Vehicle Mass Estimation via Acceleration Comparison
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Solution Overview
Problem
Existing vehicle systems face challenges in accurately estimating the mass of a vehicle, including its contents such as passengers and cargo, due to changing weights over time, which can impact the performance of systems like adaptive cruise control and automated lane change systems.
Innovation Solution
A method that determines the expected and actual acceleration of a vehicle during automated acceleration events, using sensor data from speed, trailer, environmental, and incline sensors, to calculate a vehicle mass estimate by comparing these values and adjusting for influencing factors like trailers and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle mass is estimated using fixed manufacturer data (tare weight, curb weight, GVWR), then the system is simple to implement, but the mass estimate becomes inaccurate over time as fuel, passengers, and cargo change
Solution Approach 1:
The system uses feedback by continuously monitoring actual vehicle acceleration during autonomous events and comparing it to expected acceleration. This comparison provides feedback about the actual vehicle mass, which is used to update the mass estimate over time, improving accuracy without requiring direct mass measurement sensors
Solution Approach 2:
The system performs self-service by using the vehicle's own operational data (acceleration during autonomous events) to automatically update and refine its mass estimate. No external calibration or manual input is required - the system learns from its own operation
2Measurement precision
If the system collects and processes multiple sensor inputs (speed, trailer, environmental, incline sensors), then the mass estimate accuracy improves, but the processing complexity and computational load increase
Solution Approach 1:
The system performs preliminary actions by pre-calculating expected acceleration based on vehicle dynamics models and stored manufacturer data before autonomous events occur. This allows the system to quickly compare expected versus actual acceleration during events without complex real-time processing
Solution Approach 2:
The system segments the mass estimation process into distinct phases: collecting sensor data during autonomous events, processing the acceleration comparison, and updating the mass estimate. This segmentation allows complex processing to occur in manageable stages rather than requiring simultaneous complex computations
3Adaptability or versatility
If the system uses acceleration comparison during autonomous events to estimate mass, then the mass estimate updates dynamically, but the system requires sufficient autonomous events to occur for accurate estimation
Solution Approach 1:
The system maintains continuity of useful action by continuously collecting and processing acceleration data during every autonomous event that occurs. Each event contributes to refining the mass estimate, and the system is designed to accumulate data over time without requiring specific trigger conditions beyond normal autonomous operation
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 more accurate vehicle mass estimate, improving the performance of vehicle systems by generating precise command signals, reducing errors in vehicle operation, and enhancing safety and control mechanisms.
Implementation Method 1
determining an expected acceleration of the vehicle; determining an actual acceleration of the vehicle; comparing the expected acceleration to the actual acceleration; and using the comparison of the expected acceleration to the actual acceleration to determine a vehicle mass estimate
Data Source
AI summary
A vehicle system and method that estimates or approximates the mass of a vehicle so that a more accurate vehicle mass estimate can be made available to other vehicle systems, such as an adaptive cruise control (ACC) system or an automated lane change (LCX) system. In an exemplary embodiment, the method compares an actual acceleration of the vehicle to an expected acceleration while the vehicle is under the control of an automated acceleration event. The difference between these two acceleration values, along with other potential input, may then be used to approximate the actual mass of the vehicle in a way that takes into account items such as passengers, cargo, fuel, etc. Once an accurate vehicle mass estimate is generated, the method may make this estimate available to other vehicle components, devices, modules, systems, etc. so that their performance can be improved.