Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Sustainable Mobility and Transportation (SMTS 2025)
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Sustainable Mobility and Transportation (SMTS 2025)

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 1025

Special Issue Editors

Prof. Dr. Dogossy Gábor

E-Mail Website
Guest Editor
Audi Hungaria Faculty of Vehicle Engineering, Széchenyi István University, Egyetem tér 1, Győr H-9026, Hungary
Interests: polymer composites; injection molding technologies; material testing
Dr. Szabolcs Kocsis Szürke

E-Mail Website
Guest Editor
Department at Road and Rail Vehicles, Széchenyi István University, Győr 9026, Hungary
Interests: sustainable transportation; energy efficiency; electric vehicles; battery testing; battery diagnostics

Special Issue Information

Dear Colleagues,

This Special Issue is compiled in cooperation with the 2nd Sustainable Mobility and Transportation Symposium, an interdisciplinary scientific conference organized by the Audi Hungaria Faculty of Vehicle Engineering and the Vehicle Industry Research Center of Széchenyi István University. The symposium is scheduled to take place from 16–18 October 2025 in Győr, Hungary. Following the success of last year’s event, the 2025 symposium continues to focus on the latest advancements, challenges, and innovations in automotive technology and transportation systems. The conference provides a platform for researchers, scholars, professionals, and enthusiasts from diverse fields to collaborate, share knowledge, and envision the future of sustainable mobility. We welcome submissions from participants of the symposium, with the aim being to publish selected papers in this Special Issue of Applied Sciences, thereby further disseminating high-quality research contributions from the event.

Prof. Dr. Dogossy Gábor
Dr. Szabolcs Kocsis Szürke
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

  • vehicle development
  • diagnostics and testing
  • materials technology
  • autonomous vehicle
  • fuels and lubricants
  • electromobility
  • mobility and society
  • AI and control
  • manufacturing technology

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • 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.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 7139 KiB  
Article
Synergistic Effects of CuO and ZnO Nanoadditives on Friction and Wear in Automotive Base Oil
by Ádám István Szabó and Rafiul Hasan
Appl. Sci. 2025, 15(15), 8258; https://doi.org/10.3390/app15158258 - 24 Jul 2025
Viewed by 302
Abstract
Efficient lubrication lowers friction, wear, and energy losses in automotive drivetrain components. Advanced lubricants are key to sustainable transportation performance, durability, and efficiency. This study analyzes the tribological performance of Group III base oil with CuO and ZnO nanoadditive mixtures. These additives enhance [...] Read more.
Efficient lubrication lowers friction, wear, and energy losses in automotive drivetrain components. Advanced lubricants are key to sustainable transportation performance, durability, and efficiency. This study analyzes the tribological performance of Group III base oil with CuO and ZnO nanoadditive mixtures. These additives enhance the performance of Group III base oils, making them highly relevant for automotive lubricant applications. An Optimol SRV5 tribometer performed ball-on-disk sliding contact tests with 100Cr6 steel specimens subjected to a 50 N force and a temperature of 100 °C. The test settings are designed to mimic the boundary and mixed lubrication regimes commonly seen in the automobile industry. During the tests, the effect of nanoparticles on friction was measured. Microscopic wear analysis was performed on the worn specimens. The results demonstrate that adding 0.3 wt% CuO nanoparticles to Group III base oil achieves a 19% reduction in dynamic friction and a 47% decrease in disk wear volume compared to additive-free oil. Notably, a 2:1 CuO-to-ZnO mixture produced synergy, delivering up to a 27% friction reduction and a 54% decrease in disk wear. The results show the synergistic effect of CuO and ZnO in reducing friction and wear on specimens. This study highlights the potential of nanoparticles for lubricant development and automotive applications. Full article
(This article belongs to the Special Issue Sustainable Mobility and Transportation (SMTS 2025))
►▼ Show Figures

Figure 1

10 pages, 943 KiB  
Article
The Impact of Pitch Error on the Dynamics and Transmission Error of Gear Drives
by Krisztián Horváth and Daniel Feszty
Appl. Sci. 2025, 15(14), 7851; https://doi.org/10.3390/app15147851 - 14 Jul 2025
Viewed by 225
Abstract
Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built [...] Read more.
Gear whine noise is governed not only by intentional microgeometry modifications but also by unavoidable pitch (indexing) deviation. This study presents a workflow that couples a tooth-resolved surface scan with a calibrated pitch-deviation table, both imported into a multibody dynamics (MBD) model built in MSC Adams View. Three operating scenarios were evaluated—ideal geometry, measured microgeometry without pitch error, and measured microgeometry with pitch error—at a nominal speed of 1000 r min−1. Time domain analysis shows that integrating the pitch table increases the mean transmission error (TE) by almost an order of magnitude and introduces a distinct 16.66 Hz shaft order tone. When the measured tooth topologies are added, peak-to-peak TE nearly doubles, revealing a non-linear interaction between spacing deviation and local flank shape. Frequency domain results reproduce the expected mesh-frequency side bands, validating the mapping of the pitch table into the solver. The combined method therefore provides a more faithful digital twin for predicting tonal noise and demonstrates why indexing tolerances must be considered alongside profile relief during gear design optimization. Full article
(This article belongs to the Special Issue Sustainable Mobility and Transportation (SMTS 2025))
►▼ Show Figures

Figure 1

17 pages, 1117 KiB  
Article
Driver Clustering Based on Individual Curve Path Selection Preference
by Gergo Igneczi, Tamas Dobay, Erno Horvath and Krisztian Nyilas
Appl. Sci. 2025, 15(14), 7718; https://doi.org/10.3390/app15147718 - 9 Jul 2025
Viewed by 216
Abstract
The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a [...] Read more.
The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a full user experience. Therefore, driver modeling is a key area of research for next-generation ADASs. One of the most common tasks in everyday driving is lane keeping. Drivers are assisted by lane-keeping systems to keep their vehicle in the center of the lane. However, human drivers often deviate from the center line. It has been shown that the driver’s choice to deviate from the center line can be modeled by a linear combination of preview curvature information. This model is called the Linear Driver Model. In this paper, we fit the LDM parameters to real driving data. The drivers are then clustered based on the individual parameters. It is shown that clusters are not only formed by the numerical similarity of the driver parameters, but the drivers in a cluster actually have similar behavior in terms of path selection. Finally, an Extended Kalman Filter (EKF) is proposed to learn the model parameters at run-time. Any new driver can be classified into one of the driver type groups. This information can be used to modify the behavior of the lane-keeping system to mimic human driving, resulting in a more personalized driving experience. Full article
(This article belongs to the Special Issue Sustainable Mobility and Transportation (SMTS 2025))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop