Editorial Type:
Article Category: Research Article
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Online Publication Date: 19 Jul 2021

Application of Transient- and Steady-State Acceleration Signals in Intelligent Tires

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Page Range: 190 – 210
DOI: 10.2346/tire.21.20016
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ABSTRACT

The accelerometer-based intelligent tire has gained focus in recent years for its ability to obtain both kinematics and dynamics-related information of the tire. This paper extends the previous steady-state applications of acceleration signals, which mainly estimate tire force, sideslip, and friction coefficient from the steady-state features of acceleration waveforms, to transient acceleration applications. By using the proposed tire mixed Euler–Lagrange rolling model, it is analytically demonstrated that tire rolling acceleration can be decomposed into steady-state and transient-state components from the perspective of kinematics. It is hard to analyze the transient-state component theoretically or split it from the measured signals on real road surfaces; thus, a learning-based algorithm is developed to automatically extract discriminative features without any physical models. With this method, essential information associated with tire transient acceleration could be inferred to help improve driving safety and performance.

As the application, tire wear identification with an artificial neural network is validated to be feasible based on complete acceleration signals. The prediction accuracy reaches 98.2% under different test conditions. The proposed acceleration formation mechanism is proved to be effective in explaining tire rolling acceleration as well as guiding to acquire vital information about the tire to improve vehicle safety and performance.

Keywords: intelligent tire; tire kinematics; acceleration decomposition; machine learning
FIG. 1 —
FIG. 1 —

Diagram of the accelerometer-based intelligent tire.

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FIG. 2 —

Intelligent tire prototype: (a) accelerometer at the center of the tire inner liner; (b) battery and ECU mounted on the rim; (c) screen to show information.

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FIG. 3 —

Radial and circumferential acceleration signal.

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FIG. 4 —

Mixed Euler–Lagrange method for describing the tire motion.

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FIG. 5 —

Transient excitations: (a) tread discontinuity; (b) road profile; (c) driver's operations.

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FIG. 6 —

Transformation between absolute coordinate and following-up coordinate.

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FIG. 7 —

Experiments for estimating tire vertical force: (a) field test; (b) bench test.

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FIG. 8 —

Comparison of estimated and real vertical force.

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FIG. 9 —

Estimated ply steer angle: (a) estimated ply steer angle under different inflation pressure; (b) estimated ply steer angle and measured force under different vertical load.

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FIG. 10 —

Tire beam model.

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FIG. 11 —

Bench test for estimating tire lateral force: (a) bench; (b) accelerometer for collecting signals.

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FIG. 12 —

Estimated and real lateral force.

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FIG. 13 —

Structure of ANN.

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FIG. 14 —

Field test for tire wear identification: (a) vehicle with tires of different wear degrees; (b) equipped tire.

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FIG. 15 —

Extracted radial acceleration features of the three tires in the time domain.

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FIG. 16 —

Classification accuracy of training and validation.

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FIG. 17 —

Confusion matrix of the classification result.

Contributor Notes

1 State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China
5 Corresponding author. Email: weiyt@tsinghua.edu.cn
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