Vehicle Traction Torque Estimation Using Dual Extended Kalman Filters
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Solution Overview
Problem
Existing technologies face challenges in accurately measuring and estimating vehicle tire traction torque due to limitations in sensor availability and cost, making it difficult to improve vehicle traction performance effectively.
Innovation Solution
A method and system utilizing a Dual Extended Kalman Filter (DEKF) algorithm to estimate vehicle speed and tire stiffness simultaneously, forming a unified traction torque estimation model, which is implemented using a processor and machine-readable storage medium to calculate the traction torque based on these parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional sensor-based measurement methods are used to measure tire traction torque, then measurement directness is improved, but device complexity and cost increase due to additional sensors
Solution Approach 1:
The patent replaces direct mechanical sensor measurement with a computational estimation system using extended Kalman filters. The system substitutes physical sensors with an algorithmic approach that processes existing vehicle data (wheel speeds, accelerations, steering angles) to estimate traction torque, thereby reducing hardware complexity while maintaining measurement capability
Solution Approach 2:
The patent introduces an intermediary estimation model that acts as a mediator between available vehicle sensors and the desired traction torque information. The extended Kalman filter serves as this intermediary, transforming readily available vehicle dynamics data into accurate traction torque estimates without requiring direct torque sensors
2Device complexity
If simple estimation models are used to calculate traction torque, then device complexity is reduced, but measurement precision deteriorates due to inability to capture dynamic tire characteristics
Solution Approach 1:
The patent employs dynamic estimation models (extended Kalman filters) that adapt to changing vehicle conditions in real-time. The filters continuously update tire stiffness parameters and state variables based on current vehicle dynamics, allowing the system to maintain high precision throughout transient operations like clutch engagement and slip events without requiring overly complex hardware
Solution Approach 2:
The patent dynamically changes estimation parameters (tire stiffness, state variables) based on operating conditions. The extended Kalman filter adjusts these parameters in real-time to match actual tire behavior during different vehicle operations, maintaining accuracy across varying conditions without increasing physical system complexity
3Measurement precision
If advanced dual extended Kalman filter algorithms are implemented, then measurement precision of traction torque is improved, but device complexity increases due to computational requirements
Solution Approach 1:
The patent segments the estimation problem into two separate but coordinated extended Kalman filters: one for parameter estimation (tire stiffness) and one for state estimation (vehicle dynamics). This segmentation allows each filter to focus on specific aspects of the problem, improving overall precision while managing computational complexity through modular architecture
Solution Approach 2:
The patent merges the parameter estimation and state estimation into a unified dual extended Kalman filter system. By combining these functions into an integrated algorithm that processes data simultaneously, the system achieves high precision traction torque estimation while avoiding the complexity of separate independent estimation systems
Data Source
AI summary
A method of calculating traction torque of a tire of a vehicle includes calculating a parameter vector using a first extended Kalman filter, calculating a state vector using a second extended Kalman filter, and calculating the longitudinal stiffness as a function of the parameter vector and the state vector. A method for computing traction torque of a vehicle includes computing the longitudinal stiffness of the tire using the first and second extended Kalman filters, and computing the traction torque as a function of the longitudinal stiffness of the tire and a linear speed difference between a tire speed of the tire and a vehicle longitudinal speed.