A Framework for Analysis and Expansion of Public Charging Infrastructure under Fast Penetration of Electric Vehicles

The improvement commercial competitiveness of private electric vehicles supported by the European policy for the decarbonisation of transport and with the consumers awareness-raising about CO2 emissions and climate change, are driving the increase of electric vehicles on the roads. Therefore, public charging networks are facing the challenge of supply electricity to a fast increasing number of electric cars. The objective of this paper is to establish an assessment framework for analysis and monitor of existing charging networks. The developed methodology comprises modelling the charging infrastructure electricity profile, analysing the data by using machine learning models such as functional k-means clustering and defining a novel congestion metric. The described framework has been tested against Irish public charging network historical datasets. The analyses reveal a lack of reliability of the communication network infrastructure, frequent congestion events for commercial and shopping areas in specific clusters of charge points and the presence of power peaks caused by the high number of simultaneous charging events. Several recommendations for future network expansion have been highlighted.

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Predicting Electric Vehicle Charging Station Availability Using Ensemble Machine Learning

Energies ◽

10.3390/en14237834 ◽

2021 ◽

Vol 14 (23) ◽

pp. 7834

Author(s):

Christopher Hecht ◽

Jan Figgener ◽

Dirk Uwe Sauer

Keyword(s):

Machine Learning ◽

Binary Data ◽

Training Data ◽

Gradient Boosting ◽

Traffic Density ◽

Learning Models ◽

Charging Infrastructure ◽

Ensemble Models ◽

Charging Station ◽

Machine Learning Models

Electric vehicles may reduce greenhouse gas emissions from individual mobility. Due to the long charging times, accurate planning is necessary, for which the availability of charging infrastructure must be known. In this paper, we show how the occupation status of charging infrastructure can be predicted for the next day using machine learning models— Gradient Boosting Classifier and Random Forest Classifier. Since both are ensemble models, binary training data (occupied vs. available) can be used to provide a certainty measure for predictions. The prediction may be used to adapt prices in a high-load scenario, predict grid stress, or forecast available power for smart or bidirectional charging. The models were chosen based on an evaluation of 13 different, typically used machine learning models. We show that it is necessary to know past charging station usage in order to predict future usage. Other features such as traffic density or weather have a limited effect. We show that a Gradient Boosting Classifier achieves 94.8% accuracy and a Matthews correlation coefficient of 0.838, making ensemble models a suitable tool. We further demonstrate how a model trained on binary data can perform non-binary predictions to give predictions in the categories “low likelihood” to “high likelihood”.

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Bi-LSTM Sentiment Classifier for Climate Change Issues in South Korea

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1056.0782s619 ◽

2019 ◽

Vol 8 (2S6) ◽

pp. 295-299

Keyword(s):

Climate Change ◽

Machine Learning ◽

Big Data ◽

South Korea ◽

Sentiment Analysis ◽

Training Data ◽

Learning Models ◽

Wide Range ◽

Machine Learning Models ◽

Big Data Technology

A sentiment analysis using SNS data can confirm various people’s thoughts. Thus an analysis using SNS can predict social problems and more accurately identify the complex causes of the problem. In addition, big data technology can identify SNS information that is generated in real time, allowing a wide range of people’s opinions to be understood without losing time. It can supplement traditional opinion surveys. The incumbent government mainly uses SNS to promote its policies. However, measures are needed to actively reflect SNS in the process of carrying out the policy. Therefore this paper developed a sentiment classifier that can identify public feelings on SNS about climate change. To that end, based on a dictionary formulated on the theme of climate change, we collected climate change SNS data for learning and tagged seven sentiments. Using training data, the sentiment classifier models were developed using machine learning models. The analysis showed that the Bi-LSTM model had the best performance than shallow models. It showed the highest accuracy (85.10%) in the seven sentiments classified, outperforming traditional machine learning (Naive Bayes and SVM) by approximately 34.53%p, and 7.14%p respectively. These findings substantiate the applicability of the proposed Bi-LSTM-based sentiment classifier to the analysis of sentiments relevant to diverse climate change issues.

