Analysis of Real World Data from a Range Extended Electric Vehicle Demonstrator

2014 ◽  
Vol 4 (1) ◽  
pp. 20-33 ◽  
Author(s):  
Jonathan Hall ◽  
Hannes Marlok ◽  
Michael Bassett ◽  
Marco Warth
Keyword(s):  
Electric Vehicle ◽  
Real World ◽  
Real World Data ◽  
World Data

scholarly journals Bayesian Network Based State-of-Health Estimation for Battery on Electric Vehicle Application and its Validation Through Real-World Data

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 11328-11341
Author(s):  
Qian Huo ◽  
Zhikai Ma ◽  
Xiaoshun Zhao ◽  
Tao Zhang ◽  
Yulong Zhang
Keyword(s):  
Bayesian Network ◽  
Electric Vehicle ◽  
Real World ◽  
State Of Health ◽  
Real World Data ◽  
World Data

scholarly journals Synthetic Data Generator for Electric Vehicle Charging Sessions: Modeling and Evaluation Using Real-World Data

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4211
Author(s):  
Manu Lahariya ◽  
Dries F. Benoit ◽  
Chris Develder
Keyword(s):  
Electric Vehicle ◽  
Real World ◽  
Synthetic Data ◽  
Gaussian Mixture ◽  
Real World Data ◽  
Arrival Times ◽  
World Data ◽  
Data Generator ◽  
Charging Stations ◽  
Ev Charging

Electric vehicle (EV) charging stations have become prominent in electricity grids in the past few years. Their increased penetration introduces both challenges and opportunities; they contribute to increased load, but also offer flexibility potential, e.g., in deferring the load in time. To analyze such scenarios, realistic EV data are required, which are hard to come by. Therefore, in this article we define a synthetic data generator (SDG) for EV charging sessions based on a large real-world dataset. Arrival times of EVs are modeled assuming that the inter-arrival times of EVs follow an exponential distribution. Connection time for EVs is dependent on the arrival time of EV, and can be described using a conditional probability distribution. This distribution is estimated using Gaussian mixture models, and departure times can calculated by sampling connection times for EV arrivals from this distribution. Our SDG is based on a novel method for the temporal modeling of EV sessions, and jointly models the arrival and departure times of EVs for a large number of charging stations. Our SDG was trained using real-world EV sessions, and used to generate synthetic samples of session data, which were statistically indistinguishable from the real-world data. We provide both (i) source code to train SDG models from new data, and (ii) trained models that reflect real-world datasets.

Abstract #946: Assessment of Acromegaly Patients with and Without Diabetes Treated with Lanreotide Depot: 2-Year Real World Data

2016 ◽  
Vol 22 ◽  
pp. 219
Author(s):  
Roberto Salvatori ◽  
Olga Gambetti ◽  
Whitney Woodmansee ◽  
David Cox ◽  
Beloo Mirakhur ◽  
...  
Keyword(s):  
Real World ◽  
Real World Data ◽  
World Data

Real world data of Type 2 Diabetes Mellitus (T2DM) obese patients using another GLP1 receptor agonist (GLP1–RA) and switching to Semaglutide in Spain

2020 ◽  
Author(s):  
Jersy Cardenas ◽  
Gomez Nancy Sanchez ◽  
Sierra Poyatos Roberto Miguel ◽  
Luca Bogdana Luiza ◽  
Mostoles Naiara Modroño ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Type 2 Diabetes Mellitus ◽  
Real World ◽  
Receptor Agonist ◽  
Obese Patients ◽  
Real World Data ◽  
World Data
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