Abstract
Smart tires are systems that are able to measure temperature, inflation pressure, footprint dimensions, and, importantly, tire contact forces. The integration of this additional information with the signals obtained from more conventional vehicle sensors, e.g., inertial measurement units, can enhance state estimation in production cars. This paper evaluates the use of smart tires to improve the estimation performance of an Unscented Kalman filter (UKF) based on a nonlinear vehicle dynamics model. Two UKF implementations, excluding and including smart tire information, are compared in terms of estimation accuracy of vehicle speed, sideslip angle and tire-road friction coefficient, using experimental data obtained on a high performance passenger car.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
E. A. Wan and R. Van Der Merwe, “The unscented Kalman filter for nonlinear estimation,” Adapt. Syst. Signal Process. Commun. Control Symp., 2000.
S. Antonov, A. Fehn, and A. Kugi, “Unscented Kalman filter for vehicle state estimation,” Veh. Syst. Dyn., vol. 49, no. 9, pp. 1497–1520, 2011.
H. Heidfeld, M. Schünemann, and R. Kasper, “UKF-based state and tire slip estimation for a 4WD electric vehicle,” Veh. Syst. Dyn., pp. 1–18, 2019 (in press).
M. Wielitzka, A. Busch, M. Dagen, and T. Ortmaier, “Unscented Kalman Filter for State and Parameter Estimation in Vehicle Dynamics,” Kalman Filters - Theory Adv. Appl., In Tech, pp. 56–75, 2018.
F. Braghin, M. Brusarosco, F. Cheli, A. Cigada, S. Manzoni, and F. Mancosu, “Measurement of contact forces and patch features by means of accelerometers fixed inside the tire to improve future car active control,” Veh. Syst. Dyn., vol. 44, Issue sup1, pp. 3–13, 2006.
S. C. Ergen, A. Sangiovanni-Vincentelli, X. Sun, R. Tebano, S. Alalusi, G. Audisio, and M. Sabatini, “The tire as an intelligent sensor,” IEEE Trans. Comput. Des. Integr. Circ. Syst., vol. 28, no. 7, pp. 941–955, 2009.
E. Sabbioni, D. Ivone, F. Braghin, and F. Cheli, “In-tyre sensors induced benefits on sideslip angle and friction coefficient estimation,” SAE Tech. Pap. 2015-01-1510, 2015.
K. B. Singh and S. Taheri, “Estimation of tire–road friction coefficient and its application in chassis control systems,” Syst. Sci. Control Eng., vol. 3, no. 1, pp. 39–61, 2015.
F. Cheli, E. Leo, S. Melzi, and E. Sabbioni, “On the impact of ‘smart tyres’ on existing ABS/EBD control systems,” Veh. Syst. Dyn., vol. 48, Issue sup1, pp. 255–270, 2010.
K. B. Singh, M. A. Arat, and S. Taheri, “Literature review and fundamental approaches for vehicle and tire state estimation,” Veh. Syst. Dyn., vol. 57, no. 11, pp. 1643–1665, 2019.
“Road vehicles – Vehicle dynamics and road-holding ability – Vocabulary,” BS ISO 8855, pp. 1–52, 2011.
T. Y. Kim, S. Jung, and W. S. Yoo, “Advanced slip ratio for ensuring numerical stability of low-speed driving simulation: Part I – longitudinal slip ratio,” Proc. Inst. Mech. Eng. Part D J. Automob. Eng., vol. 233, no. 8, pp. 2000-2006, 2019.
S. Haykin, Kalman Filtering and Neural Networks, John Wiley & Sons, 2001.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer-Verlag GmbH Germany, part of Springer Nature
About this paper
Cite this paper
Mazzilli, V. et al. (2021). On the vehicle state estimation benefits of smart tires. In: Pfeffer, P.E. (eds) 11th International Munich Chassis Symposium 2020. Proceedings. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-63193-5_34
Download citation
DOI: https://doi.org/10.1007/978-3-662-63193-5_34
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-63192-8
Online ISBN: 978-3-662-63193-5
eBook Packages: EngineeringEngineering (R0)