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Special Issue "Energy Management in Vehicle–Grid–Traffic Nexus"

A special issue of Energies (ISSN 1996-1073).

Deadline for manuscript submissions: closed (1 May 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Xiaosong Hu

Department of Automotive Engineering, Chongqing University, China
Website | E-Mail
Interests: Electrified vehicles; Alternative powertrains; Energy storage systems; Battery management; Vehicle-grid-home interactions; Energy management optimization
Guest Editor
Prof. Dr. Weihao Hu

Department of Electrical Engineering, Alborg University, Denmark
Website | E-Mail
Phone: +45-21370382
Interests: wind power generation; intelligent energy systems; sustainable energy; renewable power generation
Guest Editor
Dr. Chen Lv

Advanced Vehicle Engineering Centre, Cranfield University, UK
Website | E-Mail
Interests: energy conversion and management of electrified vehicles; energy-efficiency cyber-physical systems; advanced control of alternative-energy vehicles for sustainable transportation; automated vehicles

Special Issue Information

Dear Colleagues,

This Special Issue focuses on energy management in the context of vehicle–grid–traffic interactions for a sustainable energy future. Its overarching goal is to present such a synergy through an integrated vision that may come both from specialized and from interdisciplinary articles. High-caliber research and survey papers are sincerely solicited to cover a broad range of topics, including advanced energy management in electrified vehicles, smart grid, and automated/connected driving, energy analysis of vehicle–grid interplay, information-enriched energy controls in smart city. Of course, design and control issues in the vehicle–traffic–grid–home nexus and energy internet will be definitely considered, from an energy management perspective. Papers submitted to this Special Issue will be subject to a peer review procedure with the aim of rapid and wide dissemination of their contents.

Prof. Dr. Xiaosong Hu
Prof. Dr. Weihao Hu
Dr. Chen Lv
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 papers will be 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 monthly 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 1600 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 management
  • electrified vehicles
  • smart grid
  • intelligent transportation system
  • vehicle–traffic–grid nexus

Published Papers (8 papers)

