Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (16 June 2017) | Viewed by 40446

Share This Special Issue

Special Issue Editors

Dr. Hugo Morais

E-Mail
Guest Editor

Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), ISEP/IPP, Porto, Portugal
Interests: distributed energy resources management; electric vehicles integration in power systems; virtual power players, microgrids and smart grids management; multi-agents systems and power systems visualization

Prof. Dr. Juan M. Corchado

E-Mail Website
Guest Editor

1. BISITE Research Group, University of Salamanca, 37007 Salamanca, Spain
2. Air Institute, IoT Digital Innovation Hub, 37188 Salamanca, Spain
3. Department of Electronics, Information and Communication, Faculty of Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan
Interests: artificial intelligence; smart cities; smart grids
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lei Wang

E-Mail Website
Guest Editor

College of Electronic and Information Engineering, TongJi Unversity, Shanghai, China
Interests: integration of different intelligent algorithms such as neural networks, fuzzy algorithms, swarm intelligence, and apply them for intelligent decision-making

Dr. Junjie Hu

E-Mail Website
Guest Editor

Center for Electric Power and Energy, Department of Electrical Engineering, Technical University of Denmark, Copenhagen, Denmark
Interests: optimal charging of electric vehicles and storages for secure power distribution system operation and power system balancing; Transactive control for managing distributed energy resources; multi-agent theory and simulation
Special Issues, Collections and Topics in MDPI journals

Dr. Emanuele Principi

E-Mail Website
Guest Editor

Center for Electric Power and Energy, Università Politecnica delle Marche, Ancona, Italy
Interests: machine learning and pattern recognition for the smart grid (e.g., energy task scheduling, non-intrusive load monitoring, computational Intelligence for vehicle to grid) and intelligent audio analysis (e.g., multiroom voice activity detection and speaker localization)

Special Issue Information

Dear Colleagues,

The fast developments of the electric vehicles impose new challenges for power system management and planning. However, considering the recent EVs’ evolution perspectives, such as vehicle-to-grid technology, EVs also present opportunity for a smart grid in the near future. Within the smart grids context, the integration of EVs can be seen as a flexible load, as well as a generation resource, with the capacity to provide different services to the system, such as the frequency regulation and peak shaving.

Beyond the integration with power systems, the management of EVs can be integrated in a more embracing perspective like smart energy network management and planning, e.g., an integrated planning of traffic network, heating network, and power distribution network. With smart energy network planning technology, it can help in building an efficient and low-carbon society.

In the present Special Issue, we invite original and unpublished submissions concerning the integration of electric vehicles in future power systems allowing the development of the smart grids and smart energy network. Intelligent computing methods developments and applications in electric vehicles fields should be specifically addressed in the papers.

Potential topics include, but are not limited to:

Electric vehicle charging infrastructure planning
Electric vehicle fleet operation management
Multi-agents’ application on electric vehicles charging and discharging
Energy resources management considering electric vehicles
Stochastic analysis and optimization of electric vehicles management in smart grids
Use of electric vehicles/battery for frequency regulation, peak shaving and load leveling
Power quality enhancement with electric vehicles
Integrated management of electric vehicles considering the power grid and other critical infrastructures in a smart energy network context
Electric vehicle driving pattern analysis and prediction
Intelligent methods for electric motor fault detection
Impact of communication delay on system integration of electric vehicles within a smart grid context

Dr. Hugo Morais
Prof. Dr. Juan Manuel Corchado
Prof. Dr. Lei Wang
Dr. Junjie Hu
Dr. Emanuele Principi
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
smart grids
smart cities
intelligent energy resources management
electric vehicle fleet
stochastic analysis
driving patterns forecast
charging stations planning
intelligent charging technologies
battery management and operation

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

3974 KiB

Open AccessArticle

A Personalized Rolling Optimal Charging Schedule for Plug-In Hybrid Electric Vehicle Based on Statistical Energy Demand Analysis and Heuristic Algorithm

