Analysis of charging behavior when using battery electric vehicles in commercial transport

Fieltsch, Patrick; Flämig, Heike; Rosenberger, Kerstin

doi:10.1016/j.trpro.2020.03.179

Cited by 23 publications

(13 citation statements)

References 4 publications

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“…Combining historical and real-time EV data, the driving and charging states of CEVs are identified in reference [20]. Based on the long-term tracking data from the research project "Hamburg-Wirtschaft am Strom," research [21] studies the EV charging behavior characteristics in commercial transport. In addition, a hybrid logit model is leveraged in [22] to explore the influence of factors such as remaining power and detour distance on the EV drivers' selections of charging stations.…”

Section: Literature Surveymentioning

confidence: 99%

Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

et al. 2022

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The rapid development of electric vehicles (EVs) has posed challenges to power grids and transportation networks. Accurately capturing the usage patterns of EV users is a prerequisite for EVs’ interaction with electrified transportation networks. Thus, this paper proposes a full data chain (FDC) driven model to mine EVs’ comprehensive characteristics. By collecting the data of 150 private electric vehicles (PREVs), 100 commercial electric vehicles (CEVs), and 50 official electric vehicles (OEVs) in Chongqing, China, the driving characteristics are firstly mined by the adoption of origin-destination (OD) distribution and driving portrait. Moreover, the charging characteristics are extracted based on the state recognition for data chains. Then, vehicle usage characteristics of different types of users are comprehensively described based on the density-based spatial clustering of applications with noise (DBSCAN). Finally, the results of EV user characteristics are analyzed, and the effectiveness of the proposed model is verified by regional charging load analysis and urban road traffic flow comparison. The findings provide a data source and user behavior model for the planning, operation, and control of power grids and transportation networks.

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Section: Literature Surveymentioning

confidence: 99%

Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

et al. 2022

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“…Although there are studies examining the attitudes and preferences towards AFV among fleet managers in big enterprises [32][33][34][35][36][37], no similar research among microenterprises has been found. As microenterprises are usually family businesses, the decisionmaking process is closer to the one faced by the households rather than large companies.…”

Section: Factors In Making Decisions By Consumers On the Afv Marketmentioning

confidence: 99%

Key Factors of Development of Electromobility AMONG Microentrepreneurs: A Case Study from Poland

Skowrońska-Szmer

Kowalska-Pyzalska

2021

Energies

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Microentrepreneurs contribute to the growth of electromobility, and hence to sustainable transport, by the purchase of alternative fuel vehicles (AFV). This article attempts to identify key factors that may affect the growth of interest in AFV among microentrepreneurs. To find the key factors, data from 181 Polish microenterprises was collected and analyzed by means of the factor analysis. The results showed that in contrary to the popular opinion, it is not the price of the car that is crucial for its purchase, but the information that leads to knowledge about technical and utility values, and to legal and economic predictability.

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“…For the purpose of verifying the effectiveness of the proposed algorithm, we did not consider the variations in the accuracy of the forecasts. With 160 E2Ws in the charging station, we relied on the research data of [21] about the charging behavior. Accordingly, the initial SOC distribution is shown in Table III.…”

Section: Simulation and Analysismentioning

confidence: 99%

A Two-Level Desired Load Profile Tracking Algorithm for Electric Two-Wheeler Charging Stations

Huu

Ngoc

2021

Eng. Technol. Appl. Sci. Res.

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In Vietnam and in other developing countries, two-wheeled electric vehicles are potential alternatives to gasoline-powered motorbikes. The growth in the number of Electric Two-Wheelers (E2Ws) requires a large power demand of charging load. In addition, the increasing spread in the appearance and penetration of rooftop photovoltaic (PV) power systems, with their intermittence and uncertain nature, poses technical challenges that need to be addressed. The coordination of PV rooftop operation and EV charging may be an effective solution to meet the emerging load demand from EVs, increasing solar power penetration while minimizing the cost of grid reinforcement or possible upgrades. In this paper, a two-level desired load profile tracking algorithm for PV integrated electric bicycles/electric motorcycle charging stations is proposed with the purposes of load leveling, valley filling, and peak shaving. The simulation results show that the proposed algorithm is an effective solution, significantly improving the load profile, especially when compared with uncontrolled charging and constant charging power scheme.

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Analysis of charging behavior when using battery electric vehicles in commercial transport

Cited by 23 publications

References 4 publications

Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

Modeling and Analysis of Electric Vehicle User Behavior Based on Full Data Chain Driven

Key Factors of Development of Electromobility AMONG Microentrepreneurs: A Case Study from Poland

A Two-Level Desired Load Profile Tracking Algorithm for Electric Two-Wheeler Charging Stations

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