Motor Vehicle Laden Mass Estimation with Kalman Filtering on Slopes
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Solution Overview
Problem
Current methods for estimating the mass of heavy-duty motor vehicles, especially on slopes, are inaccurate and cumbersome, requiring expensive direct measurement or estimation methods that do not account for real-time variations and are imprecise due to manufacturing dispersion and reliance on unverified hypotheses.
Innovation Solution
A method using data from the CAN communication network, incorporating Kalman filters to estimate vehicle speed, slope, and mass in real-time by applying fundamental dynamics equations, with covariance matrices to correct estimation errors, allowing for precise mass determination even on slopes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct mass measurement or traditional estimation methods (ride height sensors, spring stiffness, airbag pressure) are used, then mass estimation is possible, but the methods are expensive and imprecise due to manufacturing variations and cannot be used when the vehicle is on a slope
Solution Approach 1:
The patent replaces mechanical measurement devices (ride height sensors, spring stiffness sensors, airbag pressure sensors) with a computational approach using the fundamental dynamics equation. The system uses readily available data from the vehicle's CAN communication network (engine torque, transmission gear ratio, vehicle speed, brake pedal deflection, retarder usage) combined with mathematical modeling to estimate mass, eliminating the need for expensive specialized sensors while maintaining precision even on slopes
Solution Approach 2:
The system uses data already collected by the vehicle's existing sensors and communication networks for a secondary purpose (mass estimation). By leveraging data from the CAN network that is already being collected for primary vehicle control functions, the system provides mass estimation without requiring additional dedicated measurement devices, making the solution cost-effective and easily implementable
2Measurement precision
If methods using the fundamental principle of dynamics are used, then real-time mass estimation is possible, but the methods require a certain amount of driving time to converge and are imprecise and cumbersome due to requiring numerous parameter estimates and unverified assumptions
Solution Approach 1:
The patent performs preliminary classification of road gradients (uphill, downhill, level) based on the relationship between engine torque and vehicle speed before applying the mass estimation calculation. This preliminary action allows the system to select appropriate parameters and make reasonable assumptions specific to each gradient type, reducing the convergence time and improving precision without requiring extensive driving data collection
Solution Approach 2:
The system dynamically adjusts estimation parameters based on operating conditions. By changing parameters such as rolling resistance coefficients and aerodynamic drag coefficients according to the identified road gradient and vehicle operating state, the system achieves accurate real-time mass estimation without requiring numerous fixed parameters or long convergence periods
3Adaptability or versatility
If traditional estimation methods are used, then mass can be estimated, but the methods cannot account for real-time variations and are inaccurate when the vehicle is on a slope due to stresses on the suspension
Solution Approach 1:
The patent applies the fundamental dynamics equation (F = ma) to model the vehicle's longitudinal motion dynamically, accounting for all forces acting on the vehicle including engine torque, aerodynamic drag, rolling resistance, gravitational force on slopes, and braking forces. By using real-time data from the CAN network to update the model continuously, the system accurately estimates mass under varying conditions including slopes, acceleration, and deceleration, overcoming the static nature of traditional methods
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This method provides a reliable and accurate real-time estimation of vehicle mass, reducing error margins and enabling detailed energy balance analysis, improving fuel consumption management and compliance with authorized laden weight limits.
Implementation Method 1
A method using data from the CAN communication network, incorporating Kalman filters to estimate vehicle speed, slope, and mass in real-time by applying fundamental dynamics equations
Implementation Method 2
an estimate of the total loaded mass of the vehicle, the vehicle speed, and the road gradient at a given instant is determined by applying the fundamental equation of dynamics based on the values of the total vehicle mass, the vehicle speed, and the road gradient at a previous instant
Implementation Method 3
two filters are applied in parallel... allowing the filtering of the vehicle speed and the slope of the road and a second called 'Kalman' allowing the estimation of the mass of the vehicle
Data Source
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AI summary
Method for determining the total mass of a motor vehicle from data of a communication network and vehicle parameters, wherein an estimate is determined of the total laden mass (m v,est ) of the vehicle, the speed of the vehicle (v est ) and the slope of the road (α est ) at a time (k) by applying a fundamental dynamic equation and as a function of the values of the total mass of the vehicle, the speed of the vehicle and the slope of the road at a preceding time (k-1).