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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 (19 April 2024) | Viewed by 14072

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Special Issue Editors

Dr. Cesar Eduardo Diaz-Londono

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Guest Editor

Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milan, Italy
Interests: integration of electric vehicles; demand response; smart grids; real-time simulation; optimization; flexibility services
Special Issues, Collections and Topics in MDPI journals

Dr. Yang Li

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Guest Editor

Department of Electrical Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden
Interests: health- and safety-aware fast charging control of lithium-ion batteries; advanced modelling and control of energy storage systems; renewable power generation systems, and power electronic converters

Special Issue Information

Dear Colleagues,

The Guest Editors are inviting submissions to a Special Issue of Energies on the subject area of “Smart Electric Vehicle Charging Approaches for Demand Response”. Currently, smart grids are integrating Electric Vehicles (EVs) into the power grid, leading to significant challenges for the electrical grid in maintaining the balance between the supply and the demand side. Therefore, not only smart charging strategies able to provide energy flexibility are required but also predict the charging behaviors of the EVs connected to the EV supply equipment.

This Special Issue focuses on emerging smart charging (V1G) or Vehicle-to-Grid (V2G) approaches able to increase the electrical grid operation reliability, through demand response programs. Then, it is important to discuss new strategies based for example on machine learning to forecast EV connection periods and its energy demand for scheduling the charger occupancy. Moreover, optimization techniques are proper tools to produce novel strategies capable of evaluating different demand response programs while considering the market and grid requirements.

Topics of interest include, but are not limited, to the following:

Vehicle-to-grid strategies;
Electric vehicle integration;
Machine learning-based algorithms for electric vehicles;
Strategies for smart charging stations, electric vehicles, and fleets;
Charging electric vehicles with renewable energy sources;
Electric vehicle Flexible demand response;
Optimal microgrids operation under uncertainty;
Control strategies for charging stations, electric vehicles, and fleets;
Active distribution networks.

Dr. Cesar Eduardo Diaz-Londono
Dr. Yang Li
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

demand response
electric vehicles charging
vehicle-to-grid
smart grid
renewable energy systems
sustainable e-mobility
reinforcement learning
forecasting
optimization
battery management systems

Published Papers (9 papers)

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Research

25 pages, 1893 KiB

Open AccessArticle

A Case Study of the Use of Smart EV Charging for Peak Shaving in Local Area Grids

by Josef Meiers and Georg Frey

Energies 2024, 17(1), 47; https://doi.org/10.3390/en17010047 - 21 Dec 2023

Cited by 1 | Viewed by 918

Abstract

Electricity storage systems, whether electric vehicles or stationary battery storage systems, stabilize the electricity supply grid with their flexibility and thus drive the energy transition forward. Grid peak power demand has a high impact on the energy bill for commercial electricity consumers. Using battery storage capacities (EVs or stationary battery systems) can help to reduce these peaks, applying peak shaving. This study aims to address the potential of peak shaving using a PV plant and smart unidirectional and bidirectional charging technology for two fleets of electric vehicles and two comparable configurations of stationary battery storage systems on the university campus of Saarland University in Saarbrücken as a case study. Based on an annual measurement of the grid demand power of all consumers on the campus, a simulation study was carried out to compare the peak shaving potential of seven scenarios. For the sake of simplicity, it was assumed that the vehicles are connected to the charging station during working hours and can be charged and discharged within a user-defined range of state of charge. Furthermore, only the electricity costs were included in the profitability analysis; investment and operating costs were not taken into account. Compared to a reference system without battery storage capacities and a PV plant, the overall result is that the peak-shaving potential and the associated reduction in total electricity costs increases with the exclusive use of a PV system (3.2%) via the inclusion of the EV fleet (up to 3.0% for unidirectional smart charging and 8.1% for bidirectional charging) up to a stationary battery storage system (13.3%). Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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21 pages, 3440 KiB