by Fanrong Kong, Jianhui Jiang, Zhigang Ding, Junjie Hu, Weian Guo and Lei Wang

Energies 2017, 10(9), 1333; https://doi.org/10.3390/en10091333 - 04 Sep 2017

Cited by 6 | Viewed by 3519

Abstract

To alleviate the emission of greenhouse gas and the dependence on fossil fuel, Plug-in Hybrid Electrical Vehicles (PHEVs) have gained an increasing popularity in current decades. Due to the fluctuating electricity prices in the power market, a charging schedule is very influential to driving cost. Although the next-day electricity prices can be obtained in a day-ahead power market, a driving plan is not easily made in advance. Although PHEV owners can input a next-day plan into a charging system, e.g., aggregators, day-ahead, it is a very trivial task to do everyday. Moreover, the driving plan may not be very accurate. To address this problem, in this paper, we analyze energy demands according to a PHEV owner’s historical driving records and build a personalized statistic driving model. Based on the model and the electricity spot prices, a rolling optimization strategy is proposed to help make a charging decision in the current time slot. On one hand, by employing a heuristic algorithm, the schedule is made according to the situations in the following time slots. On the other hand, however, after the current time slot, the schedule will be remade according to the next tens of time slots. Hence, the schedule is made by a dynamic rolling optimization, but it only decides the charging decision in the current time slot. In this way, the fluctuation of electricity prices and driving routine are both involved in the scheduling. Moreover, it is not necessary for PHEV owners to input a day-ahead driving plan. By the optimization simulation, the results demonstrate that the proposed method is feasible to help owners save charging costs and also meet requirements for driving. Full article

(This article belongs to the Special Issue Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks)

► Show Figures

Figure 1

2704 KiB

Open AccessArticle

A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles

by Feng Qi, Fushuan Wen, Xunyuan Liu and Md. Abdus Salam

Energies 2017, 10(8), 1159; https://doi.org/10.3390/en10081159 - 07 Aug 2017

Cited by 28 | Viewed by 5206

Abstract

Renewable energy generation and electric vehicles (EVs) have attracted much attention in the past decade due to increasingly serious environmental problems as well as less and less fossil energy reserves. Moreover, the forms of energy utilization are changing with the development of information technology and energy technology. The term “energy hub” has been introduced to represent an entity with the capability of energy production, conversion and storage. A residential quarter energy-hub-optimization model including a concentrating solar power (CSP) unit is proposed in this work, with solar energy and electricity as its inputs to supply thermal and electrical demands, and the operating objective is to minimize the involved operation costs. The optimization model is a mixed integer linear programming (MILP) problem. Demand side management (DSM) is next implemented by modeling shiftable electrical loads such as EVs and washers, as well as flexible thermal loads such as hot water. Finally, the developed optimization model is solved with the commercial CPLEX solver based on the YALMIP/MATLAB toolbox, and sample examples are provided for demonstrating the features of the proposed method. Full article

(This article belongs to the Special Issue Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks)

► Show Figures

Figure 1

12963 KiB

Open AccessArticle

Stochastic Navigation in Smart Cities

by Rubén Martín García, Francisco Prieto-Castrillo, Gabriel Villarrubia González, Javier Prieto Tejedor and Juan Manuel Corchado

Energies 2017, 10(7), 929; https://doi.org/10.3390/en10070929 - 05 Jul 2017

Cited by 3 | Viewed by 3556

Abstract

In this work we show how a simple model based on chemical signaling can reduce the exploration times in urban environments. The problem is relevant for smart city navigation where electric vehicles try to find recharging stations with unknown locations. To this end we have adapted the classical ant foraging swarm algorithm to urban morphologies. A perturbed Markov chain model is shown to qualitatively reproduce the observed behaviour. This consists of perturbing the lattice random walk with a set of perturbing sources. As the number of sources increases the exploration times decrease consistently with the swarm algorithm. This model provides a better understanding of underlying process dynamics. An experimental campaign with real prototypes provided experimental validation of our models. This enables us to extrapolate conclusions to optimize electric vehicle routing in real city topologies. Full article

(This article belongs to the Special Issue Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks)

► Show Figures

Figure 1

5532 KiB

Open AccessArticle

Incorporating Charging/Discharging Strategy of Electric Vehicles into Security-Constrained Optimal Power Flow to Support High Renewable Penetration

by Kyungsung An, Kyung-Bin Song and Kyeon Hur

Energies 2017, 10(5), 729; https://doi.org/10.3390/en10050729 - 20 May 2017

Cited by 29 | Viewed by 7012

Abstract

This research aims to improve the operational efficiency and security of electric power systems at high renewable penetration by exploiting the envisioned controllability or flexibility of electric vehicles (EVs); EVs interact with the grid through grid-to-vehicle (G2V) and vehicle-to-grid (V2G) services to ensure reliable and cost-effective grid operation. This research provides a computational framework for this decision-making process. Charging and discharging strategies of EV aggregators are incorporated into a security-constrained optimal power flow (SCOPF) problem such that overall energy cost is minimized and operation within acceptable reliability criteria is ensured. Particularly, this SCOPF problem has been formulated for Jeju Island in South Korea, in order to lower carbon emissions toward a zero-carbon island by, for example, integrating large-scale renewable energy and EVs. On top of conventional constraints on the generators and line flows, a unique constraint on the system inertia constant, interpreted as the minimum synchronous generation, is considered to ensure grid security at high renewable penetration. The available energy constraint of the participating EV associated with the state-of-charge (SOC) of the battery and market price-responsive behavior of the EV aggregators are also explored. Case studies for the Jeju electric power system in 2030 under various operational scenarios demonstrate the effectiveness of the proposed method and improved operational flexibility via controllable EVs. Full article

