Dynamic Tire Circumference Estimation for Precise Vehicle Maneuvers
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Solution Overview
Problem
Conventional methods for determining tire circumference in transportation vehicles are inaccurate due to dynamic influences such as tire slip, temperature, and load fluctuations, leading to reduced precision in driving maneuvers like parking.
Innovation Solution
A method using a Kalman filter to compute dynamic tire circumference based on yaw rate, wheel speed, steering angle, and dynamic track width, allowing for real-time adjustments and improved accuracy in driving operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional methods use predefined values of tire circumference and track width, then the device complexity is reduced, but the measurement precision deteriorates due to dynamic influences during driving operations
Solution Approach 1:
The patent applies the Dynamics principle by transitioning from static predefined values to dynamic real-time estimation of tire circumference and track width. The system continuously updates these parameters during driving operations using sensor data (wheel speeds, steering angle, yaw rate) processed through a Kalman filter, allowing the vehicle control system to adapt to changing conditions such as tire pressure variations, load changes, and temperature effects.
Solution Approach 2:
The patent implements Feedback by using measured vehicle dynamics data (wheel speeds, steering angle, yaw rate) to continuously refine the estimation of tire circumference and track width. The Kalman filter processes this feedback information to correct and update the parameter estimates, creating a closed-loop system that improves measurement precision through continuous validation and adjustment based on actual driving conditions.
2Measurement precision
If real-time dynamic tire circumference estimation is implemented, then the measurement precision is improved, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The patent applies Universality by utilizing existing vehicle sensors (wheel speed sensors, steering angle sensors, yaw rate sensors) for multiple purposes. These sensors, originally designed for other vehicle control functions, are repurposed to provide data for tire circumference and track width estimation, thereby improving measurement precision without requiring dedicated additional hardware.
Solution Approach 2:
The patent introduces the Kalman filter as an intermediary computational layer that processes raw sensor data and generates refined estimates of tire circumference and track width. This mathematical intermediary reconciles noisy measurements with physical models of vehicle dynamics, achieving high measurement precision through algorithmic processing rather than through complex physical measurement apparatus.
3Ease of operation
If predefined values are used for tire circumference, then ease of operation is maintained, but the reliability of driving maneuvers deteriorates due to inaccuracies in parking and maneuvering operations
Solution Approach 1:
The patent applies Preliminary action by pre-calculating and storing characteristic values of tire circumference and track width that are later retrieved and updated during vehicle operations. The system maintains pre-defined baseline values for ease of operation while continuously refining them through real-time estimation, allowing the vehicle control system to immediately use accurate parameters without complex real-time calculations for every maneuver.
Solution Approach 2:
The system uses feedback from vehicle dynamics measurements to continuously validate and update the tire circumference and track width parameters. This feedback mechanism ensures that the parameters remain accurate and reliable for parking and maneuvering operations while requiring minimal intervention from the driver or operator, maintaining ease of operation alongside improved reliability.
Data Source
AI summary
A method and a device for ascertaining a dynamic tire circumference of a transportation vehicle. The method includes: receiving a first signal representing a yaw rate of the transportation vehicle, a second signal representing a wheel rotation speed of a wheel of the transportation vehicle, a third signal representing a steering angle of the transportation vehicle, and a fourth signal representing a dynamic track width of the transportation vehicle; ascertaining a first output signal of a first Kalman filter that represents the dynamic tire circumference of the wheel, using the first signal, the second signal, the third signal, and the fourth signal as input signals for the first Kalman filter; and using the first output signal in a control unit of the transportation vehicle.