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Operational Methods for Charging of Electric Vehicles

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.15853 ◽

2020 ◽

Vol 48 (4) ◽

pp. 369-376

Author(s):

Bálint Csonka ◽

Márton Havas ◽

Csaba Csiszár ◽

Dávid Földes

Keyword(s):

Energy Management ◽

Electric Vehicles ◽

Transportation Network ◽

Electric Networks ◽

Charging Infrastructure ◽

Electric Cars ◽

Bus Service ◽

Bus Services ◽

Charging Stations ◽

Operational Methods

The increasing number of electric vehicles induces a new relationship between the electric vehicles, transportation network and electric network. The deployment of the charging infrastructure is a prerequisite of the widespread of electric vehicles. Furthermore, the charging process and energy management have a significant influence on the operation of both the transportation and electric networks. Therefore, we have elaborated novel operational methods that support the deployment of charging infrastructure for electric cars and buses operating in public bus service, and the energy management. Weighted sum-models were developed to assess candidate sites for public charging stations. The mathematical model of public bus services was elaborated that supports the optimization of static charging infrastructure at bus stops and terminals without schedule adjustments. The flexibility and predictability of charging sessions were identified as the main differences between charging infrastructure deployment for cars and buses. Furthermore, the flows of energy, information and value have been revealed among the components of charging with a focus on commercial locations, which is the basis of energy flow optimization on the smart grid.

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Determinants of Electric Vehicle Diffusion in China

Environmental and Resource Economics ◽

10.1007/s10640-021-00596-4 ◽

2021 ◽

Author(s):

Martin Kalthaus ◽

Jiatang Sun

Keyword(s):

Electric Vehicle ◽

Electric Vehicles ◽

Central Government ◽

License Plate ◽

Panel Estimation ◽

Charging Infrastructure ◽

Electric Cars ◽

Local Air Pollution ◽

Charging Stations ◽

Positive Effect

AbstractWe analyze the effect of four determinants of electric vehicle diffusion in China for a panel of 31 regions for the period 2010–2016. We analyze diffusion of four different electric vehicle types, namely battery electric cars and buses as well as plug-in hybrid electric cars and buses. System GMM panel estimation results show that total monetary subsidies have a positive effect only on the diffusion of battery electric cars. A closer look reveals that subsidies provided by regional governments are decisive for all types of vehicles but the subsidy provided by the central government and its degression over time dilute the overall effect of subsidies and is partly detrimental. Non-monetary ownership policies, such as license-plate lotteries, show a positive effect only for battery electric cars. Availability of public charging infrastructure increases diffusion of all vehicle types. Charging points are relevant for cars, while charging stations are especially decisive for the diffusion of electric buses. Using local environmental conditions as a novel determinant for the diffusion of electric vehicles reveals that the local air pollution influences the diffusion of buses, but not of cars.

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Vertically Integrated Supply Chain of Batteries, Electric Vehicles, and Charging Infrastructure: A Review of Three Milestone Projects from Theory of Constraints Perspective

Sustainability ◽

10.3390/su13073632 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3632

Author(s):

Michael Naor ◽

Alex Coman ◽

Anat Wiznizer

Keyword(s):

Supply Chain ◽

Electric Vehicles ◽

Lessons Learned ◽

Raw Material ◽

Theory Of Constraints ◽

Future Research ◽

Case Study Methodology ◽

Charging Infrastructure ◽

Electric Cars ◽

Integrated Supply Chain

This research utilizes case study methodology based on longitudinal interviews over a decade coupled with secondary data sources to juxtapose Tesla with two high-profile past mega-projects in the electric transportation industry, EV-1 and Better Place. The theory of constraints serves as a lens to identify production and market bottlenecks for the dissemination of electric vehicles. The valuable lessons learned from EV1 failure and Better Place bankruptcy paved the way for Tesla’s operations strategy to build gigafactories which bears a resemblance to Ford T mass production last century. Specifically, EV1 relied on external suppliers to develop batteries, while Better Place was dependent on a single manufacturer to build cars uniquely compatible with its charging infrastructure, whereas Tesla established a closed-loop, green, vertically integrated supply chain consisting of batteries, electric cars and charging infrastructure to meet its customers evolving needs. The analysis unveils several limitations of the Tesla business model which can impede its worldwide expansion, such as utility grid overload and a shortage of raw material, which Tesla strives to address by innovating advanced batteries and further extending its vertically integrated supply chain to the mining industry. The study concludes by sketching fruitful possible avenues for future research.

