Analysis of electric vehicle user recharging behaviour and the effectiveness of using financial incentives to manage recharging demand

PhD ThesisAn anticipated increase in the number of electric vehicles (EVs) on the road has created the need to understand and manage recharging demand in order to prevent localised overloading of power distribution networks during peak hours. Smart meters at home, in conjunction with off-peak energy tariffs, have been proposed as a demand management tool. This has not been tested in a region with a high density recharging infrastructure whereby drivers pay an annual fixed fee for unlimited use of non-domestic recharging infrastructure networks. This research quantified daily recharging demand profiles and assessed the effectiveness of incentivising off-peak recharging in such a region. The North East of England was used as the study area. Between 2010 and 2013, 401 home, 312 workplace and 412 public non-domestic recharging posts were installed. Recharging data were available from SwitchEV; a three year, real world EV deployment study that commenced in 2010. Sources of data were in-vehicle loggers, focus groups and questionnaires. There were 23 Private, 43 Organisation Individual users and 74 Organisation Pool users in total. Five statistically significantly different representative recharging profiles were identified. None of these profiles had high demand peaks during the off-peak hours between midnight and 07:00hrs. Interventions took place for 21 users. A 50% reimbursement for off-peak recharging was offered. At home, off-peak recharging increased by 23%. No significant changes in recharging behaviour occurred at any other recharging location. There was also no statistically significant change in the proportion of total recharging recorded at each location. Focus groups and questionnaires revealed the common theme of drivers using EV recharging posts as they offer free and convenient parking bays, rather than out of a need to recharge the battery in order to complete an upcoming trip. Furthermore, the absence of timing devices and organisation policy dictating that EVs must be recharged immediately upon returning to the premises limited the ability of organisations to deliver behavioural change. It is recommended that pay-as-you-go access to non-domestic recharging infrastructure be implemented to reduce unnecessary daytime recharging and that workplace recharging infrastructure is fitted with smart meters. These changes are required as this research has highlighted limitations of the current proposed policy

On Data Management in Pervasive Computing Environments

Abstract—This paper presents a framework to address new data management challenges introduced by data-intensive, pervasive computing environments. These challenges include a spatio-temporal variation of data and data source availability, lack of a global catalog and schema, and no guarantee of reconnection among peers due to the serendipitous nature of the environment. An important aspect of our solution is to treat devices as semiautonomous peers guided in their interactions by profiles and context. The profiles are grounded in a semantically rich language and represent information about users, devices, and data described in terms of “beliefs,” “desires, ” and “intentions. ” We present a prototype implementation of this framework over combined Bluetooth and Ad Hoc 802.11 networks and present experimental and simulation results that validate our approach and measure system performance. Index Terms—Mobile data management, pervasive computing environments, data and knowledge representation, profile-driven caching algorithm, profile driven data management, data-centric routing algorithm. æ

Mobile Databases: a Selection of Open Issues and Research Directions

International audienceThis paper reports on the main results of a specific action on mobile databases conducted by CNRS in France from October 2001 to December 2002. The objective of this action was to review the state of progress in mobile databases and identify major research directions for the French database community. Rather than provide a survey of all important issues in mobile databases, this paper gives an outline of the directions in which the action participants are now engaged, namely: copy synchronization in disconnected computing, mobile transactions, database embedded in ultra-light devices, data confidentiality, P2P dissemination models and middleware adaptability

Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids

Energy management systems (EMSs) are nowadays considered one of the most relevant technical solutions for enhancing the efficiency, reliability, and economy of smart micro/nanogrids, both in terrestrial and vehicular applications. For this reason, the recent technical literature includes numerous technical contributions on EMSs for residential/commercial/vehicular micro/nanogrids that encompass renewable generators and battery storage systems (BSS) The volume “Energy Management Systems for Optimal Operation of Electrical Micro/Nanogrids”, was released as a Special Issue of the journal Energies, published by MDPI, with the aim of expanding the knowledge on EMSs for the optimal operation of electrical micro/nanogrids by presenting topical and high-quality research papers that address open issues in the identified technical field. The volume is a collection of seven research papers authored by research teams from several countries, where different hot topics are accurately explored. The reader will have the possibility to benefit from original scientific results concerning, in particular, the following key topics: distribution systems; smart home/building; battery energy storage; demand uncertainty; energy forecasting; model predictive control; real-time control, microgrid planning; and electrical vehicles

Gaussian Process Regression for In-situ Capacity Estimation of Lithium-ion Batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2-3% RMSE.Comment: 12 pages, 10 figures, submitted to IEEE Transactions on Industrial Informatic

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

Stretching, embeddedness, and scripts in a sociotechnical transition: explaining the failure of electric mobility at Better Place (2007–2013)

Based on field research, interviews, and participant observation, this study explores the failure of Better Place—a now bankrupt company—to successfully demonstrate and deploy battery swapping stations and electric vehicle charging infrastructure. To do so, it draws from concepts in innovation studies, sociotechnical transitions, management science, organizational studies, and sociology. The study expands upon the notion of “fit-stretch”, which explains how innovations can move from an initial “fit” (with existing user practices, discourses, technical form) to a subsequent “stretch” (as the technology further develops, new functionalities are opened up, etc.) in the process of long-term transitions. It also draws from the “dialectical issue life cycle model” or “triple embeddedness framework” to explain the process whereby incumbent industry actors can introduce defensive innovations to “contain” a new niche from expanding. It lastly incorporates elements from design-driven innovation and organizational learning related to schemas and scripts, concepts that illustrate the vision-dependent and discursive elements of the innovation process. It uses the case study of Better Place to test and build upon these concepts. With a market valuation of more than $2 billion, Better Place was poised to become one of the most innovative companies in the electric mobility market. Yet after operating for five years it declared bankruptcy and saw its assets sold off for less than$500,000. We suggest here that Better Place failed because it “stretched” to the point that it “broke;” that it provoked a defensive response from both old automotive manufacturers (such as General Motors) and new ones (such as Tesla); and that the fantastic nature of its visionary scripts convinced its investors and promoters to unrealistically raise expectations and downplay persistent risks