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Smart Energy and Intelligent Transportation Systems

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 13501

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Special Issue Editors

Dr. Albert Y.S. Lam

E-Mail Website
Guest Editor

Fano Labs and Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong
Interests: evolutionary computation; optimization techniques; artificial intelligence; smart grid; smart city; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Bogusław Łazarz

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Guest Editor

Department of Road Transport, Faculty of Transport and Aviation Engineering, Silesian University of Technology, 40-019 Katowice, Poland
Interests: machine and structure dynamics; vibroacoustic behavior of machines and structures; signal processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Grzegorz Peruń

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Guest Editor

Faculty of Transport and Aviation Engineering, Silesian University of Technology, Gliwice, Poland
Interests: modeling and simulation of toothed gears; damage modeling and identification; health monitoring; vibroacoustics; signal processing; mechatronics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the Internet of things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of smart city. This facilitates a more efficient use of physical infrastructure and encourages citizen participation. Smart energy and smart mobility are among the key aspects of the smart city, in which the electric vehicle (EV) is believed to take a key role. EVs are powered by various energy sources or the electricity grid. With proper scheduling, a large fleet of EVs can be charged from charging stations and parking infrastructures. Although the battery capacity of a single EV is small, an aggregation of EVs can perform as a significant power source or load, constituting a vehicle-to-grid (V2G) system. Besides acquiring energy from the grid, in V2G, EVs can also support the grid by providing various demand response and auxiliary services. We can reduce our reliance on fossil fuels and utilize the renewable energy more effectively.

The EV market is growing very quickly, and there will likely be an abundance of EVs running on the road in the near future. EVs are also important building blocks to developing intelligent transportation systems. The self-control of autonomous vehicles (AVs) and the systematic remote control of AV fleets will bring smart energy and intelligent transportation systems into new dimensions.

This Special Issue therefore seeks to contribute to the smart energy and intelligent transportation system agenda through enhanced scientific and multi-disciplinary knowledge to improve performance and deployment by bringing some focus to electric and autonomous vehicles in order to meet technical, socio-economic, and environmental goals, as well as for energy security. We are particularly interested in investigating how smart energy technologies contribute to intelligent transportation systems, and vice versa. We therefore invite papers on innovative technical developments, reviews, and analytical as well as assessment papers from different disciplines which are relevant to the integration of smart energy and mobility. Topics of interest for publication include, but are not limited to:

Autonomous and cooperative vehicle systems;
Traffic control and management;
Mobile Internet, mobility Internet, and Internet of Things;
Vehicle-to-grid;
Vehicular energy network;
Renewable energy;
Social economics for energy and vehicle platforms;
Big data for smart energy and mobility;
Optimization for smart energy and mobility;
Network architecture for smart energy and mobility integration;
Deep learning and artificial intelligence;

Prof. Dr. Albert Y.S. Lam
Prof. Dr. Bogusław Łazarz
Prof. Dr. Grzegorz Peruń
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

electric vehicles
autonomous vehicles
vehicle-to-grid
renewable energy
artificial intelligence

Published Papers (6 papers)

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Editorial

Jump to: Research

3 pages, 152 KiB

Open AccessEditorial

Smart Energy and Intelligent Transportation Systems

by Albert Y. S. Lam, Bogusław Łazarz and Grzegorz Peruń

Energies 2022, 15(8), 2900; https://0-doi-org.brum.beds.ac.uk/10.3390/en15082900 - 15 Apr 2022

Cited by 2 | Viewed by 1337

Abstract

With the Internet of things and various information and communication technologies, a city can manage its assets in a smarter way, constituting the urban development vision of a smart city [...] Full article

(This article belongs to the Special Issue Smart Energy and Intelligent Transportation Systems)

Research

Jump to: Editorial

17 pages, 7140 KiB

Open AccessArticle

The Use of Deep Learning Methods in Diagnosing Rotating Machines Operating in Variable Conditions

by Paweł Pawlik, Konrad Kania and Bartosz Przysucha

Energies 2021, 14(14), 4231; https://0-doi-org.brum.beds.ac.uk/10.3390/en14144231 - 13 Jul 2021

Cited by 5 | Viewed by 1663

Abstract

This paper presents the use of artificial neural networks in diagnosing the technical condition of drive systems operating under variable conditions. The effects of temperature and load variations on the values of diagnostic parameters were considered. An experiment was conducted on a testing rig where a variable load was introduced corresponding to the load of the main gearbox of the bucket wheel excavator. The signals of vibration acceleration on the gearbox body, rotational speed, and current consumption of the drive motor for different values of oil temperature were measured. Synchronous analysis was performed, and the values of order amplitudes and the corresponding values of current, speed, and temperature were determined. Such datasets were the learning vectors for a set of artificial deep learning neural networks. A new approach proposed in this paper is to train the network using a learning set consisting only of data from the efficient system. The responses of the trained neural networks to new data from the undamaged system were performed against the response to data recorded for three damage states: misalignment, unbalance, and simultaneous misalignment and unbalance. As a result, a diagnostic parameter as a normalized measure of the deviation of the network results was developed for the faulted system from the result for the undamaged condition. Full article

(This article belongs to the Special Issue Smart Energy and Intelligent Transportation Systems)

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15 pages, 2267 KiB

Open AccessArticle

Classification Trees in the Assessment of the Road–Railway Accidents Mortality

by Edward Kozłowski, Anna Borucka, Andrzej Świderski and Przemysław Skoczyński

Energies 2021, 14(12), 3462; https://0-doi-org.brum.beds.ac.uk/10.3390/en14123462 - 11 Jun 2021