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Research

Open AccessArticle Study on EV Charging Peak Reduction with V2G Utilizing Idle Charging Stations: The Jeju Island Case
Energies 2018, 11(7), 1651; https://doi.org/10.3390/en11071651
Received: 26 April 2018 / Revised: 19 June 2018 / Accepted: 22 June 2018 / Published: 25 June 2018
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Abstract
Electric vehicles (EVs), one of the biggest innovations in the automobile industry, are considered as a demand source as well as a supply source for power grids. Studies have been conducted on the effect of EV charging and utilization of EVs to control
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Electric vehicles (EVs), one of the biggest innovations in the automobile industry, are considered as a demand source as well as a supply source for power grids. Studies have been conducted on the effect of EV charging and utilization of EVs to control grid peak or to solve the intermittency problem of renewable generators. However, most of these studies focus on only one aspect of EVs. In this work, we demonstrate that the increased demand resulting from EV charging can be alleviated by utilizing idle EV charging stations as a vehicle-to-grid (V2G) service. The work is performed based on data from Jeju Island, Korea. The EV demand pattern in 2030 is modeled and forecasted using EV charging patterns from historical data and the EV and charging station deployment plan of Jeju Island’s local government. Then, using a Monte Carlo simulation, charging and V2G scenarios are generated, and the effect of V2G on peak time is analyzed. In addition, a sensitivity analysis is performed for EV and charging station deployment. The results show that the EV charging demand increase can be resolved within the EV ecosystem. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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Open AccessArticle A Study on Coordinated Optimization of Electric Vehicle Charging and Charging Pile Selection
Energies 2018, 11(6), 1350; https://doi.org/10.3390/en11061350
Received: 16 April 2018 / Revised: 15 May 2018 / Accepted: 23 May 2018 / Published: 25 May 2018
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Abstract
This paper was intended to explore the mutual influences between electric vehicle (EV) charging and charging facility planning, to establish a two-stage model for optimizing the EVs’ charging and charging piles’ selection. In the first stage, the distribution pattern of the demands for
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This paper was intended to explore the mutual influences between electric vehicle (EV) charging and charging facility planning, to establish a two-stage model for optimizing the EVs’ charging and charging piles’ selection. In the first stage, the distribution pattern of the demands for EV charging, and various EVs were effectively grouped, in order to reduce the amount of computation for solving the second stage model. The goal of the second stage was to minimize the annual investment and electricity purchasing costs on the charging piles, and the coordinated optimization was carried out for EV charging and charging pile selection. The CPLEX and IP_SOLVE packages were used in MATLAB (R2014a/64 bits) to solve the established optimization model. The simulation results showed that, compared with the scheme for selecting the charging pile under the typical charging pattern (TCP), the total cost of the charging pile could be reduced by 6.32% with a scheme under the optimized charging pattern (OCP), thereby promoting the coordinated development of both the EVs and charging facilities. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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Open AccessArticle A High-Efficiency Charging Service System for Plug-in Electric Vehicles Considering the Capacity Constraint of the Distribution Network
Energies 2018, 11(4), 911; https://doi.org/10.3390/en11040911
Received: 15 March 2018 / Revised: 5 April 2018 / Accepted: 9 April 2018 / Published: 12 April 2018
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Abstract
It takes electric vehicles (EVs) a long time to charge, which is bound to influence the charging experience of vehicle owners. At the same time, large-scale charging behavior also brings about large load pressure on, and elevates the overload risk of, the power
[...] Read more.
It takes electric vehicles (EVs) a long time to charge, which is bound to influence the charging experience of vehicle owners. At the same time, large-scale charging behavior also brings about large load pressure on, and elevates the overload risk of, the power distribution network. To solve these problems, we proposed a high-efficiency charging service system based on charging reservation and charging pile binding services. The system can shorten the average charging time of EVs and improve the average immediate utilization rate of new energy sources at charging stations (CSs). In addition, the system also guarantees that the EVs are charged within the allowable range of the capacity of the distribution network and avoids overloading of the distribution network caused by the charging of EVs. The key support for the utility of the system is rooted in the three-level CS selection model and the CS energy control algorithm (CSECA) proposed in the research. Finally, the proposed model and algorithm were verified to be valid through numerous simulation experiments. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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Open AccessArticle Reduction of Electricity Prices Using the Train to Grid (T2G) System in Urban Railway
Energies 2018, 11(3), 501; https://doi.org/10.3390/en11030501
Received: 8 December 2017 / Revised: 19 January 2018 / Accepted: 9 February 2018 / Published: 27 February 2018
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Abstract
Smart transportation technologies are being rapidly developed for enhancing the smart grid establishment. Such technologies are mostly focused on electric vehicles. However, the electric railroad has advantages in various aspects such as facility construction and utilization over an electric vehicle. Therefore, in this
[...] Read more.
Smart transportation technologies are being rapidly developed for enhancing the smart grid establishment. Such technologies are mostly focused on electric vehicles. However, the electric railroad has advantages in various aspects such as facility construction and utilization over an electric vehicle. Therefore, in this paper, we introduce the train-to-grid system using the electric railroads for the smart grid, and propose a reduction method for the electricity prices. The proposed method obtains actual data from the currently operating railroad systems. Furthermore, the number of trains for charging and discharging batteries is decided by using the time-of-use price and the number of railroad operations. The electricity prices are then determined by the energy consumption calculated using the number of trains used for charging and discharging and the capacity of the energy storage system in the trains. The proposed method is simulated using real data, and its superiority is verified by comparing its electric prices with the conventional electricity prices. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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Open AccessArticle Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities
Energies 2018, 11(2), 421; https://doi.org/10.3390/en11020421
Received: 24 December 2017 / Revised: 4 February 2018 / Accepted: 6 February 2018 / Published: 12 February 2018
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Abstract
