Wheel Alignment Fault Detection Using Steering and Rack Force Errors
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Solution Overview
Problem
Existing vehicle systems such as smart cruise control and lane keep assist systems may malfunction or cause accidents if they operate when the vehicle's wheel alignment is abnormal, as these systems rely on sensors that can be affected by wheel alignment distortions.
Innovation Solution
An apparatus and method that utilize a sensor module and processor to detect abnormal wheel alignment by calculating steering angle and rack force errors, and outputting an alarm or OBD fault signal to notify the driver or enable them to check the alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous driving systems (SCC, LKAS) operate without wheel alignment checking, then system functionality is maintained, but system reliability and safety deteriorate due to potential malfunctions from abnormal wheel alignment
Solution Approach 1:
The system performs preliminary wheel alignment checking by calculating steering angle errors and rack force errors before autonomous driving systems operate. The processor continuously monitors steering angle deviations from target angles and compares estimated rack forces during straight driving conditions, enabling early detection of alignment issues before they cause system malfunctions
Solution Approach 2:
The autonomous driving system performs self-diagnosis by using its own sensors (steering angle sensor, rack force sensor) to detect wheel alignment abnormalities. The system serves itself by utilizing existing control components for alignment monitoring, eliminating the need for separate dedicated alignment checking equipment
2Ease of operation
If professional technical equipment is used to check wheel alignment in auto repair shops, then measurement precision is improved, but ease of operation and accessibility deteriorate as drivers cannot perform self-checking
Solution Approach 1:
The patent replaces traditional mechanical alignment checking equipment with an electronic sensor-based system. The processor uses electrical signals from steering angle sensors and rack force sensors to detect alignment abnormalities, substituting mechanical measurement tools with electronic sensing and computational analysis
Solution Approach 2:
The processor acts as an intermediary that translates raw sensor data (steering angle, rack force) into meaningful alignment status information. It calculates steering angle errors by comparing target versus actual angles and determines rack force errors through signal processing, mediating between physical wheel alignment and driver-observable indicators
3Object-affected harmful factors
If wheel alignment distortion is not detected, then device complexity remains low, but harmful factors increase as system performance degrades and accidents may occur
Solution Approach 1:
The system implements continuous feedback monitoring by comparing target steering angles with actual steering angles and comparing estimated rack forces during operation. When deviations exceed predetermined thresholds, the system generates warning signals to alert drivers, creating a closed-loop feedback mechanism that continuously assesses alignment health
Solution Approach 2:
The system performs preliminary detection of alignment issues through continuous monitoring of steering angle errors and rack force errors, enabling early warning before actual system failure or accidents occur. This proactive approach identifies problems before they become harmful
Data Source
AI summary
Disclosed are an apparatus and method for detecting abnormal wheel alignment. The apparatus for detecting abnormal wheel alignment includes a sensor module and a processor operatively connected to the sensor module. The processor may calculate a steering angle error indicative of a difference between a target steering angle of a vehicle and a steering angle of the vehicle that has been detected based on lane information obtained by the sensor module, may calculate a rack force error indicative of a difference between a first rack force estimated based on information that has been fed back by a motor driven power steering system (MDPS) module of the vehicle and a second rack force estimated based on information including a yaw rate and lateral acceleration obtained by the sensor module, and may determine whether wheel alignment is abnormal based on at least one of the steering angle error and the rack force error.