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Electronics
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Enabling Technologies in Electric and More Electric Transportation
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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: closed (15 November 2023) | Viewed by 11590

Special Issue Editors

Dr. Nguyen Gia Minh Thao

E-Mail Website
Guest Editor
Department of Mechanical, Electrical and Electronic Engineering, Shimane University, Matsue 690-8504, 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; mobile robots
Special Issues, Collections and Topics in MDPI journals
Dr. Ramani Kannan

E-Mail Website
Guest Editor
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 2400 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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

19 pages, 4497 KiB  
Article
Lagrange Multiplier-Based Optimization for Hybrid Energy Management System with Renewable Energy Sources and Electric Vehicles
by Huy Gia Tran, Long Ton-That and Nguyen Gia Minh Thao
Electronics 2023, 12(21), 4513; https://doi.org/10.3390/electronics12214513 - 2 Nov 2023
Cited by 7 | Viewed by 2018
Abstract
The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a [...] Read more.
The issues of energy scarcity and environmental harm have become major priorities for both business and human progress. Hence, it is important and useful to focus on renewable energy research and efficient utilization of distributed energy sources (DERs). A microgrid (MG) is a self-managed system that encompasses these energy resources as well as interconnected consumers. It has the flexibility to function in both isolated and grid-connected configurations. This study aims to design an effective method of power management for a MG in the two operating modes. The proposed optimization model seeks to strike a balance between energy usage, protecting the life of batteries, and maximizing economic benefits for users in the MG, with consideration of the real-time electricity price and constraints of the power grid. Furthermore, in order to accurately account for the dynamic nature of not only the stationary battery banks used as the energy storage systems (ESS) but also the built-in batteries of electric vehicles (EVs), the model is presented as a multi-objective, multiparametric and constrained problem. The solution is proposed to be found using the Lagrange multiplier theory, which helps to achieve good performance with less computational burden. Lastly, simulation results from both the isolated and grid-connected modes also demonstrate the effectiveness of the designed method. Full article
(This article belongs to the Special Issue Enabling Technologies in Electric and More Electric Transportation)
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17 pages, 1814 KiB  
Article
Impact of Parameter Mismatch on Three-Phase Dual-Active-Bridge Converters
by Duy-Dinh Nguyen, The-Tiep Pham, Tat-Thang Le, Ton Duc Do, Takuya Goto and Kazuto Yukita
Electronics 2023, 12(12), 2609; https://doi.org/10.3390/electronics12122609 - 9 Jun 2023
Viewed by 1555
Abstract
Three-phase dual active bridge converters (DAB3) are a widely used topology in battery charging applications thanks to their numerous advantages, such as bidirectional power flow, galvanic isolation, low output current ripple, and inherent soft-switching. In such applications, three single-phase transformers are commonly employed [...] Read more.
Three-phase dual active bridge converters (DAB3) are a widely used topology in battery charging applications thanks to their numerous advantages, such as bidirectional power flow, galvanic isolation, low output current ripple, and inherent soft-switching. In such applications, three single-phase transformers are commonly employed as the AC-link to simplify manufacturing and reduce costs. These transformers’ leakage inductance can be utilized instead of the external leakage inductance to achieve high power density. However, the assumption of uniformity in these inductances is not always accurate as they can vary significantly during fabrication. This study presents a comprehensive analysis of the impact of transformer leakage inductance variation, which can deviate by up to 24% from the desired value. The effects of this variation are investigated from different perspectives, including power transfer, soft-switching range, root-mean-square (RMS) current, and the temperature rise of the transformer winding. Although the power transfer and total copper loss of transformers are changed insignificantly even under highly mismatched leakage inductance, the currents and thermal distribution among phases are considerably impacted. Based on statistical probability, a maximum leakage inductance variation threshold of 10–15% compared to the desired value is recommended to ensure the maximum acceptable temperature rise among phases. Experimental results are presented to validate the analysis. Full article
(This article belongs to the Special Issue Enabling Technologies in Electric and More Electric Transportation)
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20 pages, 4949 KiB  
Article
Hybrid Vehicle CO2 Emissions Reduction Strategy Based on Model Predictive Control
by Carlos A. Reusser, Ramón Herrera Hernández and Tek Tjing Lie
Electronics 2023, 12(6), 1474; https://doi.org/10.3390/electronics12061474 - 21 Mar 2023
Cited by 2 | Viewed by 2023
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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17 pages, 2767 KiB  
Article
Maximizing Regenerative Braking Energy Harnessing in Electric Vehicles Using Machine Learning Techniques
by Bathala Prasanth, Rinika Paul, Deepa Kaliyaperumal, Ramani Kannan, Yellapragada Venkata Pavan Kumar, Maddikera Kalyan Chakravarthi and Nithya Venkatesan
Electronics 2023, 12(5), 1119; https://doi.org/10.3390/electronics12051119 - 24 Feb 2023
Cited by 12 | Viewed by 4362
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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