Impact of electrical vehicles on strategic planning of energy infrastructure

Schulze, Matthias; Riveros, Juliana Zapata

doi:10.1109/powercon.2010.5666608

Cited by 10 publications

(4 citation statements)

References 11 publications

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“…In ATIS, the accurately predicted traffic information can be used to reduce the uncertainty of the future traffic states, improve traffic mobility, and provide the driver with a realistic estimation of travel times, expected delays, and alternative routes to the destinations. Our research and those of others showed that an accurately predicted vehicle speed profile of an intended route is important to achieve optimal fuel economy in a hybrid electrical vehicle [1], [3]- [6] and estimate distance to go in an electric vehicle [7].…”

Section: Introductionmentioning

confidence: 80%

Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile

Park

Murphey

McGee

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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Accurate prediction of the traffic information in real time such as flow, density, speed, and travel time has important applications in many areas, including intelligent traffic control systems, optimizing vehicle operations, and the routing selection for individual drivers on the road. This is also a challenging problem due to dynamic changes of traffic states by many uncertain factors along a traveling route. In this paper, we present an Intelligent Trip Modeling System (ITMS) that was developed using machine learning to predict the traveling speed profile for a selected route based on the traffic information available at the trip starting time. The ITMS contains neural networks to predict short-term traffic speed based on the traveling day of the week, the traffic congestion levels at the sensor locations along the route, and the traveling time and distances to reach individual sensor locations. The ITMS was trained and evaluated by using ten months of traffic data provided by the California Freeway Performance Measurement System along a California Interstate I-405 route that is 26 mi long and contains 52 traffic sensors. The ITMS was also evaluated by the traffic data acquired from a 32-mi-long freeway section in the state of Michigan. Experimental results show that the proposed system, i.e., ITMS, has the capability of providing accurate predictions of dynamic traffic changes and traveling speed at the beginning of a trip and can generalize well to prediction of speed profiles on the freeway routes other than the routes the system was trained on.

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Section: Introductionmentioning

confidence: 80%

Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile

Park

Murphey

McGee

et al. 2014

IEEE Trans. Intell. Transport. Syst.

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“…Electric and hybrid electric vehicles are the future trend of motor vehicle development. Complying with this trend, if we can achieve accurate and rapid prediction of congestion on the expected route, then it can be very helpful to achieve optimal fuel economy on the vehicle, as well as to estimate the range of the vehicle [11,[14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

Yang

Luo

Gao

et al. 2019

Journal of Advanced Transportation

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In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method.

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“…Recent trends have made the plug-in electric vehicles (PEVs) inevitable components of the future systems. Therefore, it is necessary to integrate all components of the future system in an inclusive model [1].…”

Section: Introductionmentioning

confidence: 99%

Integrating the PEVs' traffic pattern in parking lots and charging stations in micro multi-energy systems

Neyestani

Damavandi

Godina

et al. 2016

2016 51st International Universities Power Engineering Conference (UPEC)

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In this paper, a mixed-integer linear programing (MILP) model for the traffic behavior of plug-in electric vehicles (PEVs) in a multi energy system (MES) is proposed. It is assumed that two micro-MESs are covering two traffic zones with different consumption patterns. The difference between these two micro-MESs is not only the different multi energy demand (MED) they provide but also different PEV traffic pattern that travel in these two micro-MESs. The PEVs traffic pattern and their behavior in using parking lot (PL) or charging station (CS) as their charging places is integrated in the MES operation model. The results showed that the improved strategy of the MES operator in using its components such as combined heat and power (CHP) unit and auxiliary boiler (AB) in response to extra added load of PEVs. The stochastic behavior of the PEVs is implemented in the model through various scenarios of arrival and departure. Index Terms-Energy hub, charging station, micro multi-energy system (MES), parking lot (PL), plug-in electric vehicle (PEV).

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Impact of electrical vehicles on strategic planning of energy infrastructure

Cited by 10 publications

References 11 publications

Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile

Intelligent Trip Modeling for the Prediction of an Origin–Destination Traveling Speed Profile

Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

Integrating the PEVs' traffic pattern in parking lots and charging stations in micro multi-energy systems

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