Data Driven Methods for EVs Charging Sessions Forecasting

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Power and Energy Forecasting".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 18352

Special Issue Editors


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Guest Editor
Department of Energy–Electrical Engineering, Politecnico di Milano, Via La Masa 34, 20156 Milano, Italy
Interests: evolutionary computation techniques; neural networks; optimization of EM devices; reflectarray antennas; electrical microgrid
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Guest Editor
Department of Energy, Politecnico di Milano, Via Lambrischini, 4, 20156 Milan, Italy
Interests: RES power forecast; load forecast; artificial neural networks; machine learning; energy management system; electric vehicles

Special Issue Information

Dear Colleagues,

Electric vehicles (EVs) are currently undergoing unprecedented growth in terms of adoption. Due to the large amount of charging power required, this poses great problems for electric networks, which must be able to manage high power volatility and balance energy supply and demand.

To help to manage the network, it is of vital importance to have an accurate forecast of the power required by EVs in the charging process, which, among others, enables smart charging approaches, with the aim of reducing the impact on the electrical grid.

This Special Issue of Forecasting on “Data-Driven Methods for EV Charging Session Forecasting” is intended to disseminate new promising methods and techniques to forecast the output power and energy of charging EVs. In particular, this Special Issue will focus on the development of statistical or machine learning methods for forecasting information related to charging sessions of EVs, such as initial state-of-charge, power required, arrival time, and charging session duration.

Possible topics include but are not limited to the following:

  • EV arrival time forecasting;
  • EV state-of-charge forecasting;
  • EV charging session duration forecasting;
  • EV required charging power;
  • Integration of electric vehicle forecasting with smart charging;
  • Impact of electric vehicle forecasting on electric grid stability and management.

Dr. Alessandro Niccolai
Dr. Alfredo Nespoli
Guest Editors

Manuscript Submission Information

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Keywords

  • electric vehicles
  • power forecasting
  • state-of-charge
  • load forecasting
  • smart charging
  • energy management system

Published Papers (1 paper)

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Review

59 pages, 24279 KiB  
Review
Comprehensive Review of Power Electronic Converters in Electric Vehicle Applications
by Rejaul Islam, S M Sajjad Hossain Rafin and Osama A. Mohammed
Forecasting 2023, 5(1), 22-80; https://doi.org/10.3390/forecast5010002 - 29 Dec 2022
Cited by 19 | Viewed by 17447
Abstract
Emerging electric vehicle (EV) technology requires high-voltage energy storage systems, efficient electric motors, electrified power trains, and power converters. If we consider forecasts for EV demand and driving applications, this article comprehensively reviewed power converter topologies, control schemes, output power, reliability, losses, switching [...] Read more.
Emerging electric vehicle (EV) technology requires high-voltage energy storage systems, efficient electric motors, electrified power trains, and power converters. If we consider forecasts for EV demand and driving applications, this article comprehensively reviewed power converter topologies, control schemes, output power, reliability, losses, switching frequency, operations, charging systems, advantages, and disadvantages. This article is intended to help engineers and researchers forecast typical recharging/discharging durations, the lifetime of energy storage with the help of control systems and machine learning, and the performance probability of using AlGaN/GaN heterojunction-based high-electron-mobility transistors (HEMTs) in EV systems. The analysis of this extensive review paper suggests that the Vienna rectifier provides significant performance among all AC–DC rectifier converters. Moreover, the multi-device interleaved DC–DC boost converter is best suited for the DC–DC conversion stage. Among DC–AC converters, the third harmonic injected seven-level inverter is found to be one of the best in EV driving. Furthermore, the utilization of multi-level inverters can terminate the requirement of the intermediate DC–DC converter. In addition, the current status, opportunities, challenges, and applications of wireless power transfer in hybrid and all-electric vehicles were also discussed in this paper. Moreover, the adoption of wide bandgap semiconductors was considered. Because of their higher power density, breakdown voltage, and switching frequency characteristics, a light yet efficient power converter design can be achieved for EVs. Finally, the article’s intent was to provide a reference for engineers and researchers in the automobile industry for forecasting calculations. Full article
(This article belongs to the Special Issue Data Driven Methods for EVs Charging Sessions Forecasting)
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