Vehicle Steering Ratio Determination via Sensor Data Matrix
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Solution Overview
Problem
Existing methods for determining the steering ratio of a vehicle are laborious and require manual input, lacking accuracy and efficiency, especially in dynamic conditions and with sensor drifts.
Innovation Solution
A method using yaw rate and steering wheel angle sensors to simulate steering ratios through a data matrix, compensating for zero-point drift and varying conditions, allowing for automatic learning and rapid calibration, thereby improving control actions in electronic stability systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual determination and input of steering ratio values is used, then device complexity is reduced, but productivity is worsened due to laborious determination and manual input
Solution Approach 1:
The system automatically determines steering ratio values through self-learning during vehicle operation. The electronic system continuously acquires steering wheel angle and yaw rate data, automatically calculates steering ratios, and stores them in a data matrix without requiring manual intervention. This self-service approach eliminates laborious manual determination while maintaining system simplicity.
Solution Approach 2:
The system performs preliminary calibration and data collection during vehicle operation before actual control actions are needed. By continuously learning and storing steering ratio data in advance through normal driving conditions, the system prepares the necessary information proactively, eliminating the need for time-consuming manual input when production or calibration is needed.
2Ease of operation
If constant steering ratio value is assumed, then ease of operation is improved, but measurement precision is worsened due to inaccuracy in representing nonlinear steering ratios
Solution Approach 1:
The steering ratio characteristic is divided into multiple discrete values stored in a data matrix, each corresponding to specific steering wheel angle and yaw rate conditions. Instead of using a single constant value, the system segments the steering range into multiple operating points, allowing accurate representation of nonlinear steering behavior while maintaining simple automated access through the data matrix structure.
Solution Approach 2:
The system transitions from a static constant steering ratio value to a dynamic multi-value data matrix that adapts to varying operating conditions. The stored steering ratio values are selected based on current steering wheel angle and yaw rate, enabling the system to automatically adjust to nonlinear steering characteristics without requiring complex real-time calculations.
3Device complexity
If sensor values are used without compensation, then device complexity is reduced, but reliability is worsened due to zero-point drift and sensor inaccuracies
Solution Approach 1:
The system incorporates feedback mechanisms to detect and compensate for zero-point drift in steering wheel angle and yaw rate sensors. By continuously monitoring sensor readings and comparing them against expected values during known operating conditions (such as straight-line driving), the system automatically identifies and corrects drift, maintaining reliability without requiring complex hardware modifications.
4Manufacturing precision
If laborious manual determination of steering ratios is performed, then manufacturing precision can be ensured, but loss of time is increased due to time-consuming calibration
Solution Approach 1:
The system automatically determines and stores accurate steering ratio values through self-learning during vehicle operation, eliminating the need for time-consuming manual calibration procedures. The electronic system continuously acquires data, calculates steering ratios, and populates the data matrix without human intervention, maintaining manufacturing precision while dramatically reducing calibration time.
Solution Approach 2:
The system performs preliminary data collection and steering ratio determination during normal vehicle operation before any actual calibration or production processes are needed. By continuously learning and storing accurate steering ratio data in advance through everyday driving conditions, the system eliminates the need for time-consuming manual calibration procedures while ensuring manufacturing precision.
Data Source
AI summary
A method for determining the steering ratio of a vehicle from sensed measured values (e.g., yaw rate, steering wheel angle, vehicle speed) in a manner that takes vehicle parameters (e.g., self-steering gradient, wheelbase and other vehicle dimensions) into consideration during stable travel of the vehicle.