Driving without anxiety: A route planner service with range prediction for the electric vehicles

At present, battery charging operations constitute one of the most critical obstacles toward a large-scale uptake of Electric Mobility (EM), due to performance issues and implementation complexities. Although several solutions based on the utilization of Information and Communication Technologies (ICTs) and on mobile applications have been investigated to assist the Electric Vehicles (EVs) drivers and to coordinate the charging operations, there is still the problem of how to evaluate and validate such solutions on realistic scenarios, due to the lack of accurate simulators integrating vehicular mobility, wireless communication and battery charging/discharging models. In this paper, we attempt to fill this gap, by proposing a novel EV simulation platform that can assist the pre-deployment of charging infrastructures and services on realistic, large-scale EM scenarios. The simulation platform, realized within the ARTEMIS EU project "Internet of Energy for Electric Mobility" (IoE), supports two utilization modes, i.e. evaluation of EM scenarios and immersive emulation of EM-related mobile applications, thanks to a semantic architecture through which virtual and real components can be integrated in a seamless way. We provide three major contributions with respect to the state of the art. First, we extend the existing co-simulation platform composed by SUMO (vehicular traffic simulator) and OMNET++ (network simulator) with realistic models of EVs, EVSEs and ontology-based communication protocols that enable the deployment of city-wide mobile services (e.g. charging reservation). Second, we validate the battery model against the consumptions data of target EVs, and we evaluate the operations of EVs on a large-scale scenario (the city of Bologna), by analyzing the effectiveness of the charging reservation process and the resulting impact to the smart grid. Finally, we introduce the Mobile Application Zoo (MAZ), a sandbox through which EM-related mobile applications can be seamlessly integrated within the simulation platform in order to be validated on virtual environments before their deployment on real scenarios, and we describe the implementation of an Android app for battery monitoring and charging reservation. , IEEE Transactions on Vehicular Technology 2 mechanical transmissions, etc). Similarly, battery models have been extensively investigated to compute the discharging of the battery over time, and to reproduce EV dynamics in an accurate way [15][16] [17]. However, none of these tools can be used to study complex dynamics of EM scenarios (like the impact of EVs to the smart grid, or the effectiveness of charging reservation policies), where it is required to model several interacting entities characterized by different time-scale behaviors (i.e. EVs, EVSEs, the electrical grid, the communication network). To this aim, the most pioneering works are [22][23] [24]. The main contribution of this paper is the design and implementation of an integrated framework to perform large-scale simulations of Electric Mobility...

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“…Through the embedding described so far, we validated the correctness of applications described in [47,48] before their real deployment.…”

Section: B Mobile Application Zoo (Maz): Embedding Real Components Imentioning

confidence: 96%

An Integrated Simulation Framework to Model Electric Vehicle Operations and Services

Bedogni

Bononi

Felice

et al. 2016

IEEE Trans. Veh. Technol.

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“…e peak hours of private PEVs to come home from work are from 17:00 to 19:00 and the charging time is from 19:00 to 6:00 the next day. So, a PEV can connect to the power grid twice a day, and the initial SOC obeys the normal distribution N(0.6, 0.01) [8]. Considering the traveling purpose of PEV users, the SOC after PEV charging or discharging must be higher than 0.6.…”

Section: E Model Of Pevs Connected To the Power Grid And Initial Socmentioning

confidence: 99%

“…In this part, we assumed that each PEV's charging power is constantly equal to 3.12 kW, the start time of charging obeys the normal distribution N(9, 0.25), N(19, 1.25), respectively, [5] and the corresponding probability of PEVs connected to the power grid is 0.5 and 0.7, respectively [8].…”

Section: E Model Of Pevs Connected To the Power Grid And Initial Socmentioning

confidence: 99%

“…e Monte Carlo simulation method is used to extract the initial SOC, the charging start time, and the charging nish time of PEVs. en, we can obtain the result of load calculation under the constraint conditions of meeting the charging time requirements [8]. e calculation model of PEVs charging load based on the Monte Carlo simulation method is shown in Figure 2.…”

Section: E Model Of Pevs Connected To the Power Grid And Initial Socmentioning

confidence: 99%

“…A new mathematical model was established in the literature [5], to increase the degree of satisfaction of PEV owners and decrease the power grid's operation cost. Based on the Monte Carlo simulation algorithm, the charging load curves of different types of PEVs were established in the literature [8,9]. Considering the economic benefit of power grid and PEV owners, a bilayer system was designed in [9].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Optimization of Plug-In Electric Vehicles Charging and Discharging Behaviors in Electrical Distribution Grid

Han

Wang

et al. 2018

Journal of Electrical and Computer Engineering

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In most countries, the problems of energy and environment are becoming worse. To deal with the environmental impacts and the dependence on fossil energy, many solutions were proposed. Plug-in electric vehicles (PEVs) is one of the best technique among these solutions. However, the large number of PEVs connected to the power grid simultaneously might increase power fluctuation or even cause the electricity shortage and thus affecting the typical use of the basic load. To cope with this issue and inspire PEV users coordinating with scheduling results, an algorithm was proposed to ensure the power transmission safety of branches and maximize the economic benefits. Considering the cost of both PEV owners and the power grid, a two-phase model of optimizing PEVs charging and discharging behaviors was built. According to the traveling purpose of PEV owners and the current electricity price, in the first phase, a novel model which defines each PEV’s charging or discharging status was established. The number of PEVs’ charging and discharging in each charging station can be obtained. Considering the constraints on the power transportation of branch, in the second phase, we built a mathematical model to maximize the benefit of both power grid and PEV owners. The genetic algorithm was used to optimize the charging and discharging power of PEVs. Simulation results show that the optimization method proposed in this paper has a better performance on the daily power curve compared with the uncoordinated PEVs charging.

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Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

Yılmaz,

Yagmahan

2024

Sustainable Energy, Grids and Networks

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Driving without anxiety: A route planner service with range prediction for the electric vehicles

Cited by 13 publications

References 6 publications

An Integrated Simulation Framework to Model Electric Vehicle Operations and Services

An Integrated Simulation Framework to Model Electric Vehicle Operations and Services

A Novel Optimization of Plug-In Electric Vehicles Charging and Discharging Behaviors in Electrical Distribution Grid

Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

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