Dynamic Rollover Prediction of Heavy Vehicles Considering Critical Frequency

Abstract

Rollover of commercial heavy vehicles can cause enormous economic losses and fatalities. It is easier for such vehicles to rollover if the driver’s steering frequency is close to the critical frequency of the vehicle’s roll motion; however, the critical roll frequency has rarely been investigated. In this study, the second-order transfer function between the steering input and roll angle was developed to calculate the critical frequency of the vehicle’s roll motion. The simulated spectrum and transfer function were then used to dynamically predict the peak lateral load transfer ratio. Laboratory experiments were conducted using a scaled vehicle to verify the critical roll frequency. The results suggest that the peak value of the lateral load transfer ratio during steering can be accurately determined from the driver’s input, and the critical roll frequency has a dominant effect on the dynamic rollover of heavy vehicles.

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

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
Fig. 16

Abbreviations

COG:

Center of gravity

LTR:

Lateral load transfer ratio

TTR:

Time to rollover

ZMP:

Zero-moment point

References

  1. 1.

    Tomar, A.: Estimation of steady state rollover threshold for high capacity transport vehicles using RCV calculation method. Dissertation, Chalmers University of Technology (2016)

  2. 2.

    Yin, Y., Rakheja, S., Boileau, P.E.: A roll stability performance measure for off-road vehicles. J. Terrramech. 64, 58–68 (2016)

    Article  Google Scholar 

  3. 3.

    Larish, C., Piyabongkarn, D., Tsourapas, V., et al.: A new predictive lateral load transfer ratio for rollover prevention systems. IEEE Trans. Veh. Technol. 62(7), 2928–2936 (2013)

    Article  Google Scholar 

  4. 4.

    Chen, B.C., Peng, H.: Differential-braking-based rollover prevention for sport utility vehicles with human-in-the-loop evaluations. Veh. Syst. Dyn. 36(4), 359–389 (2001)

    Article  Google Scholar 

  5. 5.

    Dahmani, H., Chadli, M., Rabhi, A., et al.: Vehicle dynamic estimation with road bank angle consideration for rollover detection: theoretical and experimental studies. Veh. Syst. Dyn. 51(12), 1853–1871 (2013)

    Article  Google Scholar 

  6. 6.

    Han, X., Stephant, J., Mourioux, G., et al.: A ZMP based interval criterion for rollover-risk diagnosis. IFAC-PapersOnLine 48(21), 277–282 (2015)

    Article  Google Scholar 

  7. 7.

    Stankiewicz, P.G., Brown, A.A., Brennan, S.N.: Determination of minimum state preview time to prevent vehicle rollover. Paper presented at ASME 2013 Dynamic Systems and Control Conference, Palo Alto, California, USA, 21–23 October 2013

  8. 8.

    Phanomchoeng, G., Rajamani, R.: New rollover index for the detection of tripped and untripped rollovers. IEEE Trans. Ind. Electron. 60(10), 4726–4736 (2013)

    Article  Google Scholar 

  9. 9.

    Yoon, J., Kim, D., Yi, K.: Design of a rollover index-based vehicle stability control scheme. Veh. Syst. Dyn. 45(5), 459–475 (2007)

    Article  Google Scholar 

  10. 10.

    Mashadi, B., Mostaghimi, H.: Vehicle lift-off modelling and a new rollover detection criterion. Veh. Syst. Dyn. 55(5), 1–21 (2017)

    Article  Google Scholar 

  11. 11.

    Huang, Z., Nie, W., Kou, S., et al.: Rollover detection and control on the non-driven axles of trucks based on pulsed braking excitation. Veh. Syst. Dyn. 56(12), 1–19 (2018)

    Google Scholar 

  12. 12.

    Doumiati, M., Victorino, A., Charara, A., et al.: An estimation process for vehicle wheel ground contact normal forces. IFAC Proc. Vol. 41(2), 7110–7115 (2008)

    Article  Google Scholar 

  13. 13.

    Tafner, R., Reichhartinger, M., Horn, M.: Robust vehicle roll dynamics identification based on roll rate measurements. IFAC Proc. Vol. 45(30), 72–78 (2012)

    Article  Google Scholar 

  14. 14.

    Ma, Z., Ji, X., Zhang, Y., et al.: State estimation in roll dynamics for commercial vehicles. Veh. Syst. Dyn. 55(3), 313–337 (2017)

    Article  Google Scholar 

  15. 15.

    Oh, J., Choi, S.B.: Vehicle roll and pitch angle estimation using a cost-effective six-dimensional inertial measurement unit. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 227(4), 577–590 (2013)

    Article  Google Scholar 

  16. 16.

    Kolaei, A., Rakheja, S., Richard, M.J.: An efficient methodology for simulating roll dynamics of a tank vehicle coupled with transient fluid slosh. J. Vib. Control 23(19), 3216–3232 (2017)

    MathSciNet  Article  Google Scholar 

  17. 17.

