This content is not included in your SAE MOBILUS subscription, or you are not logged in.
‘Wheel Slip-Based’ Evaluation of Road Friction Potential for Distributed Electric Vehicle
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 05, 2016 by SAE International in United States
Annotation ability available
As a typical parameter of the road-vehicle interface, the road friction potential acts an important factor that governs the vehicle motion states under certain maneuvering input, which makes the prior knowledge of maximum road friction capacity crucial to the vehicle stability control systems. Since the direct measure of the road friction potential is expensive for vehicle active safety system, the evaluation of this variable by cost effective method is becoming a hot issue all these years. A ‘wheel slip based’ maximum road friction coefficient estimation method based on a modified Dugoff tire model for distributed drive electric vehicles is proposed in this paper. It aims to evaluate the road friction potential with vehicle and wheel dynamics analyzing by using standard sensors equipped on production vehicle, and fully take the advantage of distributed EV that the wheel drive torque and rolling speed can be obtained accurately. A modified Dugoff tire model is built and analyzed, which acts as the fundamental of the road friction potential estimation algorithm. Newton-Raphson method and LMS method is introduced to estimate the maximum friction coefficient through the tire model. The simulation and a vehicle test show that this method has short convergence time and higher estimation accuracy. Numerical results verify that the estimator designed is capable of estimating tire-road friction coefficient with reasonable accuracy, and the algorithm proposed has good robustness and wide applicability under various driving conditions.
CitationChen, L., Zhang, S., Bian, M., Luo, Y. et al., "‘Wheel Slip-Based’ Evaluation of Road Friction Potential for Distributed Electric Vehicle," SAE Technical Paper 2016-01-1667, 2016, https://doi.org/10.4271/2016-01-1667.
- CHO W, CHOI J, KIM, CHOI S, et al. Unified chassis control for the improvement of agility, maneuverability, and lateral stability[J]. IEEE Transactions on Vehicular Technology, 2012, 61(3), 1008-1020.
- CHOI S, CHO D. Design of nonlinear sliding mode controller with pulse width modulation for vehicular slip ratio control[J]. Vehicle System Dynamics, 2001, 36(1(: 57-72.
- DAI Y, et al. Optimum tire force distribution for four-wheelindependent drive electric vehicle with active front steering[C]// The 11th International Symposium on Advanced Vehicle Control, 2012.
- ZHANG D, LI K, WANG J. Radar-based target identification and tracking[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2012, 226: 39-47.
- RAY L. Nonlinear tire force estimation and road friction identification: simulation and experiments[J]. Automatic, 1997, 33: 1819-1833.
- SATO Y, et al. Study on recognition method for road friction condition[J]. JSAE Transaction, 2007, 38: 51-56.
- YANADA M, et al. Road surface condition detection technique based on image taken by camera attached to vehicle rearview mirror[J]. Review of Automative Engineering, 2005, 22: 163-168.
- HONG S, ERDOGAN G, HEDRICK K, BORRELLI F. Tyre- road friction coefficient estimation based on tyre sensors and lateral tyre deflection: modeling, simulations and experiments[J]. Vehicle System Dynamics, 2013, 51(5(: 627-647.
- GUSTAFSSON F. Estimation and Change Detection of Tire-road Friction Using the Wheel Slip[C]//The American Control Conference, 1996.
- DING N, ZHAN X. Model-based recursive least square algorithm for estimation of brake pressure and road friction[C]//The FISITA 2012 World Automotive Congress, 2012.
- AHN C, PENG H, TSENG H. Estimation of road friction for enhanced active safety systems: dynamic approach[C]//American Control Conference, 2009.
- AHN C, PENG H, TSENG H. Estimation of road friction for enhanced active safety systems: algebraic approach[C]//American Control Conference, 2009.
- SATOSHI M. Innovation by in-wheel-motor drive unit[J]. Vehicle System Dynamics, 2012, 50(6(: 807-830.
- DAI, Y. Integrated longitudinal and Lateral Motion Control of Distributed Electric Vehicles[D]. Tsinghua University, China, 2013.
- CHU W, LUO Y, CHEN L, LI K. Vehicle state estimation by using unscented particle filter in distributed electric vehicle. Chinese Journal of Mechanical Engineering, 2013, 49(24(: 117-127.
- DUGOFF H, FANCHER P, SEGAL L. An analysis of tire traction properties and their influence on vehicle dynamic performance. SAE Transaction, 1970, 79: 341-366.
- Pacejka, H., "Tire and Vehicle Dynamics," (Warrendale, Society of Automotive Engineers, Inc. and Butterworth Heinemann, 2002), ISBN 978-0-7680-1126-5.
- LOPEZ A, VELEZ P, MORIANO C. Approximations to the magic formula. International Journal of Automotive. Technology, 2010, 11(2(: 155-166.
- Bian, M., Chen, L., Luo, Y., and Li, K., "A Dynamic Model for Tire/Road Friction Estimation under Combined Longitudinal/Lateral Slip Situation," SAE Technical Paper 2014-01-0123, 2014, doi:10.4271/2014-01-0123.