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Incorporated vehicle lateral control strategy for stability and enhanced energy saving in distributed drive hybrid bus.
- Source :
- Applied Soft Computing; Nov2021, Vol. 111, pN.PAG-N.PAG, 1p
- Publication Year :
- 2021
-
Abstract
- Vehicle stability and energy efficiency are important considerations in vehicle engineering. In this context, the current paper presents an energy saving strategy for hybrid electric vehicles that incorporates vehicle lateral dynamic control in conjunction with energy efficiency. To this end, we first model the nonlinear vehicle lateral dynamics of a hybrid electric bus via a Takagi–Sugeno approach and combine this model with an H_{\infty } state-feedback controller via parallel distributed compensation. The controller matrices are obtained using linear matrix inequalities through an optimal energy-to-energy performance norm of the nonlinear vehicle model. Second, we propose a reference side-slip angle generating method and a set of tire force distribution rules, which under the premise of ensuring vehicle stability, minimize the overall energy consumption of the vehicle. Finally, we put forward a new speed prediction method based on vehicle lateral dynamics for hybrid electric vehicle energy saving. Human-in-the-loop simulated driving experiments are conducted where the bus performs lane-changing maneuvers with enhanced control properties under various driving conditions, demonstrating the reliability of the proposed energy-saving performance measures. • We model the nonlinear vehicle lateral dynamics of a bus with the so-called Takagi-Sugeno fuzzy approach and combine with an H_{\infty } state-feedback controller. • We propose a novel vehicle power system layout, a reference side-slip angle generating method, and a set of tire force allocation algorithms. • A new speed prediction algorithm is introduced. • Human-in-the-loop experiment results show that the proposed method has enhanced control properties and reliable energy-saving performance. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15684946
- Volume :
- 111
- Database :
- Supplemental Index
- Journal :
- Applied Soft Computing
- Publication Type :
- Academic Journal
- Accession number :
- 152924703
- Full Text :
- https://doi.org/10.1016/j.asoc.2021.107617