基于MRAS-ANFIS的车辆制动电机控制方法

The vehicle brake motor control method based on MRAS-ANFIS solves the problem of sensor susceptibility to interference in traditional brake motor control, achieves high-precision motor state estimation and braking intention recognition, improves the robustness and energy recovery efficiency of the braking system, and adapts to stable control under complex working conditions.

CN121552945BActive Publication Date: 2026-07-17GLUBO TECHNOLOGY (YIBIN) CO LTD

Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
GLUBO TECHNOLOGY (YIBIN) CO LTD
Filing Date
2026-01-04
Publication Date
2026-07-17

AI Technical Summary

Technical Problem

Traditional brake motor control schemes rely on physical sensors, which are susceptible to electromagnetic interference, mechanical vibration, and high-temperature environments, leading to signal distortion, reduced braking control accuracy and system robustness. Furthermore, the high probability of sensor failure limits the adaptability of the braking system and its energy recovery efficiency under extreme conditions.

Method used

A vehicle brake motor control method based on MRAS-ANFIS is adopted. Electromagnetic torque and mechanical angular velocity are estimated through sensorless state, and combined with ANFIS braking intention recognition and dynamic correction, braking torque distribution and coordinated control are realized, reducing dependence on physical sensors and improving braking control accuracy and energy recovery efficiency.

Benefits of technology

It achieves high-precision motor state estimation, reduces the risk of sensor failure, improves the accuracy of braking intention recognition, enhances braking safety and energy recovery efficiency, and adapts to stable control under complex working conditions.

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Abstract

本发明公开了基于MRAS‑ANFIS的车辆制动电机控制方法,属于车辆制动电机领域,包括以下步骤:S1、采集电磁参数和逆变器参数,并在静止状态下校准三相电流信号和直流母线电压信号;S2、通过MRAS无传感器状态估算电磁转矩和机械角速度;S3、识别驾驶员的制动意图等级;S4、确定总需求制动转矩,并结合滑移率实现最大化再生能量回收;S5、动态修正电磁参数,并返回步骤S1。采用上述基于MRAS‑ANFIS的车辆制动电机控制方法,通过融合MRAS的无传感器状态估算能力与ANFIS的智能决策优势,实现了制动过程中状态精准感知‑意图动态识别‑制动力优化分配的全链路智能化控制。
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