料电池作为动力源也有助于解决许多环境问题。能源管理方案(EMSs)对这些能源的动态性能和燃料消耗有很大的影响。EMS控制不同能源之间的电力分配,以满足负载的电力需求。针对多电动飞机紧急降落场景,对不同的基于神经模糊的EMSs进行了比较分析。将在线自适应神经模糊小波控制与传统神经模糊TSK (Takagi Sugeno Kang)控制和经典PI控制进行了性能有效性检验。选择电池和超级电容器的充电状态、燃料消耗和系统整体效率作为性能指标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Online Adaptive NeuroFuzzy Based Energy Management Schemes for Fuel-Cell Based Hybrid Power System
Hybrid power systems are now extensively being used due to their low fuel consumption and greater efficiency. In this context, fuel cell integration with conventional power sources is becoming an interesting solution. Choice of fuel cell as power source also serves to address many environmental concerns. Energy Management Schemes (EMSs) have great influence on dynamic performance and fuel consumption of these sources. EMS controls the power split between different energy sources to fulfill the power demand at load. In this paper, comparative analysis of different NeuroFuzzy based EMSs for emergency landing scenario of More Electric Air-Craft is presented. The performance validity of online adaptive NeuroFuzzy Wavelet Control has been checked against conventional NeuroFuzzy Takagi Sugeno Kang (TSK) control and classical PI control. State of the charge of battery and supercapacitor, fuel consumption and overall system efficiency have been chosen as performance metrics.
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