Electric Vehicle Trial Research Articles

How Electric Vehicles and the Grid Work Together: Lessons Learned from One of the Largest Electric Vehicle Trials in the World

This article is a reprint from J. Quiros-Tortos, L. Ochoa and T. Butler, "How Electric Vehicles and the Grid Work Together: Lessons Learned from One of the Largest Electric Vehicle Trials in the World," in IEEE Power and Energy Magazine, vol. 16, no. 6, pp. 64-76, Nov.-Dec. 2018, doi: 10.1109/MPE.2018.2863060. Please access the original article on IEEE Xplore.

Capturing diversity in electric vehicle charging behaviour for network capacity estimation

This paper proposes a stochastic data-driven model for uncontrolled charging that accurately captures diversity in individual consumer behaviour. This is important because understanding the diversity between consumers is necessary to accurately estimate the number of electric vehicles’ charging a distribution network could support without reinforcements. The model combines readily available travel survey data with high resolution data from an electric vehicle trial, using clustering and conditional probabilities. We demonstrate through a case study of UK residential charging that existing approaches may overestimate the increase in peak distribution network demand by 50%, which has implications for assessing the cost of network investments required. We also show that the peak charging demand varies regionally from 0.2–1.4 kW per household, demonstrating the importance of using locally representative vehicle usage data.

Electric vehicles and rural business: Findings from the Warwickshire rural electric vehicle trial

The aim of this paper is to examine the impact of rurally based Small/Medium enterprises (SMEs) adopting electric vehicle (EV) technologies as part of their transport strategies. Existing research has demonstrated significant knowledge gaps relating to the impact of EV uptake on transport costs and planning for rurally based businesses. Instead, much of the literature has focused on the challenges faced by urban users of this technology. The paper extends the academic discourse surrounding the transition of the automotive sector to a zero emissions future by focussing on socio-technical factors in technology adoption. Importantly, the paper provides a rural emphasis on such change by reporting the results of a two-year EV demonstrator trial undertaken by sixteen businesses located in rural areas of Warwickshire, England, encouraging a much need shift in policymaking awareness. An important component of the research was to examine multi-level perspectives of technology adoption for rural firms and to question whether EVs would lead to locked-in transport technology pathways. The results of this trial gave a range of important insights into the outcomes of enterprises embracing commercial EVs for personal and business transport purposes. Evidence from the participants suggests that should EVs receive significant support in terms of infrastructure enhancement and technical developments they may prove a suitable substitute for existing transport provision in their rural locales.

How Electric Vehicles and the Grid Work Together: Lessons Learned from One of the Largest Electric Vehicle Trials in the World

In the coming years, hundreds of thousands of new electric vehicles (EVs), from plug-in hybrids to fully electric, will hit the roads around the world, adding to the current EV fleet of more than 2 million, according to the Global EV Outlook 2017. The electrification of transportation can bring environmental, health, and economic benefits when coupled with a low-carbon electricity generation portfolio; however, ensuring that this transition goes smoothly requires addressing several grid-integration challenges.

Combining samples to offset nonresponse and respondent biases

What if probabilistic-based sampling does not result in a representative sample? How can researchers overcome low respondent engagement and hypothetical choices that are perceived as being socially desirable? These questions are relevant regardless of the way primary data collection is conducted.A statistically sound sampling strategy still depends on individuals volunteering their participation. Even with extrinsic rewards, there is no guarantee the respondent will contribute an honest effort. This research reports on the data collection for a study investigating the acceptance of electric vehicles (EV) in Australia. Complementing the Western Australian Electric Vehicle Trial, this research focuses on household preferences and attitudes towards EV. The data set represents the last stage of data collection with four surveys (initially delivered to trial participants and later aimed at the broader community).An initial sample showed high interest in EV and environmentally friendly technologies, but higher education levels and higher socioeconomic status households were overrepresented. To compensate for the bias, a second sample was collected from an online panel (PureProfile). Although neither sample is representative of the population, the results from the pooled data are deemed more appropriate for understanding drivers of EV uptake in Western Australia and informing policy making accordingly.

