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Intelligent Modelling & Simulation Technology of E-Mobility
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Intelligent Modelling & Simulation Technology of E-Mobility

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

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 10090

Special Issue Editors

Prof. Dr. Zonghai Chen

E-Mail Website
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
Special Issues, Collections and Topics 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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kailong Liu

E-Mail Website
Guest Editor
School of Control Science and Engineering, Shandong University, Jinan, China
Interests: control science and engineering; artificial intelligence; machine learning; data analysis; automation; new energy; battery control; energy economy
Dr. Yujie Wang

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 24th Chinese Conference on System Simulation Technology and Application (CCSSTA 2023) will be held in Hefei, China, from 10 October to 12 October 2023. CCSSTA 2023 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 aims to focus on communicating the latest research results and progress in the field of simulation, as well as on 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)’s 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, etc. Research into intelligent e-mobility requires the support of entire fields 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 are environmentally friendly. The current Special Issue is entitled "Intelligent Modelling & Simulation Technology of E-Mobility". mainly includes the selected papers from the participants of CCSSTA2023. The topics will include, but are not limited to:

  • Sensor technologies for driverless e-mobility;
  • Intelligent vehicle-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 cyber-physical system 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 present at CCSSTA2023 will be invited to further extend their CCSSTA2023 paper, including their most recent research findings. After a second thorough round of peer review, these papers will be published in a Special Issue of the World Electric Vehicle Journal (WEVJ).

In addition, we welcome submissions from others that are not associated with this conference, but with themes focusing on the above topics. 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. Chunlin Chen
Prof. Dr. Kailong Liu
Dr. Yujie 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.

Keywords

  • transportation electrification
  • digital twin
  • prognostics and health management
  • cyber-physical system
  • battery management system
  • hydrogen fuel cell
  • hybrid electric vehicles
  • energy management
  • intelligent perceptions

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (4 papers)

