Selected Papers from MES-2025: Advances in Mechanical Engineering Solutions

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 3066

Special Issue Editors


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Guest Editor
AVL List GmbH, Advanced Simulation Technologies, Graz, Austria
Interests: engine; powertrain; modal analysis; simulation; dynamics

Special Issue Information

Dear Colleagues,

The MECHANICAL ENGINEERING SOLUTIONS sponsored by IFToMM is a forum that regularly brings together researchers and practical engineers in the field of mechanical engineering working at universities, research centers and in industry. With its cross-thematic program and broad geographical coverage, it represents an excellent networking platform that facilitates contact and promotes collaboration between scientists and engineers from different countries. In addition to the traditional IFToMM topics that focus on theoretical and applied research of mechanisms and machines, numerical methods and related software solutions as well as contributions aimed at practical/industrial applications are particularly welcome. Submissions are also encouraged considering one or more phases of the machine development life cycle: design, simulation, testing and manufacturing. Although the focus is on the technical quality characteristics of considered machines and mechanical systems (such as precision, load capacity, performance, etc.), aspects like economic efficiency, legal compliance, environmental impact, etc., will also be considered. The exhibition that will accompany the conference for the first time will offer a good opportunity to show the engineering solutions implemented in practice, to advertise and to promote engineering products, and to disseminate information on activities of universities, research centers, and industrial companies.

The conference MES- 2025 is supported by IFToMM (the International Federation for the Promotion of Mechanism and Machine Science; MMS) (https://iftomm-world.org/), with activities in the areas of Mechanical Engineering and Machine Science. It was founded in 1969 and is driven today by the fourth generation of IFToMMists, working in MMS disciplines related to different machines such as Robotics and Mechatronics, Engines and Powertrains, Transportation Machinery, Turbomachinery, Linkages and Mechanical Controls.

Deadlines for file manuscript submission and Peer-Review Process

All submitted papers that meet the criteria of originality and quality will be blind peer-reviewed for the publication in the journal. The papers must be revised and extended (in the contents, editing and English) from the conference version prior to submission to the journal Machines. Five papers, among the ten invited by the guest editors, will be published free of charge if accepted.

The special issue will consider only papers coming from conference papers as invited by the Guest Editors.

The papers must be adequately written according to journal guidelines Machines | Instructions for Authors.

Once the papers are submitted, the review process will be handled through the MDPI web system for the journal. There is no limitations on the number of pages and figures.

Prof. Dr. Marco Ceccarelli
Dr. Tigran Parikyan
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 250 words) can be sent to the Editorial Office for assessment.

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. Machines 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 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

  • robotics
  • mechatronics
  • engines
  • powertrains
  • transportation machinery
  • turbomachinery
  • linkages and mechanical controls

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Published Papers (2 papers)

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Research

17 pages, 3014 KB  
Article
Development of a Megawatt Charging Capable Test Platform
by Orgun Güralp, Norman Bucknor and Madhusudan Raghavan
Machines 2026, 14(3), 317; https://doi.org/10.3390/machines14030317 - 11 Mar 2026
Viewed by 348
Abstract
Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage [...] Read more.
Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article
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24 pages, 39327 KB  
Article
Forest Surveying with Robotics and AI: SLAM-Based Mapping, Terrain-Aware Navigation, and Tree Parameter Estimation
by Lorenzo Scalera, Eleonora Maset, Diego Tiozzo Fasiolo, Khalid Bourr, Simone Cottiga, Andrea De Lorenzo, Giovanni Carabin, Giorgio Alberti, Alessandro Gasparetto, Fabrizio Mazzetto and Stefano Seriani
Machines 2026, 14(1), 99; https://doi.org/10.3390/machines14010099 - 14 Jan 2026
Viewed by 1044
Abstract
Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation [...] Read more.
Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article
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