scholarly journals Impacts of COVID-19 on Electric Vehicle Charging Behavior: Data Analytics, Visualization, and Clustering

2022 ◽  
Vol 5 (1) ◽  
pp. 12
Author(s):  
Sakib Shahriar ◽  
A. R. Al-Ali
Keyword(s):  
Electric Vehicle ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Performance Comparison ◽  
Internal Validation ◽  
Electric Vehicle Charging ◽  
The Usa ◽  
And Performance ◽  
Ev Charging

COVID-19 pandemic has infected millions and led to a catastrophic loss of lives globally. It has also significantly disrupted the movement of people, businesses, and industries. Additionally, electric vehicle (EV) users have faced challenges in charging their vehicles in public charging locations where there is a risk of COVID-19 exposure. However, a case study of EV charging behavior and its impacts during the SARS-CoV-2 is not addressed in the existing literature. This paper investigates the impacts of COVID-19 on EV charging behavior by analyzing the charging activity during the pandemic using a dataset from a public charging facility in the USA. Data visualization of charging behavior alongside significant timelines of the pandemic was utilized for analysis. Moreover, a cluster analysis using k-means, hierarchical clustering, and Gaussian mixture models was performed to identify common groups of charging behavior based on the vehicle arrival and departure times. Although the number of vehicles using the charging station was reduced significantly due to lockdown restrictions, the charging activity started to pick up again since May 2021 due to an increase in vaccination and easing of public restrictions. However, the charging activity currently still remains around half of the activity pre-pandemic. A noticeable decline in charging session length and an increase in energy consumption can be observed as well. Clustering algorithms identified three groups of charging behavior during the pandemic and their analysis and performance comparison using internal validation measures were also presented.

scholarly journals Leveraging User Preferences to Develop Profitable Business Models for Electric Vehicle Charging

2021 ◽  
Vol 12 (2) ◽  
pp. 60
Author(s):  
Felix Röckle ◽  
Thimo Schulz
Keyword(s):  
Case Studies ◽  
Electric Vehicle ◽  
Business Models ◽  
Brand Image ◽  
User Preferences ◽  
Value Network ◽  
Electric Vehicle Charging ◽  
Utility Companies ◽  
Physical Assets ◽  
Ev Charging

To design profitable business models for electric vehicle (EV) charging it is necessary to understand user preferences. For this purpose, prior literature is analyzed to develop a conceptual framework linking a company’s assets, the surrounding value network, and user preferences. Then, survey insights from two EV charging projects (ultra-E, SLAM) are summarized to illustrate user preferences in this area. Based on this data, the framework is eventually visualized by applying it to four case studies from the EV charging market. Based on the case studies, the following six key findings are derived: 1. Companies that have a very strong position in one of the three resource classes that define the quality-of-service provision (physical assets, digital assets, brand image) demand a higher price for fast charging. 2. Utility companies leverage their existing customer base. 3. New to the industry firms leverage their brand image to enter the market. 4. Selling below cost is not sustainable. 5. Sharp price distinctions reflect the power balance within the value network. 6. Power plays may result in a fragmented market.

scholarly journals Procedural Level Generation for Sokoban via Deep Learning: An Experimental Study

2021 ◽  
Author(s):  
Yahia Zakaria ◽  
Mayada Hadhoud ◽  
Magda Fayek
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Mixture Models ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Generative Models ◽  
Generative Adversarial Networks ◽  
Sample Quality ◽  
Diversity Sampling ◽  
And Performance

Deep learning for procedural level generation has been explored in many recent works, however, experimental comparisons with previous works are rare and usually limited to the work they extend upon. This paper's goal is to conduct an experimental study on four recent deep learning procedural level generators for Sokoban to explore their strengths and weaknesses. The methods will be bootstrapping conditional generative models, controllable & uncontrollable procedural content generation via reinforcement learning (PCGRL) and generative playing networks. We will propose some modifications to either adapt the methods to the task or improve their efficiency and performance. For the bootstrapping method, we propose using diversity sampling to improve the solution diversity, auxiliary targets to enhance the models' quality and Gaussian mixture models to improve the sample quality. The results show that diversity sampling at least doubles the unique plan count in the generated levels. On average, auxiliary targets increases the quality by 24% and sampling conditions from Gaussian mixture models increases the sample quality by 13%. Overall, PCGRL shows superior quality and diversity while generative adversarial networks exhibit the least control confusion when trained with diversity sampling and auxiliary targets.

scholarly journals Comparison of dimensionality reduction and clustering methods for SARS-CoV-2 genome

2021 ◽  
Vol 10 (4) ◽  
pp. 2170-2180
Author(s):  
Untari N. Wisesty ◽  
Tati Rajab Mengko
Keyword(s):  
Dimensionality Reduction ◽  
Dimensional Reduction ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Reduction Process ◽  
Principal Component ◽  
Gaussian Mixture ◽  
Clustering Methods

This paper aims to conduct an analysis of the SARS-CoV-2 genome variation was carried out by comparing the results of genome clustering using several clustering algorithms and distribution of sequence in each cluster. The clustering algorithms used are K-means, Gaussian mixture models, agglomerative hierarchical clustering, mean-shift clustering, and DBSCAN. However, the clustering algorithm has a weakness in grouping data that has very high dimensions such as genome data, so that a dimensional reduction process is needed. In this research, dimensionality reduction was carried out using principal component analysis (PCA) and autoencoder method with three models that produce 2, 10, and 50 features. The main contributions achieved were the dimensional reduction and clustering scheme of SARS-CoV-2 sequence data and the performance analysis of each experiment on each scheme and hyper parameters for each method. Based on the results of experiments conducted, PCA and DBSCAN algorithm achieve the highest silhouette score of 0.8770 with three clusters when using two features. However, dimensionality reduction using autoencoder need more iterations to converge. On the testing process with Indonesian sequence data, more than half of them enter one cluster and the rest are distributed in the other two clusters.

