Development of processing methodologies used to form complete driving-cycle dynamometer tests based on urban on-road driving and road gradient data

Abstract

This work involves the development of 'real-world' driving cycles using on-road data from the greater area of Athens, Greece. Two different methodologies of driving-cycle development are presented here, based on the MATLAB code. Driving patterns from various test vehicles were processed to form driving cycles consisting of a specific number of driving periods. The main criterion for the design and acceptance of each method was the correlation of the resulting driving cycles with the corresponding characteristics of the processed road data. Time is an important issue when processing data. Applying the same set of data to both methods, method A needed 5 h to complete the processing compared with 9 s for method B. The corresponding average accuracies were 99.9% and 97.2% respectively. The road gradient was taken into account for the development of the driving cycles. For method A, the road gradient was included in the final result by intervening in the mean positive accelerations and the mean negative accelerations. Those were increased by 11.99% and 16.06% respectively for the Greek Urban Driving Cycle for Motorcycles. For method B, the road gradient was assimilated by a 'load cycle' expressed in kilowatts and used in combination with the developed driving cycle, thus forming a complete chassis dynamometer test. The load cycle was produced by calculating the power related to the vehicle's mass that holds or assists it when driving uphill or downhill. The Greek Urban Driving Cycle for Motorcycles was developed using method A. Its characteristics include the follwoing: a duration of 822 s, an average speed of 28.86 km/h, a stop-time percentage of 12.04 and a positive acceleration of 0.6065 m/s 2 . Method B was used for the Greek Urban Driving Cycle for passenger cars. Some of its characteristics include the following: a duration of 1175 s, an average speed of 18.67 km/h, a stop-time percentage of 31.66 and a positive acceleration of 0.692 m/s 2 .

Development of a representative operation cycle characterized by dual time series for bulldozers

“…14–17 A similar on-board measurement method was applied to collect operation data for a bulldozer in this research, the difference being that the operation data are obtained from the bulldozer’s CAN bus rather than an extraneous GPS device. 20 Also, the tested bulldozer has a hybrid electric powertrain with two independent motor-driven tracks, as shown in Figure 1. Because bulldozing processes in various bulldozers are approximately identical, the type of bulldozer powertrain does not affect collection of the data of the operation cycle.…”

“…Several parameters concerning the speed–time characteristics, such as the average speed and the average acceleration, are often utilized to characterize and develop the driving cycles of vehicles. 16–22 However, there are dual time series requiring characterization for the operation cycle of a bulldozer, namely one speed–time series and one resistance–time series. Thus, some new parameters should be introduced to represent the characteristic parameter called the bulldozing resistance, in addition to the characteristic parameters of the speed–time profile.…”

“…They are also known as synthesized and real-world driving cycles respectively. 19–21 Modal driving cycles (e.g. the ECE driving cycle, the EUDC, the New European Driving Cycle and the Japan 10–15 Driving Cycle) are synthesized according to various driving modes including parking, idling, constant acceleration, deceleration and speed.…”

“…In most studies, Global Positioning System (GPS) devices are applied to collect the vehicle speed data at defined time intervals to form the basic speed–time profiles. 20–25 This requires an additional measuring device and data storage device. The collection of operation force data must also be considered.…”

A genetic ant colony algorithm-based driving cycle generation approach for testing driving range of battery electric vehicle

“…Driving cycle is a simulation of speed and time profile, which is used to describe the characteristics of a vehicle in a specific region or traffic condition. [1][2][3] As an important common technology in the automotive industry, driving cycle is the basis of vehicle energy consumption calculation, emission testing methods and limits standards determination. 4,5 Its main purpose is to determine technical development and evaluation of new models 6 and to ascertain the risk of traffic control.…”