Electric vehicles (EV) and photovoltaic (PV) generation are widely recognized around the world. Most EV owners in the major Chinese cities are forced to charge their EV batteries at the workplace during the daytime due to the limited space near their homes, which
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Electric vehicles (EV) and photovoltaic (PV) generation are widely recognized around the world. Most EV owners in the major Chinese cities are forced to charge their EV batteries at the workplace during the daytime due to the limited space near their homes, which will increase the peak load during the daytime. On the other hand, the PV output is most likely to have a peak at around noon, which means, PVs could have a potential capability to compensate the EV charging load. An EV owner-friendly charging strategy based on PV utilization which alleviates both the EV charging constraints and the negative impact of the EV charging load on the grid is proposed. The PV utilization for compensating the unconstrained EV charging load is maximized to derive the maximum number of EVs with unconstrained charging. If the actual number of EVs exceeds the maximum number, a portion of EVs have to be charged only from the grid. Then, the line loss is introduced as the optimization objective in which the charging states are regulated. The case study shows that the proposed strategy can successfully increase the number of EVs with unconstrained charging, and reduce the peak-to-peak of the load curve. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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Open AccessArticle An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World
Energies 2017, 10(11), 1775; https://doi.org/10.3390/en10111775
Received: 24 August 2017 / Revised: 17 October 2017 / Accepted: 25 October 2017 / Published: 3 November 2017
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Abstract
Relieving the adverse effects of automobiles on the environment and natural resources has drawn the attention of numerous researchers. This paper seeks a new path to reach a target by focusing on the synergy of the vehicle and the environment. A real-time economical
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Relieving the adverse effects of automobiles on the environment and natural resources has drawn the attention of numerous researchers. This paper seeks a new path to reach a target by focusing on the synergy of the vehicle and the environment. A real-time economical route planning method for a plug-in hybrid electric vehicle (PHEV) is proposed. Three main contributions have been made. Firstly, a real comparison test is performed to provide rudimentary understanding of the difference in energy usage and route planning between PHEVs and conventional vehicles. Secondly, an approach to obtain PHEV customized data is developed for road weight calculation, which is the essential step in route planning. This method incorporates traffic data from conventional vehicles with the PHEV simulation model, obtaining the required data. Thirdly, the travel expense estimation model (TEEM) is designed. The TEEM could be applied to calculate the road weight of each road segment considering the impact on energy consumption with respect to environmental factors, providing the grounds for route planning. The proposed method to plan an economical route is evaluated, and the results justify its validation and ability to improve fuel economy. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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Open AccessArticle Transactive Demand Side Management Programs in Smart Grids with High Penetration of EVs
Energies 2017, 10(10), 1640; https://doi.org/10.3390/en10101640
Received: 18 September 2017 / Revised: 6 October 2017 / Accepted: 13 October 2017 / Published: 18 October 2017
Cited by 1 | PDF Full-text (3891 KB) | HTML Full-text | XML Full-text
Abstract
Due to environmental concerns, economic issues, and emerging new loads, such as electrical vehicles (EVs), the importance of demand side management (DSM) programs has increased in recent years. DSM programs using a dynamic real-time pricing (RTP) method can help to adaptively control the
[...] Read more.
Due to environmental concerns, economic issues, and emerging new loads, such as electrical vehicles (EVs), the importance of demand side management (DSM) programs has increased in recent years. DSM programs using a dynamic real-time pricing (RTP) method can help to adaptively control the electricity consumption. However, the existing RTP methods, particularly when they consider the EVs and the power system constraints, have many limitations, such as computational complexity and the need for centralized control. Therefore, a new transactive DSM program is proposed in this paper using an imperfect competition model with high EV penetration levels. In particular, a heuristic two-stage iterative method, considering the influence of decisions made independently by customers to minimize their own costs, is developed to find the market equilibrium quickly in a distributed manner. Simulations in the IEEE 37-bus system with 1141 customers and 670 EVs are performed to demonstrate the effectiveness of the proposed method. The results show that the proposed method can better manage the EVs and elastic appliances than the existing methods in terms of power constraints and cost. Also, the proposed method can solve the optimization problem quick enough to run in real-time. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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Open AccessArticle A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route
Energies 2017, 10(9), 1379; https://doi.org/10.3390/en10091379
Received: 1 August 2017 / Revised: 8 September 2017 / Accepted: 8 September 2017 / Published: 11 September 2017
Cited by 5 | PDF Full-text (6224 KB) | HTML Full-text | XML Full-text
Abstract
When developing a real-time energy management strategy for a plug-in hybrid electric vehicle, it is still a challenge for the Equivalent Consumption Minimum Strategy to achieve near-optimal energy consumption, because the optimal equivalence factor is not readily available without the trip information. With
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When developing a real-time energy management strategy for a plug-in hybrid electric vehicle, it is still a challenge for the Equivalent Consumption Minimum Strategy to achieve near-optimal energy consumption, because the optimal equivalence factor is not readily available without the trip information. With the help of realistic speeding profiles sampled from a plug-in hybrid electric bus running on a fixed commuting line, this paper proposes a convenient and effective approach of determining the equivalence factor for an adaptive Equivalent Consumption Minimum Strategy. Firstly, with the adaptive law based on the feedback of battery SOC, the equivalence factor is described as a combination of the major component and tuning component. In particular, the major part defined as a constant is applied to the inherent consistency of regular speeding profiles, while the second part including a proportional and integral term can slightly tune the equivalence factor to satisfy the disparity of daily running cycles. Moreover, Pontryagin’s Minimum Principle is employed and solved by using the shooting method to capture the co-state dynamics, in which the Secant method is introduced to adjust the initial co-state value. And then the initial co-state value in last shooting is taken as the optimal stable constant of equivalence factor. Finally, altogether ten successive driving profiles are selected with different initial SOC levels to evaluate the proposed method, and the results demonstrate the excellent fuel economy compared with the dynamic programming and PMP method. Full article
(This article belongs to the Special Issue Energy Management in Vehicle–Grid–Traffic Nexus)
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