Open AccessArticle

Optimal Planning Strategy for Reconfigurable Electric Vehicle Chargers in Car Parks

by Bingkun Song, Udaya K. Madawala and Craig A. Baguley

Energies 2023, 16(20), 7204; https://doi.org/10.3390/en16207204 - 23 Oct 2023

Cited by 1 | Viewed by 1250

Abstract

A conventional electric vehicle charger (EVC) charges only one EV concurrently. This leads to underutilization whenever the charging power is less than the EVC-rated capacity. Consequently, the cost-effectiveness of conventional EVCs is limited. Reconfigurable EVCs (REVCs) are a new technology that overcomes underutilization by allowing multiple EVs to be charged concurrently. This brings a cost-effective charging solution, especially in large car parks requiring numerous chargers. Therefore, this paper proposes an optimal planning strategy for car parks deploying REVCs. The proposed planning strategy involves three stages. An optimization model is developed for each stage of the proposed planning strategy. The first stage determines the optimal power rating of power modules inside each REVC, and the second stage determines the optimal number and configuration of REVCs, followed by determining the optimal operation plan for EV car parks in the third stage. To demonstrate the effectiveness of the proposed optimal planning strategy, a comprehensive case study is undertaken using realistic car parking scenarios with 400 parking spaces, electricity tariffs, and grid infrastructure costs. Compared to deploying other conventional EVCs, the results convincingly indicate that the proposed optimal planning strategy significantly reduces the total cost of investment and operation while satisfying charging demands. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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18 pages, 2591 KiB

Open AccessArticle

Enhancing Grid Operation with Electric Vehicle Integration in Automatic Generation Control

by Zahid Ullah, Kaleem Ullah, Cesar Diaz-Londono, Giambattista Gruosso and Abdul Basit

Energies 2023, 16(20), 7118; https://doi.org/10.3390/en16207118 - 17 Oct 2023

Cited by 2 | Viewed by 824

Abstract

Wind energy has been recognized as a clean energy source with significant potential for reducing carbon emissions. However, its inherent variability poses substantial challenges for power system operators due to its unpredictable nature. As a result, there is an increased dependence on conventional generation sources to uphold the power system balance, resulting in elevated operational costs and an upsurge in carbon emissions. Hence, an urgent need exists for alternative solutions that can reduce the burden on traditional generating units and optimize the utilization of reserves from non-fossil fuel technologies. Meanwhile, vehicle-to-grid (V2G) technology integration has emerged as a remedial approach to rectify power capacity shortages during grid operations, enhancing stability and reliability. This research focuses on harnessing electric vehicle (EV) storage capacity to compensate for power deficiencies caused by forecasting errors in large-scale wind energy-based power systems. A real-time dynamic power dispatch strategy is developed for the automatic generation control (AGC) system to integrate EVs and utilize their reserves optimally to reduce reliance on conventional power plants and increase system security. The results obtained from this study emphasize the significant prospects associated with the fusion of EVs and traditional power plants, offering a highly effective solution for mitigating real-time power imbalances in large-scale wind energy-based power systems. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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17 pages, 6826 KiB

Open AccessArticle

Interaction among Multiple Electric Vehicle Chargers: Measurements on Harmonics and Power Quality Issues

by Andrea Mazza, Giorgio Benedetto, Ettore Bompard, Claudia Nobile, Enrico Pons, Paolo Tosco, Marco Zampolli and Rémi Jaboeuf

Energies 2023, 16(20), 7051; https://doi.org/10.3390/en16207051 - 11 Oct 2023

Cited by 2 | Viewed by 983

Abstract

The electric vehicle (EV) market is growing rapidly due to the necessity of shifting from fossil fuel-based mobility to a more sustainable one. Smart charging paradigms (such as vehicle-to-grid (V2G), vehicle-to-building (V2B), and vehicle-to-home (V2H)) are currently under development, and the existing implementations already enable a bidirectional energy flow between the vehicles and the other systems (grid, buildings, or home appliances, respectively). With regard to grid connection, the increasingly higher penetration of electric vehicles must be carefully analyzed in terms of negative impacts on the power quality; and hence, the effects of electric vehicle charging stations (EVCSs) must be considered. In this work, the interactions of multiple electric vehicle charging stations have been studied through laboratory experiments. Two identical bidirectional DC chargers, with a rated power of 11 kW each, have been supplied by the same voltage source, and the summation phenomenon of the current harmonics of the two chargers (which leads to an amplification of their values) has been analyzed. The experiment consisted of 100 trials, which considered four different combinations of power set-points in order to identify the distribution of values and to find suitable indicators for understanding the trend of the harmonic interaction. By studying the statistical distribution of the Harmonic Summation Index, defined in the paper, the impact of the harmonic distortion caused by the simultaneous charging of multiple electric vehicles has been explored. Based on this study, it can be concluded that the harmonic contributions of the electric vehicle charging stations tend to add up with increasing degrees of similarity of the power set-points, while they tend to cancel out the more the power set-points differ among the chargers. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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26 pages, 8132 KiB

