Research Papers

Longitudinal Motion Based Lightweight Vehicle Payload Parameter Real-Time Estimations

[+] Author and Article Information
Xiaoyu Huang

e-mail: huang.638@osu.edu

Junmin Wang

e-mail: wang.1381@osu.edu
Department of Mechanical and
Aerospace Engineering,
The Ohio State University,
Columbus, OH 43210

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 24, 2011; final manuscript received June 22, 2012; published online November 7, 2012. Assoc. Editor: Xubin Song.

J. Dyn. Sys., Meas., Control 135(1), 011013 (Nov 07, 2012) (10 pages) Paper No: DS-11-1163; doi: 10.1115/1.4007554 History: Received May 24, 2011; Revised June 22, 2012

This paper proposes a longitudinal motion based payload parameter estimator (PPE) design for four-wheel-independently driven lightweight vehicles (LWVs), whose dynamics and control are substantially affected by their payload variations due to the LWVs' significantly reduced sizes and weights. Accurate and real-time estimation of payload parameters, including payload mass and its onboard planar location, will be helpful for LWV control (particularly under challenging driving conditions) and load monitoring. The proposed estimation method consists of three steps in sequential: tire effective radius identification for undriven wheels at constant speed driving; payload mass estimation during acceleration–deceleration period; and payload planar location estimation (PPLE). The PPLE is divided into two parts: a tire nominal normal force estimator (NNFE) based on a recursive least squares algorithm using signals generated by the redundant inputs, and a parameter calculator combining these estimated nominal normal forces. The prototype LWV is a lightweight electric ground vehicle (EGV) with separable torque control of the four wheels enabled by four in-wheel motors, which allow redundant input injections in the designed maneuvers. Experimental results obtained on an EGV road test show that the proposed PPE is capable of accurately estimating payload parameters, and it is independent of other unknown parameters such as tire-road friction coefficient.

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Fig. 1

Top view of LWV with payload

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Fig. 3

Individual wheel dynamics

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Fig. 4

Simplified linear tire model

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Fig. 2

Overall PPE scheme

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Fig. 5

Wheel speed signal filtering with AGF

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Fig. 6

Electric ground vehicle with in-wheel motors

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Fig. 7

Payload weights and inertia measurement system

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Fig. 9

Signals in payload mass estimation

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Fig. 10

Estimated LWV total mass

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Fig. 11

Driving (front) and braking (rear) torques

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Fig. 12

Driving torque and corresponding slip ratio (front-right wheel)

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Fig. 13

Tire nominal normal forces

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Fig. 8

Tire effective radius identification




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