What electric vehicle users want: Real-world preferences for public charging infrastructure

Anderson, John E.; Lehne, Marius; Hardinghaus, Michael

doi:10.1080/15568318.2017.1372538

Cited by 44 publications

(13 citation statements)

References 23 publications

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“…Between newer studies, the notable ones, beside the study by Smith et al [70] is study by Wang et al [71] (2017) and Anderson et al [72] (2018). Wang et al [71] in their paper presents the incentives for the purchase of EVs that are currently active in China and develop a model for the forecast of EV acceptance based on the linear regression.…”

Section: Ev Acceptancementioning

confidence: 99%

“…Other notable factors that influence the decision to purchase the EV in this research are education level and license fee. Anderson et al [72] applied survey methods to analyze EV owner's preferences about the charging infrastructure. Authors concluded that more public chargers are needed and that slower chargers are acceptable on more visited locations, while fast chargers are needed on less frequently visited locations.…”

Section: Ev Acceptancementioning

confidence: 99%

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Electric Vehicles: A Data Science Perspective Review

2019

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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.

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Section: Ev Acceptancementioning

confidence: 99%

Section: Ev Acceptancementioning

confidence: 99%

Electric Vehicles: A Data Science Perspective Review

2019

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“…5 , 6 , 7 In this effort, public charging infrastructure remains a critical complementary asset for consumers in building range confidence for trip planning and in EV purchase decisions. 8 , 9 , 10 Prior behavioral research has shown that policies designed to enhance EV adoption have largely focused on increasing the quantity of cars and connected infrastructure as opposed to the quality of the charging experience. 11 However, a fundamental challenge to deploying large-scale EV infrastructure is regular assessments of quality.…”

Section: Introductionmentioning

confidence: 99%

Topic classification of electric vehicle consumer experiences with transformer-based deep learning

Marchetto

Dharur

et al. 2021

Patterns

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Summary The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions. However, government analysts have failed to fully utilize consumer data in decisions related to charging infrastructure. This is because a large share of EV data is unstructured text, which presents challenges for data discovery. In this article, we deploy advances in transformer-based deep learning to discover topics of attention in a nationally representative sample of user reviews. We report classification accuracies greater than 91% (F1 scores of 0.83), outperforming previously leading algorithms in this domain. We describe applications of these deep learning models for public policy analysis and large-scale implementation. This capability can boost intelligence for the EV charging market, which is expected to grow to US$27.6 billion by 2027.

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“…With energy conservation and the environment, the global focus in the future will be on alternative transports such as electric vehicles (EVs). 2 Inventing rechargeable batteries found the application in electric vehicles with wide application and electricity distribution in the nineteenth century with electric motors. By charging their batteries when needed, people were more comfortable riding these electric cars in the city.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of EV charging spots along with capacitors in smart distribution network for congestion management

Sachan

Amini

2020

Int Trans Electr Energ Syst

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Summary Rapid growth of electric vehicles (EVs) has necessitated the devolvement of sustainable and easily accessible charging stations. Transport sector electrification and increased popularity of EVs make researcher to search for charging stations. In this paper, a new methodology regarding electric vehicle charging spot is proposed. In the study, allocation of the parking lot and capacitor is suggested for congestion management along with reactive power compensation. To this end, sensitivity analysis is performed by evaluating the inverse Jacobian matrix from the power flow studies. In order to optimally determine the size of parking lot, biogeography‐based optimization (BBO) technique is adopted. The effectiveness of the anticipated technique is tested on adapted IEEE 34‐bus distribution network. The outcome attained by BBO technique is equated with particle swarm optimization.

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What electric vehicle users want: Real-world preferences for public charging infrastructure

Cited by 44 publications

References 23 publications

Electric Vehicles: A Data Science Perspective Review

Electric Vehicles: A Data Science Perspective Review

Topic classification of electric vehicle consumer experiences with transformer-based deep learning

Optimal allocation of EV charging spots along with capacitors in smart distribution network for congestion management

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