Data mining of plug-in electric vehicles charging behavior using supply-side data

Siddique, Choudhury; Afifah, Fatima; Guo, Zhaomiao; Zhou, Yan

doi:10.1016/j.enpol.2021.112710

Cited by 20 publications

(12 citation statements)

References 29 publications

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“…The low utilization rate of AC stations is also due to the stationary times being much higher than the charging times [113][114][115]. This behavior could indicate that consumers are not yet aware of the time required to recharge their vehicles or do consider parking a primary need, even with respect to recharging [73,116]. Conversely, charging behavior at fast-charging stations is more akin to normal refueling behavior, with short connection times oriented toward the ability to complete the intended journey [73].…”

Section: Analysis Of Infrastructure Usagementioning

confidence: 99%

“…Conversely, charging behavior at fast-charging stations is more akin to normal refueling behavior, with short connection times oriented toward the ability to complete the intended journey [73]. Domestic charging has a longer dwell time than other CPs of equal power, while the dwell time is short on commercial premises, probably because these instances are opportunity recharges [116]. The arrival time, permanence, and inactivity at public CPs differ depending on whether the charge takes place near one's home, at work, or in a parking lot [108].…”

Section: Analysis Of Infrastructure Usagementioning

confidence: 99%

“…Data from smart electricity meters are often used to extract EV load curves [122][123][124][125]. Users who own cars with higher-capacity batteries seem to favor home charging [116].…”

Section: Analysis Of Infrastructure Usagementioning

confidence: 99%

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A Literature Review on the Charging Behaviour of Private Electric Vehicles

Andrenacci,

Valentini

2023

Applied Sciences

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Electric mobility is one of the ways of containing greenhouse gas and local pollutants emissions in urban areas. Nevertheless, the massive introduction of battery-powered electric vehicles (EVs) is introducing some concerns related to their energy demand. Modelling vehicle usage and charging behavior is essential for charge demand forecasting and energy consumption estimation. Therefore, it is crucial to understand how the charging decisions of EV owners are influenced by different factors, ranging from the charging infrastructure characteristics to the users’ profiles. This review examines the approaches used to investigate charging behavior and highlights the trends and differences between the results, remarking on any gaps worthy of further investigation.

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Section: Analysis Of Infrastructure Usagementioning

confidence: 99%

Section: Analysis Of Infrastructure Usagementioning

confidence: 99%

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A Literature Review on the Charging Behaviour of Private Electric Vehicles

Andrenacci,

Valentini

2023

Applied Sciences

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“…However, there are several unsolved problems to be addressed, one of which is the "range anxiety" problem [1]. Electric vehicle (EV) users can often feel anxious about depleting their battery before they reach their destinations.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Traffic Flow considering Electric Vehicle Market Share and Random Charging

Yan

Zha

Shi

et al. 2023

Journal of Advanced Transportation

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This paper mainly studies the multiclass stochastic user equilibrium problem considering the market share of battery electric vehicles (BEVs) and random charging behavior (RCB) in a mixed transport network containing electric vehicles and gasoline vehicles (GVs). In order to analyze the random charging and path choice behaviors of BEV users and extract the differences in travel behaviors between BEV and GV users, an improved logit-based model, multilabel algorithm, and queuing theory are applied. The influencing factors of charging possibility mainly include the initial state of charge (SOC), the SOC at the beginning of charging, and the psychologically acceptable safe SOC threshold arriving at the destination. Diversity choices of user paths and charging locations will result in changes in queuing traffic and differences in queuing time. Conversely, different stations have different queuing dwell times, which will also affect the routing and charging locations for BEVs with RCB. The path-based method of successive averages (MSA) is adopted to solve the model. Through the simulation of the test network Sioux Falls, the equilibrium traffic flow and possible charging flow under different market shares and initial SOC are predicted, and the properties of the model and the feasibility of the algorithm are verified.

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“…For the usage analysis, the authors used data from 221 charging stations with 50 491 charging events. Furthermore, Siddique et al (2022) focused on data analysis of 189 864 charging sessions collected over 13 months from 821 ChargePoint charging stations in Illinois.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal patterns of electric vehicle charging station utilization: a nationwide case study of Switzerland

Gellrich¹,

Block²,

Leikert-Böhm³

2022

Environ. Res.: Infrastruct. Sustain.

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The expansion of the public charging infrastructure for electric vehicles is seen as central to the development of electric mobility in many countries. Although national studies of charging infrastructure utilization based on real-world data would be a sound basis for demand planning, such studies are scarce. Using Switzerland as an example, this study examines the spatial and temporal patterns of charging infrastructure utilization. To this end, detailed, nationwide, real-time utilization data from 3,086 electric vehicle supply equipment units (EVSEs) at electric vehicle charging stations were collected over a period of several months and analyzed exploratively and statistically. The maximum average utilization rate of the EVSEs surveyed during the study period is between 14% and 16%, depending on the day of the week and time of day. Most charging occurs Monday through Friday during peak working hours and on Saturday during the day. The median utilization time is higher in the largest cities than the statewide average. Charging stations along major transit routes do not have higher utilization rates than in other locations. The results suggest thatpublic charging infrastructure is used primarily in cities and agglomeration during work hours. The findings from this study may help plan and make better use of funding to expand charging infrastructure.

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Data mining of plug-in electric vehicles charging behavior using supply-side data

Cited by 20 publications

References 29 publications

A Literature Review on the Charging Behaviour of Private Electric Vehicles

A Literature Review on the Charging Behaviour of Private Electric Vehicles

Prediction of Traffic Flow considering Electric Vehicle Market Share and Random Charging

Spatial and temporal patterns of electric vehicle charging station utilization: a nationwide case study of Switzerland

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