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Research Papers

Nonlinear Cascade Strategy for Longitudinal Control of Electric Vehicle

[+] Author and Article Information
F. Giri

e-mail: fouad.giri@unicaen.fr

F. Z. Chaoui

Department of Electrical Engineering,
University of Caen Basse-Normandie,
GREYC Lab UMR CNRS,
14032 Caen, France

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received September 6, 2012; final manuscript received May 31, 2013; published online September 4, 2013. Assoc. Editor: Xubin Song.

J. Dyn. Sys., Meas., Control 136(1), 011005 (Sep 04, 2013) (13 pages) Paper No: DS-12-1293; doi: 10.1115/1.4024782 History: Received September 06, 2012; Revised May 31, 2013

The problem of controlling the longitudinal motion of front-wheels electric vehicle (EV) is considered making the focus on the case where a single dc motor is used for both front wheels. Chassis dynamics are modelled applying relevant fundamental laws taking into account the aerodynamic effects and the road slope variation. The longitudinal slip, resulting from tire deformation, is captured through Kiencke's model. Despite its highly nonlinear nature the complete model proves to be utilizable in longitudinal control design. The control objective is to achieve a satisfactory vehicle speed regulation in acceleration/deceleration stages, despite wind speed and other parameters uncertainty. An adaptive controller is developed using the backstepping design technique. The obtained adaptive controller is shown to meet its objectives in presence of the changing aerodynamics efforts and road slope.

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Figures

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Fig. 1

Physical structure of electric vehicle

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Fig. 2

Physical structure of electric vehicle

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Fig. 3

The forces acting on vehicle

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Fig. 4

The controlled system including the buckconverter, the dc motor and the vehicle

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Fig. 5

Cascade control strategy for the chassis-motorassociation

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Fig. 6

Cascade control strategy for the chassis-motorassociation

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Fig. 7

Controller tracking performances

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Fig. 8

Controller robustness performances

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Fig. 9

Simulated experimental setting with PID's regulators

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Fig. 10

(a) Wheel speed responses. Solid: reference. Dashed: speed response for adaptive controller. Dotted: speed response for PID cascade controller (b) Sliding responses. Solid: sliding response for adaptive controller. Dashed: sliding response for PID cascade controller

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Fig. 11

Wheel speed responses. Solid: reference. Dashed: speed response with c = 10. Dotted: speed response with c = 10−2

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