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The increasing adoption of electric vehicles (EVs) due to technical advancements and environmental concerns requires wide deployment of public charging stations (CSs). In order to accelerate the EV penetration and predict the future CSs requirements and adopt proper policies for their deployment, studying the charging behavior of EV drivers is inevitable. This paper introduces a stochastic model that takes into consideration the behavioral characteristics of EV drivers in particular, in terms of their reaction to the EV battery charge level when deciding to charge or disconnect at a CS. The proposed model is applied in two case studies to describe the resultant collective behavior of EV drivers in a community using real field EV data obtained from a major North American campus network and part of London urban area. The model fits well to the datasets by tuning the model parameters. The sensitivity analysis of the model indicates that changes in the behavioral parameters affect the statistical characteristics of charging duration, vehicle connection time, and EV demand profile, which has a substantial effect on congestion status in CSs. This proposed model is then applied in several scenarios to simulate the congestion status in public parking lots and predict the future charging points needed to guarantee the appropriate level of service quality. The results show that studying and controlling the EV drivers’ behavior leads to a significant saving in CS capacity and results in consumer satisfaction, thus, profitability of the station owners. [ABSTRACT FROM AUTHOR]

Cellular network deployment with three sectors per site and a frequency reuse factor of one is key to supporting high data rate applications in LTE Advanced Networks. However, such configurations result in increased inter-cell interference. In this paper we model the effects of inter-cell interference on an LTE-Advanced network at two carrier frequencies. We consider two urban environments and four cell ranges. The effects of inter-cell interference are separated into inter-site interference and inter-sector interference. A site-specific 3D ray-tracing tool is used to model the communication channel between the base stations (serving and interfering links) and the user equipment terminals. The inter-site interference is first modelled in terms of its dominant interference proportion and then validated using previously reported field measurements recorded in London (U.K.). Secondly, the inter-site and inter-sector interference components are modelled as a function of user equipment relative position from the serving base stations. Our approach is compatible with the standardized ITU-R model and our proposal can easily be added to these international models. Our model requires no system layout for the interferers and reduces the required channel generation time by a factor of 12 compared with the system layout method. [ABSTRACT FROM AUTHOR]

This paper describes a novel adaptive battery model based on a remapped variant of the well-known Randles' lead-acid model. Remapping of the model is shown to allow improved modeling capabilities and accurate estimates of dynamic circuit parameters when used with subspace parameter-estimation techniques. The performance of the proposed methodology is demonstrated by application to batteries for an all-electric personal rapid transit vehicle from the Urban Light TRAnsport (ULTRA) program, which is designated for use at Heathrow Airport, U.K. The advantages of the proposed model over the Randles' circuit are demonstrated by comparisons with alternative observer/estimator techniques, such as the basic Utkin observer and the Kalman estimator. These techniques correctly identify and converge on voltages associated with the battery state-of-charge (SoC), despite erroneous initial conditions, thereby overcoming problems attributed to SoC drift (incurred by Coulomb-counting methods due to overcharging or ambient temperature fluctuations). Observation of these voltages, as well as online monitoring of the degradation of the estimated dynamic model parameters, allows battery aging (state-of-health) to also be assessed and, thereby, cell failure to be predicted. Due to the adaptive nature of the proposed algorithms, the techniques are suitable for applications over a wide range of operating environments, including large ambient temperature variations. Moreover, alternative battery topologies may also be accommodated by the automatic adjustment of the underlying state-space models used in both the parameter-estimation and observer/estimator stages. [ABSTRACT FROM AUTHOR]