OBD robustness test evaluation method and device

Abstract

The invention discloses an OBD robustness test evaluation method. The method comprises the steps that faults are continuously monitored and diagnosed and faults are periodically monitored and diagnosed; for continuous monitoring and diagnosis of faults, the statistical analysis method comprises the following steps: S1, extracting a fault monitoring enabling state from vehicle data; S2, performingtime synchronization and resampling processing on the extracted signals; and S3, performing probability distribution calculation on the extracted and processed signals, generating a probability distribution histogram, obtaining a false alarm rate or a missing report rate of the fault according to a calculation result, and evaluating the OBD robustness level. In the OBD calibration development stage, the situation of a large sample vehicle in actual use can be simulated by using a small number of sample vehicles, and OBD robustness test evaluation and calibration data optimization are completed. The fault false alarm rate and the missing report rate in actual use of vehicles after large-batch putting on the market are reduced, the maintenance cost is reduced, and manpower is saved.

Description

technical field [0001] The invention belongs to the field of on-board automatic diagnosis systems, and more specifically relates to an OBD robustness test and evaluation method. At the same time, the invention also relates to an OBD robustness test and evaluation device. Background technique [0002] The vehicle OBD system is used for fault diagnosis, through a certain diagnostic algorithm, to determine whether the vehicle has a fault. In the actual use of vehicles, due to the influence of multiple factors such as the consistency of parts production, vehicle operating environment, operating conditions, OBD calibration data, etc., the OBD system will have certain fault false positives and false negatives, and reduce false positives and false positives. It is very important to save maintenance costs and ensure the healthy operation of vehicles by increasing the reporting rate and improving the robustness of OBD. The test and evaluation of OBD robustness usually requires a la...

AI Technical Summary

Problems solved by technology

The test and evaluation of OBD robustness usually requires a large number of samples for statistical analysis, but in the calibration development, there is a lack of support for a large number of sample vehicles, and generally only a few samples are tested and evaluated, resulting in the inability to well cover the actual use of the vehicle. Therefore, , we propose an OBD robustness test and evaluation method and device

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