Research Papers: Design Automation

A Particle Filter Approach for Identifying Tire Model Parameters From Full-Scale Experimental Tests

[+] Author and Article Information
Edoardo Sabbioni

Department of Mechanical Engineering,
Politecnico di Milano
Via La Masa 1,
Milano 20156, Italy
e-mail: edoardo.sabbioni@polimi.it

Ruixin Bao

School of Mechanical Engineering,
Liaoning University,
Liaoning Shihua University,
Fushun 113001, China
e-mail: ruixinbao@126.com

Federico Cheli

Department of Mechanical Engineering,
Politecnico di Milano,
Via La Masa 1,
Milano 20156, Italy
e-mail: federico.cheli@polimi.it

Davide Tarsitano

Department of Mechanical Engineering,
Politecnico di Milano
Via La Masa 1,
Milano 20156, Italy
e-mail: davide.tarsitano@polimi.it

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 31, 2016; final manuscript received October 30, 2016; published online December 12, 2016. Assoc. Editor: Massimiliano Gobbi.

J. Mech. Des 139(2), 021403 (Dec 12, 2016) (7 pages) Paper No: MD-16-1258; doi: 10.1115/1.4035186 History: Received March 31, 2016; Revised October 30, 2016

Mathematical models simulating the handling behavior of passenger cars are extensively used at a design stage for evaluating the effects of new structural solutions or control systems. The main source of uncertainty in these type of models lies in tire–road interaction, due to high nonlinearity. Proper estimation of tire model parameters is thus of utter importance to obtain reliable results. This paper presents a methodology aimed at identifying the magic formula-tire (MF-Tire) model coefficients of the tires of an axle only based on measurements carried out on board vehicle (vehicle sideslip angle, yaw rate, lateral acceleration, speed, and steer angle) during standard handling maneuvers (step-steers, double lane changes, etc.). The proposed methodology is based on particle filtering (PF) technique. PF may become a serious alternative to classic model-based techniques, such as Kalman filters. Results of the identification procedure were first checked through simulations. Then, PF was applied to experimental data collected using an instrumented passenger car.

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Fig. 2

Step steer maneuver, maximum lateral acceleration 8 m/s2. Time histories of coefficients' estimate.

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Fig. 3

Step steer maneuver, maximum lateral acceleration 8 m/s2. Time histories of sideslip angle and yaw rate.

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Fig. 1

Single-track vehicle model

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Fig. 4

Step steer maneuver on a low adherence surface, maximum lateral acceleration 5 m/s2. Time histories of sideslip angle and yaw rate.

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Fig. 5

Experimental–numerical comparison: step-steer maneuver (steady-state lateral acceleration 8.5 m/s2). Time histories of sideslip angle and yaw rate.

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Fig. 6

Experimental–numerical comparison: double lane change maneuver (peak lateral acceleration 6.5 m/s2). Time histories of sideslip angle and yaw rate.



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