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Volume 8
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Vehicles, Volume 8, Issue 2 (February 2026) – 19 articles

Cover Story (view full-size image): Electric vehicles (EVs) can act as flexible energy resources for the electricity grid through vehicle‑to‑grid (V2G) technology. Using floating car data (FCD), this study identifies urban areas where EVs naturally park long enough to provide surplus energy. The available energy is estimated for each vehicle, and forecasting models—ARIMA (AutoRegressive Integrated Moving Average) and LSTM (Long Short‑Term Memory)—predict future grid support potential. The results show how data‑driven planning can enhance grid stability in medium‑sized cities. View this paper
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22 pages, 4722 KB  
Article
Managing Design Variants in Formula Student Race Cars: A Digital Engineering Approach Across Multiple Teams
by Julian Borowski, Hinrich Emsmann, Jannis Kneule, Rico Ruess and Stephan Rudolph
Vehicles 2026, 8(2), 43; https://doi.org/10.3390/vehicles8020043 - 23 Feb 2026
Viewed by 569
Abstract
Increasing product complexity, shorter development cycles and cross-domain integration demands pose significant challenges for modern race car engineering teams. In Formula Student teams, heterogeneous toolchains, manual data exchange, late system integration, and high personnel turnover hinder efficient collaborative development and lead to repeated [...] Read more.
Increasing product complexity, shorter development cycles and cross-domain integration demands pose significant challenges for modern race car engineering teams. In Formula Student teams, heterogeneous toolchains, manual data exchange, late system integration, and high personnel turnover hinder efficient collaborative development and lead to repeated knowledge loss. This paper presents an integrated digital-engineering framework combining graph-based design languages (GBDL), model-to-text transformations, natural-language interactions via Large Language Models (LLMs), and Git-based version control to address these issues. By formalizing design knowledge and storing it in a centralized design graph, the framework ensures digital consistency of data and models, supports automated vehicle design variant generation, and enables seamless cross-domain integration. Through case studies of three Formula Student teams, the methodology demonstrates quantifiable reductions in design iteration time, enabling the evaluation of more than 104 suspension variants within days instead of a few dozen manually created variants, while reducing hands-on engineering effort from minutes per variant to a largely unattended optimization process. The results indicate that the approach not only enhances efficiency and collaboration but also preserves design knowledge for long-term knowledge management and reuse. Looking forward, this methodology provides a scalable route toward further engineering automation, systematic variant-driven development, and early-stage design optimization supported by design languages and integrated downstream toolchains. Full article
(This article belongs to the Special Issue Vehicle Design Processes, 3rd Edition)
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39 pages, 84580 KB  
Article
FPGA Implementation and Performance Evaluation of Classic PID, IMC and DTC for BLDC Motor Control
by Jaber Ouakrim, Abdoulaye Bodian, Dina Ouardani and Alben Cardenas
Vehicles 2026, 8(2), 42; https://doi.org/10.3390/vehicles8020042 - 22 Feb 2026
Viewed by 815
Abstract
Brushless DC (BLDC) motors are widely used in mobile robotics and off-road vehicles due to their high efficiency, reliability, and compactness. However, achieving robust, high-performance speed control in embedded environments remains challenging due to nonlinearities, dead-time effects, parameter uncertainties, and strict real-time constraints. [...] Read more.
Brushless DC (BLDC) motors are widely used in mobile robotics and off-road vehicles due to their high efficiency, reliability, and compactness. However, achieving robust, high-performance speed control in embedded environments remains challenging due to nonlinearities, dead-time effects, parameter uncertainties, and strict real-time constraints. This paper presents a comprehensive experimental study of classical and robust control strategies for BLDC motor speed control, fully implemented on an FPGA platform. Classical PI and PID controllers tuned using Ziegler–Nichols, Cohen–Coon, and Chien–Hrones–Reswick methods are first investigated and discretized using both Zero-Order Hold (ZOH) and Tustin (bilinear) approximations. Model-based approaches, including IMC-based PID controllers, are then introduced to enhance robustness. In addition, a robust two-degree-of-freedom dead-time compensator (DTC) is implemented to explicitly address dead-time uncertainties inherent to inverter-based motor drives. All controllers are implemented using fixed-point arithmetic on a Xilinx Nexys A7 FPGA and validated experimentally on a BLDC motor test bench representative of semi-autonomous robotic applications. Performance is evaluated through time-domain responses and quantitative indices, including ISE, ITAE, I, control effort, and FPGA resource utilization. Experimental tests under controlled DC bus voltage disturbances are conducted to assess disturbance rejection capability and robustness under realistic operating conditions. Experimental results demonstrate that Tustin discretization consistently improves tracking performance, while IMC-PID and DTC strategies provide superior robustness against dead-time and modeling uncertainties, making them particularly suitable for embedded FPGA-based motor control. Full article
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37 pages, 3062 KB  
Systematic Review
Autonomous Vehicles in the Traffic Ecosystem: A Comprehensive Review of Integration, Impacts, and Policy Implications
by Eugen Valentin Butilă, Gheorghe-Daniel Voinea, Răzvan Gabriel Boboc and Grigore Ambrosi
Vehicles 2026, 8(2), 41; https://doi.org/10.3390/vehicles8020041 - 19 Feb 2026
Viewed by 1361
Abstract
Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance [...] Read more.
Autonomous vehicles (AVs) are expected to significantly influence road safety, traffic efficiency, and urban mobility. However, their real-world impacts depend not only on vehicle-level automation but also on interactions within the broader traffic ecosystem, including human-driven vehicles, vulnerable road users, infrastructure, and governance frameworks. This review provides a system-level synthesis of recent research on the integration of autonomous and connected autonomous vehicles in mixed traffic environments. Following PRISMA 2020 guidelines, 51 peer-reviewed studies published between 2016 and 2025 were systematically reviewed and thematically analyzed. The review addresses technological foundations, safety impacts, traffic flow and network performance, mixed traffic dynamics, infrastructure and urban systems, and policy and governance challenges. The findings indicate that AV impacts are highly non-linear and sensitive to market penetration rates, control strategies, and human behavioral adaptation. While high levels of automation and connectivity can improve safety, capacity, and traffic stability, early-stage deployment may temporarily increase delays and traffic conflicts. Policy measures—such as pricing, shared mobility integration, and regulatory oversight—are therefore critical to ensuring that AV deployment delivers sustainable and equitable mobility outcomes. Full article
(This article belongs to the Special Issue Intelligent Mobility and Sustainable Automotive Technologies)
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35 pages, 9343 KB  
Article
Collaborative Control of Rear-Wheel Independent Drive Electric Vehicles During Tire Blowouts Using Broad-Extreme Reinforcement Learning: Simulation and Scaled Prototype Verification
by Xiaozheng Wang, Pak Kin Wong, Hengli Qi, Shiron Thalagala, Ziqi Yang, Jingyu Lu and Wei Huang
Vehicles 2026, 8(2), 40; https://doi.org/10.3390/vehicles8020040 - 18 Feb 2026
Viewed by 476
Abstract
Tire blowouts represent one of the most hazardous fault scenarios for electric vehicles (EVs). While collaborative active steering control (ASC) and direct yaw moment control (DYC) can theoretically maintain stability during these events, the strong coupling effects between them make controller design challenging. [...] Read more.
Tire blowouts represent one of the most hazardous fault scenarios for electric vehicles (EVs). While collaborative active steering control (ASC) and direct yaw moment control (DYC) can theoretically maintain stability during these events, the strong coupling effects between them make controller design challenging. To address this, an adaptive control algorithm based on broad-extreme reinforcement learning (RL), named broad critic extreme actor (BCEA), is proposed. Compared to traditional controllers, the proposed BCEA architecture is simpler to design and demonstrates enhanced robustness. Crucially, it achieves significantly faster training speed than traditional RL methods such as deep deterministic policy gradient (DDPG). Both simulation and scaled prototype tests verify the ability of the BCEA-based controller to maintain vehicle stability during different types of tire blowout scenarios. Furthermore, compared to traditional RL methods, the training efficiency is improved by more than 80%. These results indicate that the proposed BCEA controller is a promising advancement for vehicle stability control under critical failure conditions. Full article
(This article belongs to the Topic Vehicle Dynamics and Control, 2nd Edition)
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18 pages, 3133 KB  
Article
Towards AI-Assisted Motorcycle Safety: Multi-Modal Video Analysis for Hazard Detection and Contextual Risk Assessment
by Fatemeh Ghorbani, Augustin Hym, Mohammed Elhenawy and Andry Rakotonirainy
Vehicles 2026, 8(2), 39; https://doi.org/10.3390/vehicles8020039 - 13 Feb 2026
Viewed by 584
Abstract
Motorcyclists face a disproportionately high risk of severe injury or death compared to other road users, highlighting the need for intelligent rider assistance technologies. This paper presents an initial, modular, and interpretable AI pipeline that generates context-aware safety advice from first-person motorcycle videos [...] Read more.
