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Speed-Acceleration Probability Distributions (a) full dataset, (b) micro-trip method, (c) Markov chain method
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Due to the technological progress, new approaches such as model-based design are spreading in the development process in the automotive industry to meet the increased requirements related to lower fuel consumption and reduced emission. This work is part of a research project which focuses on dynamic modeling of vehicles aimed at analyzing and optim...
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... we can see from the results in Table 2, both driving cycles have similar RMS error, but the driving cycle constructed with the Markov chain method has lower QoF value than the driving cycle constructed with the microtrip method. Figure 3 shows the SAPD of the driving pattern and the SAPDs of the two driving cycles obtained with the micro-trip method and the Markov chain method, respectively. It can be seen that both SAPDs look similar to the SAPD of the driving pattern, but level of similarity is higher in the case of the Markov chain method. ...
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... Using a driving cycle dataset, the average cruising speed can be found, which represents the closest steady state of that operation and can be applied for drag loss calculations. Vámosi et al. [17,18] mentioned that in the city of Debrecen, the average cruising speed is 19.197 km/h. The scope of this assessment was to examine how much could a perpendicular wind load alter the drag losses of a city bus. ...
... Using a driving cycle dataset, the average cruising speed can be found, which represents the closest steady state of that operation and can be applied for drag loss calculations. Vámosi et al. [17,18] mentioned that in the city of Debrecen, the average cruising speed is 19.197 ...
... Based on the literature review [17][18][19][20], common cruising and wind speeds in Debrecen were chosen as the input parameters. The model of the bus was created based on the schematics provided by the Public Transportation Company of Debrecen (DKV ltd.). ...
The drag coefficient is usually considered to be a constant value, which allows us to calculate the aerodynamic losses. However, at lower speeds and wind, this value could be distorted. This also applies to buses in urban environments where due to traffic, the speed is relatively low. Since the schedule of the buses is fixed, based on the driving cycle, they travel at a nominal cruising speed. This makes it possible to examine the drag losses in a quasi-steady condition. To find the magnitude of this distortion in losses, a large-eddy simulation method was used with the help of commercially available software. Symmetrical and asymmetrical flows were induced into the digital wind tunnel to assess the distribution of the forces in the cruising direction and examine the flow patterns. It was discovered that the drag forces behave differently due to the low speeds, and calculations should be performed differently compared to high-speed drag evaluations.
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