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Applied Sciences
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Advanced Technologies in Intelligent Green Vehicles and Robots
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Advanced Technologies in Intelligent Green Vehicles and Robots

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 November 2025 | Viewed by 2255

Special Issue Editors

Dr. Yue Wang

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: intelligent connected vehicle; hybrid electric vehicle; intelligent energy management; optimal control
Special Issues, Collections and Topics in MDPI journals
Dr. Lu Yang

E-Mail Website
Guest Editor
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China
Interests: special vehicle dynamics and control; advanced cross-terrain robots; automation-human collaboration control
Dr. Ning Zhao

E-Mail Website
Guest Editor
School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: intelligent logistics digital twin technology and application; intelligent manufacturing system planning; scheduling and simulation decision

Special Issue Information

Dear Colleagues,

This Special Issue calls for papers that explore the safety and energy-saving technologies used in intelligent electric vehicles and autonomous mobile robots in complex scenarios. It will present original research papers and review articles providing new results and a summary of current and emerging problems related to intelligent vehicles and autonomous mobile robots. We invite authors from all fields of science that fall under the broader umbrella of intelligent electric vehicles and autonomous mobile robots, including but not limited to energy management, speed planning, eco-driving control and dynamic control (in the fields of intelligent electric vehicles, in particular, dynamic quality, modeling, simulation, and eco-driving control), and autonomous mobile robots, (multi-vehicle or autonomous mobile robot scheduling planning, cooperative control, collaborative homework, multi-vehicle collaboration, etc.), to contribute articles.

Dr. Yue Wang
Dr. Lu Yang
Dr. Ning Zhao
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 green vehicle
  • advanced cross-terrain robots
  • planning and control
  • multi-vehicle collaboration
  • modeling

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Published Papers (3 papers)

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Research

30 pages, 970 KiB  
Article
An Unmanned Delivery Vehicle Path-Planning Method Based on Point-Graph Joint Embedding and Dual Decoders
by Jiale Cheng, Zhiwei Ni, Wentao Liu, Qian Chen and Rui Yan
Appl. Sci. 2025, 15(7), 3556; https://doi.org/10.3390/app15073556 - 25 Mar 2025
Viewed by 313
Abstract
The path-planning of unmanned delivery vehicles (UDVs) has garnered significant interest due to their extensive use in contactless delivery during severe epidemics and automated delivery of parcels in diverse scenarios. However, previous studies have focused on achieving the shortest path or time based [...] Read more.
The path-planning of unmanned delivery vehicles (UDVs) has garnered significant interest due to their extensive use in contactless delivery during severe epidemics and automated delivery of parcels in diverse scenarios. However, previous studies have focused on achieving the shortest path or time based on the comprehensive cost consumption in the transportation process and ignored the impact of different customers’ different delivery time requirements in the actual interactive system. Hence, a path-planning model is presented to tackle the routing dilemma of UDVs in logistics. This new dilemma, called the unmanned delivery vehicle routing problem (UDVRP), considers the comprehensive transportation cost consumption of distribution vehicles and the customer satisfaction of each distribution point. Customer satisfaction is defined based on the delivery time requirements of different customers. This novel deep neural network model incorporates an attention mechanism and applies a method called point-graph joint embedding and dual decoders (PGDD) to solve the problem. The network’s architecture, consisting of an encoder and two decoders, directly determines the path for unmanned delivery vehicles. In addition, the model is trained offline using a deep reinforcement-learning strategy in combination with pseudo-label learning. In this scenario, the output of one decoder serves as the label for another, overseeing its learning process to choose the most effective path. Experimental results demonstrate that PGDD reduces total costs by 8.73% on average compared to state-of-the-art algorithms in 100-node scenarios, with performance gains reaching 12.5% for larger-scale problems (400 nodes), validating its superiority in complex path-planning. Additionally, PGDD improves customer satisfaction by 15.2% and achieves a response time below 90ms in real-world deployment tests. The experimental results demonstrate that the proposed method is superior to several state-of-the-art algorithms in solving the path-planning problem of unmanned distribution vehicles. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Green Vehicles and Robots)
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25 pages, 3035 KiB  
Article
Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
by Edward Kozłowski, Magdalena Zimakowska-Laskowska, Agnieszka Dudziak, Piotr Wiśniowski, Piotr Laskowski, Michał Stankiewicz, Boris Šnauko, Norbert Lech, Maciej Gis and Jonas Matijošius
Appl. Sci. 2025, 15(4), 1681; https://doi.org/10.3390/app15041681 - 7 Feb 2025
Viewed by 770
Abstract
Using the standardised SORT, the article analyses instantaneous energy consumption and recuperation processes in an electric bus. The test includes three scenarios: SORT 1 (heavy urban traffic), SORT 2 (mixed driving conditions), and SORT 3 (suburban routes), enabling precise assessment of the energy [...] Read more.
Using the standardised SORT, the article analyses instantaneous energy consumption and recuperation processes in an electric bus. The test includes three scenarios: SORT 1 (heavy urban traffic), SORT 2 (mixed driving conditions), and SORT 3 (suburban routes), enabling precise assessment of the energy efficiency of vehicles while eliminating environmental variables. The recuperation system significantly enhances energy efficiency, though its effectiveness varies based on the driving scenario. Modelling methods were compared as follows: linear regression, KNN algorithms, and neural networks, achieving a high fit (R2 > 90%). While KNN and neural networks were better at reproducing nonlinearities, they indicated the need for additional variables and time delays to enhance accuracy. The article sets itself apart by incorporating predictive models and examining recuperation efficiency across various scenarios. It emphasizes the importance of combining SORT results with real operational data and developing adaptive energy management systems. The results indicate the potential for optimizing electric buses for public transport, including route planning and further improving recuperation technology, which can significantly reduce energy consumption and greenhouse gas emissions. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Green Vehicles and Robots)
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15 pages, 3129 KiB  
Article
Prototype System for Supporting Medical Diagnosis Based on Voice Interviewing
by Artur Samojluk and Piotr Artiemjew
Appl. Sci. 2025, 15(1), 440; https://doi.org/10.3390/app15010440 - 6 Jan 2025
Viewed by 783
Abstract
In this paper, we present the results of a study on the development of a system to support medical diagnoses based on voice-based medical interviews. The main objective is to develop a tool that improves the process of collecting information from patients, using [...] Read more.
In this paper, we present the results of a study on the development of a system to support medical diagnoses based on voice-based medical interviews. The main objective is to develop a tool that improves the process of collecting information from patients, using natural language analysis to identify key diagnostic information. The system processes the collected data to create information vectors for a selected group of diseases, allowing for an initial assessment of possible disease entities. An analysis of data mining and selected machine learning methods was carried out to develop an effective diagnosis algorithm. The system is designed to optimise patient care by automating the initial phase of the medical interview, which can lead to a reduction in errors due to subjective assessments and reduce the workload on doctors. The solution presented in this paper is part of a broader research project to develop a Medical Interview system, and this paper is the first article in a series that describes the experiences and challenges of implementing this system. Further work is planned to develop the model into advanced medical decision support techniques and validate it in a clinical setting. Full article
(This article belongs to the Special Issue Advanced Technologies in Intelligent Green Vehicles and Robots)
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