Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence

In recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power syst...

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Autores principales:	Yanying Ma, Qiang Liu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Telecommunication TK5101-6720
Acceso en línea:	https://doaj.org/article/f945d07b60e6431f9dcc334e3d3c9d2a
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spelling	oai:doaj.org-article:f945d07b60e6431f9dcc334e3d3c9d2a2021-11-22T01:11:38ZReal-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence1875-905X10.1155/2021/7666834https://doaj.org/article/f945d07b60e6431f9dcc334e3d3c9d2a2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7666834https://doaj.org/toc/1875-905XIn recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power systems. In the experiment, the open circuit voltage method and the ampere-hour integration method are used to estimate the SOC of the lithium battery and the particle swarm energy management algorithm is used to allocate the output power of the fuel cell and the lithium battery. The particle swarm algorithm module calls the dual source hybrid power system module through the sim function to convert the actual value input in the system into a fuzzy quantity suitable for fuzzy control. The energy management strategy based on particle swarm optimization and fuzzy control was tested based on working conditions under the comprehensive test bench. Finally, the matching of the hybrid system is analyzed from the structure, component parameters, control strategy, and driving cycle of the vehicle. The experimental data show that the total fuel consumption of the three sets of experiments is averaged to get a fuel consumption rate of 26.3 m3/100 km for the hybrid city bus under the optimized energy management strategy. The results show that the real-time energy management strategy based on particle swarm algorithm can significantly improve the real-time performance of traditional instantaneous energy management strategies while reducing fuel consumption.Yanying MaQiang LiuHindawi LimitedarticleTelecommunicationTK5101-6720ENMobile Information Systems, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Telecommunication TK5101-6720
spellingShingle	Telecommunication TK5101-6720 Yanying Ma Qiang Liu Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence
description	In recent years, due to the strengthening of our country’s comprehensive strength, the rapid development of science and technology and artificial intelligence has also attracted people’s attention. Artificial intelligence is a highly applicable subject, which has very good applications in power systems. In the experiment, the open circuit voltage method and the ampere-hour integration method are used to estimate the SOC of the lithium battery and the particle swarm energy management algorithm is used to allocate the output power of the fuel cell and the lithium battery. The particle swarm algorithm module calls the dual source hybrid power system module through the sim function to convert the actual value input in the system into a fuzzy quantity suitable for fuzzy control. The energy management strategy based on particle swarm optimization and fuzzy control was tested based on working conditions under the comprehensive test bench. Finally, the matching of the hybrid system is analyzed from the structure, component parameters, control strategy, and driving cycle of the vehicle. The experimental data show that the total fuel consumption of the three sets of experiments is averaged to get a fuel consumption rate of 26.3 m3/100 km for the hybrid city bus under the optimized energy management strategy. The results show that the real-time energy management strategy based on particle swarm algorithm can significantly improve the real-time performance of traditional instantaneous energy management strategies while reducing fuel consumption.
format	article
author	Yanying Ma Qiang Liu
author_facet	Yanying Ma Qiang Liu
author_sort	Yanying Ma
title	Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence
title_short	Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence
title_full	Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence
title_fullStr	Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence
title_full_unstemmed	Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence
title_sort	real-time application optimization control algorithm for energy management strategy of the hybrid power system based on artificial intelligence
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/f945d07b60e6431f9dcc334e3d3c9d2a
work_keys_str_mv	AT yanyingma realtimeapplicationoptimizationcontrolalgorithmforenergymanagementstrategyofthehybridpowersystembasedonartificialintelligence AT qiangliu realtimeapplicationoptimizationcontrolalgorithmforenergymanagementstrategyofthehybridpowersystembasedonartificialintelligence
_version_	1718418317048283136

Real-Time Application Optimization Control Algorithm for Energy Management Strategy of the Hybrid Power System Based on Artificial Intelligence

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