Research on indirect tire pressure monitoring algorithm based on machine learning

doi:10.3969/j.issn.1674-8484.2022.02.009

Abstract

Abstract:

A novel tire pressure monitoring algorithm based on machine learning was presented to solve the problem that the current indirect tire pressure monitoring systems (ITPMS) achieved poor identification accuracy under complex road status and varying driving conditions, which was implemented only with the existing wheel speed sensors in vehicles. The theoretical basis of the indirect TPMS was introduced by analyzing the rigid tire model. And the inherent error generated in speed sensors was effectively removed by the recursive least square (RLS) method and accurate wheel speed signals was obtained. The features in time and frequency domains were extracted and a decision tree was used to eliminate the abnormal speed signals and a Bayesian classifier was used to identify the tire pressure conditions overall based on features of the normal speed signals. The result shows that this method in conjunction with decision trees indicates higher correctness during indirect tire pressure monitoring, where the accuracy of identification reaches 96.36% in comparison of the method only with a Bayesian classifier.

Key words: indirect tire pressure monitoring system (ITPMS), tire vibration model, recursive least square (RLS), decision tree, Bayesian classifier

CLC Number:

U467.1+4

YU Lu, TANG Liang, WEI Lingtao, LIU Zijun. Research on indirect tire pressure monitoring algorithm based on machine learning[J]. Journal of Automotive Safety and Energy, 2022, 13(2): 290-299.

Figures/Tables 14

References 18

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轮胎纵向刚度,k_x	4 kN/m
轮胎旋转刚度,k_φ	15 (kN·m)/rad
胎面刚度,k_t	30 kN/m
悬架纵向刚度,k_s	20 kN/m
带束环质量,m_B	10.9 kg
轮辋质量,m_R	30 kg
带束环转动惯量,J_B	1.0 kg·m²
轮辋转动惯量,J_R	0.69 kg·m²
静态滚动半径,r_stat	0.35 m

轮胎纵向刚度,k_x	4 kN/m
轮胎旋转刚度,k_φ	15 (kN·m)/rad
胎面刚度,k_t	30 kN/m
悬架纵向刚度,k_s	20 kN/m
带束环质量,m_B	10.9 kg
轮辋质量,m_R	30 kg
带束环转动惯量,J_B	1.0 kg·m²
轮辋转动惯量,J_R	0.69 kg·m²
静态滚动半径,r_stat	0.35 m