A driving cycle for electrically-driven vehicles in Rome

Alessandrini, Adriano; Orecchini, Fabio

doi:10.1177/095440700321700903

Cited by 18 publications

(9 citation statements)

References 6 publications

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“…It must be questioned whether the ADR 81/02 urban test profile typifies urban Australian driving. The development of the much more realistic WLTP is a clear indication that the NEDC testing does not approximate actual driving in the EU, and real [20, 49, 50]. The drive test route used in this research had fewer periods of acceleration, higher levels of acceleration, longer periods of constant speed and a higher average speed than the NEDC/ADR profile, and could be argued to be closer to the drive cycle used in the WLTP.…”

Section: Analysis and Discussionmentioning

confidence: 99%

Limitations of testing standards for battery electric vehicles: accessories, energy usage, and range

Oakley

McHenry²,

Bräunl

2016

IET Electrical Systems in Transportation

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Section: Analysis and Discussionmentioning

confidence: 99%

Limitations of testing standards for battery electric vehicles: accessories, energy usage, and range

Oakley

McHenry²,

Bräunl

2016

IET Electrical Systems in Transportation

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“…Mean absolute acceleration for the MOVES DS ranges from 0.1 to 0.6 m/s 2 , and for the OR-217 DS from 0.2 to 0.4 m/s 2 . Beyond the unique mechanical performance of advanced vehicles, some studies have suggested that new engine/power train technologies could influence driving patterns (Alessandrini and Orecchini 2003;US Environmental Protection Agency 2006). Given the uncertainty that this is a real effect -and if it is real, what exactly the effect would be -the same DSs are used for all vehicles modeled in this study.…”

Section: Methodsmentioning

confidence: 99%

Modeling the effects of congestion on fuel economy for advanced power train vehicles

Bigazzi

Clifton

2015

Transportation Planning and Technology

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Fuel-speed curves (FSC) are used to account for the aggregate effects of congestion on fuel consumption in transportation scenario analysis. This paper presents plausible FSC for conventional internal combustion engine (ICE) vehicles and for advanced vehicles such as hybrid electric vehicles, fully electric vehicles (EVs), and fuel cell vehicles (FCVs) using a fuel consumption model with transient driving schedules and a set of 145 hypothetical vehicles. The FSC shapes show that advanced power train vehicles are expected to maintain fuel economy (FE) in congestion better than ICE vehicles, and FE can even improve for EV and FCV in freeway congestion. In order to implement these FSC for long-range scenario modeling, a bounded approach is presented which uses a single congestion sensitivity parameter. The results in this paper will assist analysis of the roles that vehicle technology and congestion mitigation can play in reducing fuel consumption and greenhouse gas emissions from motor vehicles. IntroductionTraffic congestion is ubiquitous and increasing in urban areas, leading to longer travel times, lower average speeds, and increased vehicle speed variability. These outcomes influence vehicle engine operating loads and operating duration, which in turn have an impact on fuel efficiency. At the same time, the passenger vehicle fleet continues to evolve with advanced power train types such as hybrid electric vehicles (HEVs), fuel cell vehicles (FCVs), and fully electric vehicles (EVs). This paper addresses how these advanced vehicle technologies will respond to congestion, in terms of fuel efficiency.Motor vehicle fuel efficiency can be expressed as fuel economy (FE) -in travel distance per unit volume of fuel -in the USA as miles per gallon (mpg). Fuel-speed curves (FSC) generalize the relationship between FE and congestion as the average FE at a given average travel speed, including typical acceleration and deceleration activity (usually for specific vehicle and roadway types). The average speed variable in FSC is a proxy for congestion level, indicative of both average speed and speed variability for archetypal conditions. Thus, FSC can serve to estimate the effects of congestion on aggregate fuel consumption in macroscopic traffic and transportation models.Existing research on FSC for conventional internal combustion engine (ICE) vehicles indicates that increasing levels of congestion -with lower average speeds -lead to

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“…Drive cycles can be developed by three major approaches, namely, the extrapolation method, selection method and simulation method [4]. The extrapolation method is used to build modal drive cycles.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The amount of these emissions gives the most effective understanding of the air quality impact of on-road traffic. Two different approaches are widely used to calculate vehicle emissions: emission testing and emission modeling [4,5]. Vehicle emission testing is a process in which vehicles are running through a movement pattern while equipped with emission-measuring instrument, such as the portable emission measurement system (PEMS) [6].…”

Section: Introductionmentioning

confidence: 99%

Developing a Regional Drive Cycle Using GPS-Based Trajectory Data from Rideshare Passenger Cars: A Case of Chengdu, China

Han

et al. 2021

Sustainability

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A drive cycle describes the microscopic and macroscopic vehicle activity information that is crucial for emission quantification research, e.g., emission modeling or emission testing. Well-developed drive cycles capture the driving patterns representing the traffic conditions of the study area, which usually are employed as the input of the emission models. By considering the potential of large-scale GPS trajectory data collected by ubiquitous on-vehicle tracking equipment, the objective of this study is to demonstrate the capability of GPS-based trajectory data from rideshare passenger cars for urban drive cycle development. Large-scale GPS trajectory data and order data collected by an app-based transportation vehicle was used in this study. GPS data were filtered by thresholds of instantaneous accelerations and vehicle specific powers. The micro-trip selection-to-rebuild method with operating mode distribution was used to develop a series of speed-bin categorized representative drive cycles. Sensitivity of the time-of-day and day-of-week were analyzed on the developed drive cycles. The representativeness of the developed drive cycles was verified and significant differences exist when they are compared to the default light-duty drive cycles coded in MOVES. The findings of this study can be used for helping drive cycle development and emission modeling, further improving the understanding of localized emission levels.

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A driving cycle for electrically-driven vehicles in Rome

Cited by 18 publications

References 6 publications

Limitations of testing standards for battery electric vehicles: accessories, energy usage, and range

Limitations of testing standards for battery electric vehicles: accessories, energy usage, and range

Modeling the effects of congestion on fuel economy for advanced power train vehicles

Developing a Regional Drive Cycle Using GPS-Based Trajectory Data from Rideshare Passenger Cars: A Case of Chengdu, China

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