Intelligent Modeling and Simulation Technology of E-Mobility

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

Deadline for manuscript submissions: closed (30 December 2021) | Viewed by 33386

Special Issue Editors


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Guest Editor
Department of Automation, University of Science and Technology of China, Hefei, China
Interests: hybrid mobile robots;power systems of new energy vehicles;multi-energy complementarity and collaboration of distributed micro-grid
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E-Mail Website
Guest Editor
State Key Laboratory of Mechanical Transmissions & School of Automotive Engineering, Chongqing University, No.174 Shazhengjie, Shapingba, Chongqing 400044, China
Interests: optimization and control of intelligent electric vehicle (including EV/HEV) power systems; integrated control of vehicle automatic transmissions
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Control and Systems Engineering, Nanjing University, Nanjing, China
Interests: reinforcement learning; mobile robotics; quantum control
Special Issues, Collections and Topics in MDPI journals
Department of Automation, University of Science and Technology of China, Hefei, China
Interests: power systems of new energy vehicles; modelling, simulation, and control of hybrid energy system; management and optimization control of fuel cell systems
Special Issues, Collections and Topics in MDPI journals
Department of Automation, University of Science and Technology of China, Hefei, China
Interests: mobile robot navigation; 3D environment mapping; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 22nd Chinese Conference on System Simulation Technology and Application (CCSSTA 2021) is to be held in Chongqing, China, from 10 October to 14 October 2021. CCSSTA 2021 aims to provide original communication opportunities for experts, scientists, students, technological engineers, and other young talents in the field of simulation in universities, research institutes, and enterprises. The committee of the conference focuses on fully communicating the latest research results and progress in the field of simulation and sharing practical experience in the field of simulation. With the support of the Chinese Association of Automation (CAA)—System Simulation Committee and China Simulation Federation (CAF) —Application of Simulation Technology Committee, this conference has been hosted for more than 20 years.

Vehicle intelligence involves information perception, processing, decision-making control, intelligent learning, wireless communication, intelligent operation and scheduling, advanced energy integration, and so on. Therefore, research on intelligent e-mobility requires the support of the entire field of artificial intelligence. Scholars and experts in various fields are required to communicate and jointly promote the process of intelligence in related fields. Intelligentization and electrification are important issues to ensure that vehicles operate entirely autonomously and environmentally friendly. The current Special Issue on “Intelligent Modeling and Simulation Technology of E-Mobility” mainly includes selected papers from the participants of CCSSTA2021. The topics will include but not limited to:

  • Sensor technologies for driverless e-mobility;
  • Intelligent vehicles related image, radar, and LiDAR signal processing;
  • Vehicle navigation and localization;
  • State estimation, fault diagnosis, and health prognostics for energy storage systems in e-mobility;
  • Advanced control technique for e-mobility;
  • Energy integration and cyberphysical systems for e-mobility;
  • Advanced artificial intelligence techniques for solving problems in e-mobility;
  • Human factors and human–machine interaction.

The authors of the best papers presented at CCSSTA2021 will be invited to further extend their CCSSTA2021 paper, including their most recent research findings. After a second thorough round of peer review, these papers will be published in this Special Issue of the World Electric Vehicle Journal, WEVJ.

In addition, submissions from others who are not associated with this conference but with themes focusing on the above topics are also welcome. We warmly invite emerging and pioneer investigators to contribute research papers, short communications, and review articles that focus on intelligent e-mobility.

If you have any questions, please feel free to contact the editorial office at [email protected].

Prof. Dr. Zonghai Chen
Prof. Dr. YongGang Liu
Prof. Dr. Chunlin Chen
Dr. Yujie Wang
Dr. Jikai Wang
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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.

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Published Papers (8 papers)