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Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)

Remote Sensing ◽

10.3390/rs11020128 ◽

2019 ◽

Vol 11 (2) ◽

pp. 128 ◽

Cited By ~ 16

Author(s):

Pham Hoa ◽

Nguyen Giang ◽

Nguyen Binh ◽

Le Hai ◽

Tien-Dat Pham ◽

...

Keyword(s):

Climate Change ◽

Machine Learning ◽

Neural Networks ◽

Soil Salinity ◽

River Delta ◽

Mekong River ◽

Support Vector ◽

Learning Models ◽

Mekong River Delta ◽

Machine Learning Models

Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical climates. This issue has become more severe and increasingly occurred in the Mekong River Delta of Vietnam. The main objective of this work is to map soil salinity intrusion in Ben Tre province located on the Mekong River Delta of Vietnam using the Sentinel-1 Synthetic Aperture Radar (SAR) C-band data combined with five state-of-the-art machine learning models, Multilayer Perceptron Neural Networks (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), Gaussian Processes (GP), Support Vector Regression (SVR), and Random Forests (RF). For this purpose, 63 soil samples were collected during the field survey conducted from 4–6 April 2018 corresponding to the Sentinel-1 SAR imagery. The performance of the five models was assessed and compared using the root-mean-square error (RMSE), the mean absolute error (MAE), and the correlation coefficient (r). The results revealed that the GP model yielded the highest prediction performance (RMSE = 2.885, MAE = 1.897, and r = 0.808) and outperformed the other machine learning models. We conclude that the advanced machine learning models can be used for mapping soil salinity in the Delta areas; thus, providing a useful tool for assisting farmers and the policy maker in choosing better crop types in the context of climate change.

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A Study on the Adoption of Electric Vehicles in India: The Mediating Role of Attitude

Vision The Journal of Business Perspective ◽

10.1177/0972262919875548 ◽

2019 ◽

Vol 24 (1) ◽

pp. 23-34

Author(s):

Anil Khurana ◽

V. V. Ravi Kumar ◽

Manish Sidhpuria

Keyword(s):

Electric Vehicles ◽

Internal Combustion Engines ◽

Charging Infrastructure ◽

Electric Cars ◽

Mediating Role ◽

Electric Car ◽

Toxic Emission ◽

Range Anxiety ◽

Fossil Fuel Emission ◽

The Government

Pollution of the environment is currently a global concern. Toxic emission from internal combustion engines is one of the primary air pollutants. In order to mitigate the effects of fossil fuel emission and address environmental concerns (ECs), electric vehicles (EVs) are being promoted aggressively all over the world. Various governments are encouraging people to switch to EVs by incentivizing the transition. Previous studies indicate that the high cost of the electric car, non-availability of charging infrastructure, time and range anxiety act as impediments to consumer adoption. The Government of India has given a call for ‘only Electric Vehicles’ on Road by 2030. This article is contemporary and examines the different factors that affect a consumer’s adoption of an EV. The respondents of the study are existing car owners in India. The data were analysed using Structured Equation Modelling (SEM). Attitude (ATT) emerged as a strong mediator, influencing the adoption of electric cars.

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Improving XGBoost with Imagination Sampling

Communications of the Blyth Institute ◽

10.33014/issn.2640-5652.2.1.holloway.1 ◽

2020 ◽

Vol 2 (1) ◽

pp. 3-6

Author(s):

Eric Holloway

Keyword(s):

Machine Learning ◽

General System ◽

Learning Models ◽

Starting Point ◽

Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

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