Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas

Brady, John; O’Mahony, Margaret

doi:10.1016/j.apenergy.2016.05.094

Cited by 185 publications

(103 citation statements)

References 53 publications

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“…In order to get a valid comparison of the performances of the system, the same travelled distance has been determined with a separate measuring system based on the MATLAB Mobile package of MATLAB (version 4.1.1, The MathWorks, Inc., Natick, MA, USA) which supports the acquisition of data from built-in sensors of an Android device. Such approach is similar to the one used in [54].…”

Section: Hill Climbing Forcementioning

confidence: 95%

“…The battery has to provide energy for the traction, counteracting resistive forces. For the sake of simplicity, we suppose that the route is inside a city, without major changes in elevation and the velocity of the EV can be described by the Simplified Federal Urban Driving Schedule cycle (SFUDS, Figure 8), which are a series of tests defined by the US Environmental Protection Agency (EPA) to measure tailpipe emissions and fuel economy of cars [51], but other interesting approach can be found in [52][53][54], in which the driving cycles are based on future driving information with a stochastic simplicity, we suppose that the route is inside a city, without major changes in elevation and the velocity of the EV can be described by the Simplified Federal Urban Driving Schedule cycle (SFUDS, Figure 8), which are a series of tests defined by the US Environmental Protection Agency (EPA) to measure tailpipe emissions and fuel economy of cars [51], but other interesting approach can be found in [52][53][54], in which the driving cycles are based on future driving information with a stochastic prediction method based on the Markov approach or on a telematics technology-based approach, requiring global positioning system (GPS) and intelligent transportation system (ITS) information . During the route, there are moments with high load and moments with almost zero load.…”

Section: Hill Climbing Forcementioning

confidence: 99%

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Designing a Sustainable University Recharge Area for Electric Vehicles: Technical and Economic Analysis

Miceli

Viola

2017

Energies

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This article addresses the technical and economic challenges regarding the design of a "green" recharge area for electric vehicles at the University of Palermo in order to reduce costs and pollution connected to the charging process. Based on the behaviour of the student population, the electrical load is identified and two possible solutions are evaluated to manage the peak load: an orientation of the panels increasing the power at defined time and the use of a storage system. The main strength and weakness points of two systems are investigated by taking into account the Levelized Cost of Energy (LCOE), which reaches 75.3 €/MWh for the orientation of panel and 103 €/MWh for the storage system. Furthermore, the cost of the topologies of power plant and the cost of energy are discussed in depth.

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Section: Hill Climbing Forcementioning

confidence: 95%

Section: Hill Climbing Forcementioning

confidence: 99%

Designing a Sustainable University Recharge Area for Electric Vehicles: Technical and Economic Analysis

Miceli

Viola

2017

Energies

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“…The process of the synthesis of driving cycles requires then not only the registration of data from vehicle traffic tests in real road conditions represented by urban and extraurban driving, but also the characteristics of the network of streets and roads in the region. Another application of real driving cycles concerns the design of power management algorithms, which is particularly important in the case of hybrid and electric vehicles [2,5].…”

Section: Introductionmentioning

confidence: 99%

Stochastic simulation and validation of Markov models of real driving cycles

Puchalski¹,

Komorska²

2019

Diagnostyka

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The driving cycle is a time series of the vehicle speed reflecting its movement in real road conditions. The WLTC (Worlwide harmonized Light duty Test Cycle) valid from August 2017 is based on real driving profiles derived from research and statistical analysis of road traffic in Europe, the USA and Asia. In addition to certification and comparative research, driving cycles are used in the process of virtual design of drive systems and embedded control algorithms, traffic management or intelligent road transport. Regardless of the intended use, the standard test does not guarantee the correctness of the results obtained. There is a need to generate many different driving cycles that meet the established equivalence conditions. The article discusses the methods of stochastic simulation and assessment of equivalence of obtained traffic models. The Monte Carlo method of Markov chains was used in the research. The comparative criteria are defined using the statistical parameters of the vehicle speed time series and the corresponding multifractal spectra. The synthesis was carried out at predetermined length of time series. The experiment carried out involved the study of traffic in real road conditions of urban driving and extra-urban large agglomerations during working days.