(This article belongs to the Special Issue Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks)

► Show Figures

Figure 1

2637 KiB

Open AccessArticle

Stochastic Optimal Control of Parallel Hybrid Electric Vehicles

by Feiyan Qin, Guoqing Xu, Yue Hu, Kun Xu and Weimin Li

Energies 2017, 10(2), 214; https://doi.org/10.3390/en10020214 - 13 Feb 2017

Cited by 18 | Viewed by 7722

Abstract

Energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are highly related to the fuel economy and emission performances. However, EMS constitutes a challenging problem due to the complex structure of a HEV and the unknown or partially known driving cycles. To meet this problem, this paper adopts a stochastic dynamic programming (SDP) method for the EMS of a specially designed vehicle, a pre-transmission single-shaft torque-coupling parallel HEV. In this parallel HEV, the auto clutch output is connected to the transmission input through an electric motor, which benefits an efficient motor assist operation. In this EMS, demanded torque of driver is modeled as a one-state Markov process to represent the uncertainty of future driving situations. The obtained EMS has been evaluated with ADVISOR2002 over two standard government drive cycles and a self-defined one, and compared with a dynamic programming (DP) one and a rule-based one. Simulation results have shown the real-time performance of the proposed approach, and potential vehicle performance improvement relative to the rule-based one. Full article

(This article belongs to the Special Issue Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks)

► Show Figures

Figure 1

7350 KiB

Open AccessArticle

Integration of Electric Vehicles into the Power Distribution Network with a Modified Capacity Allocation Mechanism

by Junjie Hu, Hugo Morais, Tiago Sousa, Shi You and Reinhilde D’hulst

Energies 2017, 10(2), 200; https://doi.org/10.3390/en10020200 - 10 Feb 2017

Cited by 15 | Viewed by 5362

Abstract

The growing penetration of electric vehicles (EVs) represents an operational challenge to system operators, mainly at the distribution level by introducing congestion and voltage drop problems. To solve these potential problems, a two-level coordination approach is proposed in this study. An aggregation entity, i.e., an EV virtual power plant (EV-VPP), is used to facilitate the interaction between the distribution system operator (DSO) and EV owners considering the decentralized electricity market structure. In level I, to prevent the line congestion and voltage drop problems, the EV-VPP internally respects the line and voltage constraints when making optimal charging schedules. In level II, to avoid power transformer congestion problems, this paper investigates three different coordination mechanisms, or power transformer capacity allocation mechanisms, between the DSO and the EV-VPPs, considering the case of EVs charging and discharging. The three mechanisms include: (1) a market-based approach; (2) a pro-rata approach; and (3) a newly-proposed constrained market-based approach. A case study considering a 37-bus distribution network and high penetration of electric vehicles is presented to demonstrate the effectiveness of the proposed coordination mechanism, comparing with the existing ones. Full article

(This article belongs to the Special Issue Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks)

► Show Figures

Figure 1

2169 KiB

Open AccessArticle

Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids

by Weige Zhang, Di Zhang, Biqiang Mu, Le Yi Wang, Yan Bao, Jiuchun Jiang and Hugo Morais

Energies 2017, 10(2), 147; https://doi.org/10.3390/en10020147 - 24 Jan 2017

Cited by 32 | Viewed by 6810

Abstract

A novel, fully decentralized strategy to coordinate charge operation of electric vehicles is proposed in this paper. Based on stochastic switching control of on-board chargers, this strategy ensures high-efficiency charging, reduces load variations to the grid during charging periods, achieves charge completion with high probability, and accomplishes approximate “valley-filling”. Further improvements on the core strategy, including individualized power management, adaptive strategies, and battery support systems, are introduced to further reduce power fluctuation variances and to guarantee charge completion. Stochastic analysis is performed to establish the main properties of the strategies and to quantitatively show the performance improvements. Compared with the existing decentralized charging strategies, the strategies proposed in this paper can be implemented without any information exchange between grid operators and electric vehicles (EVs), resulting in a communications cost reduction. Additionally, it is shown that by using stochastic charging rules, a grid-supporting battery system with a very small energy capacity can achieve substantial reduction of EV load fluctuations with high confidence. An extensive set of simulations and case studies with real-world data are used to demonstrate the benefits of the proposed strategies. Full article

(This article belongs to the Special Issue Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks)

► Show Figures

Journal Menu

Journal Browser

Theory and Application of Computational Intelligence in Electric Vehicles and their Integration within Smart Energy Networks

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI