NDLI: Advanced Multi-Mode Control Strategy for a Parallel Hybrid Electric Vehicle Based on Driving Pattern Recognition

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Jeon, Soonil Lee, Jang-Moo Park, Yeong-Il
Copyright Year	2003
Abstract	The adaptive multi-mode control strategy (AMMCS) is defined as the control strategy that switches control parameters for the purpose of adjusting vehicles to diverse traffic conditions and driver’s habits. This strategy is composed of off-line and on-line procedures. In the off-line procedure, several sets of control parameters are optimized under representative driving patterns (RDP). In the on-line procedure, the control parameter switching or interpolation is periodically activated based on the driving pattern recognition (DPR) algorithm, assuming that the driving pattern during the future control horizon doesn’t change significantly compared to the past pattern. The AMMCS is conceptually similar to one of predictive control theories, namely the receding horizon control which is also known as model predictive control. The AMMCS is expected to be applied well to hybrid electric vehicle (HEV) system which is very sensitive to driving patterns. Furthermore, the AMMCS can be combined with the two conventional control strategies using global and local optimization techniques to improve performances further. The design goal of the AMMCS is to minimize fuel consumption and NOx for a pre-transmission single shaft parallel HEV.
Sponsorship	Dynamic Systems and Control Division
Starting Page	373
Ending Page	383
Page Count	11
File Format	PDF
ISBN	0791837130
DOI	10.1115/IMECE2003-41857
Volume Number	Dynamic Systems and Control, Volumes 1 and 2
Conference Proceedings	ASME 2003 International Mechanical Engineering Congress and Exposition
Language	English
Publisher Date	2003-11-15
Publisher Place	Washington, DC, USA
Access Restriction	Subscribed
Subject Keyword	Hybrid electric vehicle Adaptive Control Hybrid electric vehicles Pattern recognition
Content Type	Text
Resource Type	Article

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