Vehicles, Volume 8, Issue 1 (January 2026) – 24 articles

The development of modern vehicles faces an increase in complexity, as well as a need for shorter development cycles and a seamless cross-domain integration. In order to meet these challenges, a graph-based design language which formalizes and automates engineering workflows is presented and applied in a design case study to a Formula Student race car suspension system. The proposed method uses an ontology-based vocabulary definition and executable model transformations to compile design knowledge into a central and consistent design graph. This graph enables the automatic generation of consistent 3D CAD models, domain-specific simulations and suspension kinematic analyses, replacing manual and error-prone tool and data handover processes. The design language captures both the structural and dynamic behavior of the suspension, supports variant exploration and allows for integrated validation, such as 3D collision detection. The study illustrates how graph-based design languages can serve as ‘digital DNA’ for knowledge-based product development, offering a scalable, reusable platform for engineering automation. This approach enhances the digital consistency of data, the digital continuity of processes and the digital interoperability of tools across all relevant engineering disciplines in order to support the validation of early-stage designs and the optimization of complex systems. Full article

This study focused on developing and validating machine learning models to predict pavement marking retroreflectivity using Light Detection and Ranging (LiDAR) intensity data. The retroreflectivity data was collected using a Mobile Retroreflectometer Unit (MRU) due to its increasing acceptance among states as a compliant measurement device. A comprehensive dataset was assembled spanning more than 1000 miles of roadways, capturing diverse marking materials, colors, installation methods, pavement types, and vehicle speeds. The final dataset used for model development focused on dry condition measurements and roadway segments most relevant to state transportation agencies. A detailed synchronization process was implemented to ensure the accurate pairing of retroreflectivity and LiDAR intensity values. Using these data, several machine learning techniques were evaluated, and an ensemble of gradient boosting-based models emerged as the top performer, predicting pavement retroreflectivity with an R² of 0.94 on previously unseen data. The repeatability of the predicted retroreflectivity was tested and showed similar consistency as the MRU. The model’s accuracy was confirmed against independent field segments demonstrating the potential for LiDAR to serve as a practical, low-cost alternative for MRU measurements in routine roadway inspection and maintenance. The approach presented in this study enhances roadway safety by enabling more frequent, network-level assessments of pavement marking performance at lower cost, allowing agencies to detect and correct visibility problems sooner and helping to prevent nighttime and adverse weather crashes. Full article

Vehicle exhaust gases remain one of the key sources of atmospheric air pollution and pose a serious threat to ecosystems and public health. This study presents an experimental investigation into reducing the toxicity of gasoline internal combustion engine exhaust using ultrasonic waves and infrared (IR) laser exposure. An original hybrid system integrating an ultrasonic emitter and an IR laser module was developed. Four operating modes were examined: no treatment, ultrasound only, laser only, and combined ultrasound–laser treatment. The concentrations of CH, CO, CO₂, and O₂, as well as exhaust gas temperature, were measured at idle and under operating engine speeds. The experimental results show that ultrasound provides a substantial reduction in CO concentration (up to 40%), while IR laser exposure effectively decreases unburned hydrocarbons CH (by 35–40%). The combined treatment produces a synergistic effect, reducing CH and CO by 38% and 43%, respectively, while increasing the CO₂ fraction and decreasing O₂ content, indicating more complete post-oxidation of combustion products. The underlying physical mechanisms responsible for the purification were identified as acoustic coagulation of particulates, oxidation, and photodissociation of harmful molecules. The findings support the hypothesis that combined ultrasonic and laser treatment can enhance real-time exhaust gas purification efficiency. It is demonstrated that physical treatment of the gas phase not only lowers the persistence of by-products but also promotes more complete oxidation processes within the flow. Full article

