Brake clamp force prediction method and system
By combining extended Kalman filters with motor current and piston position measurements, the spatial and cost limitations of brake clamping force estimation in brake-by-wire systems are overcome, achieving high-precision brake clamping force prediction.
CN122286969APending Publication Date: 2026-06-26NEXTEER AUTOMOTIVE SYST SUZHOU
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Patent Information
- Authority / Receiving Office
- CN Β· China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NEXTEER AUTOMOTIVE SYST SUZHOU
- Filing Date
- 2025-11-25
- Publication Date
- 2026-06-26
AI Technical Summary
Technical Problem
In existing brake-by-wire systems, installing physical clamping force sensors is limited by internal space and is costly, making it difficult to accurately estimate the braking clamping force.
Method used
An extended Kalman filter is used, and the measured values ββof motor current and piston position are used to calculate the nonlinear functions of piston speed and clamping force. Combined with Jacobian matrix and Taylor series expansion, the braking clamping force can be estimated in real time.
Benefits of technology
It can accurately estimate braking clamping force without the need for a physical clamping force sensor, reducing costs and improving prediction accuracy and precision, and is suitable for brake-by-wire systems.
β¦ Generated by Eureka AI based on patent content.
Smart Images
Figure CN122286969A_ABST
Abstract
This application provides a method and system for predicting brake clamping force. The method includes: obtaining a state measurement vector at the current moment based on the measured motor current and piston position values ββin the braking system; obtaining a pre-update state prediction vector and a pre-update covariance matrix at the current moment based on an extended Kalman filter; calculating an innovation at the current moment based on the current state measurement vector and the pre-update state prediction vector, and calculating a Kalman gain at the current moment based on the pre-update covariance matrix; updating the pre-update state prediction vector and the pre-update covariance matrix based on the current moment's innovation and Kalman gain to obtain a post-update state prediction vector and a post-update covariance matrix at the current moment, wherein the post-update state prediction vector includes a post-update brake clamping force prediction value. This application can accurately estimate the brake clamping force without the need for a physical clamping force sensor.
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