Eiman Y. ElBanhawy

INTEGRATING SPACE-SYNTAX AND DISCRETE-EVENT SIMULATION FOR E-MOBILITY ANALYSIS

Modeling and simulation of dynamic systems has been commonly used in the context of transportation, urban planning, and land use as being the basic tool for planners and policy makers. Vehicular movement modeling is one of the most popular models that deal with relevant aspects of urban regions and communities. This paper focuses on a particular mobility system; electric vehicles (EVs) clusters. It presents a study was conducted to simulate EVs population of the inner urban core of Newcastle-Gateshead via a developed 2D simulation model. The novelty of this study is the new approach proposed to simulate EV population in particular vicinity. This is by utilizing hybrid simulation technique (agent based modeling and discrete events) while applying space syntax theory and principles to predict the travel demand pattern of the urban system. The combination of these layers of modeling within the context of electrical mobility has proven successful in portraying the population and showed promising results. It aims at providing guidelines and recommendations to locate preliminary charging points and determine their numbers and capacities, which should be of interest for researchers, planning authorities and policy makers. This paper is a part of an EU project that focuses on simulating a part of the North Sea Region e-mobility system.

Analysis of space-time behaviour of electric vehicle potential user and commuter

We just have to accept the fact that transport sector is considered to be one of the major contributors to greenhouse gases emissions and it is soaring. The wide-scale adoption of privately owned low carbon emissions vehicles certainly would provide improvement. The Diffusion of electric vehicles (EVs) is on the forefront of the non-conventional powertrain technology developments. The acceptance of EV is a function of socio-economic, technical and spatiotemporal factors which potential users and even existing commuters encounter. This article explores space-time analysis of EV potential adopters and early adopters. The social aspect of a selected EV system is the core concept of this piece of work. It discusses the quantification of EV system utilization and testing the level of awareness of an existing EV network. The article presents a methodological approach to planning authorities and policy makers to tackle the major issues of owning an EV hence better understanding of the EV populations supply and demand. The selected baseline study is the inner urban core of metropolitan area of Newcastle Upon Tyne, United Kingdom.

Towards an ambient awareness interface for home battery storage system

Roof-mounted photovoltaic (PV) generation is becoming more prevalent within the domestic setting. Recently battery systems have enabled households to store excess self-generated electricity for subsequent use. However the associated user-interfaces and displays can be hard to understand, potentially preventing households from optimizing their solar usage. This paper introduces a known method being deployed in a new context. It reports on on-going research that investigates the effect of in-home ambient light displays linked to the home battery system. The paper covers the design stage and potential feedback solutions to raise awareness and influence consumer behaviour to promote energy conservation. An Ambient Light System is proposed to enable better user feedback. The study outlines the design recommendation for an ambient light display to be used in an energy consumption context. Using such a display, households can optimize use of low Carbon solar energy within the home, thus minimizing grid electricity usage.

Hybrid-OD matrix based simulation approach to identify e-charging hotspots in transport network

The use of bottom up approach simulation in transportation provides a rich framework for behaviours to emerge through interactions. The agent based modeling is a modeling technique that takes into account the attributes, constraints and variables imposed by the agent and the environment. Focusing on a alternative means of transport and in particular electric vehicles, this paper is taking place. The study proposes an integrated approach to identify electric charging hotspots in urban areas. It investigates coupling travel demand modeling to multi-model simulation technique. This is conducted through a case study; trip distribution and traffic data is provided for the given urban area. Socioeconomic and technical fundamentals of the EV system are addressed to shed light on the possibility of having integrated interactive design configurations of EV system in metropolitan areas. The outcome of the study can potentially be used in planning for future e-mobility.

