基于PI混合控制器的人工神经网络储能系统

Tushar Aggarwal, Sarthak Thareja, Shwetang Bahadur, J. Kesari
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引用次数: 0

摘要

系统设计中考虑了电动汽车混合储能技术。不断增加的电力消耗需要更大的监测和管理,以及在设计和改进具体解决方案方面的额外障碍。建筑、交通和贸易就是这样的例子。许多人拥有便携式电脑。长期储存可再生能源的能力必须与节能技术区分开来。这个问题的性质和范围需要广泛调查。如果我们了解彼此的观点,我们就能更好地理解电动汽车在混合能源存储方面的价值。作者采用神经网络和PI来提供准确,无失真的输出。本文采用神经网络和PI组合控制器对电动汽车混合动力储能系统进行了仿真。结果表明,人工神经网络和PI控制器的使用降低了输出的失真和噪声,提高了系统的使用寿命。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy Storage System with Artificial Neural Networks using PI Hybrid Controllers
EV Hybrid Energy Storage Techniques of system design are considered. Increasing electricity consumption necessitates greater monitoring and management, as well as additional obstacles in designing and refining specific solutions. Building, transportation, and trade are examples of such initiatives. A large number of people own portable computers. The capacity to store renewable energy for extended periods must be distinguished from energy saving technologies. The nature and scope of this issue need extensive investigation. We can better comprehend the value of electric cars for hybrid energy storage if we understand each other's perspectives. The authors employ neural networks and PI to provide accurate, distortion-free outputs. In this paper, a hybrid electric energy storage system for electric vehicle is simulated using Neural Network and PI combined controller. The results show that the use of artificial neural network and PI controller have reduced the distortions and noise level in output which will improve the lifetime of the system.
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