ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy

For the battery pack’s limited remaining power, two energy-aware ecological driving problems are discussed. A real-time energy-aware ecological driving control strategy is proposed to optimize energy consumption and meet the ECO driving demand. First, the vehicle longitudinal driving dynamics model...

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Autores principales:	Hongli He, Dan Liu, Xiangyang Lu, Juncai Xu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	intelligent electric vehicle ECO driving energy-aware Radau pseudo-spectral method optimal control Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/08f154dc55cf4409b78595afc5719369
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id	oai:doaj.org-article:08f154dc55cf4409b78595afc5719369
record_format	dspace
spelling	oai:doaj.org-article:08f154dc55cf4409b78595afc57193692021-11-11T15:38:07ZECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy10.3390/electronics102126132079-9292https://doaj.org/article/08f154dc55cf4409b78595afc57193692021-10-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/21/2613https://doaj.org/toc/2079-9292For the battery pack’s limited remaining power, two energy-aware ecological driving problems are discussed. A real-time energy-aware ecological driving control strategy is proposed to optimize energy consumption and meet the ECO driving demand. First, the vehicle longitudinal driving dynamics model and energy consumption model are established. Then, the optimal control problem is constructed with the maximum driving distance and the shortest driving time as the objective functions, respectively. With the multinomial Radau pseudo-spectral method, the optimization results of residual power, vehicle speed, and acceleration are obtained. The results show that in the case of in-vehicle driving the remaining power of the battery pack can be sensed in real-time, and the driving of intelligent electric vehicles can be planned in real-time to realize the most ecological driving with the largest driving distance and shortest driving time. The energy consumptions of vehicles, traveling at the same distance, are compared. The consumption obtained through optimization, is 26% less than the consumption of the vehicle that has not been optimized. The results show that the optimization process has certain advantages. In the future, as one of intelligent vehicles’ autonomous driving control strategies, the results have guiding and practical significance.Hongli HeDan LiuXiangyang LuJuncai XuMDPI AGarticleintelligent electric vehicleECO drivingenergy-awareRadau pseudo-spectral methodoptimal controlElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2613, p 2613 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	intelligent electric vehicle ECO driving energy-aware Radau pseudo-spectral method optimal control Electronics TK7800-8360
spellingShingle	intelligent electric vehicle ECO driving energy-aware Radau pseudo-spectral method optimal control Electronics TK7800-8360 Hongli He Dan Liu Xiangyang Lu Juncai Xu ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy
description	For the battery pack’s limited remaining power, two energy-aware ecological driving problems are discussed. A real-time energy-aware ecological driving control strategy is proposed to optimize energy consumption and meet the ECO driving demand. First, the vehicle longitudinal driving dynamics model and energy consumption model are established. Then, the optimal control problem is constructed with the maximum driving distance and the shortest driving time as the objective functions, respectively. With the multinomial Radau pseudo-spectral method, the optimization results of residual power, vehicle speed, and acceleration are obtained. The results show that in the case of in-vehicle driving the remaining power of the battery pack can be sensed in real-time, and the driving of intelligent electric vehicles can be planned in real-time to realize the most ecological driving with the largest driving distance and shortest driving time. The energy consumptions of vehicles, traveling at the same distance, are compared. The consumption obtained through optimization, is 26% less than the consumption of the vehicle that has not been optimized. The results show that the optimization process has certain advantages. In the future, as one of intelligent vehicles’ autonomous driving control strategies, the results have guiding and practical significance.
format	article
author	Hongli He Dan Liu Xiangyang Lu Juncai Xu
author_facet	Hongli He Dan Liu Xiangyang Lu Juncai Xu
author_sort	Hongli He
title	ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy
title_short	ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy
title_full	ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy
title_fullStr	ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy
title_full_unstemmed	ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy
title_sort	eco driving control for intelligent electric vehicle with real-time energy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/08f154dc55cf4409b78595afc5719369
work_keys_str_mv	AT honglihe ecodrivingcontrolforintelligentelectricvehiclewithrealtimeenergy AT danliu ecodrivingcontrolforintelligentelectricvehiclewithrealtimeenergy AT xiangyanglu ecodrivingcontrolforintelligentelectricvehiclewithrealtimeenergy AT juncaixu ecodrivingcontrolforintelligentelectricvehiclewithrealtimeenergy
_version_	1718434778881982464

ECO Driving Control for Intelligent Electric Vehicle with Real-Time Energy

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