The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Nov. 17, 1992

Filed:

Mar. 08, 1991
Applicant:
Inventors:

Bernhard Topfer, Stuttgart, DE;

Norman Millner, Stuttgart, DE;

Ali Aminpour, Waiblingen, DE;

Wolfgang Scheer, Stuttgart, DE;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
B60T / ; B60T / ;
U.S. Cl.
CPC ...
303100 ; 303 95 ; 303D / ; 36442601 ;
Abstract

A method utilizes components of an existing anti-lock brake system (ABS) in order thereby to achieve an automatically self-optimizing inter-axle brake-pressure distribution acting far below the wheel lock limit. For this purpose, the brake pressure and, hence, the brake-force distribution are regulated axle-specifically far below the wheel lock limit too. An immediate dynamic intervention is, on one hand, effected in the case of sufficiently large wheel-speed differences between the axles. On the other hand, an adaptive predetermination of correct brake-force distributions is made the basis for each current regulating intervention. Even before the occurrence of large speed differences, the brake-force distribution expedient in each case for these is here predictively determined, stored, and, if required, correspondingly adapted to current requirements, i.e. corrected, in the course of subsequent dynamic braking demands. Requisite determination parameters are obtained for each journey either via characteristic diagrams specific to the family of vehicles or are determined individually on the individual vehicle by a learning approximation routine.


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