Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network

Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control s...

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Autores principales:	Haitao Yan, Yongzhi Xu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/b782af62914646c4a820df96b7a3f7e1
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id	oai:doaj.org-article:b782af62914646c4a820df96b7a3f7e1
record_format	dspace
spelling	oai:doaj.org-article:b782af62914646c4a820df96b7a3f7e12021-11-29T00:56:05ZEnergy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network1875-919X10.1155/2021/7328008https://doaj.org/article/b782af62914646c4a820df96b7a3f7e12021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/7328008https://doaj.org/toc/1875-919XEnergy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.Haitao YanYongzhi XuHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Haitao Yan Yongzhi Xu Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network
description	Energy control strategy is a key technology of hybrid electric vehicle, and its control effect directly affects the overall performance of the vehicle. The current control strategy has some shortcomings such as poor adaptability and poor real-time performance. Therefore, a transient energy control strategy based on terminal neural network is proposed. Firstly, based on the definition of instantaneous control strategy, the equivalent fuel consumption of power battery was calculated, and the objective function of the minimum instantaneous equivalent fuel consumption control strategy was established. Then, for solving the time-varying nonlinear equations used to control the torque output, a terminal recursive neural network calculation method using BARRIER functions is designed. The convergence characteristic is analyzed according to the activation function graph, and then the stability of the model is analyzed and the time efficiency of the error converging to zero is deduced. Using ADVISOR software, the hybrid power system model is simulated under two typical operating conditions. Simulation results show that the hybrid electric vehicle using the proposed instantaneous energy control strategy can not only ensure fuel economy but also shorten the control reaction time and effectively improve the real-time performance.
format	article
author	Haitao Yan Yongzhi Xu
author_facet	Haitao Yan Yongzhi Xu
author_sort	Haitao Yan
title	Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network
title_short	Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network
title_full	Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network
title_fullStr	Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network
title_full_unstemmed	Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network
title_sort	energy control strategy for parallel hybrid electric vehicle based on terminal neural network
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/b782af62914646c4a820df96b7a3f7e1
work_keys_str_mv	AT haitaoyan energycontrolstrategyforparallelhybridelectricvehiclebasedonterminalneuralnetwork AT yongzhixu energycontrolstrategyforparallelhybridelectricvehiclebasedonterminalneuralnetwork
_version_	1718407730498109440

Energy Control Strategy for Parallel Hybrid Electric Vehicle Based on Terminal Neural Network

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