Vehicle Load Estimation Calibration Using Static-Dynamic Sensor Comparison
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Solution Overview
Problem
Existing vehicle weight measurement systems are inaccurate due to significant error factors in load estimation, particularly when vehicles are in motion, and require calibration during manufacturing, which adds time and cost, and accuracy can drift over time due to suspension wear, leading to potential misloading and reduced safety.
Innovation Solution
The system employs dynamic load estimation techniques in conjunction with static load estimation using sensor data from wheels and tires, with a secondary weight estimation system calibrating the primary system based on differences between stationary and moving load conditions, ensuring continuous accuracy as suspension properties change.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If calibration is performed during manufacturing, then initial measurement accuracy is improved, but manufacturing time and cost increase
Solution Approach 1:
The system performs preliminary calibration actions during manufacturing by comparing static and dynamic load measurements, establishing baseline calibration data before the vehicle enters service. This preliminary action reduces the need for extensive field calibration later.
Solution Approach 2:
The system continuously monitors load measurements during vehicle operation and uses feedback loops to adjust calibration parameters. The calibration module receives ongoing feedback from sensor comparisons to maintain accuracy without requiring repeated manual calibration procedures.
2Measurement precision
If calibration is performed during manufacturing, then initial measurement accuracy is improved, but manufacturing cost increases
Solution Approach 1:
The vehicle's load measurement system performs self-calibration during normal operation by automatically comparing static and dynamic measurements. This self-service capability eliminates the need for expensive external calibration services and reduces manufacturing overhead costs.
Solution Approach 2:
The system adjusts calibration parameters dynamically based on operating conditions and sensor performance. By changing calibration parameters rather than requiring physical recalibration, the system maintains accuracy while reducing manufacturing and maintenance costs.
3Device complexity
If static load estimation is used, then measurement simplicity is improved, but accuracy drifts over time due to suspension wear
Solution Approach 1:
The system continuously compares static and dynamic load measurements and uses feedback to detect drift caused by suspension wear. When drift is detected, the calibration module automatically adjusts calibration parameters to compensate, maintaining measurement reliability without increasing system complexity.
Solution Approach 2:
The calibration parameters are made dynamic rather than fixed, allowing the system to adapt to changing suspension characteristics over time. The system transitions from static calibration to dynamic recalibration based on operational data, maintaining accuracy despite component wear.
4Measurement precision
If dynamic load estimation is used for calibration, then continuous accuracy is improved, but system complexity increases
Solution Approach 1:
The calibration module serves multiple functions: it compares static and dynamic measurements, detects drift, recalibrates parameters, and validates sensor performance. This multi-functionality consolidates what could be multiple separate systems into a single integrated module, managing complexity while maintaining continuous accuracy.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to calibrate a weight estimation are disclosed herein. An example apparatus comprises memory including stored instructions, and a processor to execute the instructions to determine, via a first sensor, a first load estimation of a vehicle corresponding to when the vehicle under a load condition, determine, via a second sensor, a second load estimation corresponding to when the vehicle is moving and under the load condition, and calibrate, based on a difference between the second load estimation and the first load estimation, an error of the first load estimation.