Development of driving cycles for electric vehicles in the context of the city of Florence

Berzi, Lorenzo; Delogu, Massimo; Pierini, Marco

doi:10.1016/j.trd.2016.05.010

Cited by 101 publications

(40 citation statements)

References 54 publications

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“…These configurations result in either confined flows with one or more moving boundaries, or free surface flows supported by at least one substrate/base. Extensive physical and numerical studies have illustrated the profound impact of these boundary conditions to the overall flow behavior in both two-dimensional (2D) and three-dimensional (3D) situations [2,[4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. For instance, Pouliquen [8] found that base roughness is the key to the minimum thickness necessary to sustain a steady flow at a given inclination, and proposed a scaling law correlating this thickness with the mean flow velocity.…”

Section: Introductionmentioning

confidence: 99%

Characterization of base roughness for granular chute flows

et al. 2016

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Base roughness plays an important role in the dynamics of granular flows but is still poorly understood due to the difficulty of its quantification. For a bumpy base made of spheres, at least two factors should be considered in order to characterize its geometric roughness, namely, the size ratio of flow to base particles and the packing arrangement of base particles. In this paper, we propose an alternative definition of base roughness, R_{a}, as a function of both the size ratio and the distribution of base particles. This definition is generalized for random and regular packings of multilayered spheres. The range of possible values of R_{a} is presented, and optimal arrangements for maximizing base roughness are studied. Our definition is applied to granular chute flows in both two- and three-dimensional configurations, and is shown to successfully predict whether slip occurs at the base. A transition is observed from slip to nonslip conditions as R_{a} increases. Critical values of R_{a} are identified for the construction of a nonslip base at various angles of inclination.

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Section: Introductionmentioning

confidence: 99%

Characterization of base roughness for granular chute flows

et al. 2016

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“…e results indicated that the characteristics of the proposed driving cycle were able to represent the aggressive driving of the real-world well compared to existing standard driving cycles. Similar studies were also carried out in Dublin by Brady and O'Mahony [5], in Florence by Berzi et al [30], and in Southeast Michigan by Lee et al [31]. It had also found that the role of a typical driving cycle was increasingly significant, not only for vehicle certification and design but also for analyzing the impact of PHEV on the grid.…”

Section: Introductionmentioning

confidence: 63%

Study on Driving Cycle Synthesis Method for City Buses considering Random Passenger Load

Liu

Zhao

et al. 2020

Journal of Advanced Transportation

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This paper proposes a novel driving cycle construction method in consideration of velocity, road slope, and passenger load, based on a real-world bus route with a plug-in hybrid electric bus (PHEB). The main purpose is to address the disadvantage that an inaccurate reflection of the real-world driving characteristics for city buses will be caused when ignoring the passenger load in the course of a driving cycle synthesis. Two contributions are supplemented to distinguish from the previous research. Firstly, a novel station-based method is proposed aiming at developing a driving cycle with high accuracy. The kinematic segments are partitioned according to the distance of adjacent bus stops, while a two-dimensional Markov chain Monte Carlo method is employed to synthesize driving cycle between each interval of adjacent bus stops. Secondly, the random passenger load for different bus stops is treated as a discrete Markov chain model, according to the correlation analysis of the measured passenger data which are distinguished for off-peak and peak hours. Meanwhile, Monte Carlo simulation and maximum likelihood estimation are utilized to determine the most likely number of passengers for each bus stop. At last, the fuel consumption of the PHEB is simulated with the best-synthesized driving cycle and contrasted to the mean fuel consumption of the later measured data which is composed of the velocity, road slope, and the passenger load. The results demonstrate that the synthesized driving cycle has a higher accuracy on fuel consumption estimation.

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“…The k-means algorithm has been widely used in cluster computing in the existing literature [3,4,19,24,43]. The k-means clustering algorithm uses the Euclidean distance between a sample and cluster center as a criterion for similarity determination, and it has the advantages of efficiency and simplicity [19,43].…”

Section: Clustering Based On the K-means And Svmmentioning

confidence: 99%

“…Hence, it is necessary to develop a unique driving cycle for each typical city and region. Moreover, many researchers have found that the energy consumption of an EV for a real-world driving cycle greatly differs from that based on the international standard driving cycles [19,[22][23][24][25].…”

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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Development of driving cycles for electric vehicles in the context of the city of Florence

Abstract: This document is the accepted version of the article "Development of driving cycles for Electric Vehicles in the context of the city of Florence", to be used for sharing through Author Institution repository.

Cited by 101 publications

References 54 publications

Characterization of base roughness for granular chute flows

Characterization of base roughness for granular chute flows

Study on Driving Cycle Synthesis Method for City Buses considering Random Passenger Load

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

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