The Agent Based Modeling of e-mobility

Capturing vehicular travel behaviour is one of the most popular models that deal with relevant aspects of urban regions and communities. A borrowed simulation technique used in social systems is employed where representing dynamic behaviour of Electric Vehicle real-time system. The simulation approach taken is to model the vehicle and network using procedural and declarative logic in order to simplify the overall system level computational complexity, and also to capture the behaviour of the drivers battery charging decisions. It aims at providing a model that can spot the gaps in the infrastructure network hence evaluate the network reliability. This article explores a promising experimental approach so as to assist in the design phase of EV charging network. A simplified hypothetical-urban-diagram is proposed showing all key theories needed. This article works as a glossary that covers the math behind modeling an EV system. It goes through the interactions and messaging protocol which are taking place in the simulation environment, simulation paragon, the agent architecture. The present study targets social scientists, policy makers and planning authorities, who are concerned with the EVs hard and soft infrastructure.

Straight from the Horse’s Mouth: “I am an Electric Vehicle User, I am a Risk Taker.” [EV14, M, c. 30]

The car has become ubiquitous in late modern society. Electric vehicles (EVs) show potential to reduce environmental burdens of the transport sector. EV-niche market acquires more available and reliable charging infrastructure to support current and potential users. The location-allocation of the recharging facilities is not a new planning problem; however, the planning for newly-adopted low carbon emissions vehicles infrastructure has distinctive design requirements, sociotechnical and demographic factors. This paper reports on the end-user’s insight and perceptions. Using ethnographic approach, an interview-based study was carried out addressing 15 EV-users in the North East of England. The sample covered a wide spectrum of active EV-users. Clustering analysis is employed as a dimensional technique for data mining and forming the participants’ charging profiles. The model generated 3 clusters; each one is presented and discussed. This study presents a new way of capturing the social aspect of the EV-system and reports on qualitative techniques in EV-context.

The Charging Personas of the E-Mobility Users of Newcastle-Gateshead Urban Area

From a planning and policy making standpoint, the location allocation of electric vehicle (EV) refueling stations is intrinsic problem. The preposition is that the more the charging points are installed, the higher the possibility of potential users gaining confidence in driving their EVs. However, the unplanned deployment of infrastructure may cause a waste of investment. Previous research focused on the domestic charging events and the connection to the grid, slim literature covered the non-domestic charging events and patterns of EV owners. This article develops and identifies the charging personas of EV owners using non-domestic charging points. Spatiotemporal analysis was conducted based on the usage data provided by Charge Your Car (CYC) Ltd. Company, the service provider in Tyne and Wear County. This paper reports on the e-mobility system of a metropolitan area in the North East of England, Newcastle-Gateshead Area. It proposes a methodological approach to analyse current EV users’ patterns and assists planning authorities and policy makers in understanding the mobility system hence strategically plan for future EV users.

To Cluster the E-Mobility Recharging Facilities (RFs)

The world is witnessing an accelerating expansion of urban areas and intensive urbanisation. The robust relation between transport infrastructure and urban planning is reflected in how integrated and reliable a system is within the urban fabric. Designing an integrated infrastructure to support full electric vehicle (EV) use is a crucial matter, which worries planning authorities, policy makers, as well as current and potential users. Reducing range anxiety by facilitating access to public recharging facilities is designed to overcome the main barrier that stops potential users to utilise EVs. The uncertainty of having a reliable and integrated charging infrastructure also presents hurdles, and slows down the growing trend of smart ecosystems and sustainable urban communities as a whole. Automotive, battery and utility technologies have formed the cornerstone of the EV industry to compete with currently mainstream means of transport, and to gain more prominence within many regions. Strategically locating public EV charging points will help to pave the way for better market penetration of EVs. This paper analyses real information about EV users in one of these metropolitan areas. A case study of 13 charging points with 48 EV users located in the inner urban core (NE1 postcode district) of a metropolitan area in North East England, the city of Newcastle upon Tyne, incorporating space-time analysis of the EV population, is presented here. Information about usage and charging patterns is collected from the main local service provider in North East England, Charge Your Car (CYC) Ltd. The methodology employed is a clustering analysis. It is conducted as a dimensional analysis technique for data mining and for significant analysis of quantitative data sets. A spatial and temporal analysis of charging patterns is conducted using SPSS and predictive analytics software. The study outcomes provide recommendations, exploring design theory and the implementation of public EV recharging infrastructure. The chapter presents a methodological approach useful for planning authorities, policy makers and commercial agents in evaluating and measuring the degree of usability of the public electric mobility system.