Motorcyclists face a disproportionately high risk of severe injury or death compared to other road users, highlighting the need for intelligent rider assistance technologies. This paper presents an initial, modular, and interpretable AI pipeline that generates context-aware safety advice from first-person motorcycle videos with practical inference latency suitable for on-device deployment, framing large language models as interpretable cognitive support agents for motorcycle safety. The system integrates lightweight perception and reasoning components to emulate the function of an Advanced Rider Assistance System (ARAS). Video frames are processed at 1 FPS using Pixtral, a Mistral-based multimodal large language model (MLLM), to produce descriptive scene captions, while YOLOv8 identifies key objects such as vehicles, pedestrians, and road hazards. A Mistral-small language model then fuses this information to generate concise, imperative safety tips. Preliminary evaluations on publicly available motorcycle POV datasets demonstrate promising performance in terms of contextual accuracy, interpretability, and scalability, suggesting potential for real-world deployment in low-resource or embedded environments. The proposed framework offers interpretable, context-aware safety assistance that is particularly valuable for young and newly licensed riders during the transition from supervised training to independent riding, where real-time hazard interpretation support is most needed. Full article
(This article belongs to the Special Issue Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning)
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30 pages, 6249 KB  
Article
Modeling and Optimization Research on the Location Selection of Taxi Charging Stations in Severe Cold Areas
by Jiashuo Xu, Chunguang He, Ya Duan, Yazan Mualla, Mahjoub Dridi and Abdeljalil Abbas-Turki
Vehicles 2026, 8(2), 38; https://doi.org/10.3390/vehicles8020038 - 13 Feb 2026
Viewed by 385
Abstract
Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing [...] Read more.
Decarbonizing the transport sector is crucial for achieving global carbon peaking and carbon neutrality goals. Electric taxis (e-taxis), which play a vital role in urban public transportation, are central to this transition. However, their operational performance deteriorates significantly under extremely cold conditions. Existing planning models for charging infrastructure often overlook the impact of low temperatures, creating a critical research gap. To address this issue, we propose a novel planning framework using Urumqi, China (43.8° N, 87.6° E) as a case study. Urumqi is a major cold-region metropolis, where January temperatures regularly drop below −20 °C. Our methodology includes two key steps: integrating 412 driver questionnaires and 1.2 million high-resolution GPS trajectories to extract temperature-sensitive charging demand profiles; and incorporating these profiles into an integer linear programming (ILP) model to minimize lifecycle costs, considering climatic constraints, taxi operation patterns, and grid limitations. A key innovation is a temperature-correction coefficient, which dynamically adjusts vehicle energy consumption and driving range based on ambient temperature. Results show superiority over conventional (temperature-ignoring) and random plans: 14-fold lower annualized cost, 23-fold shorter average queuing time, 96.2% high-frequency demand coverage (+16.6%), and 78% charging station utilization (+50.0%). It achieves 29.8–32.3% cost savings at −5 °C (over 25.9% even at −35 °C) and scales stably for 5–50% e-taxi penetration, offering a transferable framework for cold-region e-taxi charging optimization. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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71 pages, 7602 KB  
Review
The Electric Vehicle Transition in Emerging Economies
by Ibrahima Ka, Ansoumana Noumou Djité, Seynabou Anna Chimére Diop, Godwin Kafui Ayetor and Boucar Diouf
Vehicles 2026, 8(2), 37; https://doi.org/10.3390/vehicles8020037 - 12 Feb 2026
Viewed by 1148
Abstract
The global shift toward electric mobility represents a cornerstone of sustainable energy transitions; however, developing countries face distinct structural, economic, and infrastructural challenges that constrain their participation in this transformation. This paper examines the conditions, policy frameworks, and infrastructural requirements necessary for a [...] Read more.