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Research

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22 pages, 4121 KiB  
Article
Lithium-Ion Battery SOH Estimation Method Based on Multi-Feature and CNN-BiLSTM-MHA
by Yujie Zhou, Chaolong Zhang, Xulong Zhang and Ziheng Zhou
World Electr. Veh. J. 2024, 15(7), 280; https://doi.org/10.3390/wevj15070280 - 24 Jun 2024
Viewed by 1038
Abstract
Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long [...] Read more.
Electric vehicles can reduce the dependence on limited resources such as oil, which is conducive to the development of clean energy. An accurate battery state of health (SOH) is beneficial for the safety of electric vehicles. A multi-feature and Convolutional Neural Network–Bidirectional Long Short-Term Memory–Multi-head Attention (CNN-BiLSTM-MHA)-based lithium-ion battery SOH estimation method is proposed in this paper. First, the voltage, energy, and temperature data of the battery in the constant current charging phase are measured. Then, based on the voltage and energy data, the incremental energy analysis (IEA) is performed to calculate the incremental energy (IE) curve. The IE curve features including IE, peak value, average value, and standard deviation are extracted and combined with the thermal features of the battery to form a complete multi-feature sequence. A CNN-BiLSTM-MHA model is set up to map the features to the battery SOH. Experiments were conducted using batteries with different charging currents, and the results showed that even if the nonlinearity of battery SOH degradation is significant, this method can still achieve a fast and accurate estimation of the battery SOH. The Mean Absolute Error (MAE) is 0.1982%, 0.1873%, 0.1652%, and 0.1968%, and the Root-Mean-Square Error (RMSE) is 0.2921%, 0.2997%, 0.2130%, and 0.2625%, respectively. The average Coefficient of Determination (R2) is above 96%. Compared to the BiLSTM model, the training time is reduced by an average of about 36%. Full article
(This article belongs to the Special Issue Intelligent Modelling & Simulation Technology of E-Mobility)
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15 pages, 5250 KiB  
Article
Second-Order Central Difference Particle Filter Algorithm for State of Charge Estimation in Lithium-Ion Batteries
by Yuan Chen and Xiaohe Huang
World Electr. Veh. J. 2024, 15(4), 152; https://doi.org/10.3390/wevj15040152 - 7 Apr 2024
Viewed by 1144
Abstract
The estimation of the state of charge (SOC) in lithium-ion batteries is a crucial aspect of battery management systems, serving as a key indicator of the remaining available capacity. However, the inherent process and measurement noises created during battery operation pose significant challenges [...] Read more.
The estimation of the state of charge (SOC) in lithium-ion batteries is a crucial aspect of battery management systems, serving as a key indicator of the remaining available capacity. However, the inherent process and measurement noises created during battery operation pose significant challenges to the accuracy of SOC estimation. These noises can lead to inaccuracies and uncertainties in assessing the battery’s condition, potentially affecting its overall performance and lifespan. To address this problem, we propose a second-order central difference particle filter (SCDPF) method. This method leverages the latest observation data to enhance the accuracy and noise adaptability of SOC estimation. By employing an improved importance density function, we generate optimized particles that better represent the battery’s dynamic behavior. To validate the effectiveness of our proposed algorithm, we conducted comprehensive comparisons at both 25 °C and 0 °C under the new European driving cycle condition. The results demonstrate that the SCDPF algorithm exhibits a high accuracy and rapid convergence speed, with a maximum error which never exceeds 1.30%. Additionally, we compared the SOC estimations with both Gaussian and non-Gaussian noise to assess the robustness of our proposed algorithm. Overall, this study presents a novel approach to enhancing SOC estimation in lithium-ion batteries, addressing the challenges posed by the process itself and measurement noises. Full article
(This article belongs to the Special Issue Intelligent Modelling & Simulation Technology of E-Mobility)
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12 pages, 3300 KiB  
Article
Performance Research on Heating Performance of Battery Thermal Management Coupled with the Vapor Injection Heat Pump Air Conditioning
by Weijian Yuan, Yun Guo and Yunshen Zhang
World Electr. Veh. J. 2024, 15(1), 33; https://doi.org/10.3390/wevj15010033 - 19 Jan 2024
Viewed by 1698
Abstract
Compared to the use of positive temperature coefficient (PTC) materials that consume electrical energy for low-temperature heating, heat pump air conditioners can provide more energy-efficient heating performance by absorbing and utilizing heat from the outdoor air to heat the cab in order to [...] Read more.
Compared to the use of positive temperature coefficient (PTC) materials that consume electrical energy for low-temperature heating, heat pump air conditioners can provide more energy-efficient heating performance by absorbing and utilizing heat from the outdoor air to heat the cab in order to improve the range of electric vehicles. In addition, in order to make the battery work under safe working conditions, this paper proposes battery thermal management coupled with vapor injection heat pump air conditioning. The system is modeled and analyzed through simulation, and the impact of the compressor speed and ambient temperature changes in the battery cooling performance of the system. The results show that under different compressor RPM (Revolution Per Minute) with an ambient temperature of 5 °C, the average temperature of the battery pack remains below 30 °C, and the majority of individual cell temperatures are maintained within the range of 20 to 35 °C. At a constant compressor RPM of 4000/min under varying ambient temperatures, the average temperature of the battery pack remains below 30 °C, with the majority of individual cell temperatures staying within the range of 20 to 35 °C. And the battery cooling performance still performs well. In the low temperature of −10 °C and −20 °C, the system can still maintain a relatively stable heating capacity compared with the 2009.1W, provided by the environment temperature of 5 °C at the same RPM. Full article
(This article belongs to the Special Issue Intelligent Modelling & Simulation Technology of E-Mobility)
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Review

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19 pages, 984 KiB  
Review
SLAM Meets NeRF: A Survey of Implicit SLAM Methods
by Kaiyun Yang, Yunqi Cheng, Zonghai Chen and Jikai Wang
World Electr. Veh. J. 2024, 15(3), 85; https://doi.org/10.3390/wevj15030085 - 26 Feb 2024
Cited by 1 | Viewed by 5347
Abstract
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented. NeRF-based SLAM in mapping aims to implicitly understand irregular environmental information using large-scale parameters of deep learning networks [...] Read more.
In recent years, Simultaneous Localization and Mapping (SLAM) systems have shown significant performance, accuracy, and efficiency gains, especially when Neural Radiance Fields (NeRFs) are implemented. NeRF-based SLAM in mapping aims to implicitly understand irregular environmental information using large-scale parameters of deep learning networks in a data-driven manner so that specific environmental information can be predicted from a given perspective. NeRF-based SLAM in tracking jointly optimizes camera pose and implicit scene network parameters through inverse rendering or combines VO and NeRF mapping to achieve real-time positioning and mapping. This paper firstly analyzes the current situation of NeRF and SLAM systems and then introduces the state-of-the-art in NeRF-based SLAM. In addition, datasets and system evaluation methods used by NeRF-based SLAM are introduced. In the end, current issues and future work are analyzed. Based on an investigation of 30 related research articles, this paper provides in-depth insight into the innovation of SLAM and NeRF methods and provides a useful reference for future research. Full article
(This article belongs to the Special Issue Intelligent Modelling & Simulation Technology of E-Mobility)
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