Response of electric vehicle drivers to dynamic pricing of parking and charging services: Risky choice in early reservations

When clusters of electric vehicles charge simultaneously in urban areas, the capacity of the power network might not be adequate to accommodate the additional electricity demand. Recent studies suggest that real-time control strategies, like dynamic pricing of electricity, can spread the demand and help operators to avoid costly infrastructure investments. To assess the effectiveness of dynamic pricing, it is necessary to understand how electric vehicle drivers respond to uncertain future prices when they charge their vehicle away from home. Even when data is available from electric vehicle trials, the lack of variability in electricity prices renders them insufficient for this analysis. We resolve this problem by designing a survey where we observe the stated preferences of the respondents for hypothetical charging services. A novel feature of this survey is its interface, which resembles an online or smartphone application for parking-and-charging reservations. The time-of-booking choices are evaluated within a risky-choice modelling framework, where expected utility and non-expected utility specifications are compared to understand how people perceive price probabilities. In the progress, we bring together theoretical frameworks of forward-looking behaviour in contexts where individuals were subject to equivalent price uncertainties. The results suggest that (a) the majority of the electric vehicle drivers are risk averse by choosing a certain price to an uncertain one and (b) there is a non-linearity in their choices, with a disproportional influence by the upper end of the price distribution. This approach gives new perspectives in the way people plan their travel activities in advance and highlights the impact of uncertainty when managing limited resources in dense urban centres. Similar surveys and analyses could provide valuable insights in a wide range of innovative mobility applications, including car-sharing, ride-sharing and on-demand services.

Quantifying a cellular automata simulation of electric vehicles

Within this work the Nagel–Schreckenberg (NS) cellular automata is used to simulate a basic cyclic road network. Results from SwitchEV, a real world Electric Vehicle trial which has collected more than two years of detailed electric vehicle data, are used to quantify the results of the NS automata, demonstrating similar power consumption behavior to that observed in the experimental results. In particular the efficiency of the electric vehicles reduces as the vehicle density increases, due in part to the reduced efficiency of EVs at low speeds, but also due to the energy consumption inherent in changing speeds. Further work shows the results from introducing spatially restricted speed restriction. In general it can be seen that induced congestion from spatially transient events propagates back through the road network and alters the energy and efficiency profile of the simulated vehicles, both before and after the speed restriction. Vehicles upstream from the restriction show a reduced energy usage and an increased efficiency, and vehicles downstream show an initial large increase in energy usage as they accelerate away from the speed restriction.

Urban, sub-urban or rural: where is the best place for electric vehicles?

This paper critiques the assumption that an urban setting is the most appropriate for electric vehicles. In so doing, it questions the implementation of transitions theory and socio-technical experiments in the quest to establish electric vehicles as an alternative to the existing automobility regime. For vehicle owners and infrastructure providers alike, as with society generally, the benefits of electric vehicles are maximised when usage is most intense; a difficult situation to achieve in urban areas aside from light commercial delivery and service applications. Using case study information from electric vehicle trials under the ENEVATE research programme into users of such vehicles in North West Europe, it is concluded that more policy and research attention should be given to sub-urban and rural electric vehicle applications as promising niches from which wider socio-technical change may develop.

Demonstrating Demand Management: How Intelligent EV Charging Can Benefit Everyone

This paper establishes the case for Electric Vehicle (EV) charging demand management through in-field demonstration, electricity network modelling and financial assessment. As part of the Victorian Government Electric Vehicle Trial, DiUS Computing demonstrated EV charging demand management using United Energy’s Smart Grid. Modelling of the United Energy network by the University of Melbourne found that uncontrolled charging would require network augmentation once EVs are adopted by 10% of households. In contrast, managed charging would allow the network to support in excess of 50% uptake using existing capacity and infrastructure. Furthermore, the end-to-end EV charging demand management solution demonstrated by DiUS could be implemented for one tenth the cost of the network augmentation. Although success factors were identified during the demonstration that may serve as an input for demand management program design, electricity market arrangements may be the strongest determinant of adoption generally.