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Energies
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Energy Saving in Public Transport
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Energy Saving in Public Transport

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "B: Energy and Environment".

Deadline for manuscript submissions: closed (30 November 2019) | Viewed by 19733

Special Issue Editors

Prof. Dr. Vincenzo Franzitta

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Guest Editor
Department of Engineering, University of Palermo, 90128 Palermo, Italy
Interests: HVAC; energy efficiency; energy saving in final users; sea wave; renewable energy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marzia Traverso

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Guest Editor
Institute of Sustainability in Civil Engineering, RWTH Aachen University, 52062 Aachen, Germany
Interests: life cycle assessment; sustainability assessment in building; life cycle sustainability assessment; sustainable finance; circular economy
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marco Trapanese

E-Mail Website
Guest Editor
Department of Energy, Information and Mathematical Models (DEIM), University of Palermo, 90128 Palermo, Italy
Interests: electrical drives; linear machines; magnetic materials; energy harvesting; electrical energy production from sea waves; medical equipment and medical use of nuclear radiation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays we are observing an impetuous revolution in the field of transport. Until some years ago, transport was based on the use of fossil fuels. The development of several new technologies (PV systems, electric cars, and batteries) has allowed, in the last decade, for the economical possibility to adopt new strategies in the transportation industry, starting an ongoing revolution. These technologies have opened the field to the general application of otherwise unaffordable technical solutions. However, in this new paradigm the search for higher efficiency must be extended. The use of electrical energy in the field of transportation can highly increase the overall efficiency of the system. A key factor in the development of a fully efficient approach to transportation is related to the maximization of the efficiency of the usage of produced energy to maximize the performance of the overall system.

Fortunately, nowadays several technologies that are able to maximize energy correctly and minimize losses are being studied and are becoming available. As a result, this Special Issue intends to stimulate a discussion on the available and nearly-available technologies in the field of energy saving in transportation. Papers on both the power units, typical applications, and the optimization of the overall system will be considered, and special attention will be given to studies on energy saving in vehicles.

Prof. Vincenzo Franzitta
Prof. Marzia Traverso
Prof. Marco Trapanese
Guest Editors

Manuscript Submission Information

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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

  • energy savings
  • electrical storage in electric cars
  • PV systems
  • LCA of electric cars components
  • new technologies in transportation

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Published Papers (3 papers)

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Research

33 pages, 2308 KiB  
Article
Optimizing Energy Consumption in Transportation: Literature Review, Insights, and Research Opportunities
by Canan G. Corlu, Rocio de la Torre, Adrian Serrano-Hernandez, Angel A. Juan and Javier Faulin
Energies 2020, 13(5), 1115; https://doi.org/10.3390/en13051115 - 2 Mar 2020
Cited by 48 | Viewed by 9295
Abstract
From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which [...] Read more.
From airplanes to electric vehicles and trains, modern transportation systems require large quantities of energy. These vast amounts of energy have to be produced somewhere—ideally by using sustainable sources—and then brought to the transportation system. Energy is a scarce and costly resource, which cannot always be produced from renewable sources. Therefore, it is critical to consume energy as efficiently as possible, that is, transportation activities need to be carried out with an optimal intake of energetic means. This paper reviews existing work on the optimization of energy consumption in the area of transportation, including road freight, passenger rail, maritime, and air transportation modes. The paper also analyzes how optimization methods—of both exact and approximate nature—have been used to deal with these energy-optimization problems. Finally, it provides insights and discusses open research opportunities regarding the use of new intelligent algorithms—combining metaheuristics with simulation and machine learning—to improve the efficiency of energy consumption in transportation. Full article
(This article belongs to the Special Issue Energy Saving in Public Transport)
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21 pages, 4592 KiB  
Article
Towards an Energy Efficient Solution for Bike-Sharing Rebalancing Problems: A Battery Electric Vehicle Scenario
by Muhammad Usama, Yongjun Shen and Onaira Zahoor
Energies 2019, 12(13), 2503; https://doi.org/10.3390/en12132503 - 28 Jun 2019
Cited by 9 | Viewed by 4119
Abstract
A free-float bike-sharing system faces various operational challenges to maintain good service quality while optimizing the operational cost. The primary problems include the fulfillment of the users demand at all stations, and the replacement of faulty bikes presented in the system. This study [...] Read more.
A free-float bike-sharing system faces various operational challenges to maintain good service quality while optimizing the operational cost. The primary problems include the fulfillment of the users demand at all stations, and the replacement of faulty bikes presented in the system. This study focuses on a free-float bike-sharing system rebalancing problem (FFBP) with faulty bikes using battery electric vehicles (BEVs). The target inventory of bikes at each station is obtained while minimizing the total traveling time through the presented formulation. Using CPLEX solver, the model is demonstrated through numerical experiments considering the various vehicle and battery capacities, and a cost–benefit analysis is performed for BEV and conventional internal combustion engine vehicles (ICEVs) while taking the BEV manufacturing and indirect emission into account. The results show that the annual cost incurred on an ICEV is 56.9% more as compared to the cost of using an equivalent BEV. Since BEVs consume less energy than conventional ICEVs, the use of BEVs for rebalancing the bike-sharing systems results in significant energy savings for an urban transport network. Moreover, the life cycle emissions of an ICEV are 48.3% more as compared to an equivalent BEV. Furthermore, the operational cost of a BEV significantly reduces with the increase in battery capacity. Full article
(This article belongs to the Special Issue Energy Saving in Public Transport)
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20 pages, 8723 KiB  
Article
Neural Network-Based Modeling of Electric Vehicle Energy Demand and All Electric Range
by Jakov Topić, Branimir Škugor and Joško Deur
Energies 2019, 12(7), 1396; https://doi.org/10.3390/en12071396 - 11 Apr 2019
Cited by 30 | Viewed by 5466
Abstract
A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into [...] Read more.
A deep neural network-based approach of energy demand modeling of electric vehicles (EV) is proposed in this paper. The model-based prediction of energy demand is based on driving cycle time series used as a model input, which is properly preprocessed and transformed into 1D or 2D static maps to serve as a static input to the neural network. Several deep feedforward neural network architectures are considered for this application along with different model input formats. Two energy demand models are derived, where the first one predicts the battery state-of-charge and fuel consumption at destination for an extended range electric vehicle, and the second one predicts the vehicle all-electric range. The models are validated based on a separate test dataset when compared to the one used in neural network training, and they are compared with the traditional response surface approach to illustrate effectiveness of the method proposed. Full article
(This article belongs to the Special Issue Energy Saving in Public Transport)
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