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Research

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17 pages, 5995 KiB  
Article
A Structure Optimized Method Based on AFSA for Soft Magnetic Strips of Inner Double-Layer Shield for Wireless Power Transmission of EV
by Yening Sun, Yao Wei and Yi Tian
World Electr. Veh. J. 2022, 13(3), 49; https://doi.org/10.3390/wevj13030049 - 4 Mar 2022
Cited by 2 | Viewed by 2067
Abstract
A structure optimized method based on the artificial fish swarm algorithm (AFSA) for the soft magnetic strips of the inner double-layer shield is proposed in this paper and applied to the coupler of the wireless power transfer (WPT) system of an electrical vehicle [...] Read more.
A structure optimized method based on the artificial fish swarm algorithm (AFSA) for the soft magnetic strips of the inner double-layer shield is proposed in this paper and applied to the coupler of the wireless power transfer (WPT) system of an electrical vehicle (EV). Some structure parameters including length, height, width and distances of the strips are selected to fit their relationships with the coupling coefficient, which directly effects the transfer efficiency of the coupler by the linear fitting method. Based on these relationships, a group of parameters is obtained by the AFSA with the largest coupling coefficient and suitable volume to achieve optimal transfer efficiency without lots of repetitive results of the finite element analysis. The effectiveness of the proposed method is demonstrated quantitatively according to the outcomes and comparisons among different structure parameters and algorithms. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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11 pages, 5295 KiB  
Article
Inductive Power Transmission System for Electric Car Charging Phase: Modeling plus Frequency Analysis
by Naoui Mohamed, Flah Aymen and Mohammed Alqarni
World Electr. Veh. J. 2021, 12(4), 267; https://doi.org/10.3390/wevj12040267 - 19 Dec 2021
Cited by 7 | Viewed by 3167
Abstract
The effectiveness of inductive power transfer (IPT) presents a serious challenge for improving the global recharge system performance. An electric vehicle (EVs) needs to be charged rapidly and have maximum power when it is charged with wireless technology. Based on various research, the [...] Read more.
The effectiveness of inductive power transfer (IPT) presents a serious challenge for improving the global recharge system performance. An electric vehicle (EVs) needs to be charged rapidly and have maximum power when it is charged with wireless technology. Based on various research, the performance of this recharge system is attached to several points and the frequency resonance is one of those parameters that can influence. In this paper, we try to explore the relationship between the obtained power and the signal input frequency for charging a lithium battery, solve the class imbalance problem and understand the maximum allowed frequency. To obtain the results, a mathematical model was first created to demonstrate the relationship, then the dynamic model was validated and tested using the Matlab Simulink platform. The performance of the worldwide wireless recharging system in terms of frequency variation is depicted in a summary graph. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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22 pages, 11841 KiB  
Article
Phenomenon Analysis and Improvement of Magnetic Shield Fringe Effect on Wireless Power Transmission of EV
by Yening Sun, Yao Wei and Yi Tian
World Electr. Veh. J. 2021, 12(4), 252; https://doi.org/10.3390/wevj12040252 - 25 Nov 2021
Cited by 2 | Viewed by 2492
Abstract
An increment of magnetic field strength inevitably appears at the shield edge if a magnetic shield is made of a soft magnetic material, and that increment becomes more serious if this shield is combined with the chassis of an electrical vehicle (EV). This [...] Read more.
An increment of magnetic field strength inevitably appears at the shield edge if a magnetic shield is made of a soft magnetic material, and that increment becomes more serious if this shield is combined with the chassis of an electrical vehicle (EV). This phenomenon is caused by the fringe effect, which limits the transfer efficiency of the coupler for the wireless power transmission (WPT) systems of EV. This phenomenon, and its relationships with some parameters, are analyzed in this paper, and these relationships are fitted to estimate the increment for different shield structures. A magnetic shield structure to reduce the increment of the magnetic field strength and improve coupler efficiency is herein proposed. The effectiveness and correctness of the fitting curves and the advantages of the proposed shield structure are demonstrated by finite element analyses results. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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14 pages, 821 KiB  
Article
Overview of Intelligent Vehicle Infrastructure Cooperative Simulation Technology for IoV and Automatic Driving
by Zirui Ding and Junping Xiang
World Electr. Veh. J. 2021, 12(4), 222; https://doi.org/10.3390/wevj12040222 - 8 Nov 2021
Cited by 11 | Viewed by 3233
Abstract
This paper reviews the development of vehicle road collaborative simulation in the new era, and summarizes the simulation characteristics of two core technologies in the field of transportation after entering the era of Intelligent Networking: Internet of Vehicles technology and automatic driving technology. [...] Read more.
This paper reviews the development of vehicle road collaborative simulation in the new era, and summarizes the simulation characteristics of two core technologies in the field of transportation after entering the era of Intelligent Networking: Internet of Vehicles technology and automatic driving technology. This paper analyzes and compares the mainstream Internet of Vehicles (IoV) simulation and automatic driving simulation platforms on the market, deeply analyzes the model-based IoV simulation, and explores a new mode of IoV simulation in the era of big data. According to the latest classification standard of automatic driving in 2020, we summarize the simulation process of automatic driving. Finally, we offer suggestions on the development directions of intelligent network-connected vehicle simulation. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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22 pages, 7551 KiB  
Article
A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset
by Farzin Foroughi, Zonghai Chen and Jikai Wang
World Electr. Veh. J. 2021, 12(3), 134; https://doi.org/10.3390/wevj12030134 - 26 Aug 2021
Cited by 15 | Viewed by 3581
Abstract
Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying [...] Read more.
Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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16 pages, 4361 KiB  
Article
An Approach to Complement Model-Based Vehicle Development by Implementing Future Scenarios
by Christian Raulf, Moritz Proff, Tobias Huth and Thomas Vietor
World Electr. Veh. J. 2021, 12(3), 97; https://doi.org/10.3390/wevj12030097 - 3 Jul 2021
Cited by 3 | Viewed by 3732
Abstract
Today, vehicle development is already in a process of substantial transformation. Mobility trends can be derived from global megatrends and have a significant influence on the requirements of the developed vehicles. The sociological, technological, economic, ecological, and political developments can be determined by [...] Read more.
Today, vehicle development is already in a process of substantial transformation. Mobility trends can be derived from global megatrends and have a significant influence on the requirements of the developed vehicles. The sociological, technological, economic, ecological, and political developments can be determined by using the scenario technique. The results are recorded in the form of differently shaped scenarios; however, they are mainly document-based. In order to ensure a holistic approach in the sense of model-based systems engineering and to be able to trace the interrelationships of the fast-changing trends and requirements, it is necessary to implement future scenarios in the system model. For this purpose, a method is proposed that enables the consideration of future scenarios in model-based vehicle development. The procedure of the method is presented, and the location of the future scenarios within the system architectures is named. The method is applied and the resulting system views are derived based on the application example of an autonomous people mover. With the help of the described method, it is possible to show the effects of a change of scenario (e.g., best-case and worst-case) and the connections with the highest level of requirements: stakeholder needs. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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17 pages, 4994 KiB  
Article
Optimal Control Strategy for Parallel Plug-in Hybrid Electric Vehicles Based on Dynamic Programming
by Ying Tian, Jiaqi Liu, Qiangqiang Yao and Kai Liu
World Electr. Veh. J. 2021, 12(2), 85; https://doi.org/10.3390/wevj12020085 - 8 Jun 2021
Cited by 14 | Viewed by 4608
Abstract
In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is [...] Read more.
In this paper, the dynamic programming algorithm is applied to the control strategy design of parallel hybrid electric vehicles. Based on MATLAB/Simulink software, the key component model and controller model of the parallel hybrid system are established, and an offline simulation platform is built. Based on the platform, the global optimal control strategy based on the dynamic programming algorithm is studied. The torque distribution rules and shifting rules are analyzed, and the optimal control strategy is adopted to design the control strategy, which effectively improves the fuel economy of plug-in hybrid electric vehicles. The fuel consumption rate of this parallel hybrid electric vehicle is based on china city bus cycle (CCBC) condition. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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Review

