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Research Papers

Anti-Jerk Dynamic Modeling and Parameter Identification of an Electric Vehicle Based on Road Tests

[+] Author and Article Information
Mohit Batra

Systems Design Engineering,
University of Waterloo,
Waterloo, ON, Canada
e-mail: m2batra@uwaterloo.ca

John McPhee

Professor
Systems Design Engineering,
University of Waterloo,
Waterloo, ON, Canada
e-mail: mcphee@uwaterloo.ca

Nasser L. Azad

Systems Design Engineering,
University of Waterloo,
Waterloo, ON, Canada
e-mail: nlashgarianazad@uwaterloo.ca

Contributed by the Design Engineering Division of ASME for publication in the JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS. Manuscript received December 6, 2017; final manuscript received July 9, 2018; published online August 6, 2018. Assoc. Editor: Corina Sandu.

J. Comput. Nonlinear Dynam 13(10), 101005 (Aug 06, 2018) (13 pages) Paper No: CND-17-1546; doi: 10.1115/1.4040870 History: Received December 06, 2017; Revised July 09, 2018

Model-based design facilitates quick development of vehicle controllers early in the development cycle. The goal is to develop simple, accurate, and computationally efficient physics based models that are capable of real-time simulation. We present models that serve the purpose of both plant and anti-jerk control design of electric vehicles (EVs). In this research, we propose a procedure for quick identification of longitudinal dynamic parameters for a high-fidelity plant and control-oriented model of an EV through road tests. Experimental data were gathered on our test vehicle, a Toyota Rav4EV, using an integrated measurement system to collect data from multiple sensors. A matlab/simulink nonlinear least square parameter estimator with a trust-reflective algorithm was used to identify the vehicle parameters. The models have been validated against experimental data.

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Figures

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Fig. 1

Integration of sensors through Vector CAN box

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Fig. 2

WFS and WPS sensors mounted on wheel hub: (a) WFS sensor and (b) WPS sensor

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Fig. 3

LGS and LDV sensor set mounted on wheel hub

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Fig. 4

GPS and IMU sensor set co-located with the magnetic mount

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Fig. 5

(a) Rav4EV fitted with VMS and (b) TMMC test track

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Fig. 6

Longitudinal forces acting on Rav4EV

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Fig. 7

(a) Slip ratio λ versus time and (b) normalized longitudinal force μ versus λ

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Fig. 8

Experimental versus estimated normal force

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Fig. 9

4DOF car model for identification of suspension parameters

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Fig. 10

Normal force on the front left and rear left wheels

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Fig. 11

Frontal area of Rav4EV and processed image with black and white pixels

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Fig. 12

Vehicle speed recorded during coast down testing of Rav4EV

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Fig. 13

Plot of (vx/v0) against (t/T) to estimate β

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Fig. 14

Experimental versus simulated motor angular speed (ωm)

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Fig. 15

Free-body diagram showing forces on the front (driven) wheel

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Fig. 16

Experimental and simulated wheel spin (ωw)

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Fig. 17

Full-car longitudinal dynamics model of Rav4EV in maplesim

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Fig. 18

Vehicle powertrain model

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Fig. 19

Diagram of the EV powertrain

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Fig. 20

Comparison of slip-based transients in halfshaft torsion model and transient-slip model against experiments

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Fig. 21

Comparison of longitudinal force versus wheel slip in linear and Pacejka tire models

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Fig. 22

Motor torque input measured experimentally

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Fig. 23

Comparison of wheel torque, vehicle speeds and wheel speeds—plant, control-oriented model, and experimental data

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