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Dario Pevec
University of Zagreb, Croatia

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Journal article
Published: 24 July 2020 in Journal of Cleaner Production
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Electric vehicle (EV) owners enjoy many positive aspects when driving their cars, including low running costs and zero tailpipe gas emissions, which makes EVs a clean technology provided that they are sourced through renewable sources, e.g., biomass, solar power, or wind energy. However, their driving behaviour is often negatively affected by the so-called range anxiety phenomenon, i.e., a concern that an EV might not have enough driving range to reach the desired destination due to its limited battery size. The perception of range anxiety may also affect potential buyers in their decisions on whether to purchase an internal combustion engine vehicle as opposed to an EV. This paper investigates some factors that influence range anxiety through a comparative analysis of two target groups: (i) existing EV owners, and (ii) non-EV owners (i.e., potential EV owners). The specially crafted survey was used to collect range anxiety data from more than 200 participants. In particular, participants provided their perceptions on (i) the potential relationship between existing gas station infrastructure and the desired EV charging station infrastructure, and (ii) the potential relationship between range anxiety and two influencing variables, namely the current state of charge and remaining range. Concerning the existing gas station infrastructure, evidence suggests that both target groups think that the distances between gas stations could be increased. Moreover, our analysis shows that the desired distances between charging stations correspond to the distances between the existing gas stations, which indicates that both EV owners and non-EV owners have a common view on the optimal gas station and charging station topology. Furthermore, we find that the type of settlement (urban vs rural) influences preferred distances, where both target groups living in cities desire shorter distances, and that non-EV owners, as opposed to EV owners, are more prone to be affected by the state of charge and remaining range. Quantitatively, we are able to define a measure for range anxiety, which is connected with the preferred distance between two neighbouring charging stations. Throughout our analyses, we find that the mean preferred distance between two neighbouring charging stations is 7 km, but this value significantly differs based on the settlement type of a (potential) EV owner.

ACS Style

Dario Pevec; Jurica Babic; Arthur Carvalho; Yashar Ghiassi-Farrokhfal; Wolfgang Ketter; Vedran Podobnik. A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety. Journal of Cleaner Production 2020, 276, 122779 .

AMA Style

Dario Pevec, Jurica Babic, Arthur Carvalho, Yashar Ghiassi-Farrokhfal, Wolfgang Ketter, Vedran Podobnik. A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety. Journal of Cleaner Production. 2020; 276 ():122779.

Chicago/Turabian Style

Dario Pevec; Jurica Babic; Arthur Carvalho; Yashar Ghiassi-Farrokhfal; Wolfgang Ketter; Vedran Podobnik. 2020. "A survey-based assessment of how existing and potential electric vehicle owners perceive range anxiety." Journal of Cleaner Production 276, no. : 122779.

Review
Published: 18 October 2019 in Electronics
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Current trends are showing that the popularity of electric vehicles (EVs) has significantly increased over the last few years, causing changes not only in the transportation industry but generally in business and society. This paper covers one possible angle to the (r)evolution instigated by EVs, i.e., it provides the data science perspective review of the interdisciplinary area at the intersection of green transportation, energy informatics, and economics. Namely, the review summarizes data-driven research in EVs by identifying two main research streams: (i) socio–economic, and (ii) socio–technical. The socio–economic stream includes research in: (i) acceptance of green transportation in countries and among different populations, (ii) current trends in the EV market, and (iii) forecasting future sales for the green transportation. The socio–technical stream includes research in: (i) electric vehicle battery price and capacity and (ii) charging station management. This kind of study is especially important now when the question is no longer whether the transition from internal-combustion engine vehicles to clean-fuel vehicles is going to happen but how fast it will happen and what are going to be implications for society, governmental policies, and industry. Based on the presented literature review, the paper also outlines the most significant open questions and challenges that are yet to be solved: (i) scarcity of trustworthy (open) data, and (ii) designing a generalized methodology for charging station deployment.

ACS Style

Dario Pevec; Jurica Babic; Vedran Podobnik. Electric Vehicles: A Data Science Perspective Review. Electronics 2019, 8, 1190 .

AMA Style

Dario Pevec, Jurica Babic, Vedran Podobnik. Electric Vehicles: A Data Science Perspective Review. Electronics. 2019; 8 (10):1190.

Chicago/Turabian Style

Dario Pevec; Jurica Babic; Vedran Podobnik. 2019. "Electric Vehicles: A Data Science Perspective Review." Electronics 8, no. 10: 1190.

