• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.4
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• pp.60-67
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• 2012

Most of a driving cycle is used to measure fuel consumption (FC) and emissions for a specified vehicle. A driving cycle was reflected geography and traffic characteristics for each country, also, driving pattern is affected these parameters such as vehicle dynamics, FC and emissions. Therefore, this study is an attempt to develop a driving cycle for military operational area. The proposed methodology the driving cycle using micro-trips extracted from real-world data. The methodology is that the driving cycle is constructed considering important parameters to be affected FC. Therefore, this approach is expected to be a better representation of heterogeneous traffic behavior. The driving cycle for the military operational area is constructed using the proposed methodology and is compared with real-world driving data. The running time and total distance of the final cycle is 1461 s, 13.10 km. The average velocity is 32.25 km/h and average grade is 0.43%. The Fuel economy in the final cycle is 5.93 km/l, as opposed to 6.10 km/l for real-world driving. There were about 3% differences in driving pattern between the final driving cycle and real-world driving.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.117-126
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• 2014

Recently, due to various environmental problems such as global warming, increasing of international oil prices and exhaustion of resource, a paradigm of world automobile market is rapidly changing from vehicles using internal combustion engine to eco-friendly vehicles using electric power such as EV (Electric Vehicle), HEV (Hybrid Electric Vehicle), PHEV (Plug-in Hybrid electric Vehicle) and FCEV (Fuel Cell Electric Vehicle). There are many driving cycles for performance evaluation of conventional vehicles. However there is a lack of researches on driving cycle for EV. This study is composed of part 1 and part 2. In this paper part 1, in order to develop urban driving cycle for performance evaluation of electric vehicles, Gwacheon-city patrol route of police patrol car was selected. Actual driving test was performed using EV. The driving data such as velocity, time, GPS information etc. were recorded. GUDC-EV (Gwacheon-city Urban Driving Cycle for Electric Vehicles) including road gradient was developed through the results of analyzing recorded data. Reliability of the driving cycle development method was substantiated through comparison of electricity performance. In the second part of this study, the developed driving cycle was compared to simulation result of the existing urban driving cycle. Verification of the developed driving cycle for EV performance evaluation was described.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.2
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• pp.161-168
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• 2015

Recently, due to various environmental problems such as global warming, increases of international oil prices, exhaustion of resource, a paradigm of world automobile market is rapidly changing from conventional vehicles using internal combustion engine to eco-friendly vehicles using electric power such as EV, HEV, PHEV and FCEV. Generally, in order to measure fuel consumption and pollutant emissions of cars, chassis dynamometer tests are performed on various driving cycles before actual driving test. There are many driving cycles for performance evaluation of conventional vehicles. However, there is a lack of researches on driving cycle for EV. In this study, the urban driving cycle for performance evaluation of electric vehicles was developed. This study is composed of two parts. In the part 1, the urban driving cycle 'GUDC-EV(Gwacheon-city Urban Driving Cycle for Electric Vehicles)' was developed by using driving data, which were obtained through actual driving experiment, and statistic analysis with chronological table. In this paper part 2, in order to verify the developed driving cycle GUDC-EV, virtual EV platforms were configured and simulations were performed with actual driving data using In addition, simulation results were compared with existing driving cycles such as FTP-72, NEDC and Japan 10-15.

• Journal of Electrical Engineering and Technology
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• v.13 no.4
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• pp.1578-1585
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• 2018

This work investigates two different motor drive technologies, switched reluctance motor (SRM) and induction motor (IM). They are designed optimally to meet the desired performances for electric scooters. The comparison of both motors is described in terms of performances and material cost. With the similar constraint, induction motor performs slightly better than switched reluctance motor. But this must be traded-off with higher weight and cost. Both drive systems are, however, suitable for electric scooter application. Finally, the range simulations are conducted on a European urban driving cycle, ECE15 driving cycle and a more realistic cycle, Bangkok driving cycle. The e-scooter ranges are varied from 36 to 109 km depending on driving cycle, motor technology and number of passengers.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.5
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• pp.8-13
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• 2020

In this paper, the electric vehicle (EV) and internal combustion engine vehicle (ICEV) are compared for different driving cases. The EV exhibits a lower powertrain efficiency when driven on the aggressive driving cycle than when driven on the moderate cycle. In particular, EV powertrain efficiency is low when the battery state of charge (SOC) is low, but ICEV efficiency increases when the driving cycle changes from the moderate cycle to the aggressive cycle. Based on these results, attempts can be made to increase EV powertrain efficiency. EV charging before the battery power drops to a low charging state can reduce energy consumption by 2.7% for an urban area. Furthermore, ECO driving has a more significant effect on EVs than on ICEVs.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.3
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• pp.105-112
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• 2013

