Public charging choices of electric vehicle users: A review and conceptual framework

Potoglou, Dimitris; Song, Rongqiu; Santos, Georgina

doi:10.1016/j.trd.2023.103824

Cited by 18 publications

(8 citation statements)

References 54 publications

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“…There have been many empirical studies on EV charging behavior. Potogolu et al [23] conducted a comprehensive review assisting policymakers in understanding the public charging preferences of potential and current EV users, which aims to facilitate the implementation of effective government policies that support the expansion of public charging infrastructure and encourage the widespread adoption of EVs. Battery capacity, midnight indicator, initial state of charging (SOC), and several past charging events are the parameters that determined the charging choice behavior and location, using the mixed logit model (MXL) among 500 BEV users in Japan [11].…”

Section: Charging Behaviormentioning

confidence: 99%

An Analysis of Electric Vehicle Charging Intentions in Japan

Hanni,

Yamamoto,

Nakamura

2024

Sustainability

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This study focuses on charging-related decisions for fast charging at highway service and parking areas, slow charging at home, fast charging at commercial facilities, and fast and slow charging at workplaces. This research contributes to the existing literature by estimating the charging behavior variables, as well as understanding the role of explanatory variables in influencing charging-related decisions. Responses from the stated preference (SP) survey in Japan in 2021 were analyzed with a mixed logit model (MXL). The results showed that, (1) when the battery level is 75% or higher, users of battery electric vehicles (BEVs) are not keen to charge their vehicles, but when the next trip is anticipated to be 50 or more kilometers, they choose to charge their vehicles; (2) individuals are not willing to tolerate any waiting time for their vehicles to be charged at each location; and (3) the recurrence of charging at the target location affects the charging decision of BEV users. We found significant relationships between socioeconomic characteristics and charging decisions. Furthermore, we examined the practical applications of the empirical findings in this study for policymaking and charging infrastructure planning.

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Section: Charging Behaviormentioning

confidence: 99%

An Analysis of Electric Vehicle Charging Intentions in Japan

Hanni,

Yamamoto,

Nakamura

2024

Sustainability

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“…The study explores population characteristics, mode shift, and mode interaction to provide a causal analysis to evaluate the nature of the relationship between e-scooters and transit trips in the whole city. A synthesis and critical discussion of the most up-to-date evidence on public charging choices is provided [15]. The findings indicate that useful criteria for charging behavior include home charging dominance, perceived insufficiency of public infrastructure, charging prices, comfort and waiting time, location matters, charging power, and frequency of use.…”

Section: Sustainable E-trans Infrastructurementioning

confidence: 99%

A Hybrid Deep Learning Model Utilizing Cross-Structural Multi-Behavioral Comparative Recommendation for Sustainable Electric Transportation Infrastructure

Xu,

Chu

2024

Applied Sciences

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Ensuring the sustainability of transportation infrastructure for electric vehicles (e-trans) is increasingly imperative in the pursuit of decarbonization goals and addressing the pressing energy shortage. By prioritizing the development and maintenance of resilient e-trans platforms through the optimization of the public charging network, electric vehicle businesses can effectively meet the needs of users, thereby contributing to efforts aimed at improving environmental quality. To achieve this goal, researching the dynamics of vehicle user behaviors plays a crucial role. In this paper, we propose cross-structure multi-behavior contrastive learning for recommendation (C-MBR), which takes into account the dynamic preferences of users, and develops model profiles from the global structure module, local structure module, cross-behavior contrastive learning module, cross-structure contrastive learning module, and model prediction and optimization. C-MBR is mainly designed to learn user preferences from the diversity of users’ behaviors in the process of interacting with the project, so as to grasp the different behavioral intentions of users. The experimental and analytical research is further conducted and validated for dealing with cold start problems. The results indicate that C-MBR has a strong ability to deal with the problem of sparse data. Compared with the ablation experiment, the model performance of C-MBR is significantly enhanced, showing that the C-MBR model can fully apply the information of a global structure and local structure in cross-structure comparative learning and multi-behavioral comparative learning to further alleviate the problem of data sparsity. As a result, the e-trans infrastructure will be significantly enhanced by addressing the issue of data-driven disruption.

