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Smart Traction Control Systems for Electric Vehicles Using Acoustic Road-type Estimation

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Abstract

The application of traction control systems (TCS) for electric vehicles (EV) has great potential due to easy implementation of torque control with direct-drive motors. However, the control system usually requires road-tire friction and slip-ratio values, which must be estimated. While it is not possible to obtain the first one directly, the estimation of latter value requires accurate measurements of chassis and wheel velocity. In addition, existing TCS structures are often designed without considering the robustness and energy efficiency of torque control. In this work, both problems are addressed with a smart TCS design having an integrated acoustic road-type estimation (ARTE) unit. This unit enables the road-type recognition and this information is used to retrieve the correct look-up table between friction coefficient and slip-ratio. The estimation of the friction coefficient helps the system to update the necessary input torque. The ARTE unit utilizes machine learning, mapping the acoustic feature inputs to road-type as output. In this study, three existing TCS for EVs are examined with and without the integrated ARTE unit. The results show significant performance improvement with ARTE, reducing the slip ratio by 75% while saving energy via reduction of applied torque and increasing the robustness of the TCS.

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  • T A Johansen
  • J Kalkkuhl
  • J Luedemann
I. Petersen,T.A. Johansen, J. Kalkkuhl, J. Luedemann, "Wheel slip control in ABS brakes using gain scheduled constrained LQR", IEEE European Control Conference (ECC'01), 4-7 Sept 2011, Porto, Portugal.
Recursive Identification of Cornering Stiffness Paramters for an Enhanced SingleTrack Model
  • C Lundquist
  • T B Schön
C. Lundquist, T.B. Schön, "Recursive Identification of Cornering Stiffness Paramters for an Enhanced SingleTrack Model", IFAC Proceedings, 42 (10), pp. 1726-1731, 2009.
Robust estimation of road frictional coefficient
  • C Ahn
  • H Peng
  • H E Tseng
C. Ahn, H. Peng, H.E. Tseng, "Robust estimation of road frictional coefficient", IEEE Transactions on Control Systems Technology, 21 (1), pp. 1-13, Jan 2013.
Intelligent Traction Control in Electric Vehicles using a Novel Acoustic Approach for Online Estimation of Road-Tire Friction
  • P Boyraz
  • D Dogan
P. Boyraz, D. Dogan, "Intelligent Traction Control in Electric Vehicles using a Novel Acoustic Approach for Online Estimation of Road-Tire Friction", IEEE Int. Vehicle Symposium 2013, 23-26 June 2013, Gold coast; Australia.
Modeling and analysis of longitudinal tire dynamics based on the LuGre friction model
  • J Deur
J. Deur, Modeling and analysis of longitudinal tire dynamics based on the LuGre friction model. In Advances in Automotive Control 2001. Proceedings of the 3rd IFAC Workshop.