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WEVJ
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Theory, Method and Application of New Energy and Intelligent Transportation
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Theory, Method and Application of New Energy and Intelligent Transportation

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

Deadline for manuscript submissions: 30 November 2024 | Viewed by 25390

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. Zhiling Wang

E-Mail Website
Guest Editor
Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
Interests: driverless vehicles; environmental perception and understanding; computer vision and machine learning
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
Dr. Jikai Wang

E-Mail Website
Guest Editor
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
Dr. Kailong Liu

E-Mail Website
Guest Editor
Warwick Manufacturing Group, University of Warwick, Coventry, UK
Interests: modeling, optimization, and control with applications to electrical/hybrid vehicles; energy storage, and battery manufacture and management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, as the number of on-road vehicles is growing fast, the number of accidents and fatalities on the roads is expected to also increase steadily in the coming years. Furthermore, with the acceleration of urban development, the problems of traffic congestion, environmental pollution, and energy shortages are becoming more and more prominent. How to rely on technological innovations to solve traffic problems is a serious challenge and a pressing issue. Intelligent Transportation Systems (ITS) have provided unquestioned proof for improving road safety, sustainability, efficient road traffic, and vehicle management. It has become a research hotspot and is urgently requires wide application.

Intelligent transportation is a new generation of smart civil infrastructure that integrates IoT, big data, artificial intelligence, advanced sensing technologies, automated piloting, green energy, and sustainable and resilient materials working together to achieve high-quality road service and efficient operations. As a future trend, it will significantly change the form of traditional transportation infrastructures. However, due to the high complexity of ITS, the challenges are also tremendous. The basic theories, key methods, and technologies are still developing. The construction of a large-scale, usable, and complete intelligent transportation systems are still being explored. To promote such processes, the cross-disciplinary cooperation, complex system simulation and control, ultra-large-scale data communication and processing, and distributed management will be highly involved.

This Special Issue aims at providing a platform for researchers and practitioners to exchange and publish the latest research trends and results on intelligent and sustainable vehicle and transportation systems. Researchers are encouraged to explore key concepts of AI, deep learning, new energy, complex system management, and simulations that can be utilized for ITS. The main objective of this Special Issue is to collect novel discoveries and achievements, and discuss progress in current applications and future developments for technologies in transportation infrastructure systems, including:

  • Advanced sensing, monitoring ,and analysis;
  • Smart management and maintenance;
  • Green energy for transportation infrastructures;
  • Multi-purpose road services;
  • Resilient infrastructures;
  • New deep learning paradigms and their application to ITS;
  • Computer vision over edge devices with applications to ITS;
  • Complex system simulation, model, and control;
  • Energy storage for transportation;
  • Energy infrastructure for electrical transportation;
  • Hybrid electrical powertrain systems;
  • Transportation applications of Internet of Things and energy Internet;
  • Power electronics for traction purposes;
  • Energy management and control systems;
  • Safety, durability, and reliability;
  • Artificial intelligence applied to transportation.

Prof. Dr. Zonghai Chen
Prof. Dr. Zhiling Wang
Dr. Yujie Wang
Dr. Jikai Wang
Dr. Kailong Liu
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

  • intelligent transportation system
  • artificial intelligent
  • complex system
  • power systems of new energy vehicles

Published Papers (9 papers)

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Research

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21 pages, 3317 KiB  
Article
Towards Sustainable Transport in the Moroccan Context: The Key Determinants of Electric Cars Adoption Intention
by Omar Boubker, Marwan Lakhal, Youssef Ait Yassine and Hicham Lotfi
World Electr. Veh. J. 2024, 15(4), 136; https://doi.org/10.3390/wevj15040136 - 27 Mar 2024
Viewed by 809
Abstract
In recent years, many countries have actively promoted sustainable mobility as part of their efforts to decarbonize transportation through automotive electrification. Therefore, identifying the factors that influence individuals’ interest in using electric cars (ECs) is crucial for guiding public opinion toward choosing this [...] Read more.
In recent years, many countries have actively promoted sustainable mobility as part of their efforts to decarbonize transportation through automotive electrification. Therefore, identifying the factors that influence individuals’ interest in using electric cars (ECs) is crucial for guiding public opinion toward choosing this sustainable mode of transportation. Consequently, the present study mobilized the theory of planned behavior and the technology acceptance model to interpret the various factors influencing the intention to adopt ECs in a developing country. Following the developed model, data were collected from individuals using cars in Morocco through an online questionnaire. Data analysis using structural equation modeling revealed a positive influence of relative advantage on both the perceived ease of use and green perceived usefulness. Furthermore, the perceived ease of use, green perceived usefulness, environmental concern, and social influence positively affected attitudes toward using ECs. Similarly, these results confirmed that green perceived usefulness and individual attitudes positively enhance ECs adoption intention. These findings contribute to the literature related to ECs adoption and offer guidance to policymakers on promoting ECs adoption in developing countries. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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18 pages, 3209 KiB  
Article
A Game Lane Changing Model Considering Driver’s Risk Level in Ramp Merging Scenario
by Guo Yang, Shihuan Liu, Ming Ye, Chengcheng Tang, Yi Fan and Yonggang Liu
World Electr. Veh. J. 2023, 14(7), 172; https://doi.org/10.3390/wevj14070172 - 27 Jun 2023
Viewed by 1242
Abstract
A ramp merging decision as an important part of the lane change model plays a crucial role in the efficiency and safety of the entire merging process. However, due to the inevitability of on-ramp merging, the limitations of the road environment, and the [...] Read more.
A ramp merging decision as an important part of the lane change model plays a crucial role in the efficiency and safety of the entire merging process. However, due to the inevitability of on-ramp merging, the limitations of the road environment, and the conflict between the merging vehicle and the following vehicle on the main road, it is difficult for human drivers to make optimal decisions in complex merging scenarios. First, based on the NGSIM dataset, a gain function is designed to represent the interaction between the ego vehicle (EV) and the surrounding vehicles, and the gain value is then used as one of the characteristic parameters. The K-means algorithm is employed to conduct a cluster analysis of the driving style under the condition of changing lanes. This paper models the interaction and conflict between the ego vehicle (vehicle merging) and the mainline lagging vehicle as a complete information non-cooperative game process. Further, various driving styles are coupled in the ramp decision model to mimic the different safety and travel efficiency preferences of human drivers. After EV decision-making, a quintic polynomial method with multi-constraints is proposed to implement merging trajectory planning. The proposed algorithm is tested and analyzed in an on-ramp scenario, and the results demonstrate that drivers with different driving styles can make correct decisions and complete the ramp merging. The changing trend of the speed and trajectory tests are also in line with the features of the driver’s driving style, offering a theoretical foundation for individualized on-ramp merging decisions. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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17 pages, 2848 KiB  
Article
Deep Reinforcement Learning Algorithm Based on Fusion Optimization for Fuel Cell Gas Supply System Control
by Hongyan Yuan, Zhendong Sun, Yujie Wang and Zonghai Chen
World Electr. Veh. J. 2023, 14(2), 50; https://doi.org/10.3390/wevj14020050 - 10 Feb 2023
Viewed by 1295
Abstract
In a proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor affecting the output characteristics of the PEMFC, and there is a coordination problem in the flow control of both. To ensure real-time gas supply [...] Read more.
In a proton exchange membrane fuel cell (PEMFC) system, the flow of air and hydrogen is the main factor affecting the output characteristics of the PEMFC, and there is a coordination problem in the flow control of both. To ensure real-time gas supply in the fuel cell and improve the output power and economic benefits of the system, a deep reinforcement learning controller with continuous state based on fusion optimization (FO-DDPG) and a control optimization strategy based on net power optimization are proposed in this paper, and the effects of whether the two gas controls are decoupled or not are compared. The experimental results show that the undecoupled FO-DDPG algorithm has a faster dynamic response and more stable static performance compared to the fuzzy PID, DQN, traditional DRL algorithm, and decoupled controllers, demonstrated by a dynamic response time of 0.15 s, an overshoot of less than 5%, and a steady-state error of 0.00003. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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18 pages, 7951 KiB  
Article