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18 pages, 6606 KiB  
Review
A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles
by Deyun Dai, Zonghai Chen, Peng Bao and Jikai Wang
World Electr. Veh. J. 2021, 12(3), 139; https://doi.org/10.3390/wevj12030139 - 30 Aug 2021
Cited by 19 | Viewed by 7875
Abstract
In recent years, electric vehicles have achieved rapid development. Intelligence is one of the important trends to promote the development of electric vehicles. As a result, autonomous driving system is becoming one of the core systems of electric vehicles. Considering that environmental perception [...] Read more.
In recent years, electric vehicles have achieved rapid development. Intelligence is one of the important trends to promote the development of electric vehicles. As a result, autonomous driving system is becoming one of the core systems of electric vehicles. Considering that environmental perception is the basis of intelligent planning and safe decision-making for intelligent vehicles, this paper presents a survey of the existing perceptual methods in vehicles, especially 3D object detection, which guarantees the reliability and safety of vehicles. In this review, we first introduce the role of perceptual module in autonomous driving system and a relationship with other modules. Then, we classify and analyze the corresponding perception methods based on the different sensors. Finally, we compare the performance of the surveyed works on public datasets and discuss the possible future research interests. Full article
(This article belongs to the Special Issue Intelligent Modeling and Simulation Technology of E-Mobility)
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