scholarly journals Selecting Locations of Electric Vehicle Charging Stations Based on the Traffic Load Eliminating Method

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1650 ◽  
Author(s):  
Bong-Gi Choi ◽  
Byeong-Chan Oh ◽  
Sungyun Choi ◽  
Sung-Yul Kim
Keyword(s):  
Electric Vehicle ◽  
Electric Vehicles ◽  
Traffic Load ◽  
Queueing Model ◽  
Traffic Data ◽  
Electric Vehicle Charging ◽  
Supply Equipment ◽  
Charging Stations ◽  
Ev Charging ◽  
Charging Demand

Establishing electric vehicle supply equipment (EVSE) to keep up with the increasing number of electric vehicles (EVs) is the most realistic and direct means of promoting their spread. Using traffic data collected in one area; we estimated the EV charging demand and selected priority fast chargers; ranging from high to low charging demand. A queueing model was used to calculate the number of fast chargers required in the study area. Comparison of the existing distribution of fast chargers with that suggested by the traffic load eliminating method demonstrated the validity of our traffic-based location approach.

Video Image Segmentation Using Gaussian Mixture Models Based on the Differential Evolution-Based Parameter Estimation

2011 ◽  
Vol 474-476 ◽  
pp. 442-447
Author(s):  
Zhi Gao Zeng ◽  
Li Xin Ding ◽  
Sheng Qiu Yi ◽  
San You Zeng ◽  
Zi Hua Qiu
Keyword(s):  
Image Segmentation ◽  
Differential Evolution ◽  
Mixture Models ◽  
Clustering Algorithms ◽  
Gaussian Mixture Models ◽  
Expectation Maximization Algorithm ◽  
Image Data ◽  
Gaussian Mixture ◽  
Parameters Estimation ◽  
Video Image

In order to improve the accuracy of the image segmentation in video surveillance sequences and to overcome the limits of the traditional clustering algorithms that can not accurately model the image data sets which Contains noise data, the paper presents an automatic and accurate video image segmentation algorithm, according to the spatial properties, which uses the Gaussian mixture models to segment the image. But the expectation-maximization algorithm is very sensitive to initial values, and easy to fall into local optimums, so the paper presents a differential evolution-based parameters estimation for Gaussian mixture models. The experiment result shows that the segmentation accuracy has been improved greatly than by the traditional segmentation algorithms.

scholarly journals Performance Estimation of Wireless Electric Vehicle Charging System Based On Magnetic Coupling

2019 ◽  
Vol 8 (6) ◽  
pp. 4389-4394
Keyword(s):  
Electric Vehicle ◽  
Magnetic Coupling ◽  
Operating Regime ◽  
Performance Estimation ◽  
Ohmic Loss ◽  
Design Guideline ◽  
Charging System ◽  
Electric Vehicle Charging ◽  
Power Transfer Efficiency ◽  
Ev Charging

In order to achieve an efficient wireless Electric Vehicle (EV) charging system in non-ideal practical scenarios, a proper design guideline has been delineated through the simulation, theoretical calculation as well as experimental investigation. It is examined that the wireless power transfer efficiency (WPTE) is invariably affected by the configuration of the charging coils (coil radius & number of turns), coupling to loss ratio, ohmic loss, radiation resistance, operating frequency, magnetic coupling as well as physical air gap between the coils. It is found that there is a certain operating regime at which maximum WPTE can be uphold. The acquired results provide a comprehensive strategic plan that can be used in EV charging system

scholarly journals A Review of High Efficiency Non-Isolated DC-DC Power Converters for Electric Vehicle Charging Application

2020 ◽  
Vol 6 (1) ◽  
pp. 60-74
Author(s):  
Ratil H Ashique
Keyword(s):  
Electric Vehicle ◽  
High Efficiency ◽  
Power Converters ◽  
Renewable Energy Sources ◽  
Pulse Frequency ◽  
Soft Switching ◽  
Electric Vehicle Charging ◽  
Dc Power ◽  
Vehicle Charging ◽  
Ev Charging

The electric vehicle (EV) charging systems employ dc-dc power converters as EV chargers. Currently, the expected high penetration of electric vehicle (EV) demands for the integration of the renewable energy sources (RES) into the electric vehicle charging system as a promising solution to cut down the load on the electrical grid. These systems interface with RES by implementing dc-dc power converters. Moreover, with the advent of high-power dc charging, the charging efficiency is largely dependent on the performance of the power converters. Hence, to improve the charging, the soft switching dc-dc converters are implemented to maintain low switching losses and to achieve high-efficiency operation. This paper reviews the non-isolated, soft switching dc-dc power converters for EV charging application. For this purpose, different types of soft switching topologies, namely the snubber, the series resonant, the shunt resonant and the pulse frequency modulated converters are investigated. The advantages and the disadvantages associated with these converters are highlighted. Furthermore, to perform a comparative evaluation, the topologies are simulated in a standard simulation platform. Consequently, the relative standing of the converters depending on several parameters, i.e. the component count, the output voltage and current ripple, the soft switching range, and the power losses are established. Finally, based on these results, the optimum applicability of the converters in the EV charging application is determined. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 6(1), Dec 2019 P 60-74

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