Vehicle Sideslip Angle Estimation Using Kalman Filters: Modelling and Validation

  • Cristiano Pieralice
  • Basilio LenzoEmail author
  • Francesco Bucchi
  • Marco Gabiccini
Conference paper
Part of the Mechanisms and Machine Science book series (Mechan. Machine Science, volume 68)


The knowledge of the vehicle sideslip angle provides useful information about the state of the vehicle and it is often considered to increase the performance of the car as well as to develop safety systems, especially in the vehicle equipped with Torque Vectoring control systems. This paper describes two methods, based on the use of Kalman filters, to estimate the vehicle sideslip angle and the tire forces of a vehicle starting from the longitudinal and yaw velocity data. In particular, these data refer to on-track testing of a Range Rover Evoque performing ramp steer maneuvers at constant speed. The results of the sideslip estimation method are compared with the actual vehicle sideslip measured by a Datron sensor and are also used to estimate the tire lateral forces.


Sideslip angle Kalman filter Vehicle State estimation Random walk method 


  1. 1.
    Bucchi, F., Forte, P., Frendo, F.: Analysis of the torque characteristic of a magnetorheological clutch using neural networks. J. Intell. Material Syst. Struct. 26(6), 680–689 (2015)CrossRefGoogle Scholar
  2. 2.
    Bucchi, F., et al.: The effect of the front-to-rear wheel torque distribution on vehicle handling: an experimental assessment. In: 25th International Symposium IAVSD 2017, Rockhampton, Australia (2017)Google Scholar
  3. 3.
    Chen, B.-C., Hsieh, F.-C.: Sideslip angle estimation using extended Kalman filter. Veh. Syst. Dyn. 46, 353–364 (2008)CrossRefGoogle Scholar
  4. 4.
    Chindamo, D., Lenzo, B., Gadola, M.: On the vehicle sideslip angle estimation: a literature review of methods, models, and innovations. Appl. Sci. 8, 355 (2018)CrossRefGoogle Scholar
  5. 5.
    De Filippis, G., Lenzo, B., et al.: Energy-efficient torque-vectoring control of electric vehicles with multiple drivetrains. IEEE Trans. Veh. Technol. 67, 6 (2018)CrossRefGoogle Scholar
  6. 6.
    Doumiati, M., Victorino, A., Charara, A., Lechner, D.: A method to estimate the lateral tire force and the sideslip angle of a vehicle: experimental validation. In: Proceedings of 2010 American Control Conference (2010)Google Scholar
  7. 7.
    Farroni, F., et al.: A comparison among different methods to estimate vehicle sideslip angle. In: Proceedings of the World Congress on Engineering, vol. 2 (2015)Google Scholar
  8. 8.
    Gadola, M., Chindamo, D., Romano, M., Padula, F.: Development and validation of a Kalman filter-based model for vehicle slip angle estimation. Veh. Syst. Dyn. 52, 68–84 (2014)CrossRefGoogle Scholar
  9. 9.
    Ghosh, J., Tonoli, A., Amati, N., Chen, W.: Sideslip angle estimation of a formula SAE racing vehicle. SAE Int. J. Passeng. Cars-Mech. Syst. 9, 944–951 (2016)CrossRefGoogle Scholar
  10. 10.
    Guiggiani, M.: The Science of Vehicle Dynamics. Springer (2014)Google Scholar
  11. 11.
    Gurney, K.: An Introduction to Neural Networks. CRC press (2014)Google Scholar
  12. 12.
    Lenzo, B., Sorniotti, A., Gruber, P., Sannen, K.: On the experimental analysis of single input single output control of yaw rate and sideslip angle. Int. J. Autom. Technol. 18, 5 (2017)CrossRefGoogle Scholar
  13. 13.
    Lenzo, B., De Filippis, G., et al.: Torque distribution strategies for energy-efficient electric vehicles with multiple drivetrains. ASME J. Dyn. Syst. Meas. Control 139, 12 (2017)CrossRefGoogle Scholar
  14. 14.
    Madhusudhanan, A.K., Corno, M., Holweg, E.: Vehicle sideslip estimator using load sensing bearings. Control Eng. Pract. 54 (2016)CrossRefGoogle Scholar
  15. 15.
    Ouahi, M., Stéphant, J., Meizel, D.: Simultaneous observation of the wheels’ torques and the vehicle dynamic state. Veh. Syst. Dyn. 51, 737–766 (2013)CrossRefGoogle Scholar
  16. 16.
    Pacejka, H.B.: Tire and Vehicle Dynamics (2006)Google Scholar
  17. 17.
    Selmanaj, D., Corno, M., Panzani, G., Savaresi, S.M.: Vehicle sideslip estimation: a kinematic based approach. Control Eng. Pract. 67, 1–12 (2017)CrossRefGoogle Scholar
  18. 18.
    Tota, A., et al.: On the experimental analysis of integral sliding modes for yaw rate and sideslip control of an electric vehicle with multiple motors. Int. J. Autom. Technol. (2018)Google Scholar
  19. 19.
    Wang, Z., Montanaro, U., Fallah, S., et. al.: A gain scheduled robust linear quadratic regulator for vehicle direct yaw moment control. Mechatronics 51, 31–45 (2018)CrossRefGoogle Scholar
  20. 20.
    Welch, G., Bishop, G.: An Introduction to the Kalman filter. University of North Carolina at Chapel Hill, Department of Computer Science, TR 95-041 (2004)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Cristiano Pieralice
    • 1
    • 2
  • Basilio Lenzo
    • 2
    Email author
  • Francesco Bucchi
    • 1
  • Marco Gabiccini
    • 1
  1. 1.Università di PisaPisaItaly
  2. 2.Sheffield Hallam UniversitySheffieldUK

Personalised recommendations