Skip to main content
Log in

Development of Indoor Wear Test Method for Passenger Car Tires Reflecting Road Driving Conditions

  • Connected Automated Vehicles and ITS, Electric, Fuel Cell, and Hybrid Vehicle, Vehicle Dynamics and Control
  • Published:
International Journal of Automotive Technology Aims and scope Submit manuscript

Abstract

This study presents a method for developing a tire indoor wear test mode that reflects road driving conditions using a Flat-trac. Using a machine learning model, the slip angle, slip ratio, longitudinal force, and lateral force change according to vehicle speed and acceleration changes are estimated. Reduced data representing the estimated data are calculated using a peak–valley (PV) algorithm. Through the blocking process, representative test modes for driving and braking, right turning and left turning are derived and converted into a test mode for application to the Flat-trac. The evolution of tire tread wear is observed through 120 repeated tests, and the applicability of the test mode developed in this study is discussed.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Data Availability

The data that support the findings of this study are not available due to manufacturer's request.

References

  • AIR5797. (2013). Aircraft tire wear profile development and execution for laboratory testing. SAE International.

    Google Scholar 

  • Gadola, M., Chindamo, D., Romano, M., & Padula, F. (2014). Development and validation of a Kalman filter-based model for vehicle slip angle estimation. Vehicle System Dynamics, 52(1), 68–84.

    Article  Google Scholar 

  • Gipser, M. (2005). FTire : A physically based application-oriented tyre model for use with detailed MBS and finite-element suspension models. Vehicle System Dynamics, 43(sup1), 76–91.

    Article  Google Scholar 

  • Gultlinger, J., Gauterin, F., Brandau, C., Schlittenhard, J., & Wies, B. (2014). Investigations of road wear caused by studded tires. Tire Science and Technology, 42(1), 2–15.

    Article  Google Scholar 

  • Herbert, A., & Fischer, G. (2004). Load program development and testing of super single wheels in the biaxle wheel test rig and numerical pre-design. SAE Technical paper.

    Google Scholar 

  • ISO. (2018). ISO 28580:2009(E), Passenger car, truck and bus tyres - methods of measuring rolling resistance - single point test and correlation of measurement results

  • Jeong, D., Lee, J., Choi, S., Kim, M. (2018). Tire load estimation using intelligent tire with accelerometer, in Proceedings of the Korean Society of Automotive Engineers - Daejeon/Sejong/Chungcheong branch, pp. 6–9.

  • Jian, K., Yang, D., Xie, S., Xiao, Z., Victorino, A., & Charara, A. (2019). Real-time estimation and prediction of tire forces using digital map for driving risk assessment. Transportation Research Part C, 107, 463–489.

    Article  Google Scholar 

  • Jung, S., & Jo, J. (2018). Test method of rolling resistance of a tire considering cornering condition. Transactions of the Korean Society of Automotive Engineers, 26(6), 736–744.

    Article  Google Scholar 

  • Knisley, S. (2002). A correlation between rolling tire contact friction energy and indoor tread wear. Tire Science and Technology, 30(2), 83–89.

    Article  MathSciNet  Google Scholar 

  • Mathissen, M., Grochwicz, J., Schmidt, C., Vogt, R., Hagen, F., Grabiec, T., Steven, H., & Grigoratos, T. (2018). A novel real-world braking cycle for studying brake wear particle emissions. Wear, 414–415, 219–226.

    Article  Google Scholar 

  • Matsuzaki, R., Hiraoka, N., Todoroki, A., & Mizutani, Y. (2012). Strain monitoring and applied load estimation for the development of intelligent tires using a single wireless CCD camera. Journal of Solid Mechanics and Materials Engineering, 6(9), 935–949.

    Article  Google Scholar 

  • Nopiah, Z., Khairir, M., Abdullah, S., & Nizwan, C. (2008). Peak-valley segmentation algorithm for fatigue time series data. Wseas Transactions on Mathematics, 7(12), 698–707.

    Google Scholar 

  • Nurkala, L., & Wallace, R. (2004). Development of the SAE biaxial wheel test load file. SAE Technical paper.

    Google Scholar 

  • Pacejka, H., & Bakker, E. (1992). The magic formula tyre model. Vehicle System Dynamics, 21(sup1), 1–18.

    Article  Google Scholar 

  • Quddus, M., & Washington, S. (2015). Shortest path and vehicle trajectory aided map-matching for low frequency GPS data. Transportation Research Part C, 55, 328–339.

    Article  Google Scholar 

  • Ray, L. (1997). Nonlinear tire force estimation and road friction identification: Simulation and experiments. Automatica, 33(10), 1819–1833.

    Article  MathSciNet  Google Scholar 

  • Scikit-learn, Regression, (2022). https://scikit-learn.org/stable/supervised_learning.html#supervised-learning. Accessed 10 Oct 2022.

  • Seo, Y., Kwak, S., Lee, L., Lee, H., Liu, H., Jo, H., Park, S., & Lee, E. (2018). Vehicle load measurement using tire deformation values. Journal of Korean Institute of Intelligent Systems, 28(2), 170–176.

    Article  Google Scholar 

  • Seo, Y., Kwak, S., & Yang, J. (2021). Tire speed measurement using strain gauge sensor. Journal of Korean Institute of Intelligent Systems, 13(5), 376–382.

    Article  Google Scholar 

  • Singh, K., & Taheri, S. (2019). Accelerometer based method for tire load and slip angle estimation. Vibration, 2, 174–186.

    Article  Google Scholar 

  • Smith, K., Kennedy, R., & Knisley, S. (2008). Prediction of tire profile wear by steady-state FEM. Tire Science and Technology, 36(4), 290–303.

    Article  Google Scholar 

  • Snoek, J., Larochelle, H., & Adams, R. (2012). Practical Bayesian optimization of machine learning algorithms. Advances in Neural Information Processing Systems, 25, 1–9.

    Google Scholar 

  • Stalnaker, D., & Turner, J. (2002). Vehicle and course characterization process for indoor tire wear simulation. Tire Science and Technology, 30(2), 100–121.

    Article  Google Scholar 

  • Stalnaker, D., Turner, J., Parekh, D., Whittle, B., & Norton, R. (1996). Indoor simulation of tire wear: Some case studies. Tire Science and Technology, 24(2), 94–118.

    Article  Google Scholar 

  • UN/ECE. (2008). UNECE Regulation No. 30, uniform provisions concerning the approval of pneumatic tyres for motor vehicles and their trailers.

  • Wilkin, M., Manning, W., Crolla, D., & Levesley, M. (2006). Use of an extended Kalman filter as a robust tyre force estimator. Vehicle System Dynamics, 44(sup1), 50–59.

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the Ministry of Trade, Industry, and Energy and the Korea Evaluation Institute of Industrial Technology (KEIT) in 2021 (20015841, sustainable material-based eco-friendly tire-manufacturing technology development).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sungpil Jung.

Ethics declarations

Conflict of Interest

All authors declare that they have no conflicts of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jung, S., Lee, J. Development of Indoor Wear Test Method for Passenger Car Tires Reflecting Road Driving Conditions. Int.J Automot. Technol. 25, 413–425 (2024). https://doi.org/10.1007/s12239-024-00034-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12239-024-00034-6

Keywords

Navigation