Cited by 11 | Viewed by 1882

Abstract

A special element of road safety research is accidents at the interface of the road and rail system. Due to their low share in the total number of incidents, they are not a popular subject of analyses but rather an element of collective studies, whereas the specificity of the road–rail accidents requires a separate characteristic, allowing, on the one hand, to categorize these types of incidents, and on the other, to specify the factors that affect them, along with an assessment of the strength of this impact. It is important to include in such analyses all potential predictors, both qualitative and quantitative. Moreover, the literature considers most often a number of accidents while, according to the authors, it does not fully reflect the scale of the danger. A better evaluation would be the victim’s degree of injury. Therefore, the purpose of this article is to assess the likelihood of occurrence of various effects of road–rail accidents in the aspect of selected factors. Due to the ordinal form of the dependent variable, the classification trees method was used. The results obtained not only allow the characterization and assessment of the danger but also constitute guidelines for taking preventive actions. Full article

(This article belongs to the Special Issue Smart Energy and Intelligent Transportation Systems)

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27 pages, 6013 KiB

Open AccessArticle

Optimal Pricing of Vehicle-to-Grid Services Using Disaggregate Demand Models

by Charilaos Latinopoulos, Aruna Sivakumar and John W. Polak

Energies 2021, 14(4), 1090; https://0-doi-org.brum.beds.ac.uk/10.3390/en14041090 - 19 Feb 2021

Cited by 4 | Viewed by 2161

Abstract

The recent revolution in electric mobility is both crucial and promising in the coordinated effort to reduce global emissions and tackle climate change. However, mass electrification brings up new technical problems that need to be solved. The increasing penetration rates of electric vehicles will add an unprecedented energy load to existing power grids. The stability and the quality of power systems, especially on a local distribution level, will be compromised by multiple vehicles that are simultaneously connected to the grid. In this paper, the authors propose a choice-based pricing algorithm to indirectly control the charging and V2G activities of electric vehicles in non-residential facilities. Two metaheuristic approaches were applied to solve the optimization problem, and a comparative analysis was performed to evaluate their performance. The proposed algorithm would result in a significant revenue increase for the parking operator, and at the same time, it could alleviate the overloading of local distribution transformers and postpone heavy infrastructure investments. Full article

(This article belongs to the Special Issue Smart Energy and Intelligent Transportation Systems)

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16 pages, 2336 KiB

Open AccessArticle

Substantiation of Loading Hub Location for Electric Cargo Bikes Servicing City Areas with Restricted Traffic

by Vitalii Naumov

Energies 2021, 14(4), 839; https://0-doi-org.brum.beds.ac.uk/10.3390/en14040839 - 05 Feb 2021

Cited by 10 | Viewed by 3087

Abstract

Electric cargo bicycles have become a popular mode of transport for last-mile goods deliveries under conditions of restricted traffic in urban areas. The indispensable elements of the cargo bike delivery systems are loading hubs: they serve as intermediate points between vans and bikes ensuring loading, storage, and e-vehicle charging operations. The choice of the loading hub location is one of the basic problems to be solved when designing city logistics systems that presume the use of electric bicycles. The paper proposes an approach to justifying the location of a loading hub based on computer simulations of the delivery process in the closed urban area under the condition of stochastic demand for transport services. The developed mathematical model considers consignees and loading hubs as vertices in the graph representing the transport network. A single request for transport services is described based on the set of numeric parameters, among which the most significant are the size of the consignment, its dimensions, and the time interval between the current and the previous requests for deliveries. The software implementation of the developed model in Python programming language was used to simulate the process of goods delivery by e-bikes for two cases—the synthetically generated rectangular network and the real-world case of the Old Town district in Krakow, Poland. The loading hub location was substantiated based on the simulation results from a set of alternative locations by using the minimum of the total transport work as the efficiency criterion. The obtained results differ from the loading hub locations chosen with the use of classical rectilinear and center-of-gravity methods to solve a simple facility location problem. Full article

(This article belongs to the Special Issue Smart Energy and Intelligent Transportation Systems)

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19 pages, 1699 KiB

Open AccessArticle

Multi-Criteria Stochastic Selection of Electric Vehicles for the Sustainable Development of Local Government and State Administration Units in Poland

by Paweł Ziemba

Energies 2020, 13(23), 6299; https://0-doi-org.brum.beds.ac.uk/10.3390/en13236299 - 29 Nov 2020

Cited by 44 | Viewed by 2143

Abstract

Increasing the popularity of electric vehicles is one way of reducing greenhouse gas emissions and making the economy more sustainable. In Poland, the use of electric vehicles is to be increased by the adoption of the Act on Electromobility and Alternative Fuels. This Act obliges local government units and state administration to expand the electric vehicle fleet. The expansion of the fleet should be carried out on a planned basis, based on rational decisions supported by economic analyses. Therefore, the aim of this article is to provide a recommendation of an electric vehicle that meets the needs of local and state administration to the greatest extent possible. The aim has been achieved using the multi-criteria decision analysis method called PROSA-C (PROMETHEE for Sustainability Assessment—Criteria) combined with the Monte Carlo method. The PROSA-C method allows promoting more sustainable vehicles with high technical, economic, environmental and social parameters. The Monte Carlo method, on the other hand, is a stochastic simulation tool that allows for taking into account the uncertainty of parameters describing vehicles. As a result of the research, the most and least attractive vehicles were identified from the perspective of the needs of local government units and state administration. Moreover, the conducted research allowed confirming the effectiveness and usefulness of the research methodology proposed in the article and the procedural approach combining the PROSA-C and Monte Carlo methods. Full article

(This article belongs to the Special Issue Smart Energy and Intelligent Transportation Systems)

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