Vehicle Mass Estimation Using CAN Data on Road Slopes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for estimating the mass of heavy goods vehicles are inaccurate and expensive, especially when the vehicle is on a slope, and existing solutions require extensive parameters and assumptions that are not always satisfied, leading to high error margins and inability to provide real-time energy balance.
Innovation Solution
A method using data from the CAN communication network, including vehicle parameters and sensors, applies Kalman filters to estimate the total mass of the vehicle by determining motive force, aerodynamic friction, rolling resistance, and gravitational force, while accounting for slope and speed, to provide a reliable real-time estimation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct measurement of mass or estimation with attitude sensors and suspension stiffness is used, then mass estimation is possible, but the cost is high and accuracy is insufficient due to manufacturing variability
Solution Approach 1:
The patent replaces direct mechanical measurement systems (scales, load cells) and complex sensor systems (attitude sensors, suspension stiffness sensors) with a computational approach using the fundamental principle of dynamics. The system uses readily available CAN bus data (speed, acceleration, engine torque) combined with aerodynamic and rolling resistance models to calculate mass, eliminating the need for expensive dedicated measurement hardware.
2Adaptability or versatility
If estimation methods using attitude sensors and suspension stiffness are used, then mass can be estimated, but the method cannot estimate mass when the vehicle is situated on a slope
Solution Approach 1:
The patent introduces slope angle as an explicit parameter in the dynamic equation and uses accelerometer data to determine slope conditions. By modifying the fundamental principle of dynamics to include slope compensation (F_gravity = m*g*sin(θ)), the system can accurately estimate mass on slopes, whereas traditional suspension-based methods fail because slope changes alter suspension loading independently of mass changes.
3Ease of operation
If methods applying the fundamental principle of dynamics at a single instant are used, then mass estimation is possible, but the methods require a certain rolling time before converging to accurate estimation and are inaccurate and unwieldy to implement
Solution Approach 1:
The patent uses feedback from multiple sensors (accelerometer, speed sensor, engine torque sensor) continuously fed into the dynamic calculation. The system constantly updates mass estimation based on real-time feedback from these sensors, allowing immediate accurate estimation without requiring rolling time for convergence. The feedback loop compensates for variations in operating conditions and maintains accuracy across different driving scenarios.
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 approach allows for accurate and efficient estimation of the vehicle's mass in real-time, even on slopes, reducing error margins and enabling detailed energy balance analysis, thus improving the management of braking and fuel consumption.
Implementation Method 1
applies Kalman filters to estimate the total mass of the vehicle by determining motive force, aerodynamic friction, rolling resistance, and gravitational force
Implementation Method 2
positioning system, especially of global positioning system type termed 'Global Positioning System' or 'GPS', which uses position data emitted in real time by a constellation of geostationary satellites
Implementation Method 3
the force induced by gravity is determined, at an instant, as a function of the vehicle mass estimated at a previous instant, of the gravity constant and of the slope of the road
Data Source
AI summary
A method determines the total mass of an automotive vehicle on the basis of data of a communication network and parameters of the vehicle, in which an estimation of the total laden mass (mv,est) of the vehicle, of the speed of the vehicle (vest) and of the slope of the road (αest) is determined at an instant (k) by applying the fundamental equation of dynamics and as a function of the values of the total mass of the vehicle, of the speed of the vehicle and of the slope of the road at a previous instant (k−1).