Adaptive On-Board Diagnostic System for Vehicle Hardware
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Solution Overview
Problem
On-board diagnostic systems for motor vehicles face a challenge in optimizing the ratio between the number of diagnoses performed and their accuracy, as stringent legal requirements demand frequent diagnoses while maintaining high accuracy, which is compromised by conditions such as engine temperature and vehicle speed, especially in vehicles with short journeys.
Innovation Solution
A method that dynamically adjusts the conditions for performing on-board hardware diagnoses based on vehicle use, using a set of calibrations that adjust the level of accuracy in steps to ensure compliance with predetermined diagnosis ratios, allowing for more diagnoses while maintaining accuracy by modifying conditions such as coolant temperature and vehicle speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the conditions for performing on-board hardware diagnoses are made stringent (e.g., requiring high engine temperature and specific vehicle speed ranges), then the accuracy of the diagnosis is improved, but the number of times the diagnosis can be performed decreases
Solution Approach 1:
The patent applies dynamics by making the diagnostic conditions adaptive rather than fixed. The system dynamically adjusts the conditions for performing diagnoses based on actual vehicle operating patterns and accumulated data. This allows the conditions to evolve over time, becoming less stringent when sufficient diagnostic coverage is achieved, thereby increasing the number of diagnoses performed while maintaining acceptable accuracy levels through intelligent adaptation.
Solution Approach 2:
The patent changes parameters by modifying the diagnostic conditions (such as temperature thresholds, speed ranges, and duration requirements) based on vehicle usage patterns. The system monitors actual operating conditions and adjusts the parameters of diagnostic thresholds to optimize the balance between accuracy and frequency. This parameter adaptation enables more diagnoses to be performed under varying operational contexts.
2Productivity
If the conditions for performing on-board hardware diagnoses are relaxed to increase the number of diagnoses, then the productivity increases, but the accuracy of the diagnosis deteriorates
Solution Approach 1:
The system dynamically adapts diagnostic conditions based on accumulated vehicle usage data and actual operating patterns. Rather than using fixed stringent conditions, the system evolves the diagnostic thresholds over time, adjusting them to be less restrictive when sufficient diagnostic coverage is achieved. This dynamic approach enables increased diagnostic frequency while maintaining acceptable accuracy through intelligent adaptation to real-world usage.
Solution Approach 2:
The patent modifies diagnostic parameters (temperature thresholds, speed ranges, duration requirements) based on observed vehicle usage patterns. The system monitors actual operating conditions and adjusts these parameters to optimize the balance between diagnostic frequency and accuracy. This parameter adaptation allows the system to perform more diagnoses by adjusting thresholds to match actual operational realities.
3Measurement precision
If fixed stringent conditions are used for on-board hardware diagnoses, then the diagnosis accuracy is maintained, but the system cannot adapt to different vehicle usage patterns (e.g., short journeys at low speed)
Solution Approach 1:
The patent applies dynamics by transforming fixed diagnostic conditions into adaptive ones that evolve based on vehicle usage patterns. The system monitors actual operating conditions over time and adjusts diagnostic thresholds accordingly. This enables the system to adapt to different usage scenarios such as short journeys, low-speed operation, or varying temperature conditions, while maintaining diagnostic accuracy through intelligent adaptation rather than rigid fixed conditions.
Solution Approach 2:
The system performs self-service by automatically monitoring its own diagnostic performance and adapting its conditions based on accumulated data. It uses feedback from actual vehicle operation to self-adjust the diagnostic thresholds and conditions, eliminating the need for manual reconfiguration. This self-adaptation enables the system to handle diverse usage patterns autonomously while maintaining diagnostic quality.
Data Source
AI summary
An automatic calibration method capable of adapting the set of conditions for performing hardware diagnostics, in an OBD system, with a view to optimizing the compromise between the number of diagnostics performed, in particular in order to comply with legislation relating to the ratio between the number of diagnostics performed on a component and the number of operating cycles, and the accuracy of the diagnostics.