Tire Force Estimation via CAN-Bus Sensor Fusion
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Solution Overview
Problem
Current systems fail to accurately and robustly estimate tire normal, lateral, and longitudinal forces in real-time during vehicle operation, especially over the lifetime of a tire tread, due to reliance on indirect and load-dependent sensor measurements.
Innovation Solution
A tire state estimation system utilizing CAN-bus accessible vehicle sensors for input data, including acceleration, angular velocities, and steering wheel angle, employs normal, lateral, and longitudinal force estimators to calculate tire forces without relying on GPS or suspension sensors, using models for wheel rotational dynamics, planar vehicle models, and adaptive inertial parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If GPS and suspension sensors are used for tire force estimation, then measurement data availability is improved, but system reliability deteriorates due to load-dependent inertial parameters and sensor errors
Solution Approach 1:
The patent extracts and removes GPS and suspension sensors from the tire force estimation system. By eliminating these sensors that introduce load-dependent inertial parameters and measurement errors, the system achieves more reliable and accurate tire force estimates without compromising data availability through alternative sensor fusion approaches.
2Device complexity
If indirect sensor measurements are used for tire force estimation, then system complexity is reduced, but measurement precision deteriorates over tire tread lifetime
Solution Approach 1:
The patent changes the parameters used in tire force estimation by eliminating load-dependent inertial parameters from the estimation algorithm. This parameter change allows the system to maintain simplicity while improving measurement precision across the entire tire tread lifetime, as the estimation no longer degrades with tire wear or load variations.
3Adaptability or versatility
If load-dependent inertial parameters are included in the estimation model, then model completeness is improved, but estimation robustness deteriorates during vehicle operation
Solution Approach 1:
The patent extracts and removes load-dependent inertial parameters from the tire force estimation model. This extraction eliminates the source of estimation errors and degradation over time, achieving robust real-time estimation that maintains accuracy throughout the tire's operational lifetime without requiring complex compensations.
Data Source
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AI summary
A tire state estimation system is provided for estimating normal force, lateral force and longitudinal forces based on CAN-bus accessible sensor inputs; the normal force estimator generating the normal force estimation from a summation of longitudinal load transfer, lateral load transfer and static normal force using as inputs lateral acceleration, longitudinal acceleration and roll angle derived from the input sensor data; the lateral force estimator estimating lateral force using as inputs measured lateral acceleration, longitudinal acceleration and yaw rate; and the longitudinal force estimator estimating the longitudinal force using as inputs wheel angular speed and drive/brake torque derived from the input sensor data.