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“…While less developed, there is also growing literature pertaining to the nature and determinants of public attitudes and intentions to use EV chargers and/or charging initiatives (e.g., smart charging) [8,32,33]. For instance, the provision (or lack thereof) of public charging infrastructure, alongside the anticipated speed and cost of charging, are commonly cited concerns among prospective end-users [34][35][36]. We drew upon this literature in developing the survey for use in the current study.…”

Section: Modelling Public Acceptance Of Evs and Charging Infrastructurementioning

confidence: 99%

“…The three output quality concerns related to the charge time, sufficiency of charge, and the limited availability of charge. The charging time and charge sufficiency are noted concerns with EV charging [34][35][36], while the limited availability of charge is something that could affect an off-grid system like FEVER. The control beliefs/facilitating conditions concerns are related to the cost of charging, availability of chargers, whether the chargers would be working, whether technical support would be on hand, and whether there would be a need to reserve the chargers in advance.…”

Section: Current Study and Hypothesesmentioning

confidence: 99%

“…The control beliefs/facilitating conditions concerns are related to the cost of charging, availability of chargers, whether the chargers would be working, whether technical support would be on hand, and whether there would be a need to reserve the chargers in advance. These are not only recognised concerns that EV users have with charging infrastructure [34][35][36], but are also pertinent to FEVER, being that it presents a novel EV charging solution. Consistent with the TAM3, the 'output quality' concerns were modelled as negative predictors of PU, while the control beliefs/facilitating conditions were modelled as negative predictors of PEOU.…”

Section: Current Study and Hypothesesmentioning

confidence: 99%

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Modelling Public Intentions to Use Innovative EV Chargers Employing Hybrid Energy Storage Systems: A UK Case Study Based upon the Technology Acceptance Model

Jones,

Elgueta,

Chudasama

et al. 2024

Energies

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The current study investigates public intentions to use an innovative, off-grid renewably powered EV charging technology called FEVER (Future Electric Vehicle Energy networks supporting Renewables). We report the findings of a questionnaire-based survey (QBS) conducted at a zoo in the south of England, exploring the prospect of demonstrating FEVER. The QBS was designed around a context-specific technology acceptance model (TAM) and administered both face-to-face (n = 63) and online (n = 158) from April to May 2023. The results indicate that most participants were willing to pay to use FEVER, particularly where revenue would benefit the zoo. The participants agreed they intended to use the chargers, and that they would be useful and easy to use. The participants agreed that there would be normative pressure to use the chargers, but that their use would be enjoyable. Of greatest concern was that the chargers would be blocked by others. The participants were ambivalent about concerns over charging duration and charge sufficiency. Structural equation modelling confirmed that the context-specific TAM explained 58% of people’s use intentions. The core relationships of the TAM were confirmed, with ‘perceived usefulness’ additionally predicted by subjective norms and ‘perceived ease of use’ additionally predicted by anticipated enjoyment. Of the other variables, only concern that the chargers would be blocked was retained as a marginal predictor of ‘perceived ease of use’. The implications of these findings for the co-design and demonstration of FEVER are discussed.

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Public charging choices of electric vehicle users: A review and conceptual framework

Cited by 18 publications

References 54 publications

An Analysis of Electric Vehicle Charging Intentions in Japan

An Analysis of Electric Vehicle Charging Intentions in Japan

A Hybrid Deep Learning Model Utilizing Cross-Structural Multi-Behavioral Comparative Recommendation for Sustainable Electric Transportation Infrastructure

Modelling Public Intentions to Use Innovative EV Chargers Employing Hybrid Energy Storage Systems: A UK Case Study Based upon the Technology Acceptance Model

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