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Special Issue "Intelligent Modeling and Simulation Technology of E-Mobility"

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

Deadline for manuscript submissions: 30 September 2021.

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Special Issue Editors

Prof. Dr. Zonghai Chen
E-Mail Website
Guest Editor

Department of Automation, University of Science and Technology of China, Hefei 230027, China
Interests: hybrid mobile robots; power systems of new energy vehicles; multi-energy complementarity and collaboration of distributed micro-grid

Prof. Dr. Yonggang Liu
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 and Collections in MDPI journals

Prof. Dr. Chunlin Chen
E-Mail Website
Guest Editor

Department of Control and Systems Engineering, Nanjing University, Nanjing, China
Interests: reinforcement learning; mobile robotics; quantum control

Dr. Yujie Wang
E-Mail Website

SciProfiles

Guest Editor

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

Dr. Jikai Wang
E-Mail Website
Guest Editor

Department of Automation, University of Science and Technology of China, Hefei, China
Interests: mobile robot navigation; environment mapping and understanding; 3D Lidar SLAM; machine learning and knowledge representation

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 25 July to 29 July 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 papers will be 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 quarterly 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 1000 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.

Published Papers (4 papers)

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Research

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Open AccessArticle

A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset

Farzin Foroughi

Zonghai Chen

and

Jikai Wang

World Electr. Veh. J. 2021, 12(3), 134; https://doi.org/10.3390/wevj12030134 - 26 Aug 2021

Viewed by 229

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 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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Open AccessArticle

An Approach to Complement Model-Based Vehicle Development by Implementing Future Scenarios

and

World Electr. Veh. J. 2021, 12(3), 97; https://doi.org/10.3390/wevj12030097 - 03 Jul 2021

Viewed by 652

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 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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Open AccessArticle

Optimal Control Strategy for Parallel Plug-in Hybrid Electric Vehicles Based on Dynamic Programming

and

World Electr. Veh. J. 2021, 12(2), 85; https://doi.org/10.3390/wevj12020085 - 08 Jun 2021

Viewed by 661

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 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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Open AccessReview

A Review of 3D Object Detection for Autonomous Driving of Electric Vehicles

and

World Electr. Veh. J. 2021, 12(3), 139; https://doi.org/10.3390/wevj12030139 - 30 Aug 2021

Viewed by 217

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 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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Special Issue "Intelligent Modeling and Simulation Technology of E-Mobility"

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