Special Issue "Enabling Technologies in Electric and More Electric Transportation"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: 15 November 2023 | Viewed by 1670

Special Issue Editors

Graduate School of Engineering & International Exchange Promotion Center, Toyota Technological Institute, Nagoya 468-8511, Japan
Interests: control systems engineering; intelligent control (fuzzy logic, artificial neural networks); nonlinear control; hybrid control design; electrical engineering; renewable energy; photovoltaic and wind power; energy management systems and algorithms; electric motors and drives; motor losses evaluation; electric vehicles (EVs); applied electromagnetics; electromagnetic characterization; high-frequency magnetics; finite element method; power electronics applications; machine learning; and mobile robots
Special Issues, Collections and Topics in MDPI journals
Department of Electrical and Electronics Engineering, Universiti Teknologi Petronas, Seri Iskandar 32610, Malaysia
Interests: power electronics and drives; renewable energy systems; radiation in power semiconductor devices; artificial intelligence applications to power electronics

Special Issue Information

Dear Colleagues, 

In recent years, electric vehicles (EVs), hybrid EVs and electric transportation systems have been rapidly developed, and more and more broadly used in the world. This Special Issue is focused on the recent advances and developments in key technologies and solutions used for EVs, hybrid EVs, electric transportation systems, and related applications. All relevant research and review papers of analysis, design, simulation, evaluation and/or experiment are welcomed to contribute to the Special Issue.

Topics of interests include (but are not limited to) the following:

1. Electric motors, motor drives, and related issues in EVs:

  • Design, analysis, and evaluation of high-efficient motors for EVs.
  • New fault diagnosis techniques for electric motors and drives.
  • Motor drives using wide-bandgap semiconductor devices (SiC/GaN).
  • EMI issues in motor drive systems for EVs, and related solutions.

2. Power electronic converters, magnetic materials and components for EVs:

  • High-frequency and high-power converters in EVs and hybrid EVs.
  • Advanced topologies of high-efficient power electronic converters for EVs.
  • Design and characterization of magnetic materials and components for power converters in EVs.
  • Topologies and control of quick chargers for EVs and hybrid EVs.

3. Control methods and algorithms for EVs and electric transportation:

  • Dynamics and modeling of EVs and related transportation systems.
  • Advanced control methods for EVs and hybrid EVs.
  • Driving control algorithms for intelligent EVs.
  • Advances in autonomous and manual operation modes of EVs.

4. Energy management and AI applications in EVs and electric transportation:

  • Energy management and optimization algorithms for EVs and hybrid EVs.
  • Thermal analysis and management for EVs and hybrid EVs.
  • Application and implementation of artificial intelligence (AI) in EVs.
  • Control and mechanism of power grid with high penetration of EVs.

Dr. Nguyen Gia Minh Thao
Dr. Ramani Kannan
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. Electronics 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 2000 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

  • high-efficient motors and drives
  • high-frequency converters and magnetic materials
  • quick chargers for electric vehicles
  • advanced control of EVs
  • driving control for intelligent EVs
  • energy management algorithms for EVs
  • hybrid EVs

Published Papers (2 papers)

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Research

Article
Hybrid Vehicle CO2 Emissions Reduction Strategy Based on Model Predictive Control
Electronics 2023, 12(6), 1474; https://doi.org/10.3390/electronics12061474 - 21 Mar 2023
Viewed by 464
Abstract
This work proposes a hybrid drive controlled configuration, using a minimum emissions search algorithm, which ensures the operation of the Internal Combustion Engine (ICE) in its fuel efficiency range, minimizing CO2 emissions by controlling the power flow direction of the Electric Machine [...] Read more.
This work proposes a hybrid drive controlled configuration, using a minimum emissions search algorithm, which ensures the operation of the Internal Combustion Engine (ICE) in its fuel efficiency range, minimizing CO2 emissions by controlling the power flow direction of the Electric Machine (EM). This action is achieved by means of Power Converters, in this case a bi-directional DC-DC Buck-Boost Converter in the DC-side and a DC-AC T-type Converter as the inverting stage. Power flow is controlled by means of a bi-directional Model Predictive Control (MPC) scheme, based on an emissions optimization algorithm. A novel drivetrain configuration is presented where both, the ICE and the EM are in tandem arrangement. The EM is driven depending on the traction requirements and the emissions of the ICE. The EM is capable of operates in motor and generator mode ensuring the Minimum Emission Operating Point (MEOP) of the ICE regardless of the mechanical demand at the drivetrain. Simulation and validation results using a Hardware in the Loop (HIL) virtual prototype under different operation conditions are presented in order to validate the proposed overall optimization strategy. Full article
(This article belongs to the Special Issue Enabling Technologies in Electric and More Electric Transportation)
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Article
Maximizing Regenerative Braking Energy Harnessing in Electric Vehicles Using Machine Learning Techniques
Electronics 2023, 12(5), 1119; https://doi.org/10.3390/electronics12051119 - 24 Feb 2023
Viewed by 407
Abstract
Innovations in electric vehicle technology have led to a need for maximum energy storage in the energy source to provide some extra kilometers. The size of electric vehicles limits the size of the batteries, thus limiting the amount of energy that can be [...] Read more.
Innovations in electric vehicle technology have led to a need for maximum energy storage in the energy source to provide some extra kilometers. The size of electric vehicles limits the size of the batteries, thus limiting the amount of energy that can be stored. Range anxiety amongst the crowd prevents the entire population from shifting to a completely electric mode of transport. The extra energy harnessed from the kinetic energy produced due to braking during deceleration is sent back to the batteries to charge them, a process known as regenerative braking, providing a longer range to the vehicle. The work proposes efficient machine learning-based methods used to harness maximum braking energy from an electric vehicle to provide longer mileage. The methods are compared to the energy harnessed using fuzzy logic and artificial neural network techniques. These techniques take into consideration the state of charge (SOC) estimation of the battery, or the supercapacitor and the brake demand, to calculate the energy harnessed from the braking power. With the proposed machine learning techniques, there has been a 59% increase in energy extraction compared to fuzzy logic and artificial neural network methods used for regenerative energy extraction. Full article
(This article belongs to the Special Issue Enabling Technologies in Electric and More Electric Transportation)
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