Open AccessArticle

Practical Nonlinear Model Predictive Control for Improving Two-Wheel Vehicle Energy Consumption

by Yesid Bello, Juan Sebastian Roncancio, Toufik Azib, Diego Patino, Cherif Larouci, Moussa Boukhnifer, Nassim Rizoug and Fredy Ruiz

Energies 2023, 16(4), 1950; https://doi.org/10.3390/en16041950 - 15 Feb 2023

Viewed by 1451

Abstract

Increasing the range of electric vehicles (EVs) is possible with the help of eco-driving techniques, which are algorithms that consider internal and external factors, like performance limits and environmental conditions, such as weather. However, these constraints must include critical variables in energy consumption, such as driver preferences and external vehicle conditions. In this article, a reasonable energy-efficient non-linear model predictive control (NMPC) is built for an electric two-wheeler vehicle, considering the Paris-Brussels route with different driving profiles and driver preferences. Here, NMPC is successfully implemented in a test bed, showing how to obtain the different parameters of the optimization problem and the estimation of the energy for the closed-loop system from a practical point of view. The efficiency of the brushless DC motor (BLCD) is also included for this test bed. In addition, this document shows that the proposal increases the chance of traveling the given route with a distance accuracy of approximately 1.5% while simultaneously boosting the vehicle autonomy by almost 20%. The practical result indicates that the strategy based on an NMPC algorithm can significantly boost the driver’s chance of completing the journey. If the vehicle energy is insufficient to succeed in the trip, the algorithm can guide the minimal State of Charge (SOC) required to complete the journey to reduce the driver energy-related uncertainty to a minimum. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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20 pages, 5869 KiB

Open AccessArticle

Soft Switched Current Fed Dual Active Bridge Isolated Bidirectional Series Resonant DC-DC Converter for Energy Storage Applications

by Kiran Bathala, Dharavath Kishan and Nagendrappa Harischandrappa

Energies 2023, 16(1), 258; https://doi.org/10.3390/en16010258 - 26 Dec 2022

Cited by 1 | Viewed by 1906

Abstract

This paper proposes a high-frequency isolated current-fed dual active bridge bidirectional DC–DC series resonant converter with an inductive filter for energy storage applications, and a steady-state analysis of the converter is carried out. The performance of the proposed converter has been compared with a voltage-fed converter with a capacitive output filter. The proposed converter topology is operated in continuous conduction mode with zero circulation current (ZCC), less current stress and high efficiency. The conditions required for soft switching are determined, and it is found that the converter operates with soft switching of all switches for a wide variation in load and input voltage without loss of duty cycle. Current-fed converters are suitable for low-voltage renewable energy applications because of their inherent boosting capability. An inductive output filter is chosen to make the output current ideal for fast charging and high-power-density battery storage applications. Simple single-phase shift control is used to control the switches. The performance of the converter is studied using PSIM simulation software. These results are confirmed by an experiment on a 135 W converter on an OPAL-RT real-time simulator. The maximum efficiency obtained in simulation is 96.31%. Simulation and theoretical results are given in the comparison table for both forward and reverse modes of operation. A breakdown of the losses of this converter is also presented. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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17 pages, 2072 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Agnostic Battery Management System Capacity Estimation for Electric Vehicles