Journal article
Published: 12 November 2018 in Journal of Cleaner Production
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Electric vehicles (EVs) are currently the most promising technology with the potential to transform transportation and energy landscapes to make industry and society more sustainable. Although their adoption has been much faster than anticipated a few years ago, resulting in a significant increase from 400,000 EVs sold in 2013 to more than 3 million in 2017, the coming years will be crucial for determining whether EVs are to become a new transportation industry “standard”, the role of which has been held by internal combustion engine (ICE) vehicles over the last hundred years. Not only will innovation in EV batteries determine a “win” or “fail” for EVs, but a significant role in its acceptance will be successful development of a comprehensive EV eco-system, establishing an efficient network of EV chargers as well as attractive and affordable charging tariffs. Furthermore, having insight as to how much money EV users are willing to pay for the charging service (i.e., willingness to pay) in various circumstances is one of the essential factors in building a successful EV eco-system. That being said, this paper utilizes a gamified survey for exploring a person's willingness to pay for EV charging. The experimental setup includes both the classic (text-only) questionnaire, as well as the game-based questionnaire. Therefore, not only have interesting insights on the willingness to pay for EV charging been identified, but analysis of user experience analysis from the questionnaires suggests that use of gamification is a good approach to performing a survey as it exhibits superior hedonic quality in comparison to classic methods (i.e., text-only questionnaire).

ACS Style

Lara Dorcec; Dario Pevec; Hrvoje Vdovic; Jurica Babic; Vedran Podobnik. How do people value electric vehicle charging service? A gamified survey approach. Journal of Cleaner Production 2018, 210, 887 -897.

AMA Style

Lara Dorcec, Dario Pevec, Hrvoje Vdovic, Jurica Babic, Vedran Podobnik. How do people value electric vehicle charging service? A gamified survey approach. Journal of Cleaner Production. 2018; 210 ():887-897.

Chicago/Turabian Style

Lara Dorcec; Dario Pevec; Hrvoje Vdovic; Jurica Babic; Vedran Podobnik. 2018. "How do people value electric vehicle charging service? A gamified survey approach." Journal of Cleaner Production 210, no. : 887-897.

Article
Published: 19 February 2018 in International Journal of Energy Research
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Current trends suggest that there is a substantial increase in the overall usage of electric vehicles (EVs). This, in turn, is causing drastic changes in the transportation industry and, more broadly, in business, policy making, and society. One concrete challenge brought by the increase in the number of EVs is a higher demand for charging stations. This paper presents a methodology to address the challenge of EV charging station deployment. The proposed methodology combines multiple sources of heterogeneous real-world data for the sake of deriving insights that can be of a great value to decision makers in the field, such as EV charging infrastructure providers and/or local governments. Our starting point is the business data, ie, data describing charging infrastructure, historical data about charging transactions, and information about competitors in the market. Another type of data used are geographical data, such as places of interest located around chargers (eg, hospitals, restaurants, and shops) and driving distances between available chargers. The merged data from different sources are used to predict charging station utilization when EV charging infrastructure and/or contextual data change, eg, when another charging station or a place of interest is created. On the basis of such predictions, we suggest where to deploy new charging stations. We foresee that the proposed methodology can be used by EV charging infrastructure providers and/or local governments as a decision support tool that prescribes an optimal area to place a new charging station while keeping a desired level of utilization of the charging stations. We showcase the proposed methodology with an illustrative example involving the Dutch EV charging infrastructure through the period from 2013 to 2016. Specifically, we prescribe the optimal location for new ELaadNL charging stations based on different objectives such as maximizing the overall charging network utilization and/or increasing the number of chargers in scarcely populated areas.

ACS Style

Dario Pevec; Jurica Babic; Martin A. Kayser; Arthur Carvalho; Yashar Ghiassi-Farrokhfal; Vedran Podobnik. A data-driven statistical approach for extending electric vehicle charging infrastructure. International Journal of Energy Research 2018, 42, 3102 -3120.

AMA Style

Dario Pevec, Jurica Babic, Martin A. Kayser, Arthur Carvalho, Yashar Ghiassi-Farrokhfal, Vedran Podobnik. A data-driven statistical approach for extending electric vehicle charging infrastructure. International Journal of Energy Research. 2018; 42 (9):3102-3120.

Chicago/Turabian Style

Dario Pevec; Jurica Babic; Martin A. Kayser; Arthur Carvalho; Yashar Ghiassi-Farrokhfal; Vedran Podobnik. 2018. "A data-driven statistical approach for extending electric vehicle charging infrastructure." International Journal of Energy Research 42, no. 9: 3102-3120.