In–wheel motor electric vehicle state estimation by using unscented particle filter
Vehicle state parameters are essential for active safety stability control and very valuable in chassis design evaluation. In this paper, a method for vehicle state parameters estimation is developed for in–wheel motor (IWM) electric vehicle (EV). The observer is based on information fusion combining standard sensor suite in today's typical vehicle and feedback signals from IWM. This paper utilises unscented particle filter (UPF) for tyre lateral force, longitudinal velocity, lateral velocity and yaw rate estimation, which is based on a numerically efficient nonlinear stochastic estimation technique. Planar vehicle model and dynamic tyre model are developed to describe behaviour of IWM EV. Detailed simulation verifies the validation and robustness of proposed estimation algorithm.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/14775360
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Supplemental Notes:
- © 2015 Inderscience Enterprises Ltd.
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Authors:
- Chu, Wenbo
- Luo, Yugong
- Dai, Yifan
- Li, Keqiang
- Publication Date: 2015
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 115-136
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Serial:
- International Journal of Vehicle Design
- Volume: 67
- Issue Number: 2
- Publisher: Inderscience Enterprises Limited
- ISSN: 1477-5360
- Serial URL: http://www.inderscience.com/jhome.php?jcode=IJVD
Subject/Index Terms
- TRT Terms: Dynamic models; Electric vehicles; Estimation theory; Filters; Motors; Simulation; Stability (Mechanics); Tire forces; Vehicle design; Wheels
- Uncontrolled Terms: In wheel motors
- Subject Areas: Design; Highways; Vehicles and Equipment; I91: Vehicle Design and Safety;
Filing Info
- Accession Number: 01561338
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 27 2015 9:48AM