Axle Load Sensor Calibration via Dynamic Disturbance Compensation
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Solution Overview
Problem
Existing methods for calibrating axle or wheel load sensors in commercial vehicles lack cost-effectiveness, simplicity, and reliability, leading to inaccurate measurements, especially under varying environmental conditions and during vehicle operation.
Innovation Solution
A method involving multiple sensors on axles or wheels, with initial measurements taken at standstill under identical conditions, followed by continuous measurements during driving, using a self-learning algorithm to adapt to changing environmental conditions and correct for disturbance variables like temperature, allowing for precise weight determination and load distribution analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simple calibration methods are used, then cost and implementation simplicity are improved, but measurement accuracy deteriorates under varying environmental conditions
Solution Approach 1:
The calibration method transitions from static calibration to dynamic calibration by performing measurements during actual vehicle operation under varying environmental conditions. The system continuously adapts the calibration parameters based on real-time temperature and disturbance data, ensuring measurement accuracy without requiring complex pre-calibration procedures.
Solution Approach 2:
The system performs self-calibration by automatically detecting disturbance variables during operation and adjusting calibration parameters without external intervention. The control unit processes measurement data and temperature information to autonomously update calibration constants, eliminating the need for manual recalibration under different conditions.
2Ease of operation
If calibration is performed only under stationary conditions, then simplicity is improved, but reliability deteriorates during vehicle operation
Solution Approach 1:
The system performs preliminary calibration measurements under stationary conditions to establish baseline calibration parameters. These preliminary values are then refined during vehicle operation by continuously comparing measurements with temperature-compensated reference values, ensuring both initial simplicity and ongoing reliability.
Solution Approach 2:
The system implements continuous feedback by monitoring measurement values during vehicle operation and comparing them against calibration data obtained under stationary conditions. The control unit uses temperature information and disturbance variables to adjust calibration parameters in real-time, maintaining measurement reliability across varying operational conditions.
3Device complexity
If temperature compensation is not implemented, then device complexity is reduced, but measurement accuracy deteriorates under changing ambient conditions
Solution Approach 1:
The system introduces temperature information as an intermediary variable that mediates between the physical measurement and the calibration process. Temperature sensors provide data to the control unit, which uses this information to compensate for thermal effects on measurement values, achieving accurate measurements without complex hardware modifications.
Solution Approach 2:
The system changes calibration parameters based on temperature conditions by storing multiple calibration constants for different temperature ranges. The control unit selects and applies the appropriate calibration parameters based on current temperature measurements, maintaining accuracy across varying ambient conditions without requiring complex real-time calculations.
Data Source
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AI summary
A method for calibrating a system with a plurality of sensors (28), wherein each of the sensors is arranged on an element of a vehicle (10), comprises: performing a first number of measurements of an axle load or wheel load with each of the plurality of sensors (28) when the unloaded vehicle (10) is stationary; performing a first number of measurements of an axle load or wheel load with each of the plurality of sensors (28) when the loaded vehicle (10) is stationary, wherein each of the measurements is performed under the influence of identical disturbances as one of the measurements performed with the unloaded vehicle (10); performing a plurality of measurements of an axle load or wheel load at regular intervals with each of the plurality of sensors (28) while the vehicle (10) is in motion; determining at least one disturbance at regular intervals while the vehicle (10) is in motion; and creating an algorithm.which takes into account the measured values determined when the unloaded vehicle (10) is stationary, the measured values determined when the loaded vehicle (10) is stationary, the measured values determined during the movement of the loaded vehicle (10), as well as the disturbance variables when the vehicle (10) is stationary and during the movement of the vehicle (10).