Vehicle Weight Interval Estimation via Dynamic Force Segmentation
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Solution Overview
Problem
Current methods for estimating the weight of an automobile vehicle are either expensive, imprecise, or require costly installations, and existing dynamic-based methods are particularly imprecise and onerous, failing to quickly determine whether a vehicle is lightly or heavily loaded without exact weight determination.
Innovation Solution
A method and device that define two weight intervals and calculate probabilities using driving profiles and Bayes theorem, determining the risk of incorrect interval choice based on cost and instantaneous mass, allowing for a quick and low-cost estimation of the weight interval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of weight by displacement sensors is used, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces mechanical displacement sensors with a computational approach using the fundamental principle of dynamics. Weight is estimated by calculating from measured forces (aerodynamic drag, road resistance, engine torque) and acceleration data, substituting mechanical sensing with mathematical modeling and data processing.
Solution Approach 2:
The patent introduces an intermediary computational model that processes multiple measured parameters (forces, acceleration) to derive weight information. This intermediary processing layer allows weight estimation without direct mechanical sensing, using the relationship F=ma as the mediating principle.
2Device complexity
If dynamic-based weight estimation methods are used, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent segments the weight estimation problem into distinct force components (aerodynamic drag, road resistance, engine torque, braking friction) that can be measured or calculated separately. Each force component is estimated independently using specific sensors and models, then combined through the dynamics equation to obtain total weight, improving precision through systematic decomposition.
Solution Approach 2:
The patent changes the approach from direct weight measurement to measuring multiple related parameters (forces, acceleration) and deriving weight through computational relationships. By measuring several parameters and using the fundamental dynamics equation, the system achieves accurate weight estimation with simpler individual sensors.
3Speed
If single-moment weight estimation is used, then response speed is improved, but measurement precision deteriorates due to assumption dependencies
Solution Approach 1:
The patent performs preliminary measurements and calculations of force components (aerodynamic drag, road resistance, engine torque) before computing weight. By pre-measuring and storing these force parameters, the system can quickly calculate weight when acceleration data is available, improving response speed while maintaining precision through pre-prepared force data.
Data Source
AI summary
A method for estimating the interval within which the total weight of an automobile vehicle is situated includes defining at least two intervals of weights, including determining a first situation in which the total weight of the vehicle belongs to the first interval and a second situation in which the total weight of the vehicle belongs to the second interval, calculating probabilities of being in the first or second situation knowing a value of the weight, calculating a risk of choosing the wrong interval as a function of the calculated probabilities and of a cost associated with an erroneous decision, and determining the interval within which the total weight of the vehicle is situated as a function of the risk.