Robust Vehicle Mass Estimation With Uncertainty-Aware Dynamic Models
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Solution Overview
Problem
Existing methods for estimating the mass of a motor vehicle are either expensive due to the need for additional sensors or inaccurate due to model error deviations, especially in varying driving contexts, which affects energy management and vehicle dynamics.
Innovation Solution
A method that estimates vehicle mass in real-time using vehicle data and propagates sensor uncertainties in a dynamic model, applying least squares linear regression to calculate a robust mass estimate with quantified accuracy, and includes a posteriori supervision to ensure precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement methods using specific sensors (ultrasonic, angle sensors) are used to measure vehicle mass parameters, then measurement precision is improved, but device complexity and cost increase due to additional expensive sensors
Solution Approach 1:
The patent uses existing vehicle sensors (accelerometer, wheel speed sensor) as intermediaries to indirectly measure vehicle mass through dynamic model analysis, avoiding the need for direct mass measurement sensors. The sensors measure intermediate parameters (acceleration, wheel speed) that are then processed through mathematical models to derive mass information.
Solution Approach 2:
The patent replaces physical direct measurement systems with a computational approach using dynamic models and mathematical regression. Instead of using mechanical sensors to directly measure mass, the system uses computational algorithms (least squares linear regression) to calculate mass from other measured parameters, substituting mechanical measurement with computational analysis.
2Device complexity
If indirect methods using existing sensors are used to estimate vehicle mass, then device complexity is reduced, but measurement precision deteriorates due to model error deviations
Solution Approach 1:
The patent implements feedback through iterative regression analysis and model validation. The system continuously refines mass estimates by comparing predicted vehicle behavior with actual sensor measurements, adjusting the dynamic model parameters to minimize errors. This feedback loop compensates for model deviations and improves estimation accuracy over time.
Solution Approach 2:
The patent dynamically adjusts model parameters based on operating conditions. The dynamic model incorporates variable parameters that change with vehicle state (speed, acceleration, load conditions), allowing the system to adapt to different driving scenarios and maintain precision across varying conditions rather than using fixed parameters.
3Measurement precision
If context selection methods are used to improve mass estimation accuracy, then measurement precision is improved in specific conditions, but productivity decreases due to slow convergence time
Solution Approach 1:
The patent uses a dynamic estimation approach that continuously updates mass estimates in real-time as new data becomes available, rather than requiring static convergence to a fixed value. The system adapts the estimation process dynamically based on incoming sensor data, allowing continuous improvement of precision without requiring the system to reach a final converged state before providing useful estimates.
Solution Approach 2:
The patent applies preliminary regression analysis and model calibration during vehicle operation to establish initial mass estimates quickly. By performing preliminary calculations using available data and then refining estimates as more data accumulates, the system achieves faster initial convergence while maintaining the ability to improve precision over time through continuous refinement.
Data Source
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AI summary
Method (40) for determining the mass (mr) of a vehicle in which: - a value of the mass (mest) of the vehicle is estimated in real time from data (x) of the vehicle and a residual (e) is estimated; - an uncertainty is quantified capable of quantifying the accuracy (Δmest) of the previously calculated mass (mest) estimate of the vehicle as a function of the residual (e), the data (x) of the vehicle and their uncertainties (Δx); and - a post-hoc supervision is performed to interpret the accuracy (Δmest) of the mass estimate and to determine a robust estimate of the mass (mr) and its accuracy (Δmr) over a given journey.