Vehicle Cornering Stiffness Estimation Using Tire Relaxation Dynamics
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Solution Overview
Problem
Existing vehicle stability control systems face challenges in accurately estimating tire cornering stiffness, which varies with tire type and age, leading to unnecessary system interventions due to slow update rates and failure to account for tire relaxation dynamics.
Innovation Solution
A method that uses sensor data from vehicle longitudinal velocity, lateral acceleration, yaw rate, and steering angle to determine observability and estimate cornering stiffness parameters through a bicycle model incorporating tire relaxation dynamics, formulated as a weighted linear least squares problem using recursive least squares techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a slow loop process is used to determine updated cornering stiffness parameters, then the system can account for variations in tire cornering stiffness, but the update rate is too slow (about one to two seconds) to respond to rapid changes in handling characteristics
Solution Approach 1:
The patent transitions from a static, slow-update cornering stiffness estimation to a dynamic, real-time estimation that continuously adapts to changing tire conditions. The system dynamically updates cornering stiffness parameters at each control cycle based on current vehicle state measurements, enabling rapid response to changing handling characteristics while maintaining accuracy through continuous observation of vehicle dynamics.
2Device complexity
If traditional stability control systems use fixed cornering stiffness values, then the system design is simpler, but the system performs unnecessary interventions when tire cornering stiffness varies due to tire aging, type changes, or environmental conditions
Solution Approach 1:
The patent implements a feedback mechanism where the stability control system continuously observes actual vehicle behavior (yaw rate, lateral acceleration, steering angle) and uses this information to estimate and adapt cornering stiffness parameters in real-time. This feedback loop enables the system to automatically adjust to varying tire conditions without requiring complex manual reconfiguration, thereby improving robustness while maintaining operational simplicity.
Solution Approach 2:
The system performs self-adjustment by automatically estimating cornering stiffness parameters based on observed vehicle dynamics without requiring external input or manual intervention. The cornering stiffness values are self-updated through the estimation algorithm that processes standard vehicle sensor data, allowing the system to adapt to changing tire conditions autonomously and prevent unnecessary stability control interventions.
3Device complexity
If cornering stiffness parameters are not updated to reflect tire relaxation dynamics, then the computational model is simpler, but the estimation accuracy deteriorates because tire relaxation effects are not accounted for
Solution Approach 1:
The patent incorporates tire relaxation dynamics into the estimation algorithm by pre-defining the relationship between slip angle and lateral tire force using a relaxation function. This preliminary modeling of tire behavior allows the system to account for the time-dependent nature of tire response without requiring complex real-time calculations, thereby improving estimation accuracy while keeping the computational burden manageable through efficient mathematical formulation.
Data Source
AI summary
A method, arrangement and system are described for estimating one or more vehicle cornering stiffness parameters (cf, cr) in a linear vehicle operating region. The method includes reading sensor data representative of at least vehicle (1) longitudinal velocity (vx), vehicle lateral acceleration (ay), vehicle yaw rate (ωz) and vehicle steering angle (δ), determining from the read sensor data if the cornering stiffness parameters (cf, cr) are observable, and if so providing an estimate of the cornering stiffness parameters (cf, cr) using a bicycle model that includes a model of tire relaxation dynamics.