Real Driving Emissions—Event Detection for Efficient Emission Calibration

Abstract

The systematic analysis of measurement data allows a large amount of information to be obtained from existing measurements in a short period of time. Especially in vehicle development, many measurements are performed, and large amounts of data are collected in the process of emission calibration. With the introduction of Real Driving Emissions Tests, the need for targeted analysis for efficient and robust calibration of a vehicle has further increased. With countless possible test scenarios, test-by-test analysis is no longer possible with the current state-of-the-art in calibration, as it takes too much time and can disregard relevant data when analyzed manually. In this article, therefore, a methodology is presented that automatically analyzes exhaust measurement data in the context of emission calibration and identifies emission-related critical sequences. For this purpose, moving analyzing windows are used, which evaluate the exhaust emissions in each sample of the measurement. The detected events are stored in tabular form and are particularly suitable for condensing the collected measurement data to a required amount for optimization purposes. It is shown how different window settings influence the amount and duration of detected events. With the example used, a total amount of 454 events can be identified from 60 measurements, reducing 184,623 s of measurements to a relevant amount of 12,823 s.