To ensure the usability and reliability of the collected data, one Hikvision monitoring camera (iDS-TCV900-AE/25) is deployed at the entrance of Taijia Expressway in Shanxi province in China for image capturing. This camera is installed on the roadside pole with a height of 5.8 meters and uses the infrared flash as the supplementary lighting. The captured images cover two lanes of the expressway, with the resolution being 4096*2160. All images are captured during the period of November 2019 to April 2020.

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Simulation results from EVI-OnDemand estimating need for infrastructure to support electric ride-hailing vehicles in the US.

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This paper proposes methods of predicting and preventing thermal failure within high-power ferrite structures of electric vehicle (EV) wireless charging inductive power transfer (IPT) by improving their ferrite layouts. A high-power IPT magnetic design suitable for wirelessly charging an EV at 50 kW using a heuristic approach is presented where the chosen design achieves reduced heating within the magnetic structure. Recommendations are made that both avoid ferrite fracturing due to magnetic hotspots and cause temperature differentials across ferrite tiles, and regarding airgap distribution

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This paper proposes methods of predicting and preventing thermal failure within high-power ferrite structures of electric vehicle (EV) wireless charging inductive power transfer (IPT) by improving their ferrite layouts. A high-power IPT magnetic design suitable for wirelessly charging an EV at 50 kW using a heuristic approach is presented where the chosen design achieves reduced heating within the magnetic structure. Recommendations are made that both avoid ferrite fracturing due to magnetic hotspots and cause temperature differentials across ferrite tiles, and regarding airgap distribution

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2D Map is collection of pure lines which are connected to form different features like roundabouts, culdisec, buildings, special figures..etc. In real Maps these lines are created using the latitude and longitude. The idea in this letter identify and validate the roundabouts using the geometry of the Map features by spline technique where the geometries is pass over the non rational uniform B spline function to get the knot vectors and hence the new control points, then the curvature($\kappa$) is calculated and hence the radius.

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The SiCWell Dataset contains data of battery electric vehicle lithium-ion batteries for modeling and diagnosis purposes. In this experiment, automotive-grade lithium-ion pouch bag cells are cycled with current profiles plausible for electric vehicles. 

The analysis of current ripples in electric vehicles and the corresponding aging experiments of the battery cells result in a dataset, which is composed of the following parts: 

 

Instructions: 

Cell Aging Scenarios

The battery cells are cycled in groups of three cells in series. The scenarios for each cell are the following:

  • Ka01, Ka02: Calendar test 35°C 80% SoC
  • Ka03, Ka04: Calendar test 35°C 45% SoC
  • Ka05, Ka06: Calendar test 45°C 80% SoC
  • Ka07, Ka08: Calendar test 45°C 20% SoC
  • Ka09, Ka10: Calendar test 45°C 45% SoC
  • Ka11, Ka12: Calendar test 45°C 60% SoC
  • DC01, DC02, DC03: DC cycling
  • AC01, AC02, AC03: Sinusoidal cycling 10 kHz, 12.5 A
  • AC04, AC05, AC06: Sinusoidal cycling 10 kHz, 25.0 A
  • AC07, AC08, AC09: Sinusoidal cycling 10 kHz, 6.25 A
  • AC10, AC11, AC12: Sinusoidal cycling 40 kHz, 12.5 A
  • AC13, AC14, AC15: Sinusoidal cycling 20 kHz, 12.5 A
  • AC16, AC17, AC18: Sinusoidal cycling 40 kHz, 6.25 A
  • AC19, AC20, AC21: Artificial ripple cycling
  • AC22, AC23, AC24: Realistic ripple cycling
  • AC25, AC26, AC27: Realistic ripple cycling

Current Ripple Evaluation

The evaluation results of current ripples in a battery-electric vehicle are stored in the “current_ripple_evaluation” directory. It contains the following files:

  • input_sWLTP.csv/input_UDDS.csv: The speed, torque, and power of the sWLTP and UDDS cycles at every second of the simulated battery-electric vehicle.
  • cycler_sWLTP.csv/cycler_UDDS.csv: The compressed current values for every second of the sWLTP and UDDS cycles. More details about the compression can be found at [1].
  • parameters.csv: The parameters for simulation of the battery-electric vehicle and the drivetrain.
  • sWLTP.h5/UDDS.h5: The simulated current and voltage of the battery-electric vehicle in the time-domain sampled with 500 kHz. Every second has its operating point, which is simulated for a second.

