An anti-interference adaptive new energy vehicle electronic hydraulic brake system control method

By introducing a third-order polynomial nonlinear model and a recursive least squares method with a variable forgetting factor into the electro-hydraulic braking system of new energy vehicles, combined with a model predictive controller and an extended Kalman filter, the nonlinearity and frictional disturbance problems of the system are solved, and the robustness and economy of the control are improved.

CN122211345APending Publication Date: 2026-06-16EAST CHINA JIAOTONG UNIVERSITY
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Patent Information

Application Number
CN202610685780.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2026-05-19
Publication Date
2026-06-16

AI Technical Summary

Technical Problem

Existing electro-hydraulic braking systems for new energy vehicles lack robustness and smoothness in the face of strong nonlinearity, time-varying parameters, and complex disturbances during braking. They are unable to cope with brake pad wear, hydraulic oil temperature changes, and nonlinear friction effects, and rely on high-cost sensors.

Method used

A third-order polynomial nonlinear model combined with a recursive least squares method with a variable forgetting factor is used for online updating. A multi-constraint model predictive controller and an extended Kalman filter are designed to achieve real-time identification and compensation of friction parameters, reducing the dependence on physical sensors.

Benefits of technology

Active compensation for nonlinear frictional disturbances is achieved, which improves the robustness and smoothness of control, reduces the dependence on high-cost sensors, and enhances the economy and reliability of the system.

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Abstract

The application discloses a new energy vehicle electronic hydraulic brake system control method with anti-interference self-adaptation, comprising the following steps: establishing a nonlinear model between a master cylinder piston position and a brake pressure; adopting a recursive least square method with a variable forgetting factor to update coefficients of a third-order polynomial in real time online; converting a desired target brake pressure into a target piston position trajectory, and designing a multi-constrained model predictive controller; adopting an improved extended LuGre friction model to describe a friction behavior inside the system, and designing an extended Kalman filter to identify friction parameters online in real time, and then feeding the identification results to the model predictive controller; based on the nonlinear model, the model predictive controller and the extended Kalman filter, using motor rotation angle, current and piston position information, estimating the master cylinder pressure in real time, and finally realizing closed-loop pressure control. The application can improve the robustness and smoothness of control.
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