System and method for upgrading metamodel for friction coefficient prediction of brake pad, and brake control system using the metamodel
AssigneesHyundai Motor Company, Kia Corporation
InventorsSung Hyun Cho, Sung Hoon Bang
Life of this patent
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Abstract
A system and method includes upgrading a metamodel for friction coefficient prediction of a brake, in which the metamodel for friction coefficient prediction may be constructed using various derivative parameters relating to the speed, temperature and pressure of a brake disc in addition to basic parameters, such as the speed, temperature and pressure of the brake disc, to greatly improve performance and accuracy in friction coefficient prediction using the metamodel for friction coefficient prediction and to improve accuracy in evaluation of the driving performance of a vehicle through an increase in accuracy of determination of brake torque.
Claims17
A system of upgrading a metamodel for friction coefficient prediction of a brake pad, the system comprising:
- a data preprocessor configured to preprocess raw data including a rotation speed, a temperature and a pressure of a brake disc to validate the raw data;
- a derivative parameter preprocessor configured to generate derivative parameters from basic parameters, as output by the data preprocessor, including the rotation speed, the temperature and the pressure of the brake disc;
- and
- a metamodel processor configured to generate the metamodel for friction coefficient prediction by performing machine learning according to the derivative parameters output by the derivative parameter preprocessor,
- wherein the derivative parameter preprocessor is configured to define the derivative parameters as principal factors influencing a change in a friction coefficient by analyzing relations between the basic parameters and the friction coefficient at a point in time when a friction coefficient difference occurs under an identical condition of each of the basic parameters preprocessed by the data preprocessor.
A method of upgrading a metamodel for friction coefficient prediction of a brake pad, the method comprising:
- preprocessing, by a data preprocessor, raw data including a rotation speed, a temperature and a pressure of a brake disc to validate the raw data;
- generating, by a derivative parameter preprocessor, derivative parameters from basic parameters, as output by the data preprocessor, including the rotation speed, the temperature and the pressure of the brake disc;
- and
- generating, by a machine learning processor, the metamodel for friction coefficient prediction by performing machine learning according to the derivative parameters output by the derivative parameter preprocessor,
- wherein, in the generating of the derivative parameters, the derivative parameters are defined as principal factors influencing a change in a friction coefficient by analyzing relations between the basic parameters and the friction coefficient at a point in time when a friction coefficient difference occurs under an identical condition of each of the basic parameters preprocessed by the data preprocessor.
A method of controlling a brake system, the method comprising:
- preprocessing, by a data preprocessor, raw data including a rotation speed, a temperature and a pressure of a brake disc of the brake system to validate the raw data;
- generating, by a derivative parameter preprocessor, derivative parameters from basic parameters, as output by the data preprocessor, including the rotation speed, the temperature and the pressure of the brake disc;
- and
- generating, by a metamodel processor, a metamodel for friction coefficient prediction by performing machine learning according to the derivative parameters output by the derivative parameter preprocessor,
- determining, by a brake controller, a target brake torque according to a friction coefficient output by the metamodel for friction coefficient prediction and applying a hydraulic brake pressure control signal corresponding to the determined target brake torque to the brake system,
- wherein the derivative parameter preprocessor is configured to define the derivative parameters as principal factors influencing a change in a friction coefficient by analyzing relations between the basic parameters and the friction coefficient at a point in time when a friction coefficient difference occurs under an identical condition of each of the basic parameters preprocessed by the data preprocessor.
Dependencies are inferred from the claim text.