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Vehicles Challenges

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 July 2025 | Viewed by 730

Special Issue Editors


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Guest Editor
Department of Energy, Politecnico di Milano, Via Giuseppe La Masa, 34, 20156 Milano, Italy
Interests: heat transfer; thermal energy storage; phase change materials; energy efficiency; building thermal simulation; renewable energy; photovoltaic systems; wind systems; electrical storage; solar greenhouses; electric vehicles; ground-source heat pump system; artificial neural networks; multi-objective optimization
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Guest Editor
Department of Vehicles and Engines, Faculty of Mechanical Engineering, Technical University of Liberec, Studentská 1402, 46117 Liberec, Czech Republic
Interests: combustion engines; alternative fuels; ignition systems; sustainable energy; emission; vehicles; electromobility; batteries; hydrogen; friction; mixed lubrication

Special Issue Information

Dear Colleagues,

Vehicles Challenges offers a critical platform for exploring the latest challenges and groundbreaking innovations in the field of vehicles and the automotive industry. The journal delves into cutting-edge topics such as autonomous driving, electrification, electric vehicles, and intelligent transportation systems. It also addresses pressing issues related to vehicle safety, cybersecurity, and environmental sustainability and the impact of legislative changes on vehicle development and manufacturing.

In addition to traditional powertrains, Vehicles Challenges investigates the potential of alternative fuels, including hydrogen, biofuels, and synthetic fuels. By examining these emerging technologies, including advancements in electric vehicles, the journal provides new perspectives and solutions for the future of transportation. The aim is to foster a deeper understanding of the complex issues facing both vehicles and the automotive industry and to stimulate innovative solutions.

Vehicles Challenges covers a wide range of topics that contribute to the advancement of the vehicle sector. By promoting interdisciplinary collaboration and knowledge exchange among researchers, industry professionals, and policymakers, the journal seeks to drive innovation and address the multifaceted issues impacting vehicles and transportation systems today.

Dr. Domenico Mazzeo
Dr. Aleš Dittrich
Guest Editors

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Keywords

  • vehicle challenges
  • automotive industry
  • autonomous driving
  • electrification
  • electric vehicles
  • intelligent transportation systems
  • safety
  • cybersecurity
  • sustainability
  • legislation
  • powertrains
  • internal combustion engines
  • alternative fuels
  • hydrogen
  • biofuels
  • synthetic fuels

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Published Papers (1 paper)

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Research

17 pages, 858 KiB  
Article
Deep Learning as a New Framework for Passive Vehicle Safety Design Using Finite Elements Models Data
by Mar Lahoz Navarro, Jonas Siegfried Jehle, Patricia A. Apellániz, Juan Parras, Santiago Zazo and Matthias Gerdts
Appl. Sci. 2024, 14(20), 9296; https://doi.org/10.3390/app14209296 - 12 Oct 2024
Viewed by 557
Abstract
In recent years, passive vehicle safety has become one of the major concerns for the automotive industry due to the considerable increase in the use of cars as a means of daily transport. Since real crash testing has a high financial cost, finite [...] Read more.
In recent years, passive vehicle safety has become one of the major concerns for the automotive industry due to the considerable increase in the use of cars as a means of daily transport. Since real crash testing has a high financial cost, finite element simulations are generally used, which entail high computational cost and long simulation times. In this paper, we make use of the recent advances in the deep learning field to propose an affordable method to provide reliable approximations of the finite element simulator model that significantly reduce the computational load and time required. We compare the prediction performance in crash tests of different models, namely feed-forward neural networks and bayesian neural networks, as well as two multi-output regression methods. Our results show promising results, as deep learning models are able to drastically reduce the engineering costs while providing a feasible first approximation to the passenger’s injuries in a crash event, thus being a potential game changer in the vehicle safety design process. Full article
(This article belongs to the Special Issue Vehicles Challenges)
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