Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour

Morrissey, Patrick; Weldon, Peter; O’Mahony, Margaret

doi:10.1016/j.enpol.2015.12.001

Cited by 356 publications

(154 citation statements)

References 9 publications

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“…These findings have since then been confirmed in a growing body of literature around the world. Descriptive studies in The United states (Idaho National Laboratory, 2015b), Australia (Jabeen, Olaru, Smith, Braunl, & Speidel, 2013;Speidel, Jabeen, Olaru, & Harries, 2012), England (Robinson, Blythe, Bell, Hübner, & Hill, 2013;Wardle, 2015), Canada (Toronto Atmospheric Fund, 2015), Ireland (Morrissey, Weldon, & Mahony, 2016) and the Netherlands (Hoed, Helmus, Vries, & Bardok, 2014;Spoelstra & Helmus, 2015) confirm such behavioural patterns. In particular, these studies generally indicate two peaks in starts of charging sessions, one in the morning, reflecting "business charging", and one in the late afternoon, reflecting "home charging".…”

Section: Literature Review 21 Charging Behaviourmentioning

confidence: 96%

Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments

Wolbertus

Kroesen

Hoed

et al. 2018

Transportation Research Part D: Transport and Environment

104

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Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experimentsWolbertus, Rick; Kroesen, Maarten; van den Hoed, Robert; Chorus, Caspar G. AbstractPolicy makers are looking for effective ways to promote the adoption of electric vehicles (EVs). Among the options is the roll-out and management of charging infrastructure to meet the EV drivers' refuelling needs. However, policies in this area do not only have a long-term effect on the adoption of EVs among prospective owners, they also have short-term impacts on the usage of public charging infrastructure among current EV owners and vice-versa. Presently, studies focusing on both effects simultaneously are lacking, missing out on possible cross-pollination between these areas. This study uniquely combines stated and revealed preference data to estimate the effect of particular policy measures aimed at EV adoption, on the one hand, and charging behaviour, on the other. Using a large dataset (1.7 million charging sessions) related to charging behaviour using public charging infrastructure in the Netherlands we quantify the effects of (i) daytime-parking (to manage parking pressure) and (ii) free parking (to promote purchase of EVs) policies on charging behaviour. To estimate the effects of these particular policies on EV purchase intentions, a stated choice experiment was conducted among potential EV-buyers. Results show that cross-pollinations between EV charging and adaptation policies exist and should be taken into account when designing policies for EV adoption.©2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. https://creativecommons.org/licenses/by-nc-nd/4.0/

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Section: Literature Review 21 Charging Behaviourmentioning

confidence: 96%

Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments

Wolbertus

Kroesen

Hoed

et al. 2018

Transportation Research Part D: Transport and Environment

104

View full text Add to dashboard Cite

show abstract

“…It is generally accepted that the charging of a large population of EVs will have two major effects on the grid; it will increase the overall power load needed and it will increase the load on local distribution networks. For example, Morrissey et al (2016) found that electric vehicles users prefer to charge at home in the evening at peak electricity demand times. Either of these effects could become the limiting factor but it is believed that the local load distribution problems will become prevalent sooner than the more general overall power supply issue (Denholm et al, 2006;Liu et al, 2011;Duvall et al, 2007;Clement-Nyns et al, 2010).…”

Section: Introductionmentioning

confidence: 98%

Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data

Brady

O’Mahony

2016

Sustainable Cities and Society

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154

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“…To analyse its effectiveness, the paper applied this strategy to quick CSs where there are multiple types of EVs and high traffic inflow. In fact, the size of EV traffic inflow is relative to the size of total CS capacity [3,4]. When the traffic inflow is known, EV traffic inflow is greater and increases as the total CS capacity decreases.…”

Section: Parameter Settings Of Csmentioning

confidence: 99%

“…Currently, electric vehicles (EV), as a kind of low-carbon and environmentally friendly mode of transport, have become a research focus [1,2] of concern to many national governments, vehicle manufacturers, and energy enterprises. With large-scale popularisation of EVs, charging stations (CS) constructed on highways will provide favourable conditions for long-distance driving of EVs [3,4]. Limitations in charging time for EVs, which can affect the convenience of EV charging, are mainly caused by the constraints of the power level of charging facilities [5] and acceptable charging profile of EV batteries [6,7].…”

Section: Introductionmentioning

confidence: 99%

Study of a New Quick-Charging Strategy for Electric Vehicles in Highway Charging Stations

et al. 2016

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Abstract:To solve the problem, because of which conventional quick-charging strategies (CQCS) cannot meet the requirements of quick-charging for multiple types of electric vehicles (EV) on highways where vehicle inflow is excessive, this paper proposed a new quick-charging strategy (NQCS) for EVs: on the premise of not affecting those EVs being charged, the remaining power of the quick-charging pile with multiple power output interfaces is used to provide a synchronous charging service for EVs waiting in the queue. To verify the effectiveness of this strategy, a power distribution model of charging pile and a queuing model of charging station (CS) were constructed. In addition, based on an actual highway service area where vehicle inflow is excessive during the simulation period (0:00-24:00), charging situations of CQCS and NQCS were respectively simulated in a charging station (CS), with different number of chargers, by basic queuing algorithm and an improved queuing algorithm. The simulation results showed that when the relative EV inflow is excessive, compared to CQCS, NQCS not only can reduce user waiting time, charging time, and stay time, but also can improve the utilisation rate of charging infrastructure and service capacity of CS and reduce the queue length of CS. At the same time, NQCS can reduce the impact on the power grid. In addition, in NQCS, the on-demand power distribution method is more efficient than the average power distribution method. Therefore, NQCS is more suitable for quick-charging for multiple types of EVs on highways where vehicle inflow is excessive.

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Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour

Cited by 356 publications

References 9 publications

Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments

Policy effects on charging behaviour of electric vehicle owners and on purchase intentions of prospective owners: Natural and stated choice experiments

Modelling charging profiles of electric vehicles based on real-world electric vehicle charging data

Study of a New Quick-Charging Strategy for Electric Vehicles in Highway Charging Stations

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