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Advanced Automotive Technologies and Sustainable Road Transport: Real-World Emissions, Electrification, Energy Modeling, and Data-Driven Mobility

A Special Issue of Applied Sciences (ISSN 2076-3417) belonging to the section "Transportation and Future Mobility".

Deadline for manuscript submissions: 20 February 2027 | Viewed by 1408

Editors


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Guest Editor
Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszów, Poland
Interests: emission; exhaust gases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to invite researchers to contribute to this Special Issue on advanced automotive technologies and sustainable road transport. This issue aims to present recent scientific developments addressing the transition toward cleaner, smarter, and more energy-efficient mobility systems. Particular attention will be given to the integration of real-world vehicle testing, electrification, traffic and energy modeling, artificial intelligence, and transport-system optimization in support of decarbonization goals.

The automotive and transport sectors are undergoing rapid transformation driven by environmental regulations, the expansion of electric and hybrid vehicles, digitalization, and the need for evidence-based mobility planning. New methods based on portable emissions measurement systems, on-board diagnostics, battery diagnostics, microsimulation, and machine learning are enabling more accurate assessment of vehicle emissions, energy use, traffic impacts, and operational efficiency under real driving conditions.

This Special Issue welcomes original research and review papers focused on innovative vehicle technologies, sustainable transport solutions, low- and zero-emission mobility, predictive modeling, and intelligent transport tools. Interdisciplinary contributions linking vehicle engineering, transport planning, energy analysis, and environmental performance assessment are especially encouraged.

Research areas may include, but are not limited to, the following:

  • Real-world vehicle emissions and energy consumption analysis using PEMS, OBD, and road data;
  • Electric vehicles, hybrid vehicles, and plug-in hybrid electric vehicles in real operating conditions;
  • Battery diagnostics, degradation, safety, and state-of-health estimation in Evs;
  • AI and machine learning methods for emission, energy, and traffic prediction;
  • Microsimulation of road traffic linked with emission and energy models;
  • Decarbonization strategies for the road mobility ecosystem and automotive sector;
  • Sustainable urban mobility, public transport transition, and zero-emission transport systems;
  • Intelligent transport systems, digital twins, and data-driven mobility management;
  • Vehicle–infrastructure interaction at intersections, roundabouts, and urban nodes;
  • Environmental assessment of emerging automotive technologies and transport operations.

Dr. Maksymilian Mądziel
Dr. Tiziana Campisi
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-anonymized 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

  • sustainable transport
  • automotive innovation
  • real-world emissions
  • electric vehicles
  • PHEV
  • energy modeling
  • machine learning
  • traffic microsimulation
  • decarbonization
  • smart mobility

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Published Papers (2 papers)

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Research

25 pages, 5624 KB  
Article
Remaining Useful Life Prediction of Retired Lithium-Ion Batteries Under Second-Life Energy Storage Conditions Using Wavelet Packet Energy Entropy
by Lin Chen, Minling Pan, Zihao Liu, Kang Yu, Bing Ji, Yuan Gao and Haihong Pan
Appl. Sci. 2026, 16(16), 8018; https://doi.org/10.3390/app16168018 - 12 Aug 2026
Viewed by 262
Abstract
Retired lithium-ion batteries retain considerable residual value for second-life energy storage applications, but significant variations in health conditions and complex operating scenarios make accurate remaining useful life (RUL) prediction challenging. To address the limited availability of capacity measurements and the poor adaptability of [...] Read more.
Retired lithium-ion batteries retain considerable residual value for second-life energy storage applications, but significant variations in health conditions and complex operating scenarios make accurate remaining useful life (RUL) prediction challenging. To address the limited availability of capacity measurements and the poor adaptability of conventional models to dynamically fluctuating degradation trajectories, a hybrid RUL prediction framework integrating Wavelet Packet Energy Entropy (WPEE), a Fractional-Order Grey Model (FGM), and an Unscented Kalman Filter (UKF) is proposed. WPEE extracted from discharge voltage signals is employed as a degradation indicator, while a Box–Cox transformation enhances its correlation with capacity. An Adaptive Mutation Particle Swarm Optimization (AMPSO) algorithm is used to determine the optimal fractional-order parameter, and the optimized FGM is incorporated into the UKF state-transition process for recursive state correction. Validation was conducted using four retired lithium-ion cells and two series-connected battery packs with different health conditions at prediction starting points of 20, 25, and 30 cycles. The results show that the proposed method effectively tracks degradation evolution, with RUL prediction errors within 7 cycles for retired cells and within 6 cycles for battery packs. Across all 18 prediction cases, FGM–UKF achieved an overall mean AE of 3.111 cycles, lower than those of FGM (5.111 cycles), GM(1, 1) (4.000 cycles), and LR (4.278 cycles). These results demonstrate the effectiveness and robustness of the proposed framework for lifetime assessment in second-life battery energy storage systems. Full article
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23 pages, 2645 KB  
Article
An Adaptive ADAS Support Framework Based on Microwave Signal Conversion to Address Radar Perception Limitations
by Hojae Kim, Juneyoung Park, Kang-Dae Lee and Eunbi Jeong
Appl. Sci. 2026, 16(15), 7377; https://doi.org/10.3390/app16157377 - 23 Jul 2026
Viewed by 614
Abstract
Advanced Driver Assistance Systems (ADAS) can exhibit perception limitations involving large or stationary vehicles, occluded downstream hazards, and the finite detection range of onboard radar. This study proposes a microwave signal conversion based support framework comprising a vehicle-mounted V2V–V2V system and a roadside [...] Read more.
Advanced Driver Assistance Systems (ADAS) can exhibit perception limitations involving large or stationary vehicles, occluded downstream hazards, and the finite detection range of onboard radar. This study proposes a microwave signal conversion based support framework comprising a vehicle-mounted V2V–V2V system and a roadside I2I–I2V system. The algorithms convert and retransmit radar signals according to vehicle speed and spacing states or downstream congestion, while longitudinal responses were behaviorally represented in VISSIM-COM through conditional Desired Speed modification for equipped trucks and buses. A calibrated 12-km Seoul Tollgate corridor was evaluated using V2V–V2V market penetration rates of 0–100%, a binary I2I–I2V condition, and a combined full-deployment scenario. Run-level TTC and conflict frequency were summarized using means, standard deviations, and 95% confidence intervals, and both measures were compared using two-sided paired t-tests. Increasing V2V–V2V penetration was associated with higher TTC and fewer conflicts. Within the tollgate influence area, combined deployment increased mean TTC from 0.72 to 1.27 s and reduced mean conflict frequency from 1486 to 764; both paired comparisons were statistically significant (p < 0.001). These findings suggest potential surrogate-safety benefits from coordinating vehicle- and infrastructure-based recognition support and advance speed adjustment. Full article
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