This study investigates cross-cultural differences in public perception of mobility electrification by applying natural language processing (NLP) techniques to social media discourse in Germany and China. Using a large language model (LLM), this study conducted sentiment analysis and zero-shot text classification on over 10,000 posts to explore how citizens in each country engage with the topic of electric mobility. Results reveal that while infrastructure readiness is a dominant concern in both contexts, German discourse places greater emphasis on environmental impact, often reflecting skepticism toward sustainability claims. On the other hand, Chinese discussions highlight technological advancement and infrastructure expansion, with comparatively limited focus on environmental concerns. These findings show the importance of culturally tailored policy and communication strategies in supporting the public acceptance of electric mobility. By demonstrating how artificial intelligence-driven large-scale social media data analysis can be used to analyze public sentiment across linguistic and cultural contexts, this study contributes methodologically to the emerging field of computational social science and offers practical insights for mobility policy in diverse national settings. Full article

Predicting the severity of traffic accidents remains challenging due to the limited ability of existing methods to extract deep semantic information from unstructured accident narratives, as traditional approaches typically depend on structured data alone. This study proposes a severity prediction approach enhanced by semantic risk reasoning derived from large language models (LLMs). A prompt-engineering template is designed to guide LLMs in extracting proxy semantic features from accident descriptions, forming an enriched feature set that incorporates causal logic. These semantic features are fused with traditional structured features through three integration strategies—direct feature concatenation, optimized feature selection, and model-level fusion. Experiments based on 4013 accident records from expressways in Yunnan Province, China, demonstrate that models using LLM-derived semantic features significantly outperform those relying solely on structured features. Notably, the LightGBM model utilizing semantic features within a balanced learning framework achieves a severe accident recall of 77.8%. While model-level fusion proves optimal for XGBoost (improving Macro-F1 to 0.6356), we identify a “feature dilution” effect in other classifiers, where high-quality semantic reasoning is compromised by low-quality structured noise. These findings indicate that the proposed approach effectively enhances the identification of high-risk accidents and offers a novel semantic-aware solution for traffic safety management. Furthermore, the obtained results provide actionable insights for traffic management agencies to optimize emergency response resource allocation and formulate targeted accident prevention strategies. Full article

This research paper proposes a solution to reduce CO₂ emissions from a spark ignition engine’s exhaust gases by installing a filtration system on the vehicle’s exhaust pipe. The analyzed filtration system was not patented and was in the testing stage. Tests will also be carried out on the stand. The tested system can be used to reduce CO₂ levels in automotive exhaust gases and for static applications (generators, internal combustion engine test stands, fossil fuel power generation systems). The need for a system to reduce pollutant emissions emerged with the average age in Europe. In proper conditions, some vehicles can use this type of filtration system. The tested vehicle is a vehicle (produced in 2009) equipped with a 75HP Spark Ignition Engine. The CO₂ filtration system consists of a container containing a reactive aqueous solution comprising water, CaO, and MgO. Four tests were performed: the first without a filter, and the other three with the filter placed at different distances from the exhaust pipe end to the reactive solution surface. The tests consisted of evaluating the exhaust gases from the cold start of the engine and running (idle engine speed) until the engine reached the optimal operating temperature. The test procedure involved saving the data collected by the analyzer every 10 s for each of the four tests performed (the duration of a test was 1050 s). The first test (No. 1) was performed without the use of the filtering system. Tests 2, 3, and 4 were carried out using the filtering system and changing the distance between the exhaust gases’ outlet point and the surface of the aqueous substance. All tests were carried out under similar conditions. Data specific to the test of engines were collected—emissions (CO₂, CO, NO_x), ambient temperature, and exhaust temperature. The tests were analyzed and compared, and the highest CO₂ reductions without increases in CO or NO_x were observed in Tests 3 and 4. Based on the detailed analysis of the values obtained from the four tests, the system was efficient. The tests will continue on experimental engines from test stands, to develop a prototype filter for primarily static applications with internal combustion engines: test stands for engines and generators, and, after homologation, directly on vehicles. The paper aims to partially solve an important problem—reducing the level of CO₂ from the exhaust gases. The presented solution may have applicability in the automotive industry but is also feasible for static applications. Another objective is to reduce emissions from older vehicles, which are widespread in certain regions of Europe and worldwide. Full article