by Lisa Calearo, Charalampos Ziras, Andreas Thingvad and Mattia Marinelli

Energies 2022, 15(24), 9656; https://doi.org/10.3390/en15249656 - 19 Dec 2022

Cited by 7 | Viewed by 2254

Abstract

Battery degradation is a main concern for electric vehicle (EV) users, and a reliable capacity estimation is of major importance. Every EV battery management system (BMS) provides a variety of information, including measured current and voltage, and estimated capacity of the battery. However, these estimations are not transparent and are manufacturer-specific, although measurement accuracy is unknown. This article uses extensive measurements from six diverse EVs to compare and assess capacity estimation with three different methods: (1) reading capacity estimation from the BMS through the central area network (CAN)-bus, (2) using an empirical capacity estimation (ECE) method with external current measurements, and (3) using the same method with measurements coming from the BMS. We show that the use of BMS current measurements provides consistent capacity estimation (a difference of approximately 1%) and can circumvent the need for costly experimental equipment and DC chargers. This data can simplify the ECE method only by using an on-board diagnostics port (OBDII) reader and an AC charger, as the car measures the current directly at the battery terminals. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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24 pages, 9335 KiB

Open AccessArticle

Risk Assessment of Industrial Energy Hubs and Peer-to-Peer Heat and Power Transaction in the Presence of Electric Vehicles

by Esmaeil Valipour, Ramin Nourollahi, Kamran Taghizad-Tavana, Sayyad Nojavan and As’ad Alizadeh

Energies 2022, 15(23), 8920; https://doi.org/10.3390/en15238920 - 25 Nov 2022

Cited by 22 | Viewed by 1836

Abstract

The peer-to-peer (P2P) strategy as a new trading scheme has recently gained attention in local electricity markets. This is a practical framework to enhance the flexibility and reliability of energy hubs, specifically for industrial prosumers dealing with high energy costs. In this paper, a Norwegian industrial site with multi-energy hubs (MEHs) is considered, in which they are equipped with various energy sources, namely wind turbines (WT), photovoltaic (PV) systems, combined heat and power (CHP) units (convex and non-convex types), plug-in electric vehicles (EVs), and load-shifting flexibility. The objective is to evaluate the importance of P2P energy transaction with on-site flexibility resources for the industrial site. Regarding the substantial peak power charge in the case of grid power usage, this study analyzes the effects of P2P energy transaction under uncertain parameters. The uncertainties of electricity price, heat and power demands, and renewable generations (WT and PV) are challenges for industrial MEHs. Thus, a stochastically based optimization approach called downside risk constraint (DRC) is applied for risk assessment under the risk-averse and risk-neutral modes. According to the results, applying the DRC approach increased by 35% the operation cost (risk-averse mode) to achieve a zero-based risk level. However, the conservative behavior of the decision maker secures the system from financial losses despite a growth in the operation cost. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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17 pages, 23778 KiB

Open AccessArticle

Resiliency-Sensitive Decision Making Mechanism for a Residential Community Enhanced with Bi-Directional Operation of Fuel Cell Electric Vehicles

by Fatma Gülşen Erdinç, Alper Çiçek and Ozan Erdinç

Energies 2022, 15(22), 8729; https://doi.org/10.3390/en15228729 - 20 Nov 2022

Cited by 1 | Viewed by 1139

Abstract

The trend regarding providing more distributed solutions compared to a fully centralized operation has increased the research activities conducted on the improvement of active regional communities in the power system operation in the last decades. In this study, an energy management-oriented decision-making mechanism for residential end-users based local community is proposed in a mixed-integer linear programming context. The proposed concept normally includes inflexible resiliency-sensitive load–demand activated as flexible during abnormal operating conditions, fuel cell electric vehicles (FCEVs) fed via the hydrogen provided by an electrolyzer unit connected to the residential community and capable of acting in vehicle-to-grid (V2G) mode, common energy storage and photovoltaic (PV) based distributed generation units and dispersed PV based generating options at the end-user premises. The combination of the hydrogen–electricity chain with the V2G capability of FCEVs and the resiliency-sensitive loads together with common ESS and generation units provides the novelty the study brings to the existing literature. The concept was tested under different case studies also with different objective functions. Full article

(This article belongs to the Special Issue Smart Electric Vehicle Charging Approaches for Demand Response)

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