State of Charge Estimation for Power Battery Base on Improved Particle Filter
by Xingtao Liu, Xiaojie Fan, Li Wang and Ji Wu
World Electr. Veh. J. 2023, 14(1), 8; https://doi.org/10.3390/wevj14010008 - 28 Dec 2022
Cited by 6 | Viewed by 2028
Abstract
In this paper, an improved particle filter (Improved Particle Swarm Optimized Particle Filter, IPSO-PF) algorithm is proposed to estimate the state of charge (SOC) of lithium-ion batteries. It solves the problem of inaccurate posterior estimation due to particle degradation. The algorithm divides the [...] Read more.
In this paper, an improved particle filter (Improved Particle Swarm Optimized Particle Filter, IPSO-PF) algorithm is proposed to estimate the state of charge (SOC) of lithium-ion batteries. It solves the problem of inaccurate posterior estimation due to particle degradation. The algorithm divides the particle population into three parts and designs different updating methods to realize self-variation and mutual learning of particles, which effectively promotes global development and avoids falling into local optimum. Firstly, a second-order RC equivalent circuit model is established. Secondly, the model parameters are identified by the particle swarm optimization algorithm. Finally, the proposed algorithm is verified under four different driving conditions. The results show that the root mean square error (RMSE) of the proposed algorithm is within 0.4% under different driving conditions, and the maximum error (ME) is less than 1%, showing good generalization. Compared with the EKF, PF, and PSO-PF algorithms, the IPSO-PF algorithm significantly improves the estimation accuracy of SOC, which verifies the superiority of the proposed algorithm. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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15 pages, 5708 KiB  
Article
A Path Planning Method for Autonomous Vehicles Based on Risk Assessment
by Wei Yang, Cong Li and Yipeng Zhou
World Electr. Veh. J. 2022, 13(12), 234; https://doi.org/10.3390/wevj13120234 - 6 Dec 2022
Cited by 4 | Viewed by 2900
Abstract
In order to meet the requirements of vehicle automatic obstacle avoidance, a lane change trajectory planning method is proposed to meet the requirements of safety, comfort, and lane change efficiency. Firstly, the potential collision points that may exist are analyzed using information about [...] Read more.
In order to meet the requirements of vehicle automatic obstacle avoidance, a lane change trajectory planning method is proposed to meet the requirements of safety, comfort, and lane change efficiency. Firstly, the potential collision points that may exist are analyzed using information about surrounding vehicle movement and the road. Then, the safe lane change range for vehicles is obtained. Secondly, the control points of the fifth order Bézier curve are constrained to generate a series of path clusters in the optimal range. At the same time, the driver’s style and reaction time are taken into account in the risk assessment stage of the route using the improved artificial potential field method. Finally, the optimal path is selected by comprehensively considering lane-changing efficiency and comfort. In order to further verify the accuracy of the algorithm, real-vehicle experiments have been carried out on the autonomous vehicle platform. Under different driving styles, the vehicle can avoid obstacles perfectly while ensuring the smoothness of the path. Simulation and real-vehicle experiment results show that the proposed algorithm can provide an excellent solution for autonomous vehicles for lane changing and obstacle avoidance. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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20 pages, 6144 KiB  
Article
LiDAR-Only Ground Vehicle Navigation System in Park Environment
by Kezhi Wang, Jianyu Li, Meng Xu, Zonghai Chen and Jikai Wang
World Electr. Veh. J. 2022, 13(11), 201; https://doi.org/10.3390/wevj13110201 - 27 Oct 2022
Cited by 1 | Viewed by 1954
Abstract
In this paper, a novel and complete navigation system is proposed for mobile ground vehicles in a park environment. LiDAR map representation and maintenance, dynamic objects detection and removal, hierarchal path planning and model-free local planning are developed in the system. The system [...] Read more.
In this paper, a novel and complete navigation system is proposed for mobile ground vehicles in a park environment. LiDAR map representation and maintenance, dynamic objects detection and removal, hierarchal path planning and model-free local planning are developed in the system. The system is formulated in three layers. In the global layer, given the global point cloud map of the environment, the traverse area is detected and its skeleton graph is extracted to represent the global topology of the environment. Then, in the middle layer, the global map is divided into several submaps and each submap is represented by a modified multi-layer grid map. In the local layer, considering the dynamics of the environment, according to the real-time LiDAR observation, a probabilistic distribution-based representation and its updating mechanism are proposed. Based on the hierarchal environment map representation, the path planning and local planning are performed in a hierarchal way. Considering the complexity of the motion model estimation, a model free local planner is used. Extensive experiments are conducted in the real environment and the source code will be made open for the robotics community. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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14 pages, 3791 KiB  
Article
State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration
by Laijin Luo, Chaolong Zhang, Youhui Tian and Huihan Liu
World Electr. Veh. J. 2022, 13(8), 148; https://doi.org/10.3390/wevj13080148 - 5 Aug 2022
Cited by 5 | Viewed by 2238
Abstract
An accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging [...] Read more.
An accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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Review