In this paper, EV (Electric Vehicle) and ICE (Internal Combustion Engine) vehicle simulators are developed to compare maximum driving range of EV and ICE vehicle according to different driving patterns. And, simulations are performed for fourteen constant velocity cases (20, 30, 40, ${\ldots}$, 150 km/h) and four different driving cycles. From the simulation results of constant velocity, it is found that the decreasing rate of maximum driving range for EV is larger than the one for ICE as both the vehicle velocity and the driving power increase. It is because the battery efficiency of EV decreases as both the velocity and the driving power increase, whereas the engine and transmission efficiencies of ICE vehicle increase. From the results of four driving cycle simulation, the maximum driving range of EV is shown to decrease by 50% if the average driving power of driving cycle increases from 10 to 20kW. It is because the battery efficiency decreases as the driving power increases. In contrast, the maximum driving range of ICE vehicle also increases as the average driving power of driving cycle increases. It is because the engine and transmission efficiencies also increase as the driving power increases.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.4
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• pp.454-462
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• 2016

This study was conducted in order to estimate the exhaust emissions analysis method of the real driving emission(RDE). The Association for Emissions Control by Catalyst(AECC) has developed a test procedure by using a random cycle method based on the chassis dynamometer. In order to confirm this approach in Korea, Euro 5(DPF), Euro 6(DPF + LNT), and Euro 6(DPF + SCR) were performed on three different vehicles to determine the exhaust gas characteristics of the random cycle, real-road driving test(PEMS), and emission certification driving mode(NEDC). Six different random cycle driving modes were generated by the vehicle specifications(e.g. curb weight, engine power, gear ratio, and maximum acceleration). The NOx emissions were increased in the NEDC, random cycle, and PEMS order in this study regardless of the test vehicles. The random cycle method has the advantage because it utilizes a chassis dynamometer in the laboratories for a repeatable data collection, and it allows any eminent emission improvement checked prior to a real-road driving test with PEMS.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.505-510
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• 2000

Vehicle performances such as fuel consumption and catalyst-out emissions are affected by a driving pattern, which is defined as a driving cycle with the grade in this study. We developed an algorithm to recognize a current driving pattern by using a neural network. And this algorithm can be used in adapting the driving control strategy to the recognized driving pattern. First, we classified the general driving patterns into 6 representative driving patterns, which are composed of 3 urban driving patterns, 2 suburban driving patterns and 1 expressway driving pattern. A total of 24 parameters such as average cycle velocity, positive acceleration kinetic energy, relative duration spent at stop, average acceleration and average grade are chosen to characterize the driving patterns. Second, we used a neural network (especially the Hamming network) to decide which representative driving pattern is closest to the current driving pattern by comparing the inner products between them. And before calculating inner product, each element of the current and representative driving patterns is transformed into 1 and -1 array as to 4 levels. In the end, we simulated the driving pattern recognition algorithm in a temporary pattern composed of 6 representative driving patterns and, verified the reliable recognition performance.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.6
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• pp.123-134
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• 2013

Strengthening vehicle emission regulation is one of important policies to improve air quality in urban area. Due to the limitation of specified driving cycles for certification test to reflect real driving conditions, additional off-cycle emission regulations have been adopted in US and being developed in Europe. The driving cycles of US or Europe have been used in emission certification for Korean light-duty vehicles, but it has not been known how well the driving cycles reflect various real driving patterns in Korea. In that point of view, it is required to estimate vehicle emission based on real road driving conditions to raise the effectiveness of vehicle emission regulation in Korea. In this study, real driving emission measurements have been conducted for three Korean light-duty vehicles with PEMS. The driving routes consisted of urban, rural and motorway in Seoul and Incheon. The data have been analyzed with various averaging methods including moving averaging windows method and compared to emission limits set with emission certification modes applied to tested vehicles. The results have shown that the real driving pollutant emissions of a gasoline and a LPG vehicles have been ranged quite lower than those of emission limits on CVS-75 driving cycle. But real driving NOx of a light duty diesel vehicle has been considerably higher than emission limit of NEDC driving cycle. The higher than expected NOx emission of a diesel vehicle might be caused by different strategy to control EGR in real driving condition from NEDC driving.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1934-1944
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• 2017

As a key component of high-voltage power conversion system for electric vehicles (EVs), bidirectional DC/DC (Bi-DC/DC) is required to have high efficiency and light weight. Conventional design methods optimize the Bi-DC/DC at the maximum power dissipation point (MPDP). For EVs application, the work condition of the Bi-DC/DC is not strict as the MPDP, where the design method using MPDP may not be optimal during travel of EVs. This paper optimizes the Bi-DC/DC converter targeting efficiency and weight based on the driving cycle. By analyzing the two-phase interleaved Bi-DC/DC for hybrid energy storage systems (HESS) of EVs, its power dissipation is calculated, and an efficiency model is derived. On this basis, weight models of capacitor, inductor and heat sink are built, as well as a dynamic temperature model of heat sink. Based on these models, a method using New European Driving Cycle (NEDC) for optimal design of Bi-DC/DC which simultaneously considered efficiency and weight is proposed. The simulation result shows that compare with conventional optimization methods revealed that the optimization approach based on driving cycle allowed significant weight reduction while meeting the efficiency requirements.