Reconfigurable Tire Force Estimation Algorithm for Vehicle Dynamics
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Solution Overview
Problem
Existing tire force estimation methods are sensitive to road surface conditions and unable to handle various driveline configurations, leading to inaccurate results due to uncertainties in tire model parameters and road friction variations.
Innovation Solution
A reconfigurable algorithm for estimating longitudinal and lateral tire forces at each vehicle corner, independent of road conditions, suitable for different driveline configurations (AWD, RWD, FWD) and actuation types, using measured vehicle acceleration, yaw rate, and wheel speed to calculate torques and form inertia matrices, without requiring additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing tire-based or torque-based estimation methods are used, then the estimation process is simple, but the accuracy deteriorates due to uncertainties in tire model parameters and road friction variations
Solution Approach 1:
The patent introduces an intermediary observer system that mediates between available vehicle sensors and tire force estimation. The observer uses measured wheel speeds, vehicle acceleration, and yaw rate as inputs, processes them through a structured algorithm involving inertia matrices and torque calculations, and produces accurate tire force estimates without requiring additional sensors or complex tire models.
Solution Approach 2:
The estimation algorithm is designed to be universal across different driveline configurations (AWD, RWD, FWD) and actuation types (eLSD, open differential, electric motors, gasoline engines). The same core algorithm adapts to various configurations through reconfigurable torque calculations, eliminating the need for configuration-specific estimation methods.
2Reliability
If existing algorithms are used, then the implementation is straightforward, but the reliability deteriorates due to sensitivity to road surface conditions
Solution Approach 1:
The patent changes the approach from using tire model parameters and road friction coefficients (which vary with conditions) to using directly measurable vehicle dynamics parameters. By formulating the estimation around measured wheel speeds, accelerations, and yaw rates combined with inertia matrices, the algorithm becomes insensitive to road surface variations and tire parameter uncertainties.
Solution Approach 2:
The observer implementation incorporates feedback mechanisms that continuously monitor measured vehicle states and adjust torque calculations accordingly. The algorithm uses feedback from wheel speed measurements and vehicle acceleration data to maintain accurate tire force estimates under varying driving conditions, enhancing reliability.
3Measurement precision
If additional sensors are added to improve estimation accuracy, then the measurement precision improves, but the device complexity and cost increase
Solution Approach 1:
The system uses existing vehicle sensors (wheel speed sensors, accelerometers, yaw rate sensors) that are already part of the vehicle's standard equipment for other functions. The tire force estimation algorithm processes data from these existing sensors, making the system self-sufficient without requiring additional measurement devices, thereby avoiding increased complexity and cost.
4Measurement precision
If complex estimation algorithms are used, then the accuracy improves, but the computational complexity increases
Solution Approach 1:
The estimation algorithm is segmented into distinct computational modules: forming inertia matrices from measured wheel speeds, calculating torques at corners using the inertia matrices and engine torque, and estimating tire forces from measured acceleration and wheel speed data. This modular segmentation improves computational efficiency by organizing calculations in a systematic sequence that can be efficiently implemented in real-time control systems.
Data Source
AI summary
A method for estimation of a vehicle tire force includes: receiving, by a controller of a vehicle, a measured vehicle acceleration of the vehicle; receiving, by the controller, a measured wheel speed and a measured yaw rate of the vehicle; forming, by the controller, inertia matrices based on an inertia of rotating components of the vehicle; calculating torques at corners of the vehicle using the inertia matrices; estimating tire forces of the vehicle based on the measured vehicle acceleration, the measured wheel speed, and the inertia matrices; and controlling, by the controller, the vehicle, based on the plurality of estimated longitudinal and lateral tire forces.