Tire-Based Road Friction Estimation Using Longitudinal Stiffness
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Solution Overview
Problem
Existing systems for estimating road surface friction face challenges in achieving reliable and accurate friction estimation due to the difficulty in attaining the requisite level and persistence of vehicle motion excitations, which is problematic for advanced vehicle control systems like adaptive cruise control, anti-lock braking systems, and electronic stability programs.
Innovation Solution
A tire-based system that estimates road surface friction using model-based and actual longitudinal stiffness estimations, incorporating tire parameters such as temperature, pressure, and wear state, along with vehicle parameters like wheel speed and torque, to derive a tire road friction estimation through a comparative analysis and adaptive algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle motion excitations (accelerating, decelerating, steering) are used to estimate road friction, then friction estimation can be achieved, but it becomes problematic to attain the requisite level and persistence of excitation in practice
Solution Approach 1:
The patent replaces the mechanical approach of requiring actual vehicle motion excitations with a signal processing approach. By injecting pseudorandom binary sequence (PRBS) signals into the wheel torque and measuring the resulting wheel speed responses, the system can estimate friction coefficients without requiring the vehicle to actually undergo large accelerations or steering maneuvers. This substitution allows friction estimation to proceed using normal driving conditions combined with signal injection, resolving the contradiction between measurement accuracy and ease of operation.
2Measurement precision
If tire-based parameters (temperature, pressure, wear state) are incorporated into the estimation model, then estimation accuracy improves, but system complexity increases
Solution Approach 1:
The patent leverages existing tire pressure monitoring system (TPMS) sensors that already measure tire pressure and temperature, and extends their utility to also provide data for friction estimation. By making these existing sensors serve multiple functions (original pressure/temperature monitoring plus friction estimation), the system improves measurement precision without proportionally increasing device complexity. The wear state is estimated from the existing hub vertical acceleration sensor data, further avoiding additional hardware.
Solution Approach 2:
The patent introduces a longitudinal stiffness estimation as an intermediary parameter that connects the measurable quantities (wheel speed, torque, acceleration) to the desired friction coefficient. This intermediary allows the system to process complex tire parameter effects through a structured mathematical model, making the overall system more manageable despite the multiple inputs involved.
3Reliability
If model-based longitudinal stiffness estimation with multiple adaption factors is used, then reliability of friction estimation improves, but computational requirements and processing time increase
Solution Approach 1:
The patent pre-calculates and stores the relationship between tire parameters (pressure, temperature, wear state) and longitudinal stiffness in lookup tables or pre-computed models. During real-time operation, the system simply queries these pre-prepared data structures rather than performing complex calculations from scratch, significantly reducing processing time while maintaining the reliability benefits of incorporating multiple adaption factors.
Solution Approach 2:
The patent transforms the complex multi-parameter estimation problem into a series of simpler parameter adjustments. By expressing the longitudinal stiffness as a base value modified by multiplicative adaption factors for pressure, temperature, and wear state, the system can efficiently update the stiffness estimate by applying these factors to a baseline model, reducing computational burden compared to solving the full problem from scratch at each time step.
Data Source
AI summary
A tire-based system and method for estimating road surface friction includes a model-based longitudinal stiffness estimation generator using tire-based parameter inputs and vehicle-based parameter inputs; an actual longitudinal stiffness estimation generator using real-time vehicle-based parameter inputs; and a tire road friction estimation generator for deriving a tire road friction estimation from a comparative analysis between the actual longitudinal stiffness estimation and the model-based longitudinal stiffness estimation. A road surface classifier algorithm is employed to generate a road surface type analysis from the road friction estimation, an ambient air temperature measurement, and an ambient air moisture measurement.