Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles

Bucher, Dominik; Henry, Martin; Hamper, Jannik; Jaleh, Atefeh; Becker, Henrik; Zhao, Pengxiang; Raubal, Martin

doi:10.5194/agile-giss-1-2-2020

Cited by 6 publications

(15 citation statements)

References 21 publications

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“…Previous literature stated that EVs with small batteries increase the experience of range anxiety for users (Bucher et al, 2020; Philipsen et al, 2018, 2019). Accordingly, range anxiety decreases for users with larger EV batteries as they can travel longer distances.…”

Section: Discussionmentioning

confidence: 99%

Investigating Factors Affecting Electric Vehicle Users’ Procrastination Charging Behavior

Khaleghikarahrodi,

Herrera Santos,

Lynch-Smith

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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The existence of electric vehicle users who procrastinate their charging activities – delay in charging until a critically low state of charge (i.e., almost empty battery) – is acknowledged in the previous survey studies and data-driven analyses. However, the underlying reasons affecting such behavior have not been explored. This study attempts to justify whether procrastination in charging is due to larger battery capacities and users’ close proximity to charging stations. The results indicate that battery sizes can have a significant effect on EV users’ procrastination charging behavior. This study found that users with battery capacities greater than 21kWh tend to procrastinate charging since they can travel longer distances. The findings will be able to serve a broad range of EV industry stakeholders due to the emerging market and continuous improvement for achieving mass EV adoption.

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Section: Discussionmentioning

confidence: 99%

Investigating Factors Affecting Electric Vehicle Users’ Procrastination Charging Behavior

Khaleghikarahrodi,

Herrera Santos,

Lynch-Smith

et al. 2023

Proceedings of the Human Factors and Ergonomics Society Annual

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“…Motif, for example, is a mobility feature that describes typical movements by considering the time series of visited locations on a semantic level (Song et al, 2010b;Schneider et al, 2013;Xu et al, 2018). Besides, socio-demographic factors can be considered to model the mobility decision-making behaviors if data are available (Bucher et al, 2020). Second, our current study only considers days with valid energy consumption and parking duration in the forecasting.…”

Section: Peak-shaving Effectsmentioning

confidence: 99%

“…However, in reality, smart charging should also consider days without valid values. Future research could consider adding the prediction of whether an EV is used on a particular day, whose influencing factors have been discussed in Bucher et al (Bucher et al, 2020). Third, the simulation of smart charging in our research has not considered renewable energy sources, which has been discussed in Martin et al (2022).…”

Section: Peak-shaving Effectsmentioning

confidence: 99%

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Optimizing Electric Vehicle Charging Schedules Based on Probabilistic Forecast of Individual Mobility

Cai

Xin²,

Martin³

et al. 2022

AGILE GIScience Ser.

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Abstract. The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if the charging of many EVs is not coordinated. Among the many strategies to cope with this challenge, next-day EV energy demand forecasting plays a key role. Existing studies have focused on predicting the next-day energy demand of EVs on the aggregated and individual levels. However, these studies have not yet extensively considered individual user mobility behaviors, which exhibit a high level of predictability. In this study, we consider several mobility features of individual users when forecasting the next-day energy demand of individual EVs. Three types of quantile regression models are used to generate probabilistic forecasts of energy demand, particularly the next-day energy consumption and parking duration. Based on the prediction results, two time-shifting smart charging strategies are designed: unidirectional and bidirectional smart charging. These two strategies are compared with an uncontrolled charging baseline to evaluate their financial benefits and peak-shaving effects. Our results show that human mobility features can partially improve the prediction of next-day individual EV energy demand. Additionally, users and distribution grids can benefit from smart charging strategies both financially and technically.

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“…The same cannot be affirmed for charging stations dedicated to recharging EVs. Their numbers are not so high and widespread in a country's territory, despite the recent growth and availability being critical for ensuring an 'on-the-route' recharge, affecting the range [6]. In addition, the different charging sockets are not always unified for the same charging spot and the different performances for recharging operations influence the total time spent or wasted along the route.…”

Section: Introductionmentioning

confidence: 99%

Full Implementation of Electric Mobility in a Countryside Region of Spain

et al. 2022

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The ongoing spread of electric sustainable mobility is transforming the local ways of transport in metropolitan areas. This is meant to be extended outside of big cities in the near future thanks to new technological developments. Little towns should adapt to these changes, as they are located geographically far from the big cities and are generally characterized by low economic and demographic indicators. Hence, little towns must keep pace with these changes in mobility to avoid being isolated from the main cities in a country. People living in the countryside usually move toward big cities for various reasons, either related to work or living necessities. Therefore, it must be possible to conduct usual displacements through the use of electric vehicles (EVs), i.e., reaching the destinations and supplying the batteries through charging infrastructures. This paper studies the full implementation of electric mobility applied in the case of Cuenca, a city located in middle Spain. A brief geographical context is provided, together with the routes and destinations of interest considered. Then, different EVs are considered and an analytical vehicle model is provided. The model was exploited to simulate the electrical energy demand to reach the destinations chosen; the results allow comparing the performances offered by different types of EVs. This aspect is then considered as the basis to propose further upgrades in the charging infrastructures where needed, to comply with the widespread use of electric mobility.

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Exploring Factors that Influence Individuals’ Choice Between Internal Combustion Engine Cars and Electric Vehicles

Cited by 6 publications

References 21 publications

Investigating Factors Affecting Electric Vehicle Users’ Procrastination Charging Behavior

Investigating Factors Affecting Electric Vehicle Users’ Procrastination Charging Behavior

Optimizing Electric Vehicle Charging Schedules Based on Probabilistic Forecast of Individual Mobility

Full Implementation of Electric Mobility in a Countryside Region of Spain

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