Vehicle Reference Speed Fusion Using Stochastic Estimators
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Solution Overview
Problem
Existing methods for determining vehicle reference speed in motor vehicles, particularly in all-wheel drive vehicles during drive slip or braking, are inaccurate due to reliance on wheel speed alone, and face challenges with incomplete or noisy sensor data from longitudinal acceleration and engine torque signals.
Innovation Solution
A method that fuses wheel speed information with additional driving state sensors using weighting factors, incorporating a hierarchical structure of stochastic estimators to improve accuracy and robustness, including models for drive train, tire, and longitudinal dynamics, and utilizing Kalman filters for signal fusion and noise reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If wheel speed information alone is used to determine vehicle reference speed, then the device complexity is reduced, but the measurement precision deteriorates especially during braking and drive slip conditions
Solution Approach 1:
The patent combines multiple sensor signals (wheel speed sensors, longitudinal acceleration sensors, and optionally engine torque sensors) into a unified vehicle reference speed determination system. This merging of sensor data sources resolves the contradiction by maintaining low device complexity through integrated processing while significantly improving measurement precision through multi-signal fusion, especially during braking and drive slip conditions where single-sensor methods fail.
Solution Approach 2:
The control unit is designed to process multiple types of sensor signals (wheel speed, acceleration, torque) and adaptively determine vehicle reference speed across various driving conditions (normal driving, braking, drive slip). This multi-functional capability allows the system to maintain precision across diverse operational scenarios without requiring separate dedicated systems for each condition.
2Measurement precision
If additional sensors (longitudinal acceleration, engine torque) are incorporated to improve vehicle reference speed determination, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The control unit serves as an intermediary that receives, processes, and fuses data from multiple sensor types (wheel speed sensors, longitudinal acceleration sensors, engine torque sensors). This intermediary processing layer integrates the additional sensors into the existing vehicle control architecture, improving measurement precision while managing device complexity through centralized signal fusion rather than distributed complex processing.
3Reliability
If weighting factors are applied to fuse multiple sensor signals, then the reliability of vehicle reference speed determination improves under varying driving conditions, but the computational complexity increases
Solution Approach 1:
The patent implements dynamic weighting factors that adaptively adjust the contribution of each sensor signal based on current driving conditions (normal driving, braking, drive slip). This dynamic approach improves reliability by optimizing signal fusion for each operational scenario while managing computational complexity through condition-based weight selection rather than continuous complex optimization algorithms.
Data Source
Figure 1
AI summary
A method for determining a vehicle reference speed and a vehicle controller having such a method, in which directly or indirectly determined or estimated vehicle status signals, including weighting factors associated with each of the vehicle status signals, are merged in a merging module, wherein the merging module includes at least two stochastic estimators, which exchange signals with one another that correspond to physical vehicle parameters, wherein the association of the estimators is selected in accordance with a physics model for the vehicle behavior.