The global shift toward electric mobility represents a cornerstone of sustainable energy transitions; however, developing countries face distinct structural, economic, and infrastructural challenges that constrain their participation in this transformation. This paper examines the conditions, policy frameworks, and infrastructural requirements necessary for a successful electric vehicle (EV) transition in developing countries, with particular attention to the interplay between energy access, transportation policy, and grid readiness. Using a mixed-methods approach that integrates policy analysis, partial life-cycle assessment (LCA) with the second-hand market, and case studies across sub-Saharan Africa and South Asia, the study evaluates the implications of limited electricity access, unreliable power grids, and the dominance of informal transport systems on EV adoption. The findings reveal that, while EVs offer significant potential for reducing emissions and improving urban air quality, their deployment depends critically on coordinated investments in renewable-based electricity generation, charging infrastructure, and supportive regulatory frameworks. Policy strategies such as fiscal incentives, public–private partnerships, and decentralized charging networks can accelerate uptake when aligned with energy-access goals. The paper argues that the EV transition in developing economies must be policy-driven and context-adapted, integrating mobility electrification with broader agendas of energy justice, rural electrification, and industrial development. Ultimately, the research provides a roadmap for aligning electric mobility policies with sustainable infrastructure development to ensure that the global EV revolution becomes both inclusive and equitable. Full article
(This article belongs to the Special Issue Sustainable Traffic and Mobility—2nd Edition)
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15 pages, 2410 KB  
Article
Smart Vision Traffic Surveillance: Vehicle Re-Identification and Tracking Using Vision Transformer
by Muhammad Shoaib Hanif, Zubair Nawaz and Muhammad Kamran Malik
Vehicles 2026, 8(2), 36; https://doi.org/10.3390/vehicles8020036 - 10 Feb 2026
Cited by 1 | Viewed by 411
Abstract
Intelligent transportation systems (ITSs) are crucial for modern traffic management and law enforcement. This paper addresses the challenge of monitoring and managing extensive vehicle traffic in large cities like Lahore, Pakistan. We propose a deep learning based ITS utilizing Vision Transformers combined with [...] Read more.
Intelligent transportation systems (ITSs) are crucial for modern traffic management and law enforcement. This paper addresses the challenge of monitoring and managing extensive vehicle traffic in large cities like Lahore, Pakistan. We propose a deep learning based ITS utilizing Vision Transformers combined with convolutional feature extraction to accurately identify vehicle type, color, make/model, and license plates. Experiments were conducted on a comprehensive dataset collected from multiple checkpoints across Lahore under varying environmental conditions. Our proposed model achieved high accuracy rates: 98.0% for vehicle type classification, 96.0% for color detection, 95.0% for make/model identification, and 89.0% for license plate recognition. These results demonstrate the system’s potential to significantly enhance traffic management and road safety and support law enforcement operations in developing urban environments. Full article
(This article belongs to the Special Issue Intelligent Connected Vehicles)
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36 pages, 4643 KB  
Article
System Readiness Assessment for Emerging Multimodal Mobility Systems Using a Hybrid Qualitative–Quantitative Framework
by Fabiana Carrión, Gregorio Romero, Jose-Manuel Mira and Jesus Félez
Vehicles 2026, 8(2), 35; https://doi.org/10.3390/vehicles8020035 - 9 Feb 2026
Viewed by 1033
Abstract
This paper presents a hybrid qualitative–quantitative framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with specific application to the Pods4Rail project. The methodology integrates expert-based Technology Readiness Level (TRL) assessment with a probabilistic System Readiness Level (SRL) [...] Read more.
This paper presents a hybrid qualitative–quantitative framework for assessing the technical feasibility and system readiness of emerging multimodal mobility concepts, with specific application to the Pods4Rail project. The methodology integrates expert-based Technology Readiness Level (TRL) assessment with a probabilistic System Readiness Level (SRL) estimation that incorporates uncertainties in both TRLs and Integration Readiness Levels (IRLs). The qualitative component uses expert judgment and visual heat maps to identify subsystem-specific maturity gaps, particularly in automation, digitalization, and sustainability. The quantitative component explicitly separates three methodological layers often treated implicitly in prior research: (i) the probabilistic model representing uncertainties in TRL and IRL, (ii) the uncertainty-propagation problem linking these variables to system-level readiness, and (iii) the Monte Carlo algorithm employed to solve this problem. This structure enables the derivation of SRL distributions that reflect uncertainty more realistically than deterministic approaches, allowing statistical analysis of different characteristics of these distributions and exploratory sensitivity analysis. Results show that the Pods4Rail system is positioned between SRL 1 and SRL 2, corresponding to concept refinement and technology development stages. While hardware-related subsystems such as the Transport Unit and Rail Carrier Unit exhibit relatively higher maturity, planning, logistics, and operational management functionalities remain at early development stages. By combining interpretative insight with statistical rigor, the proposed framework offers a transparent and reproducible approach to early-phase readiness assessment. Its transferability makes it suitable for other innovative mobility systems facing similar challenges of incomplete information, uncertain integration pathways, and high conceptual complexity. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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21 pages, 2609 KB  
Article
An Adaptive Full-Order Sliding-Mode Observer Based-Sensorless Control for Permanent Magnet Synchronous Propulsion Motors Drives
by Shengqi Huang, Yuqing Huang, Le Wang, Lei Shi and Junwu Zhang
Vehicles 2026, 8(2), 34; https://doi.org/10.3390/vehicles8020034 - 7 Feb 2026
Viewed by 520
Abstract
In electric vehicle and marine propulsion applications, the stable operation of permanent-magnet synchronous motor (PMSM) drive systems relies on accurate rotor position information. Such information is typically obtained from position sensors, which are prone to high temperature, humidity, vibration, and electromagnetic interference, leading [...] Read more.
