Driving cycle developments and their impacts on energy consumption of transportation

Achour, Hussam; Olabi, Abdul–Ghani

doi:10.1016/j.jclepro.2015.08.007

Cited by 79 publications

(35 citation statements)

References 36 publications

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“…The need to expand the knowledge on road energy consumption in a variety of scenarios, reflecting road geometry, vehicle characteristics, and control strategies, is more pertinent today than ever before. Numerous studies have addressed the question of quantifying the energy demand and emissions exhibited by different types of vehicles, in order to develop fuel consumption and emissions models [1][2][3][4][5][6][7], to adjust driving cycles to real-world conditions [8][9][10][11][12][13][14][15], to determine the energy-efficiency of electrical vehicles (EV) or the benefits of regenerative braking in a hybrid powertrain [16][17][18][19], and to adopt eco-friendly driving strategies through vehicle control optimization [20][21][22] or the practice of eco-routing [23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Impact of Road Geometry on Vehicle Energy Consumption and CO2 Emissions: An Energy-Efficiency Rating Methodology

2019

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This study presents a methodology for classifying road traffic energy efficiency. The indicators defined discriminate the impact of the road vertical and horizontal alignments upon energy consumption, disclosing the improvement potential of the road as a function of the traffic origin–destination matrix. The methodologic approach is based on basic physical principals, thus guarantying its generality, as opposed to the usual empirical mesoscale approaches. A simplified algebraic procedure is also proposed, resorting to simplified driving cycles and a constant speed assumption (CSA), thus avoiding the intricacy of microscale/microsimulation models. The simplified methodology was validated against field data acquired on the Portuguese highway A25. A microscale vehicle specific power analysis combined with detailed fuel models is compared against CSA results. The findings demonstrate its adequacy for free-flow traffic conditions and the importance of classifying road traffic energy-efficiency. For the case studied, it was found that 49.5% of the round trip propulsive energy expended by a 37-ton truck on the A25, a modern road, was degraded as heat through braking. The difference found between the microscale analysis and CSA approach is 0.8%, despite the speed unevenness, varying between 32 and 96 km/h, with a standard deviation of 24% of the average speed.

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Section: Introductionmentioning

confidence: 99%

Impact of Road Geometry on Vehicle Energy Consumption and CO2 Emissions: An Energy-Efficiency Rating Methodology

2019

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“…The details of the test route are shown in Table 1. Because Xi'an is located in a plain area, the slopes of the urban roads are 32 mainly caused by the viaducts, and the influence of the road gradient on data acquisition is not considered in the test route selection process.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of this approach is that the collected data can accurately represent the actual driving cycle based on large-scale studies. However, the test costs to obtain a reasonable sample size can be extremely high, and the data processing workload is large [32,34,36]. Chase car method is widely used to collect speed-time data from real-world driving cycles.…”

Section: Introductionmentioning

confidence: 99%

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

Zhao

et al. 2018

Journal of Advanced Transportation

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This paper proposes a scientific and systematic methodology for the development of a representative electric vehicle (EV) urban driving cycle. The methodology mainly includes three tasks: test route selection and data collection, data processing, and driving cycle construction. A test route is designed according to the overall topological structure of the urban roads and traffic flow survey results. The driving pattern data are collected using a hybrid method of on-board measurement method and chase car method. Principal component analysis (PCA) is used to reduce the dimensionality of the characteristic parameters. The driving segments are classified using a hybrid k-means and support vector machine (SVM) clustering algorithm. Scientific assessment criteria are studied to select the most representative driving cycle from multiple candidate driving cycles. Finally, the characteristic parameters of the Xi’an EV urban driving cycle, international standard driving cycles, and other city driving cycles are compared and analyzed. The results indicate that the Xi’an EV urban driving cycle reflects more aggressive driving characteristics than the other cycles.

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“…Driving cycles (DC) are time series of speeds that represent driving patterns [2,4] They have been constructed in the way that the values of the CPs of the DC are approximately equal to the values of the CPs that describe the driving patterns. DCs describe the workloads imposed on the vehicles and therefore have been used for assessing the environmental impact of traffic [5], and for optimizing new vehicles' powertrain configurations and engine control strategies to reduce fuel consumption (FC) [6]. In this manuscript, we distinguish two types of DC: local and type-approval DC.…”

Section: Introductionmentioning

confidence: 99%

“…The key issue relative to local DCs is their representativeness of the real-world local driving patterns. Researchers look for DCs that provide a synthesized representation of the local driving patterns [5,15,16].…”

Section: Introductionmentioning

confidence: 99%

Driving Cycles Based on Fuel Consumption

et al. 2018

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Type-approval driving cycles currently available, such as the Federal Test Procedure (FTP) and the Worldwide harmonized Light vehicles Test Cycle (WLTC), cannot be used to estimate real fuel consumption nor emissions from vehicles in a region of interest because they do not describe its local driving pattern. We defined a driving cycle (DC) as the time series of speeds that when reproduced by a vehicle, the resulting fuel consumption and emissions are similar to the average fuel consumption and emissions of all vehicles of the same technology driven in that region. We also declared that the driving pattern can be described by a set of characteristic parameters (CPs) such as mean speed, positive kinetic energy and percentage of idling time. Then, we proposed a method to construct those local DC that use fuel consumption as criterion. We hypothesized that by using this criterion, the resulting DC describes, implicitly, the driving pattern in that region. Aiming to demonstrate this hypothesis, we monitored the location, speed, altitude, and fuel consumption of a fleet of 15 vehicles of similar technology, during 8 months of normal operation, in four regions with diverse topography, traveling on roads with diverse level of service. In every region, we considered 1000 instances of samples made of m trips, where m varied from 4 to 40. We found that the CPs of the local driving cycle constructed using the fuel-based method exhibit small relative differences (<15%) with respect to the CPs that describe the driving patterns in that region. This result demonstrates the hypothesis that using the fuel based method the resulting local DC exhibits CPs similar to the CPs that describe the driving pattern of the region under study.

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Driving cycle developments and their impacts on energy consumption of transportation

Cited by 79 publications

References 36 publications

Impact of Road Geometry on Vehicle Energy Consumption and CO2 Emissions: An Energy-Efficiency Rating Methodology

Impact of Road Geometry on Vehicle Energy Consumption and CO2 Emissions: An Energy-Efficiency Rating Methodology

Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm

Driving Cycles Based on Fuel Consumption

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