Charging behaviour of users utilising battery electric vehicles and extended range electric vehicles within the scope of a field test

Dost, Philip; Spichartz, P.

doi:10.1109/icrera.2015.7418592

Cited by 16 publications

(6 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Equations (13) and (14) illustrate the mechanism of the calculating calendar (D CAL p,s ) and cyclic (D CYC p,s ) degradation [21].…”

Section: Problem Statementmentioning

confidence: 99%

“…In addition, considering only a single PEV fleet in the simulation as a proof of concept or assuming that all vehicles have the same features are other shortcomings of prior studies [8][9][10][11][12]. The limitations of the distribution network are essential to be considered in the modeling framework, while they are neglected in some prior studies [13][14][15]. On-peak charging might cause serious problems such as voltage instability, reserve capacity, and voltage collapse, voltage insecurity, and power loss.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

View full text Add to dashboard Cite

The negative environmental impacts of using fossil fuel-powered vehicles underlined the need for inventing an alternative eco-friendly transportation fleet. Plug-in electrical vehicles (PEVs) are introduced to cut the continuing increase in energy use and carbon emission of the urban mobility. However, the increased demand for mobility, and therefore energy, can create constraints on the power network which can reduce the benefits of electrification as a certain and reliable source. Thus, the rise in the use of electric vehicles needs electric grids to be able to feed the increased energy demand while the current infrastructure supports it. In this paper, we introduce a methodological framework for scheduling smart PEVs charging by considering the uncertainties and battery degradation. This framework includes an economic model for charging and discharging of PEVs which has been implemented in a 21-node sample distribution network with a wind turbine as a distributed generation (DG) unit. Our proposed approach indicates that the optimal charging of the PEVs has a high impact on the distribution network operation, particularly under the high market penetration of PEVs. Thus, the smart grid to vehicle (G2V) charging mode is a potential solution to maximize the PEV’s owner profit, while considering the battery degradation cost of the PEVs. The simulation result indicates that smart charging effectuation is economical.

show abstract

“…Equations (13) and (14) illustrate the mechanism of the calculating calendar (D CAL p,s ) and cyclic (D CYC p,s ) degradation [21].…”

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Such flexibility favors applications of the proposed method in battery systems operating under various conditions. 27 Third, the transfer-learning technique enables the fast development of a new DNN for different batteries with a less computational burden and higher accuracy. We demonstrate that a pre-trained DNN based on one dataset can quickly adapt to other datasets where different batteries undergo different aging tests.…”

Section: Context and Scalementioning

confidence: 99%

Deep neural network battery charging curve prediction using 30 points collected in 10 min

Tian

Xiong

Shen

et al. 2021

Joule

212

View full text Add to dashboard Cite

“…The battery's capacity determines the driving range of EVs, and the complexity of the engine also plays a factor. Recent research has focused on increasing the range of EVs [8]. The primary goal of internet of things (IoT) is to improve the quality of human existence [9].…”

Section: Introductionmentioning

confidence: 99%

Internet of things based real-time electric vehicle and charging stations monitoring system

Mohammed

Qahtan

Ali

2022

IJEECS

View full text Add to dashboard Cite

Due to a shortage of fuel sources and the increment in environmental pollution, efficient techniques should be introduced. The best solution is to move to the use of electric vehicles. The article aims to develop a solution for electric vehicle (EV) charging station locations that utilize the internet of things (IoT) technology. The IoT is a paradigm that uses sensors and transmitting networks to provide current facilities with a real-time global communication perspective of the physical world. This paper proposes a real-time system to provide a real-time update to EV location and charging stations (CSs) location to reduce time lost by users searching CSs, and provides real-time charging station (CS) recommendations for EV users by displaying the nearest CS, provide estimation arrival time to the nearest CS, display distance between nearest CS and EV real-time updated. The work of the proposed system was tested, and the most significant error rate (17 meters) is represented by the difference in the distance obtained from the system and the distance obtained from Google Map. The total accuracy of the design for the tested case is (98.014%).

show abstract

Charging behaviour of users utilising battery electric vehicles and extended range electric vehicles within the scope of a field test

Cited by 16 publications

References 7 publications

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Optimal Charging of Plug-In Electric Vehicle: Considering Travel Behavior Uncertainties and Battery Degradation

Deep neural network battery charging curve prediction using 30 points collected in 10 min

Internet of things based real-time electric vehicle and charging stations monitoring system

Contact Info

Product

Resources

About