In electric vehicle and marine propulsion applications, the stable operation of permanent-magnet synchronous motor (PMSM) drive systems relies on accurate rotor position information. Such information is typically obtained from position sensors, which are prone to high temperature, humidity, vibration, and electromagnetic interference, leading to elevated failure rates; moreover, sensor installation introduces additional interfaces and wiring, thereby reducing system reliability. To address these issues, this paper proposes a sensorless control method based on an adaptive full-order sliding-mode observer (SMO). The proposed method employs the SMO output as the observer feedback correction term rather than the estimated back EMF, thereby avoiding substantial high-frequency noise. Furthermore, an S-shaped nonlinear function is designed to replace the conventional switching function, mitigating high-frequency chattering when the system operates in sliding mode; an adaptive sliding-mode gain function is designed, the sliding-mode gain and the boundary-layer thickness are adaptively tuned as a function of motor speed, which effectively enhances the back EMF estimation accuracy over a wide operating-speed range. The effectiveness of the proposed method is validated on a 2.3-kW PMSM experimental platform. Full article
(This article belongs to the Special Issue Next-Generation Power Electronics and Charging Technologies for Electric Vehicles)
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26 pages, 1782 KB  
Article
An Integrated User-Centered E-Scooter Design Framework for Enhancing User Satisfaction, Performance, and Terrain Adaptation in Budapest City
by Basheer Wasef Shaheen and Ahmed Jaber
Vehicles 2026, 8(2), 33; https://doi.org/10.3390/vehicles8020033 - 6 Feb 2026
Viewed by 757
Abstract
Electric scooters and other micromobility innovations are becoming standard fare in urban transportation networks. Yet there are several obstacles that must be overcome, including concerns about users’ satisfaction and safety. This study aimed primarily at developing a user-centered methodological framework that combined different [...] Read more.
Electric scooters and other micromobility innovations are becoming standard fare in urban transportation networks. Yet there are several obstacles that must be overcome, including concerns about users’ satisfaction and safety. This study aimed primarily at developing a user-centered methodological framework that combined different user-centered engineering tools such as voice of customers analysis, needs–metrics mapping, Pugh’s matrix and morphological design, strategic analysis approaches such as SWOT and PESTEL, and, a key innovation, the smart terrain-adaptive power management system (STAPMS), an AI-based feature that dynamically adjusts power output and regenerative braking based on Budapest’s varied topography and road conditions to improve energy efficiency and ride comfort. This innovative framework offers insights into redesign options aimed at enhancing customer satisfaction, product quality, and business growth. The proposed framework was validated on Lime electric scooters, particularly the S2 generation type. Three design concepts were generated and evaluated through a systematic approach to provide an optimal balance between users’ needs, technical performance, and strategic feasibility. The proposed user-centered framework shows significant potential to improve users’ satisfaction, enhanced usability, extended range, and increased market competitiveness, validating its viability for micromobility innovative solutions. The findings also demonstrate the necessity for systematic frameworks that link user experience with engineering design and can be generalized to other micromobility products. Full article
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30 pages, 6506 KB  
Review
Driving Simulator-Based Driving Behavioural Research: A Bibliometric and Narrative Review Providing Key Insights for New and Emerging Researchers
by Muhammad Hussain, Muladilijiang Baikejuli, Jing Shi, Amjad Pervez, Matthew A. Albrecht, Etikaf Hussain, Razi Hasan and Teresa Senserrick
Vehicles 2026, 8(2), 32; https://doi.org/10.3390/vehicles8020032 - 6 Feb 2026
Viewed by 680
Abstract
The driving simulator’s ability to provide practical, safe, and controlled environments has made it a widely used tool for evaluating driving behaviours in the realm of road safety. To consolidate the fragmented research in this area, this study is divided into two parts: [...] Read more.
