An EV Charging Management System Concerning Drivers' Trip Duration and Mobility Uncertainty

With continually increased attention on Electric Vehicles (EVs) due to environment impact, public Charging Stations (CSs) for EVs will become common. However, due to the limited electricity of battery, EV drivers may experience discomfort for long charging waiting time during their journeys. This often happens when a large number of (on-the-move) EVs are planning to charge at the same CS, but it has been heavily overloaded. With this concern, in an EV charging management system, we focus on CS-selection decision making and propose a scheme to manage EVs' charging plans, to minimize drivers' trip duration through intermediate charging at CSs. The proposed scheme jointly considers EVs' anticipated charging reservations (including arrival time, expected charging time) and parking duration at CSs. Furthermore, by tackling mobility uncertainty that EVs may not reach their planned CSs on time (due to traffic jams on the road), a periodical reservation updating mechanism is designed to adjust their charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our proposal, in terms of minimized drivers' trip duration, as well as charging performance at the EV and CS sides

Electric Vehicle Charging Recommendation and Enabling ICT Technologies: Recent Advances and Future Directions

The introduction of Electric Vehicles (EV) will have a significant impact on the sustainable economic development of urban city. However, compared with traditional gasoline-powered vehicles, EVs currently have limited range, which necessitates regular recharging. Considering the limited charging infrastructure currently available in most countries, infrastructure investments and Renewable Energy Sources (RES) are critical. Thus, service quality provisioning is necessary for realizing EV market. Unlike numerous previous works which investigate "charging scheduling" (referred to when/whether to charge) for EVs already been parked at home/Charging Stations (CSs), a few works focus on “charging recommendation” (refer to where/which CS to charge) for on-the-move EVs. The latter use case cannot be overlooked as it is the most important feature of EVs, especially for driving experience during journeys. On-the-move EVs will travel towards appropriate CSs for charging based on smart decision on where to charge, so as to experience a shorter waiting time for charging. The effort towards sustainable engagement of EVs has not attracted enough attention from both industrial and academia communities. Even if there have been many charging service providers available, the utilization of charging infrastructures is still in need of significant enhancement. Such a situation certainly requires the popularity of EVs towards the sustainable, green and economic market. Enabling the sustainability requires a joint contribution from each domain, e.g., how to guarantee accurate information involved in decision making, how to optimally guide EV drivers towards charging place with the least waiting time, how to schedule charging services for EVs being parked within grid capacity. Achieving this goal is of importance towards a positioning of efficient, scalable and smart ICT framework, makes it feasible to learn the whole picture of grid: - Necessary information needs to be disseminated between stakeholders CSs and EVs, e.g., expected queuing time at individual CSs. In this context, how accurate CSs condition information plays an important role on the optimality of charging recommendation. - Also, it is very time-consuming for the centralized Global Controller (GC) to achieve optimization, by seamlessly collecting data from all EVs and CSs, The complexity and computation load of this centralized solution, increases exponentially with the number of EVs. This paper summaries the recent interdisciplinary research works on EV charging recommendation along with novel ICT frameworks, with an original taxonomy on how Intelligent Transportation Systems (ITS) technologies support the EV charging use case. Future directions are also highlighted to promote the future research

Applying DTN Routing for Reservation-Driven EV Charging Management in Smart Cities

Charging management for Electric Vehicles (EVs) on-the-move (moving on the road with certain trip destinations) is becoming important, concerning the increasing popularity of EVs in urban city. However, the limited battery volume of EV certainly influences its driver’s experience. This is mainly because the EV needed for intermediate charging during trip, may experience a long service waiting time at Charging Station (CS). In this paper, we focus on CS-selection decision making to manage EVs’ charging plans, aiming to minimize drivers’ trip duration through intermediate charging at CSs. The anticipated EVs’ charging reservations including their arrival time and expected charging time at CSs, are brought for charging management, in addition to taking the local status of CSs into account. Compared to applying traditionally applying cellular network communication to report EVs’ charging reservations,we alternatively study the feasibility of applying Vehicle-to-Vehicle (V2V) communication with Delay/Disruption Tolerant Networking (DTN) nature, due primarily to its flexibility and cost-efficiency in Vehicular Ad hoc NETworks (VANETs). Evaluation results under the realistic Helsinki city scenario show that applying the V2V for reservation reporting is promisingly cost-efficient in terms of communication overhead for reservation making, while achieving a comparable performance in terms of charging waiting time and total trip duration

