Artificial Intelligence Applied to Edge Computing of Electric Vehicle Applications

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: closed (1 May 2022) | Viewed by 6281

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Department of Electronic Engineering, National Yunlin University of Science and Technology, Douliu, Taiwan
Interests: advanced nanomaterials and nanoparticles; MEMS sensing design; hardware/EE/RF circuit and IC design; antenna/microwave wireless design; EMC/EMI design; millimeter-wave and terahertz communication; artificial intelligence
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International Center, Chiba University of Commerce, Chiba, Japan
Interests: social media analysis; big data analysis; social media marketing in multi-cultural environment

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Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology (BUET), Dhaka, Bangladesh
Interests: machine and deep learning; image; video; biomedical and power signal; robotics
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Special Issue Information

Dear Colleagues

The aim of this Special Issue is to collect the latest developments from researchers working in the field of artificial intelligence (AI). This Special Issue will be a collection of articles from Editorial Board Members and leading researchers discussing new knowledge or new cutting-edge developments covers all studies related to ADAS and AI. Prospective authors are invited to submit original contributions that include, but are not limited to, the following topics of interest:

  • Control techniques and optimization algorithms
  • Advanced driver assistant systems (ADAS)
  • Artificial intelligence, machine learning, deep learning
  • Sensing and fusion technology (multi-cameras image processing, Lidar, Radar, etc.)
  • Communication and network of vehicles (RF, mmWave, 5G, 6G, etc.)
  • System-on-a-chip (SoC) and AIoT
  • Power electronics components
  • Wireless power transfer and charging
  • Battery of electric vehicles
  • Intelligent vehicle-to-grid systems
  • Neural network optimization (CNN, DNN, GAN, etc.)
  • Big data analysis and cloud computing
  • Natural language processing
  • Computer vision
  • Performance of embedded system (high mean average precision, low latency, etc.)
  • Edge computing of electric vehicles

This Special Issue intends to bring together researchers from academia and industries working on emerging topics of intelligent electric vehicle systems.

Dr. Wen-Cheng Lai
Prof. Dr. Takako Hashimoto
Prof. Dr. Celia Shahnaz
Prof. Dr. Valentina E. Balas
Guest Editors

Manuscript Submission Information

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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. World Electric Vehicle Journal is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • ADAS
  • AI
  • AIoT
  • sensing and fusion technology
  • neural network optimization
  • computer vision
  • performance of embedded system
  • edge computing of electric vehicle

Published Papers (2 papers)

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16 pages, 4000 KiB  
Article
Bidirectional Converter for Plug-In Hybrid Electric Vehicle On-Board Battery Chargers with Hybrid Technique
by Gopinath Anjinappa, Divakar Bangalore Prabhakar and Wen-Cheng Lai
World Electr. Veh. J. 2022, 13(11), 196; https://doi.org/10.3390/wevj13110196 - 22 Oct 2022
Cited by 5 | Viewed by 2267
Abstract
Recently, Plug-in Hybrid Electric Vehicles (PHEVs) have gathered a lot of attention by integrating an electric motor with an Internal Combustion Engine (ICE) to minimize fuel consumption and greenhouse gas emissions. The On-Board Chargers (OBCs) are selected in this research because they are [...] Read more.
Recently, Plug-in Hybrid Electric Vehicles (PHEVs) have gathered a lot of attention by integrating an electric motor with an Internal Combustion Engine (ICE) to minimize fuel consumption and greenhouse gas emissions. The On-Board Chargers (OBCs) are selected in this research because they are limited by dimensions and mass, and also consume low amounts of power. The Equivalent Series Resistance (ESR) of a filter capacitor is minor, so the zero produced by the ESR is positioned at a high frequency. In this state, the system magnitude gradually drops, causing a ripple in the circuit that generates a harmful impact on the battery’s stability. To improve the stability of the system, a Neural Network with an Improved Particle Swarm Optimization (NN–IPSO) control algorithm was developed. This study establishes an isolated converter topology for PHEVs to preserve battery-charging functions through a lesser number of power electronic devices over the existing topology. This isolated converter topology is controlled by NN–IPSO for the PHEV, which interfaces with the battery. The simulation results were validated in MATLAB, indicating that the proposed NN–IPSO-based isolated converter topology minimizes the Total Harmonic Distortion (THD) to 3.69% and the power losses to 0.047 KW, and increases the efficiency to 99.823%, which is much better than that of the existing Switched Reluctance Motor (SRM) power train topology. Full article
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20 pages, 23357 KiB  
Article
High Gain Converter with Improved Radial Basis Function Network for Fuel Cell Integrated Electric Vehicles
by Balasubramanian Girirajan, Himanshu Shekhar, Wen-Cheng Lai, Hariraj Kumar Jagannathan and Parameshachari Bidare Divakarachar
World Electr. Veh. J. 2022, 13(2), 31; https://doi.org/10.3390/wevj13020031 - 31 Jan 2022
Cited by 6 | Viewed by 2685
Abstract
In a recent trend, electric vehicles (EV) have been facing various power quality issues, so fuel cells (FC) are considered the best choice for integrating EV technology to enhance performance. A fuel cell electric vehicle (FCEV) is a type of EV that uses [...] Read more.
In a recent trend, electric vehicles (EV) have been facing various power quality issues, so fuel cells (FC) are considered the best choice for integrating EV technology to enhance performance. A fuel cell electric vehicle (FCEV) is a type of EV that uses a fuel cell combined with a small battery or super-capacitor to power its on-board electric motor. However, the power obtained from the FC system is much less and is not enough to drive the EV. So, another energy source is required to deliver the demanded power, which should contain high voltage gain with high conversion efficiency. The traditional converter produces a high output voltage at a high duty cycle, which generates various problems, such as reverse recovery issues, voltage spikes, and less lifespan. High switching frequency and voltage gain are essential for the propulsion of FC-based EV. Therefore, this paper presents an improved radial basis function (RBF)-based high-gain converter (HGC) to enhance the voltage gain and conversion efficiency of the entire system. The RBF neural model was constructed using the fast recursive algorithm (FRA) strategy to prune redundant hidden-layer neurons. The improved RBF technique reduces the input current ripple and voltage stress on the power semiconductor devices to increase the conversion ratio of the HGC without changing the duty cycle value. In the end, the improved RBF with HGC achieved an efficiency of 98.272%, vehicle speed of 91 km/h, and total harmonic distortion (THD) of 3.12%, which was simulated using MATLAB, and its waveforms for steady-state operation were analyzed and compared with existing methods. Full article
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