The driving simulator’s ability to provide practical, safe, and controlled environments has made it a widely used tool for evaluating driving behaviours in the realm of road safety. To consolidate the fragmented research in this area, this study is divided into two parts: a bibliometric analysis and a narrative review: (a) the bibliometric analysis identified 4992 studies, expanding from 2000 to June 2025, sourced from four databases—Web of Science, Scopus, TRID, and Google Scholar (supplementary)—and examined trends over the years, the general topics covered, the countries where studies were conducted, and the main research fields associated with driving simulators; and (b) the narrative review further analysed 48 selected studies from eight domains (distraction, fatigue and drowsiness, traffic-calming measures, impairment from psychoactive drugs, road curves, intersections, tunnels, and adverse weather conditions) to provide insights into how driving simulators have contributed to these fields, the methodologies employed by researchers, and the practical applications of the findings. The study aims to provide clear and essential insights for new and emerging researchers, offering an accessible overview of how driving simulators have evolved, why they are important, how they measure different driving metrics, and how they ultimately improve road safety. The findings indicate that driving simulator studies are increasingly prominent in research on driver behaviour (e.g., driving speed, lateral movement, and acceleration/deceleration). Full article
(This article belongs to the Special Issue Road Safety, Aberrant Driver Behaviour and Sustainable Transportation Planning)
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17 pages, 1285 KB  
Article
Joint Optimization of Dynamic Pricing and Flexible Refund Fees for Railway Services
by Wuyang Yuan, Zhen Ren, Zhongrui Zhou and Yu Ke
Vehicles 2026, 8(2), 31; https://doi.org/10.3390/vehicles8020031 - 6 Feb 2026
Viewed by 436
Abstract
This study explores strategies for dynamic pricing and flexible refund fee setting in railway line services, aiming to optimize ticket sales revenue by integrating refund mechanisms into the revenue management framework. By introducing a consistent concept of opportunity cost applicable to both passengers [...] Read more.
This study explores strategies for dynamic pricing and flexible refund fee setting in railway line services, aiming to optimize ticket sales revenue by integrating refund mechanisms into the revenue management framework. By introducing a consistent concept of opportunity cost applicable to both passengers and railway operators, we propose an integrated approach that combines dynamic pricing with flexible refund fees grounded in the demand-driven opportunity cost of seat resources. A dynamic programming model is constructed to quantify the opportunity cost of seat resources. To address the computational challenges arising from the model’s scale, state and time dimension compression methods are applied to develop an approximate linear programming model with fewer constraints. The proposed model is solved using a turning point search algorithm and a constraint generation algorithm. Numerical experiments and ticket sales simulations are conducted to verify the feasibility of the proposed methods and to explore the application effects of different pricing strategy combinations. The results demonstrate that the integration of dynamic pricing and flexible refund fees can significantly enhance ticket sales revenue, particularly in scenarios of supply shortfall. Full article
(This article belongs to the Special Issue Models and Algorithms for Railway Line Planning Problems)
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19 pages, 2621 KB  
Article
Electric Vehicles to Support Grid Needs: Evidence from a Medium-Sized City
by Antonio Comi, Eskindir Ayele Atumo and Elsiddig Elnour
Vehicles 2026, 8(2), 30; https://doi.org/10.3390/vehicles8020030 - 4 Feb 2026
Viewed by 557
Abstract
Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study [...] Read more.
Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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18 pages, 2469 KB  
Article
Fires in Urban Passenger Transport Vehicles Engine—Case Study
by Hugo Raposo, Jorge Raposo, José Torres Farinha and J. Edmundo de-Almeida-e-Pais
Vehicles 2026, 8(2), 29; https://doi.org/10.3390/vehicles8020029 - 2 Feb 2026
Viewed by 1266
Abstract
Passenger transport companies have often been affected by fires in their vehicles, causing considerable damage. As a result, it is important to study the causes and effects of these fires, as well as to define the maintenance policies and strategies to be implemented [...] Read more.