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24 pages, 6631 KiB  
Review
Research on the Application and Control Strategy of Energy Storage in Rail Transportation
by Dixi Xin, Jianlin Li and Chang’an Liu
World Electr. Veh. J. 2023, 14(1), 3; https://doi.org/10.3390/wevj14010003 - 23 Dec 2022
Cited by 3 | Viewed by 1751
Abstract
With the development of the global economy and the increase in environmental awareness, energy technology in transportation, especially the application of energy storage technology in rail transportation, has become a key area of research. Rail transportation systems are characterized by high energy consumption [...] Read more.
With the development of the global economy and the increase in environmental awareness, energy technology in transportation, especially the application of energy storage technology in rail transportation, has become a key area of research. Rail transportation systems are characterized by high energy consumption and poor power quality due to the more flexible regulation capability of energy storage technology in these aspects. This paper summarizes the latest research results on energy storage in rail transportation systems, matches the characteristics of energy storage technologies with the energy storage needs of rail transportation, and analyzes the operation of energy storage systems in different scenarios. The adaptability of batteries, supercapacitors, and flywheels as energy storage systems for rail transportation is summarized and compared. The topologies and integration methods of various energy storage systems are studied. The control strategies under each control of rail transportation are summarized and proposed. The future development direction of energy storage system for rail transportation prospects and the corresponding reference is provided for the engineering of energy storage technology in the field of rail transportation. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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19 pages, 13522 KiB  
Review
Fuel Cell Hybrid Electric Vehicles: A Review of Topologies and Energy Management Strategies
by Pengli Yu, Mince Li, Yujie Wang and Zonghai Chen
World Electr. Veh. J. 2022, 13(9), 172; https://doi.org/10.3390/wevj13090172 - 16 Sep 2022
Cited by 26 | Viewed by 9564
Abstract
With the development of the global economy, the automobile industry is also developing constantly. In recent years, due to the shortage of environmental energy and other problems, seeking clean energy as the power source of vehicles to replace traditional fossil energy could be [...] Read more.
With the development of the global economy, the automobile industry is also developing constantly. In recent years, due to the shortage of environmental energy and other problems, seeking clean energy as the power source of vehicles to replace traditional fossil energy could be one of the measures to reduce environmental pollution. Among them, fuel cell hybrid electric vehicles (FCHEVs) have been widely studied by researchers for their advantages of high energy efficiency, environmental protection, and long driving range. This paper first introduces the topology of common FCHEVs and then classifies and introduces the latest energy management strategies (EMSs) for FCHEVs. Finally, the future trends of EMSs for FCHEVs are discussed. This paper can be useful in helping researchers better understand the recent research progress of EMSs for FCHEVs. Full article
(This article belongs to the Special Issue Theory, Method and Application of New Energy and Intelligent Transportation)
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