    Zheng, X., Li, X., Ren, Y., et al.: Rollover stability analysis of tank vehicle impacted by transient liquid sloshing. J. Jilin Univ. (Eng. Technol. Edn.) 44(3), 625–630 (2014)

    Google Scholar 

  18. 18.

    Bouteldja, M., Cerezo, V.: Low rate error modeling of articulated heavy vehicle dynamics and experimental validation. Int. J. Control Autom. Syst. 15(5), 2203–2212 (2017)

    Article  Google Scholar 

  19. 19.

    Chou, T., Chu, T.: An improvement in rollover detection of articulated vehicles using the grey system theory. Veh. Syst. Dyn. 52(5), 679–703 (2014)

    Article  Google Scholar 

  20. 20.

    Stijn, D.B., Herman, V.A., Paola, D., et al.: Online estimation of vehicle inertial parameters for improving chassis control systems. IFAC Proc. 44(1), 1814–1819 (2011)

    Article  Google Scholar 

  21. 21.

    Huang, X., Wang, J.: Center of gravity height real-time estimation for lightweight vehicles using tire instant effective radius. Control Eng. Pract. 21(4), 370–380 (2013)

    Article  Google Scholar 

  22. 22.

    Stensson, T.A., Rothhämel, M., Pauwelussen, J., et al.: Advanced vehicle dynamics of heavy trucks with the perspective of road safety. Veh. Syst. Dyn. 55(10), 1–46 (2017)

    Google Scholar 

  23. 23.

    Chondros, T.G., Michalos, G., Michaelides, P., et al.: An approximate method for the evaluation of the roll stiffness of road tankers. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 221(12), 1499–1512 (2007)

    Article  Google Scholar 

  24. 24.

    Jiang, G., Liu, L., Guo, C., et al.: A novel fusion algorithm for estimation of the side-slip angle and the roll angle of a vehicle with optimized key parameters. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 231(2), 161–174 (2016)

    MathSciNet  Article  Google Scholar 

  25. 25.

    Huston, R.L., Kelly, F.A.: Another look at the static stability factor (SSF) in predicting vehicle rollover. Int. J. Crashworth. 19(6), 567–575 (2017)

    Article  Google Scholar 

  26. 26.

    Wei, H., Zhang, Y., Liu, R., et al.: A novel dynamic rollover threshold model of top-heavy vehicle. In: Paper presented at 2018 3rd International Conference on Automation, Mechanical and Electrical Engineering, Shanghai, China, 22–23 July 2018

  27. 27.

    Czechowicz, M.P., Mavros, G.: Analysis of vehicle rollover dynamics using a high-fidelity model. Veh. Syst. Dyn. 52(5), 608–636 (2014)

    Article  Google Scholar 

  28. 28.

    Chen, Y., Andrew, W.P., Ce, Z., et al.: A simulation-based comparative study on lateral characteristics of trucks with double and triple trailers. Int. J. Veh. Saf. 11(2), 136–157 (2019)

    Article  Google Scholar 

  29. 29.

    Dahmani, H., Chadli, M., Rabhi, A., et al.: Detection of impending vehicle rollover with road bank angle consideration using a robust fuzzy observer. Int. J. Autom. Comput. 12(1), 93–101 (2015)

    Article  Google Scholar 

  30. 30.

    Zhang, X., Yang, Y., Guo, K.H.: Contour line of load transfer ratio for vehicle rollover prediction. Veh. Syst. Dyn. 55(11), 1748–1763 (2017)

    Article  Google Scholar 

  31. 31.

    Yin, Y., Rakheja, S., Yang, J., et al.: Effect of articulated frame steering on the transient yaw responses of the vehicle. Proc. Inst. Mech. Eng. Part D J. Automob. Eng. 232(3), 384–399 (2017)

    Article  Google Scholar 

  32. 32.

    Robert W.G.: Development of a rollover-warning device for road vehicles. Dissertation, The Pennsylvania State University (2001)

  33. 33.

    Shi, H., Huang, Q.: Vibration Systems: Analyzing, Modeling, Testing, Control. Huazhong University of Science & Technology Press, Wuhan (2016)

    Google Scholar 

  34. 34.

    Phanomchoeng, G., Rajamani, R.: Prediction and prevention of tripped rollovers. Dissertation, University of Minnesota (2012)

  35. 35.

    Yu, Z.: Theory of Vehicle. China Machine Press, Beijing (2013)

    Google Scholar 

Download references

Acknowledgements

This project is supported by the National Natural Science Foundation of China (NSFC) under Grant 51905483.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Yuming Yin.

Ethics declarations

Conflict of Interest

On behalf of all authors, the corresponding authors state that there are no conflicts of interest.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ye, Z., Xie, W., Yin, Y. et al. Dynamic Rollover Prediction of Heavy Vehicles Considering Critical Frequency. Automot. Innov. 3, 158–168 (2020). https://doi.org/10.1007/s42154-020-00099-w

Download citation

Keywords

  • Vehicle rollover
  • Dynamic rollover prediction
  • Lateral load transfer ratio
  • Critical frequency