This paper presents a kind of dual-channel coupled radial magnetic field resolver (DCCRMFR). The exciting winding and signal winding of this resolver adopt the structure of orthogonal phase. The number of turns and distribution of the four phase signal winding have been designed. The rotor has a double-wave magnetic conductive material structure. The variable reluctance mechanism between the stator and the rotor is derived by analytical method, and the feasibility of changing the coupling area for variable reluctance is obtained. The inductance of DCCRMFR was theoretically derived through the winding function method and combined with the finite element simulation method to obtain the inductance variation law and verify the correctness of the resolver design. Then simulation analysis was conducted on the output signal of DCCRMFR to extract the total harmonic distortion (THD) of the envelope of the electromotive force (EMF) output from the signal winding. Taking THD as the optimization objective, the optimized DCCRMFR simulation model is obtained by analyzing the air-gap length between the stator and the rotor and the thickness ratio of rotor. Finally, experimental measurements were conducted on a prototype model of a two pole pairs DCCRMFR, and the measurement results were compared and analyzed with simulation results to verify the correctness of the structural design and optimization of this DCCRMFR. Full article

Vehicle repeated passes over soft terrain alter the soil’s bearing and shear behavior, thereby affecting vehicle mobility and energy consumption. To address this issue, this study conducted cyclic compression and shear tests on beach sand with moisture contents of 5%, 15%, and 25%. A constitutive model incorporating the coupling effects of loading cycles (N) and moisture content (ω) was developed based on the Bekker and Janosi model framework. The model expresses compression parameters as functions of N and ω, and describes shear behavior through the strength evolution function k(N,ω) and deformation modulus function h(N,ω). Results show excellent agreement between the model predictions and experimental data (R² > 0.92). Furthermore, a vehicle–soil coupled dynamics model was established based on the proposed constitutive model, forming a comprehensive analytical framework that integrates soil meso-mechanics with full vehicle–terrain interaction. This work provides valuable theoretical and technical support for predicting vehicle trafficability on coastal soft soils and optimizing vehicle suspension systems. Full article

Object detection for autonomous vehicles under adverse weather conditions—such as rain, fog, snow, and low light—remains a significant challenge due to severe visual distortions that degrade image quality and obscure critical features. This paper presents YOLOv11-TWCS, an enhanced object detection model that integrates TransWeather, the Convolutional Block Attention Module (CBAM), and Spatial-Channel Decoupled Downsampling (SCDown) to improve feature extraction and emphasize critical features in weather-degraded scenes while maintaining real-time performance. Our approach addresses the dual challenges of weather-induced feature degradation and computational efficiency by combining adaptive attention mechanisms with optimized network architecture. Evaluations on DAWN, KITTI, and Udacity datasets show improved accuracy over baseline YOLOv11 and competitive performance against other state-of-the-art methods, achieving mAP@0.5 of 59.1%, 81.9%, and 88.5%, respectively. The model reduces parameters and GFLOPs by approximately 19–21% while sustaining high inference speed (105 FPS), making it suitable for real-time autonomous driving in challenging weather conditions. Full article

Improving the ride comfort of commercial vehicles is crucial for driver health and operational safety. This study focuses on optimizing the parameters of a cab suspension system to improve its vibration isolation performance. Initially, nonlinear fitting was applied to experimental data characterizing air spring stiffness and damping, which informed the development of a multi-body rigid-flexible coupled dynamic model of the suspension system; its dynamic characteristics were subsequently validated through modal analysis. Road excitation data, filtered through the chassis suspension, were collected during vehicle testing, and displacement excitations for ride comfort simulation were reconstructed using virtual iteration technology. Thereafter, an integrated ISIGHT platform, combining ADAMS and MATLAB, was employed to systematically optimize suspension parameters and key bushing stiffness via a multi-island genetic algorithm. The optimization results demonstrated significant performance improvements: on General roads, the overall weighted root-mean-square acceleration was markedly reduced with enhanced isolation efficiency; on Belgian pave roads, resonance in the cab’s X-axis direction was effectively suppressed; and on Cobblestone roads, the pitch angle was successfully constrained within the design limit. This research provides an effective parameter matching methodology for performance optimization of cab suspension systems. Full article

