Vehicle Parameter Estimation via Residual Detection
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Solution Overview
Problem
Current vehicle control systems face challenges in accurately estimating changes in vehicle mass, rolling resistance, air resistance, and road gradient, leading to suboptimal control decisions that can result in higher fuel consumption, increased wear, and safety risks due to reliance on inaccurate parameter values.
Innovation Solution
A method using a longitudinal vehicle model and residual-based detection to quickly identify changes in these parameters, allowing for rapid recalibration of estimation algorithms and providing indications for control systems to adjust calculations accordingly, thereby ensuring more robust decision-making.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If estimation algorithms continuously update vehicle parameters, then measurement precision improves, but computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary detection using a residual-based method to identify when parameter changes occur. Instead of continuously updating estimations, the system only triggers recalibration of estimation algorithms when a change is detected, reducing computational complexity while maintaining measurement precision.
Solution Approach 2:
The system uses feedback from residual analysis to control the estimation process. When residuals indicate a parameter change, the system triggers an update; when residuals are within normal ranges, the system maintains current estimates. This feedback mechanism optimizes the balance between accuracy and computational effort.
2Measurement precision
If parameter estimation is performed frequently, then measurement precision improves, but loss of time increases due to continuous calculations
Solution Approach 1:
The residual-based detection method serves as a preliminary filter that quickly identifies when parameter changes occur. This preliminary action avoids the need for time-consuming continuous estimation calculations, as updates are only performed when necessary.
Solution Approach 2:
Instead of continuous estimation updates, the system employs periodic action by triggering estimations only at specific moments when parameter changes are detected. This periodic approach significantly reduces processing time while maintaining estimation accuracy.
3Device complexity
If the system uses simple estimation methods, then device complexity is reduced, but measurement precision deteriorates leading to suboptimal control decisions
Solution Approach 1:
The system segments the parameter estimation process into two distinct parts: a simple residual-based detection stage and a more complex estimation stage. The segmentation allows the system to use simple methods for monitoring while reserving complex estimation techniques for when they are actually needed, thus maintaining both simplicity and precision.
Solution Approach 2:
The residual-based detection method acts as an intermediary between simple monitoring and complex estimation. This intermediary layer filters when complex estimation is necessary, allowing the system to maintain simplicity in normal operation while ensuring precision when parameter changes occur.
4Measurement precision
If the system triggers estimation updates immediately upon detecting changes, then measurement precision improves, but productivity decreases due to frequent recalibrations
Solution Approach 1:
The residual-based detection serves as a preliminary verification step before triggering full estimation updates. This preliminary action filters out false positives and unnecessary updates, ensuring that productivity is not compromised by excessive recalibrations while still maintaining measurement precision when genuine changes occur.
Data Source
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AI summary
The present invention presents a method and a system for the management of changes in actual values for parameters that impact a driving resistance force F drivingres for a vehicle. The system comprises a model device, arranged to define a longitudinal vehicle model, wherein the model comprises representations of forces with horizontal effect on the vehicle in a valid driving condition. The system also comprises a determination device, arranged to determine whether a change in one or several of a representation of an actual rolling resistance force F act roll an actual mass m act for the vehicle has occurred, by determining whether the representation of the forces with horizontal effect on the vehicle cancel each other out when an estimated mass m est and a representation of an estimated rolling resistance force F est roll are introduced into the vehicle model. The system also comprises a utility device, arranged to use the determination of whether or not a change has occurred.