Vehicle Controller Monitoring via Correction Value Extrapolation
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Solution Overview
Problem
Existing vehicle monitoring systems typically react only when emission or control limits are exceeded, leading to unexpected vehicle failures and high follow-up costs, particularly in commercial environments like construction and mining vehicles, where proactive maintenance is often not possible until symptoms appear.
Innovation Solution
A method that determines correction values, records their course, extrapolates them to predict potential errors, and provides timely warnings to drivers or operators, allowing for preventive measures, with the option to perform calculations outside the vehicle in a 'cloud' for fleet management, and displaying the remaining route or time until an error is likely to occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If reactive diagnosis is used (waiting for emission limits or control limits to be exceeded), then the system complexity remains low, but the reliability deteriorates due to unexpected vehicle failures
Solution Approach 1:
The system performs preliminary action by extrapolating the current development trend of correction values to predict future errors before they occur. The control unit calculates whether a future error is likely to occur based on the current course of correction values, enabling preventive maintenance before the actual error manifests, thus improving reliability without requiring complex additional hardware
Solution Approach 2:
The system implements feedback by continuously monitoring correction values and using them to predict future errors. The control unit receives correction values from learning and control functions, evaluates their development trend, and provides feedback about predicted future errors to the driver, creating a closed-loop system that improves reliability through continuous monitoring and prediction
2Reliability
If continuous monitoring and extrapolation are performed in the vehicle, then the reliability improves through early error detection, but the use of energy increases due to continuous calculations
Solution Approach 1:
The system applies partial action by performing extrapolation calculations only when correction values indicate a developing trend toward future errors. Rather than continuously extrapolating all parameters, the control unit selectively processes correction values from learning and control functions that show significant deviations, reducing energy consumption while maintaining reliable error prediction
Solution Approach 2:
The control unit utilizes existing multi-functional capabilities by using the same processing unit that handles emission control and learning functions to also perform error prediction extrapolation. This allows the existing control unit to serve multiple purposes - emission management, learning, and predictive diagnostics - without requiring dedicated additional hardware that would increase energy consumption
3Loss of time
If early warning system is implemented, then the loss of time for maintenance is reduced, but the device complexity increases due to extrapolation functions
Solution Approach 1:
The system implements self-service by using the control unit's existing learning and control functions to generate correction values that are then fed into the extrapolation process. The system serves itself by utilizing its own operational data (correction values from emission control) to predict future errors, eliminating the need for separate dedicated sensing systems and reducing overall device complexity while enabling early warning
Data Source
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AI summary
A method for monitoring a vehicle controller is described. Correction values are determined by means of a correction function. The progression of the correction values is recorded and extrapolated. An error is predicted based on the extrapolated correction values.