Towards Anycasting-driven Reservation System for Electric Vehicle Battery Switch Service

Electro-Mobility has become an increasingly important research problem in urban city. Due to the limited electricity of battery, Electric Vehicle (EV) drivers may experience discomfort for long charging waiting time. Different from plug-in charging technology, we investigate the battery switch technology to improve EV drivers’ comfort (e.g., reduce the service waiting time from tens of minutes to a few minutes), by benefiting from switchable (fully-recharged) batteries cycled at Charging Stations (CSs). Since demand hotspot may still happen at CSs (e.g., running out of switchable batteries), incoming EVs may wait additional time to get their battery switched, and thus the EV driver’s comfort is degraded. Firstly, we propose a centralized reservation enabling service, considering EVs’ reservations (including arrival time, expected charging time of their batteries to be depleted) to optimally coordinate their battery switch plans. Secondly, a decentralized system is further proposed, by facilitating the Vehicle-to-Vehicle (V2V) anycasting to deliver EV’s reservations. This helps to address some of the privacy issues that can be materialized in centralized system and reduce communication cost (e.g., through cellular network for reservation making). Results under the Helsinki city scenario show a trade-off between comparable performance (e.g., service waiting time, number of switched batteries) and cellular network cost for EVs’ reservations delivery

Mobile Charging as a Service: A Reservation-Based Approach

This paper aims to design an intelligent mobile charging control mechanism for Electric Vehicles (EVs), by promoting charging reservations (including service start time, expected charging time, and charging location, etc.). EV mobile charging could be implemented as an alternative recharging solution, wherein charge replenishment is provided by economically mobile plug-in chargers, capable of providing on-site charging services. With intelligent charging management, readily available mobile chargers are predictable and could be efficiently scheduled towards EVs with charging demand, based on updated context collected from across the charging network. The context can include critical information relating to charging sessions as well as charging demand, etc. Further with reservations introduced, accurate estimations on charging demand for a future moment are achievable, and correspondingly, optimal mobile chargersselection can be obtained. Therefore, charging demands across the network can be efficiently and effectively satisfied, with the support of intelligent system-level decisions. In order to evaluate critical performance attributes, we further carry out extensive simulation experiments with practical concerns to verify our insights observed from the theoretical analysis. Results show great performance gains by promoting the reservation-based mobile charger-selection, especially for mobile chargers equipped with suffice power capacity

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

An EV Charging Management System Concerning Drivers’ Trip Duration and Mobility Uncertainty

With continually increased attention on electric vehicles (EVs) due to environment impact, public charging stations (CSs) for EVs will become common. However, due to the limited electricity of battery, EV drivers may experience discomfort for long charging waiting time during their journeys. This often happens when a large number of (on-the-move) EVs are planning to charge at the same CS, but it has been heavily overloaded. With this concern, in an EV charging management system, we focus on CS-selection decision making and propose a scheme to manage EVs' charging plans, to minimize drivers' trip duration through intermediate charging at CSs. The proposed scheme jointly considers EVs' anticipated charging reservations (including arrival time and expected charging time) and parking duration at CSs. Furthermore, by tackling mobility uncertainty that EVs may not reach their planned CSs on time (due to traffic jams on the road), a periodical reservation updating mechanism is designed to adjust their charging plans. Results under the Helsinki city scenario with realistic EV and CS characteristics show the advantage of our proposal, in terms of minimized drivers' trip duration, as well as charging performance at the EV and CS sides

Towards Efficient, Scalable and Coordinated On-the-move EV Charging Management

Unlike traditional Internal Combustion Engine Vehicles ICEVs), the introduction of Electric Vehicles (EVs) is a significant step towards green environment. Public Charging Stations (CSs) are essential for providing charging services for on-the-move EVs (e.g., EVs moving on the road during their journeys). Key technologies herein involve intelligent selection of CSs to coordinate EV drivers’ charging plans, and provisioning of cost-efficient and scalable communication infrastructure for information exchange between power grid and EVs. In this article, we propose an efficient and scalable Publish/Subscribe (P/S) communication framework, in line with a coordinated onthe-move EV charging management scheme. The case study under the Helsinki city scenario shows the advantage of proposed CS-selection scheme, in terms of reduced charging waiting time and increased number of charged EVs, as charging performance metrics at EV and CS sides. Besides, the proposed P/S communication framework shows its low communication cost (in terms of signallings involved for charging management), meanwhile with great scalability for supporting increasing EVs’ charging demands