Adaptive Tire Model Segmentation for Vehicle Stability Control
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Solution Overview
Problem
Current control methods for wheeled vehicles are unsuitable for off-road driving and high-speed conditions due to their reliance on linear tire models, which fail to accurately represent non-linear tire behavior, especially when empirical parameters are difficult to define in real-time.
Innovation Solution
A method that uses a model of tire behavior based on sideslip angle, identifying zones of linear, linear-to-nonlinear transition, and nonlinear variation to apply appropriate models (affine, DUGOFF, and constant) for estimating lateral force, allowing for real-time adaptation and improved control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a linear tire model is used for vehicle control, then control simplicity is maintained, but accuracy deteriorates under non-ideal conditions such as off-road driving and high speeds
Solution Approach 1:
The tire behavior curve is segmented into three distinct zones (linear, transition, nonlinear) based on sideslip angle, with each zone having its own simplified model. This segmentation allows the system to maintain simplicity within each zone while achieving high accuracy across the entire range by selecting the appropriate model for current conditions.
Solution Approach 2:
The control system dynamically switches between different tire models (linear, DUGOFF, constant) based on the current operating zone determined by sideslip angle. This dynamic adaptation allows the system to maintain both simplicity and accuracy by using the most appropriate model for each operating condition without requiring complex real-time parameter identification.
2Measurement precision
If a non-linear tire model such as PACEJKA is used, then accuracy is improved, but device complexity increases due to numerous empirical parameters
Solution Approach 1:
The complex non-linear tire behavior is segmented into three zones, each represented by a simplified model with fewer parameters. This avoids the need to implement the full complexity of models like PACEJKA while maintaining accuracy in each specific operating range.
Solution Approach 2:
Different levels of model complexity are applied locally to different operating zones. The linear model is used where it is sufficient (small sideslip angles), the DUGOFF model is used in the transition zone, and the constant model is used when the tire is saturated. This local adaptation optimizes the balance between accuracy and complexity for each specific condition.
3Measurement precision
If real-time parameter identification is performed for accurate tire modeling, then measurement precision is improved, but processing time increases
Solution Approach 1:
The tire behavior curve is pre-characterized and divided into three zones before real-time operation. The boundaries and characteristics of each zone are determined in advance, allowing the control system to simply identify which zone the current operating point falls into without performing complex real-time parameter identification.
Solution Approach 2:
Instead of identifying multiple empirical parameters in real-time, the system changes its approach by using sideslip angle as the primary parameter to determine the operating zone. This single parameter approach dramatically reduces computational complexity while maintaining accuracy.
Data Source
AI summary
A method of controlling a vehicle having wheels provided with tires resting on a surface, the method using a model of the physical behavior of each tire as a function of a sideslip angle (βij) for each tire relative to the surface. The model is obtained by implementing an adaptive algorithm that selectively applies an affABREGEine model (Z1), a DUGOFF model (Z2), or a constant model (Z3).