Algorithm to generate Synthetic Driving Cycle using Real driving data

Examensarbete för masterexamen
Nagaraj, Subramanya
The growth of technology has led to much increase in pollution levels. The European Union has enforced strict rules for car manufacturers to reduce the emission levels for vehicles. The regulation of the European Union includes a test for Real Driving Emissions. The automobile manufacturers are forced to test their vehicles for Real Driving Emissions. The available driving cycles like WLTC or NEDC lack real-world driving characteristics. This makes it is highly essential to develop a driving cycle by using real driving data. An algorithm is created to produce a driving cycle delivering the parameters within the Real Driving Emissions test parameters. In this master thesis, micro-trip based construction model is applied for the vast data collected from real driving trips. The process includes use of unsupervised learning algorithm by utilizing k-means clustering technique to group the data. The statistical CH index is used to evaluate the performance of clustering and the trips are filtered with the Real Driving Emissions parameters before deploying D-optimal design to maximize the created design matrix from the filtered data. The microtrips are selected in a ratio of 7:1:1 with urban, rural and motorway sections to stay within the required duration limits. The selected micro-trips are combined to form complete driving cycles, and are simulated using a simulation model constructed by using QSS toolbox in Simulink. The model comprises a normal IC engine with manual transmission, capable enough to determine the fuel consumption. The developed driving cycles are analyzed and their parameters are compared with real driving emission test criteria. The results show that the cycles are valid. The results of the simulation are dependent on the engine operating points. The transmission model needs to be calibrated and evaluated with the real scenario to increase accuracy. The regression analysis carried forward from the simulations, predicts the relation of VApos with fuel consumption. The aggressiveness of the cycle tends to increase fuel consumption. Hence, it helps to understand the variation in fuel consumption based on the driving cycle parameters
Micro trip based construction model, K-means clustering, CH Index, D-optimal design, Driving cycle, fuel consumption
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