Adaptive Tire Modeling for Real-Time Vehicle Control
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Solution Overview
Problem
Existing tire models are inadequate for capturing the complex and unpredictable nature of tire-road interactions, failing to adapt to real-time driving conditions and introducing safety risks due to computational inefficiencies and reliance on additional sensors.
Innovation Solution
A method and system for creating an adaptive tire model using vehicle sensor data to estimate, penalize, normalize, and compress data points, fitting them into a predefined tire model to dynamically adjust to varying conditions without additional sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional static tire models with fixed parameters are used, then the model structure is simple and computational demands are low, but the model accuracy deteriorates in capturing complex and unpredictable tire-road interactions under varying driving conditions
Solution Approach 1:
The patent transforms the static tire model into a dynamic adaptive model that automatically adjusts its parameters in real-time based on driving conditions. The model uses online identification methods to continuously update tire parameters from sensor data, enabling it to adapt to varying road surfaces, loads, and speeds without requiring complex manual recalibration or additional sensors.
2Adaptability or versatility
If offline tire fitting using test bench data is used, then the tire model parameters are well-defined, but the model cannot adapt to real-world driving conditions due to prohibitive computational demands and requirement for static conditions
Solution Approach 1:
The tire model performs self-identification by automatically extracting parameters from vehicle sensor data during normal operation. The system uses online identification algorithms that process sensor measurements in real-time to update tire parameters, eliminating the need for separate offline calibration procedures or test bench experiments. This self-service capability enables continuous adaptation to changing driving conditions.
3Loss of information
If additional sensors measuring tire sidewall deflection or internal temperature are added, then the tire model data completeness improves, but the system complexity and cost increase without fundamentally solving the core adaptability issues
Solution Approach 1:
The patent extracts the necessary tire parameter information from existing vehicle sensor data without requiring additional specialized sensors. By using data already available from standard vehicle sensors (wheel speeds, steering angle, accelerometers), the system extracts tire friction coefficients and other parameters through mathematical identification algorithms, eliminating the need for complex additional sensing systems.
4Reliability
If existing methods process data into black-box models, then the model can handle complex nonlinear behavior, but safety risks increase due to unpredictable outputs and robustness is undermined
Solution Approach 1:
The patent implements a feedback mechanism where the adaptive tire model continuously monitors its own performance and adjusts parameters based on actual vehicle behavior. The online identification system compares predicted versus actual tire forces and uses this feedback to refine parameter estimates, ensuring predictable and reliable outputs while maintaining the ability to capture complex nonlinear tire behavior.
Data Source
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AI summary
A method for providing an adaptive tire model for controlling a vehicle and/or for providing tire-related information of the vehicle, the method comprising the steps of: estimating (S 1) a series of data points of forces and slips of a tire of the vehicle based on vehicle sensor data; penalizing (S2) and/or excluding (S3) data points having a predefined dynamic behaviour and/or an accuracy being lower than a predefined threshold from the series of data points to provide an adapted series of data points; normalizing (S4) the adapted series of data points to provide a normalized series of data points; compressing (S5) the normalized series of data points to provide a compressed series of data points; and fitting (S6) the compressed series of data points with a predefined tire model to obtain the adaptive tire model.