 

Cell Cycles

The raw current, voltage, and temperature measurements of the cycled battery cells. The results are stored in the “cell_cycling_*” directory with the following files:

 

  • cell_cycling_sinusoidal/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the sinusoidal cycling experiments.
  • cell_cycling_artificial_ripple/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the artifical ripple cycling experiments.
  • cell_cycling_realistic_ripple/[cell id]/[cell id]_cycle[cycle id].hdf5: The raw measurements of the realistic ripple cycling experiments.

The measurements have been taken periodically by an external 2MHz measurement system. Each .hdf5 file contains the measurements of a specific cycle number of the experiment. In the sinusoidal and artificial ripple tests, a measurement has been taken for every 1 % State-of-Charge. In the realistic ripple tests, a measurement has been taken every 5 seconds. Every measurement has a duration of 100 ms and a sampling rate of 2 MS/s.

Each measurement is a group in the hdf5 file, with the voltage and current as 1d 32bit floating-point arrays. Each measurement also has a Unix UTC timestamp of the time of the measurement, the cell temperature, capacity, and resistance[10s] stored as attributes. The capacity and resistance are synchronized with the checkups and interpolated linearly over the number of cycles.

 

Cell Checkups

The periodic checkups of the battery cells are composed of capacity, internal resistance, EIS, OCV, and qOCV measurements. Measurements that take longer, such as EIS and OCV, are not taken at every checkup. The results are stored in the “cell_checkups” directory with the following files:

 

  • Overview.csv: List of every checkup of every battery cell. For every checkup, the date, number of cycles, capacity, 10s resistance, and references to the more detailed checkup files are stored.
  • EIS/[cell id]_CheckUp[checkup id]_[date]_EIS.csv: The results of the electrochemical impedance spectroscopy from 0.001 to 50,000 Hz using an EIS-meter.
  • OCV/[cell id]_CheckUp[checkup id]_[date]_OCV.csv: Results of the open-circuit voltage measurement between 0 and 100 % State of Charge in 5 % steps.
  • qOCV/[cell id]_CheckUp[checkup id]_[date]_qOCV.csv: Results of the quasi-open-circuit voltage measurement between 0 and 100 % State of Charge in 1 % steps. It contains the quasi-open-circuit voltage, 1s resistance, and 10s resistance.
  • OCV_raw/[cell id]_CheckUp[checkup id]_[date]_OCV_raw.csv: Raw current, voltage, and temperature values of the OCV measurement.
  • qOCV_raw/[cell id]_CheckUp[checkup id]_[date]_qOCV_raw.csv: Raw current, voltage, and temperature values of the qOCV measurement.
  • Capacity_raw/[cell id]_CheckUp[checkup id]_[date]_Cap_raw.csv: Raw current, voltage, and temperature values of the capacity measurement.
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We propose a novel high-resolution dataset named, “Dataset for Indian Road Scenarios (DIRS21)” for developing perception systems for advanced driver assistance systems.

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We design a solution to achieve coordinated localization between two unmanned aerial vehicles (UAVs) using radio and camera perception. We achieve the localization between the UAVs in the context of solving the problem of UAV Global Positioning System (GPS) failure or its unavailability. Our approach allows one UAV with a functional GPS unit to coordinate the localization of another UAV with a compromised or missing GPS system. Our solution for localization uses a sensor fusion and coordinated wireless communication approach.

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his corpus was approved by Air Traffic Management Bureau, Civil Aviation Administration of China (CAAC). All speech had been recorded by real radiotelephony between air traffic controllers (ATCs) and pilots from December 15, 2020 to January 14, 2021. The raw data comprised around 700,000 segments of ATCs speech involving all work stations  over 3 daily periods (0200–0700, 1000–1200 and 1330–1530 hours). Finally, seven controllers with different genders, age groups, controller levels and control positions were identified, namely ATCs_1 to ATCs_7.

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