Skip to main content
Log in

Tire Dynamic Rolling Radius Compensation Algorithm Based on Ax Sensor Offset Estimation for i-TPMS

  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript

Abstract

Indirect Tire Pressure Monitoring System (i-TPMS) monitors inflation pressures in pneumatic tires using wheel speed sensor signals acquired from Anti-lock Braking System (ABS). In order to monitor the tire pressures indirectly, frequency analysis and wheel radius analysis are operating in parallel. This manuscript focuses on the wheel radius analysis. Changes in vehicle weight can influence the difference in rolling radius of the front and rear tires. In this manuscript, a tire rolling radius compensation algorithm using a longitudinal acceleration (Ax) sensor is proposed, without any additional cost. The Ax sensor offset caused by loaded mass is estimated when the vehicle is standstill as well as in motion. The tire rolling radius can be compensated using the estimated offset of the Ax sensor. The compensation amount of the tire rolling radius can be obtained based on the difference between the estimated value from the TPMS learning phase and the value from the TPMS detecting phase. Vehicle tests have been conducted in order to evaluate the proposed estimation algorithm. It has been shown from the test results that the proposed method provides satisfactory performance for compensating the tire rolling radius for the changes in vehicle weight.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Abbreviations

I w :

wheel rotational moment of inertia, kg · m2

α w :

wheel rotational acceleration, rad/s2

T D :

drive torque, N/m

T B :

brake torque, N/m

T w :

wheel torque, N/m

ω w :

measured rotational angular velocity, rad/s

F x :

longitudinal tire force, N

C x :

longitudinal tire stiffness, N

v w :

wheel speed, m/s

v veh :

vehicle speed, m/s

r const :

constant tire rolling radius, m

r eff :

effective tire rolling radius, m

A x :

longitudinal acceleration, m/s2

FL:

front left wheel

FR:

front right wheel

RL:

rear left wheel

RR:

rear right wheel

f :

front axle

r :

rear axle

References

  • Grieβer, M., Köbe, A., Edling, F., Koukes, V., Cunz, J., Gootjes, L., Kohn, J., Runge, I. and Hofmann, I. (2011). Method for indirect tire pressure monitoring. U.S. Patent No. 7,991,523.

  • Hong, S., Lee, C., Borrelli, F. and Hedrick, J. K. (2014). A novel approach for vehicle inertial parameter identification using a dual kalman filter. IEEE Trans. Intelligent Transportation Systems 16, 1, 151–161.

    Article  Google Scholar 

  • Huh, K., Lim, S., Jung, J., Hong, D., Han, S., Han, K., Jo, H. Y. and Yun, J. M. (2007). Vehicle mass estimator for adaptive roll stability control. SAE Technical Paper No. 2007-01-0820.

  • Kang, J. (2019). Resonance frequency estimation of tire for detecting decrease in tire inflation pressure. Trans. Korean Society of Automotive Engineers 27, 11, 847–852.

    Article  Google Scholar 

  • Kidambi, N., Harne, R. L., Fujii, Y., Pietron, G. M. and Wang, K. W. (2014). Methods in vehicle mass and road grade estimation. SAE Int. J. Passenger Cars-Mechanical Systems, 7, 981–991.

    Article  Google Scholar 

  • Lee, M. S. and Lim, J. H. (2010). A consideration on the safety standard of the motor vehicle equipped with tire pressure monitoring system. KSAE Spring Conf. Proc. St. Louis, MO, USA.

  • Pence, B. L., Fathy, H. K. and Stein, J. L. (2009). Sprung mass estimation for off-road vehicles via base-excitation suspension dynamics and recursive least squares. American Control Conf., St. Louis, MO, USA.

  • Persson, N., Gustafsson, F. and Drevö, M. (2002). Indirect tire pressure monitoring using sensor fusion. SAE Trans. 111, 6, 1657–1662.

    Google Scholar 

  • Suender, R., Prokop, G. and Roscher, T. (2015). Comparative analysis of tire evaluation methods for an indirect tire pressure monitoring system (iTPMS). SAE Int. J. Passenger Cars-Mechanical Systems 8, 1, 110–118.

    Article  Google Scholar 

  • Thiriez, K. K. (2003). Evaluation of indirect tire pressure monitoring systems using data from NCSA’s tire pressure special study. Proc. Int. Technical Conf. Enhanced Safety Vehicles, 2003, 8.

    Google Scholar 

  • Vahidi, A., Stefanopoulou, A. and Peng, H. (2005). Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments. Vehicle System Dynamics 43, 1, 31–55.

    Article  Google Scholar 

  • Zhao, J., Su, J., Zhu, B. and Shan, J. (2016). An indirect TPMS algorithm based on tire resonance frequency estimated by AR model. SAE Int. J. Passenger Cars-Mechanical Systems 9, 1, 99–106.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ju Yong Kang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ga, H.S., Kang, J.Y. Tire Dynamic Rolling Radius Compensation Algorithm Based on Ax Sensor Offset Estimation for i-TPMS. Int.J Automot. Technol. 22, 1579–1587 (2021). https://doi.org/10.1007/s12239-021-0136-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12239-021-0136-x

Key Words

Navigation