Global optimal energy management control strategies for connected four-wheel-drive hybrid electric vehicles

This study presents a novel decentralised hierarchical global energy management control strategy for a group of connected four-wheel-drive hybrid electric vehicles (HEVs) in urban road conditions. In the higher level controller, signal phase and timing information and the optimal cruising velocity a...

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Veröffentlicht in:IET intelligent transport systems Jg. 11; H. 5; S. 264 - 272
Hauptverfasser: Qiu, Lihong, Qian, Lijun, Zomorodi, Hesam, Pisu, Pierluigi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United Kingdom The Institution of Engineering and Technology 01.06.2017
Institution of Engineering and Technology (IET)
Schlagworte:
connected four‐wheel‐drive hybrid electric vehicles
decentralised hierarchical global energy management control strategy
dynamic programming
dynamic programming problem
energy management systems
fuel economy
global energy management optimisation
global optimal energy management control strategy
HEVs
hybrid electric vehicles
level control
level controller
model predictive control framework
optimal control
optimal cruising velocity
predictive control
Research Article
target velocity generation
timing information
urban road conditions
wheels
wheels
dynamic programming problem
dynamic programming
connected four-wheel-drive hybrid electric vehicles
decentralised hierarchical global energy management control strategy
optimal control
model predictive control framework
global energy management optimisation
energy management systems
fuel economy
level controller
timing information
hybrid electric vehicles
global optimal energy management control strategy
optimal cruising velocity
HEVs
predictive control
target velocity generation
level control
urban road conditions
ISSN:1751-956X, 1751-9578
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Beschreibung
Zusammenfassung:This study presents a novel decentralised hierarchical global energy management control strategy for a group of connected four-wheel-drive hybrid electric vehicles (HEVs) in urban road conditions. In the higher level controller, signal phase and timing information and the optimal cruising velocity are combined to generate the target velocities for the HEVs. A model predictive control framework that focuses on the tracking of the target velocity and the associated desired control variable for every individual vehicle is proposed for the prediction of the optimal velocity that compromises fuel economy, mobility and safety. In the lower level controller, a dynamic programming problem is formulated that utilises the predicted velocity for the global energy management optimisation of every individual HEV. Simulation results validate the advantages of the proposed higher and lower level controllers.
Bibliographie:USDOE
ISSN:1751-956X
1751-9578
DOI:10.1049/iet-its.2016.0197