Passenger transport companies have often been affected by fires in their vehicles, causing considerable damage. As a result, it is important to study the causes and effects of these fires, as well as to define the maintenance policies and strategies to be implemented to minimize the probability of this type of accident occurring. The support for this paper was based on the study of an accident that occurred in Portugal involving a passenger bus that suffered a fire in the engine compartment, which spread to the passenger compartment and caused the destruction of the vehicle, with no personal injuries. This study used infrared image analysis technology, oil ignition temperature analysis, maintenance history, accident history and operator interviews to determine the possible cause of the ignition. It was found that the cause was due to oil leaks from the engine compartment cooling system. The present communication will share a set of explanatory elements of the circumstances in which the accident occurred. In addition to identifying the causes of the accident, the study warns of the importance of more effective and efficient maintenance, particularly when using Condition Based Maintenance (CBM), including periodic visual inspections of the various mechanical and electrical components that make up the vehicles. The conclusions presented in the study also show that these events are not unrelated to the poor or even non-existent maintenance policy for the entire fleet, including the applicable standards. Full article
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19 pages, 5725 KB  
Article
Real-Time 3D Scene Understanding for Road Safety: Depth Estimation and Object Detection for Autonomous Vehicle Awareness
by Marcel Simeonov, Andrei Kurdiumov and Milan Dado
Vehicles 2026, 8(2), 28; https://doi.org/10.3390/vehicles8020028 - 2 Feb 2026
Viewed by 822
Abstract
Accurate depth perception is vital for autonomous driving and roadside monitoring. Traditional stereo vision methods are cost-effective but often fail under challenging conditions such as low texture, reflections, or complex lighting. This work presents a perception pipeline built around FoundationStereo, a Transformer-based stereo [...] Read more.
Accurate depth perception is vital for autonomous driving and roadside monitoring. Traditional stereo vision methods are cost-effective but often fail under challenging conditions such as low texture, reflections, or complex lighting. This work presents a perception pipeline built around FoundationStereo, a Transformer-based stereo depth estimation model. At low resolutions, FoundationStereo achieves real-time performance (up to 26 FPS) on embedded platforms like NVIDIA Jetson AGX Orin with TensorRT acceleration and power-of-two input sizes, enabling deployment in roadside cameras and in-vehicle systems. For Full HD stereo pairs, the same model delivers dense and precise environmental scans, complementing LiDAR while maintaining a high level of accuracy. YOLO11 object detection and segmentation is deployed in parallel for object extraction. Detected objects are removed from depth maps generated by FoundationStereo prior to point cloud generation, producing cleaner 3D reconstructions of the environment. This approach demonstrates that advanced stereo networks can operate efficiently on embedded hardware. Rather than replacing LiDAR or radar, it complements existing sensors by providing dense depth maps in situations where other sensors may be limited. By improving depth completeness, robustness, and enabling filtered point clouds, the proposed system supports safer navigation, collision avoidance, and scalable roadside infrastructure scanning for autonomous mobility. Full article
(This article belongs to the Special Issue Emerging Solutions and Technologies for Smart Mobility and Vehicle Safety in Transportation)
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20 pages, 3498 KB  
Article
Design and Optimization of a Non-Contact Current Sensor for EVs Based on a Hybrid Semi-Circular Array of Hall-Effect and TMR Elements
by Xiaopeng Yuan, Haoyu Wang and Lei Zhang
Vehicles 2026, 8(2), 27; https://doi.org/10.3390/vehicles8020027 - 1 Feb 2026
Viewed by 588
Abstract
This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in [...] Read more.
