使用基于ANFIS的MPPT算法对质子交换膜燃料电池产生最佳功率.
Devakirubakaran S1, Bharatiraja C2, Narasimha Prasad T3
1Center for Smart Energy Systems, Chennai Institute of Technology, Chennai, Tamilnadu, India.
Scientific reports
|November 3, 2024
概括
本研究介绍了燃料电池电动汽车 (EV) 适应性神经模糊推理系统 (ANFIS) 最大功率点跟踪 (MPPT) 方法. 与传统方法相比,ANFIS-MPPT提高了功率稳定性和响应时间.
科学领域:
- 可再生能源系统可再生能源系统
- 汽车工程 汽车工程
- 控制系统 控制系统
背景情况:
- 燃料电池对于未来的能源需求至关重要,特别是在电动汽车 (EV) 中.
- 将燃料电池集成到电动汽车中,在管理动态运行行为方面提出了挑战.
- 为了最大限度地提高燃料电池效率,需要先进的追踪方法来实现最佳功率输出.
研究的目的:
- 为燃料电池提出和评估基于适应性神经模糊推理系统 (ANFIS) 的最大功率点跟踪 (MPPT) 方法.
- 提高汽车应用中的燃料电池系统的效率和动态性能.
- 将拟议的ANFIS-MPPT方法与传统的MPPT算法进行比较.
主要方法:
- 开发了一个ANFIS-MPPT算法,考虑的流速,压力和堆温度.
- 将ANFIS-MPPT算法与1.26kW的燃料电池模型集成到MATLAB/Simulink中.
- 在压力,堆温度和负载的动态变化下验证了系统.
主要成果:
- ANFIS-MPPT算法显示,电源稳定性比扰乱和观察 (P&O) 和增量传导 (InC) 方法有10-15%的改善.
- ANFIS-MPPT的响应速度比P&O快30%,比InC快23%.
- ANFIS实现了2.5秒的响应时间,提供1.26千瓦,优于P&O (3.6秒,1.13千瓦) 和InC (4.5秒,1.19千瓦).
结论:
- 基于ANFIS的MPPT方法在动力稳定性和燃料电池系统的响应时间方面提供了卓越的性能.
- 这种先进的MPPT技术对于优化燃料电池电动汽车 (FCEV) 运行非常有效.
- 拟议的方法为管理动态行为和最大限度地提高燃料电池应用中的功率输出提供了强大的解决方案.
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