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“…Sensitivity of UF to different vehicle attributes such as age, class, annual VMT, and charging behavior depending on dwelling unit type is examined, and their analyses indicates that UF is largely insensitive to vehicle class and dwelling unit type, but highly sensitive to annual VMT, age, and charging behavior [14]. With the availability of real-world driving data collected using loggers albeit from ICEs, efforts have been undertaken to develop a more realistic PHEV driving cycle compared to dynamometer cycles [10] in order to better estimate their real-world energy consumption and emissions [15]. The scope of such efforts expanded by incorporating additional charging opportunities based on dwelling times and location.…”

mentioning

confidence: 99%

Influence of User Preferences on the Revealed Utility Factor of Plug-In Hybrid Electric Vehicles

Raghavan¹,

Tal²

2019

WEVJ

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Plug-in hybrid electric vehicles (PHEVs) are an effective intermediate vehicle technology option in the long-term transition pathway towards light-duty vehicle electrification. Their net environmental impact is evaluated using the performance metric Utility Factor (UF), which quantifies the fraction of vehicle miles traveled (VMT) on electricity. There are concerns about the gap between Environmental Protection Agency (EPA) sticker label and real-world UF due to the inability of test cycles to represent actual driving conditions and assumptions about their driving and charging differing from their actual usage patterns. Using multi-year longitudinal data from 153 PHEVs (11-53 miles all-electric range) in California, this paper systematically evaluates how observed driving and charging, energy consumption, and UF differs from sticker label expectations. Principal Components Analysis and regression model results indicated that UF of short-range PHEVs (less than 20-mile range) was lower than label expectations mainly due to higher annual VMT and high-speed driving. Long-distance travel and high-speed driving were the major reasons for the lower UF of longer-range PHEVs (at least 35-mile range) compared to label values. Enhancing charging infrastructure access at both home and away locations, and increasing the frequency of home charging, improves the UF of short-range and longer-range PHEVs respectively. 3 of 30 than 65 miles. While the J2841 UF is strictly a distance based metric, Ref. [14] proposes an energy based UF. Sensitivity of UF to different vehicle attributes such as age, class, annual VMT, and charging behavior depending on dwelling unit type is examined, and their analyses indicates that UF is largely insensitive to vehicle class and dwelling unit type, but highly sensitive to annual VMT, age, and charging behavior [14]. With the availability of real-world driving data collected using loggers albeit from ICEs, efforts have been undertaken to develop a more realistic PHEV driving cycle compared to dynamometer cycles [10] in order to better estimate their real-world energy consumption and emissions [15]. The scope of such efforts expanded by incorporating additional charging opportunities based on dwelling times and location. High resolution GPS enabled travel data collected over a span of 18 months from 400 ICEs in the Seattle metropolitan area is utilized in [16] to investigate how UF would change if only home based tours are considered. Their study reports that gasoline and electricity prices have no statistically significant impact on the UF, and that workplace or away from home charging increases the UF only if the AER of PHEV is less than 40 miles. Studies also applied UF by utilizing longitudinal data from ICEs for evaluating the life-cycle costs, emissions, and value proposition of PHEVs [17,18], and optimal battery size design and its impact on market acceptance [19,20].Around early 2011, a nationwide PEV demonstration and charging infrastructure deployment was undertaken as part of the EV project...

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Development of a driving cycle to evaluate the energy economy of electric vehicles in urban areas

Cited by 185 publications

References 53 publications

Designing a Sustainable University Recharge Area for Electric Vehicles: Technical and Economic Analysis

Designing a Sustainable University Recharge Area for Electric Vehicles: Technical and Economic Analysis

Stochastic simulation and validation of Markov models of real driving cycles

Influence of User Preferences on the Revealed Utility Factor of Plug-In Hybrid Electric Vehicles

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