Vehicle Velocity Estimation Using Slope-Compensated Acceleration
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Solution Overview
Problem
Current methods for determining vehicle velocity in dynamic control systems are prone to errors due to noise in wheel speed measurements, accelerometer bias, wheel radius variations, and non-linear tire models, especially in scenarios with slipping wheels and varying road slopes.
Innovation Solution
A method that estimates longitudinal vehicle velocity by compensating acceleration measurements for road slope and using a Kalman filter to combine slope and velocity estimation, with adjustments for excessive wheel slip and slope gradient limits, ensuring accurate velocity estimation even on sloped roads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If wheel speed measurements are used for velocity estimation, then the velocity can be determined rapidly, but the accuracy deteriorates due to noise in wheel speed signals and slipping wheels
Solution Approach 1:
The patent combines multiple velocity estimation methods (direct wheel speed method and acceleration integration method) into a unified system that selects and switches between them based on operating conditions. This merging allows the system to maintain rapid response when wheel speeds are reliable while achieving accurate integration when wheels are slipping, thereby resolving the contradiction between speed and accuracy of velocity estimation.
Solution Approach 2:
The system dynamically adapts its velocity estimation approach based on real-time detection of wheel slip conditions. When wheel slip is detected, the system transitions from relying on wheel speed measurements to using acceleration integration, and vice versa. This dynamic adaptation allows the system to maintain high accuracy across varying operating conditions while preserving rapid response capability.
2Reliability
If acceleration integration is used to determine vehicle velocity, then velocity can be estimated without direct wheel speed reliance, but the accuracy deteriorates due to accelerometer bias accumulation over time
Solution Approach 1:
The system employs feedback mechanisms where the estimated velocity from acceleration integration is continuously compared with direct wheel speed measurements when available. This feedback loop allows the system to detect and correct drift caused by accelerometer bias, thereby maintaining long-term accuracy while preserving the ability to estimate velocity reliably under wheel slip conditions where direct wheel speed measurement fails.
Solution Approach 2:
The patent maintains continuous velocity estimation by seamlessly switching between wheel speed-based methods and acceleration integration based on operating conditions. This continuity ensures that velocity estimation remains reliable and accurate across all driving scenarios, including transitions between normal operation and wheel slip conditions, without interruption or significant error accumulation.
3Measurement precision
If GPS signals are used to correct velocity estimation, then accuracy can be improved, but the system complexity increases
Solution Approach 1:
The system uses readily available, low-cost sensors (accelerometers and wheel speed sensors) that are already present in modern vehicles, rather than relying on expensive GPS hardware. By processing and integrating data from these existing sensors through intelligent algorithms, the system achieves GPS-level or better accuracy without adding expensive hardware, thereby improving accuracy while minimizing system complexity.
4Reliability
If tire models are used to estimate tire force, then the system becomes less sensitive to noise, but estimation error increases in non-linear situations
Solution Approach 1:
The system dynamically adapts its approach to tire force estimation by switching between model-based methods and direct measurement-based methods depending on operating conditions. In linear operating conditions, the tire model provides noise-resistant estimates, while in non-linear situations such as hard braking or rapid acceleration, the system transitions to methods that directly account for the non-linear tire behavior, thereby maintaining both noise immunity and accuracy across all conditions.
Data Source
AI summary
A method is described for estimating longitudinal velocity of a vehicle on a road surface. The method includes obtaining a measured value of vehicle acceleration, which is dependent on longitudinal acceleration of the vehicle and vertical acceleration of the vehicle when a slope of the road surface is non-zero. The method includes determining an initial estimate of the slope. The method includes determining a difference between the initial estimate of the slope and a prior estimate of the slope and, based on the difference, setting a current estimate of the slope to be equal to the initial estimate or the prior estimate. The method includes estimating the longitudinal velocity of the vehicle based on the current estimate of the slope and the measured value of vehicle acceleration. The method includes controlling at least one wheel of the plurality of wheels based on the estimated longitudinal velocity of the vehicle.