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Transportation

Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation.

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High-voltage batteries in battery electric vehicles face significant load fluctuations due to driving behavior. This dynamic performance of the powertrain is contrasted by the almost constant load of the auxiliary consumers. The highest auxiliary consumption is generated by the heating and air conditioning system, which decreases the vehicles range significantly. 72 real driving trips with a BMW i3 (60 Ah) were recorded, serving for model validation of a full vehicle model consisting of the powertrain and the heating circuit.

 Each trip contains:

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This provides the code and data used in the paper "Optimal EV Scheduling in Residential Distribution Networks Considering Customer Charging Preferences" by Mailys Le Cam and Barry Hayes. 

Some material has been adapated from the OpenDSS help files: http://smartgrid.epri.com/SimulationTool.aspx Some data has been taken from the IEEE test feeders archive: http://sites.ieee.org/pes-testfeeders/

 

 

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We propose a driver pattern dataset consists of 51 features extracted from CAN (Controller Area Network) of Hyundai YF Sonata while four drivers drove city roads of Seoul, Republic of Korea. Under the belief that different driving patterns implicitly exist at CAN data, we collected CAN diagnosis data from four drivers in pursuit of research on driver identification, driver profiling, and abnormal driving behavior detection. Four drivers are named A, B, C, and D.

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An inductive power transfer (IPT) system is envisaged as the best solution to conveniently charge electric vehicles (EVs). While stationary IPT systems are becoming commercialized, significant research is being conducted to address the challenges related to dynamic IPT systems. Dynamic or in-motion IPT systems require a fully electrified roadway with embedded inductive couplers with accompanying circuitry. The large number of electronic components required, however, increases the system complexity, reducing the reliability and economic viability of dynamic IPT systems proposed to-date.

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The trace location in Seoul, South Korea has an area spanning over 2.5 × 1.5 km where there are are more than 30 intersections. A number of simulated vehicles are moving in the road topology. Each vehicle shows following properties for every second: 

timestep(simulation) secondsThe time step described by the values within this timestep-element

ididThe id of the vehicle

typeidThe name of the vehicle type

speedm/sThe speed of the vehicle

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In this paper, a web-based application for DC Railways networks analysis is presented. The paper provides the guidelines to develop an integrated simulation framework containing different elements like server, databases, visual analytic tools using open-source software. In this case, the proposed application allows to design a DC railway feeding system and analyse the impact of the different agents like vehicles, substations, overhead feeding systems, on-board and wayside energy storage systems, etc.

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This work contains data gathered by a series of sensors (PM 10, PM 2.5, temperature, relative humidity, and pressure) in the city of Turin in the north part of Italy (more precisely, at coordinates 45.041903N, 7.625850E). The data has been collected for a period of 5 months, from October 2018 to February 2019. The scope of the study was to address the calibration of low-cost particulate matter sensors and compare the readings against official measures provided by the Italian environmental agency (ARPA Piemonte).

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