This paper presents a semi-circular, non-contact current sensor designed to simplify the layout of automotive wiring harnesses and enhance measurement convenience and reliability. The sensor integrates a hybrid sensing array consisting of Hall-effect and tunnel magnetoresistance (TMR) elements. To address common challenges in automotive power systems and vehicle wiring—such as conductor eccentricity and magnetic interference from adjacent cables—two key techniques are proposed. First, an eccentricity error compensation algorithm is developed, achieving a measurement accuracy of 97.07% under specific misalignment conditions. Second, an equivalent modeling method based on eccentricity principles is introduced to characterize interference fields in complex wiring environments, maintaining 94.31% accuracy in the presence of external disturbances. When the conductor is centered within the array, the average measurement accuracy reaches 99.05%. Experimental results demonstrate that the proposed sensor can reliably measure large currents from 0 to 210 A, making it highly suitable for applications in electric vehicles, high-voltage harness monitoring, power electronics, and intelligent transportation systems. Full article
(This article belongs to the Special Issue Intelligent Vehicle Infrastructure Cooperative System (IVICS))
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32 pages, 6959 KB  
Article
Handling Stability Control for Multi-Axle Distributed Drive Vehicles Based on Model Predictive Control
by Hongjie Cheng, Zhenwei Hou, Zhihao Liu, Jianhua Li, Jiashuo Zhang, Yuan Zhao and Xiuyu Liu
Vehicles 2026, 8(2), 26; https://doi.org/10.3390/vehicles8020026 - 1 Feb 2026
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Abstract
Multi-axle vehicles are commonly used for heavy-duty special operations, which easily leads to high driving torque demands when adopting distributed electric drive configurations. This study achieves the objective of reducing the driving torque of each in-wheel motor while controlling the stability of multi-axle [...] Read more.
Multi-axle vehicles are commonly used for heavy-duty special operations, which easily leads to high driving torque demands when adopting distributed electric drive configurations. This study achieves the objective of reducing the driving torque of each in-wheel motor while controlling the stability of multi-axle vehicles. Taking a five-axle distributed drive test vehicle as the research object, a hierarchical control strategy integrating active all-wheel steering and direct yaw moment control is proposed. The upper layer is implemented based on model predictive control, with fuzzy control introduced to dynamically adjust control weights; the lower layer accomplishes the allocation of targets calculated by the upper layer through minimizing the objective function of tire load ratio. A linear parameter varying (LPV) tire model is introduced into the vehicle model to improve the calculation accuracy of tire lateral forces, and a neural network method is employed to solve the real-time performance issue of the model predictive control (MPC) controller. The proposed strategy is verified through a combination of simulation and real vehicle tests. High-speed condition simulations demonstrate that the AWS/DYC strategy significantly outperforms the ARS/DYC approach: compared to the active rear-wheel steering strategy, while the sideslip angle is reduced by 90.98%, the peak driving torque is reduced by 30.78%. Notably, tire slip angle analysis reveals that AWS/DYC maintains relatively uniform slip angle distribution across axles with a maximum of 4.7°, entirely within the linear working region, optimally balancing tire performance utilization with lateral stability while preserving safety margin, whereas ARS/DYC causes slip angles to exceed 11.9° at the rear axle, entering saturation. Low-speed real vehicle tests further confirm the engineering applicability of the strategy. The proposed method is of significant importance for the application of distributed drive configurations in the field of special vehicles. Full article
(This article belongs to the Topic Dynamics, Control and Simulation of Electric Vehicles)
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21 pages, 4512 KB  
Article
Interpretability Evaluation Method for Driving Stability on Curved Road Sections with Trajectory Uncertainty
by Xiaoyang Li, Tao Chen, Lebin Zhao, Yang Luo, Pengfei Zhang and Meng Wang
Vehicles 2026, 8(2), 25; https://doi.org/10.3390/vehicles8020025 - 1 Feb 2026
Viewed by 472
Abstract
This study was conducted in order to enrich the safety evaluation system of vehicles on complex road sections and provide quantitative support for speed control and driving decision-making. To address the driving stability issue caused by trajectory uncertainty on curved roads, we analyzed [...] Read more.
This study was conducted in order to enrich the safety evaluation system of vehicles on complex road sections and provide quantitative support for speed control and driving decision-making. To address the driving stability issue caused by trajectory uncertainty on curved roads, we analyzed lane-changing stability and found that trajectory variations induce a step change in centrifugal force, aggravating lateral instability. Secondly, we developed a variety of simulation schemes to determine the stability limit speed under multi-source information fusion and constructed the corresponding database. Finally, we established an interpretable driving stability evaluation method based on the Differential Evolution-Extended Belief Rule Base-Shapley Additive Explanations (DB-EBRB-SHAP) model. This model incorporates driving behavior as a qualitative variable into the hybrid framework, and its accuracy was further enhanced through parameter optimization. The results demonstrate that the model achieves high evaluation accuracy for driving stability on curved road sections (MAE = 0.0306 and RMSE = 0.0363). Interpretability analysis reveals that curve radius and lane-changing behavior are the key influencing parameters; the negative interaction effect between the two on driving stability will weaken as the curve radius increases. Full article
(This article belongs to the Special Issue Intelligent Vehicle Infrastructure Cooperative System (IVICS))
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