Method for Estimating of the Friction Potential

Müller, Gerd; Müller, Steffen

doi:10.1007/s38311-016-0030-8

Cited by 2 publications

(2 citation statements)

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“…In addition to the pure estimation of the adhesion limit, there are also investigations with the aim of detecting the condition of the road surface and, in particular, wetness [15][16][17][18][19]. The early patent application [16] proposes the measurement of the splashing water noise or the rolling noise of a wheel by a sensor attached to the vehicle.…”

Section: State Of the Art-estimation Of Tyre-road Conditionsmentioning

confidence: 99%

“…As an outlook, the study highlights the importance for advanced driver assistance systems (ADAS). The study [17] is devoted to classify of the friction coefficient also differentiating between dry and wet conditions. In addition to the vehicle sensors the method requires data from at least one weather station and one ice warning system.…”

Section: State Of the Art-estimation Of Tyre-road Conditionsmentioning

confidence: 99%

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A Concept for Using Road Wetness Information in an All-Wheel-Drive Control

Warth¹,

Sieberg

Unterreiner³

et al. 2022

Energies

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This paper presents a concept for using road wetness information in an all-wheel-drive (AWD) control that distributes drive torques in the longitudinal direction. Driving on wet roads requires special attention. Not only does the road surface friction coefficient decrease, but driving dynamics targets must be adjusted to prevent vehicle instability under wet conditions. As an exemplary application, the otherwise generic control concept is implemented on an AWD vehicle with a torque-on-demand transfer case. Therefore, the AWD topology of a drive train with a torque-on-demand transfer case is analysed in advance in terms of occurring torques and rotational speeds. In the following, the vehicle dynamics goals for driving in wet road conditions are described—divided into primary and secondary goals. Starting from a state-of-the art AWD control, an adaptive control strategy is derived by superimposing a wetness coordination unit. With the knowledge of occurring road wetness, this unit adapts newly introduced parameters in order to meet the target driving behaviour under wet conditions. Lastly, the derived AWD control is implemented into a 14-DOF, non-linear vehicle model in Matlab/Simulink, which is used as a virtual plant. The performance of the developed concept is assessed by the driving maneuver “Power On Cornering“ (PON), which means an acceleration out of steady-state circular motion. As its essential benefit, the AWD control enables a maximum spread between driving stability, agility and traction under combined dynamics when using wetness information. The newly introduced wetness coordination unit uses only a few additional and physically interpretable key parameters for this purpose, without significantly increasing the controller complexity.

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