Direct injection in hydrogen engines enables flexible combustion control, improves engine efficiency, and reduces the risk of abnormal combustion. However, implementing this injection strategy is challenging due to the need to provide a relatively high volumetric fuel flow rate, achieve a specified degree of mixture stratification, and account for the functional and technological limitations of the injection system. These challenges highlight the relevance and objectives of the present study. The mathematical model of a turbocharged engine cycle has been refined to account for the influence of injection parameters on combustion kinetics. On the basis of mathematical modeling, the injection pressure and injector area were determined to ensure the specified injection conditions. For the late injection strategy, a method was proposed to select the start of injection based on a specified value of the “relative ignition timing” criterion. Engine operation was simulated across the full range of operating modes for both early and late injection strategies. The results show that the late injection strategy increases the maximum indicated thermal efficiency by approximately 2%, reduces peak in-cylinder pressure by about 1 MPa, lowers maximum nitrogen oxide emissions by a factor of 1.4, and ensures knock-free operation across all modes compared to early injection. Full article

To address the issue of insufficient trajectory tracking accuracy and the stability of distributed drive mining dump trucks under complex working conditions, this paper proposes a model predictive control (MPC) strategy based on genetic-particle swarm optimization (GAPSO). This strategy overcomes the limitations of traditional MPC controllers—where the weight matrix is fixed—by constructing a hierarchical optimization architecture that enables adaptive weight adjustment. An MPC-based trajectory tracking controller is developed using a three-degree-of-freedom vehicle dynamics model. Furthermore, to address the challenge of tuning MPC weight parameters, a GAPSO-based fusion optimization algorithm is introduced. This algorithm integrates the global search capability of genetic algorithms with the local convergence advantages of particle swarm optimization, enabling joint optimization of the state and control weight matrices. Simulation results demonstrate that under complex scenarios such as double lane change maneuvers, varying vehicle speeds, and different road adhesion coefficients, the proposed GAPSO-MPC controller significantly outperforms conventional MPC and PSO-MPC approaches in terms of lateral position tracking root mean square error. The method effectively enhances the robustness of trajectory tracking for distributed drive mining vehicles under disturbance conditions, offering a viable technical solution for high-precision control in autonomous mining systems. Full article

A device under test, when applied to the test rig, often does not come with much information about its mechanical properties to the user. There are different applications in which specific properties of the device under test are of interest to the user. Therefore, a suitable model approach and a parameterisation method are required. If there is a torsional model of the plant, including the device under test and the load machines, it can, for example, be used in a model predictive control architecture. The focus of the publication is on the frequency range of driveability ($f<$ 30 Hz) and, in particular, on the phenomenon of the vehicle shuffle mode, which is important for driving comfort. The model approach has to map these characteristics. To make this possible, the publication presents a suitable, simplified modelling approach for electric powertrains in the hardware-in-the-loop environment and the possibility of indirect parameterisation for the moment of inertia and stiffness. The investigations demonstrate that the model possesses the essential eigenmodes and frequencies observed in the measurements on the test rig. Taking into account extensions, the model enables the incorporation of the properties of an open differential, including delta speeds. The natural frequency matches the measured one with deviations less than 1%. The results also show that the parameters are smaller than assumed. The authors will revise the developed method on this basis to achieve higher information value and a better confidence interval. This further work will discuss the influence of the confidence interval on the resulting parameters. Full article

High-altitude long-endurance (HALE) UAVs require navigation payloads that are both fully autonomous and lightweight. This paper presents a full-parameter calibration method for a dual-axis rotational-modulation RINS/CNS integrated system in which the IMU is mounted on a two-axis indexing mechanism and the reconnaissance camera is reused as the star sensor. We establish a unified error propagation model that simultaneously covers IMU device errors (bias, scale, cross-axis/installation), gimbal non-orthogonality and encoder angle errors, and camera exterior/interior parameters (EOPs/IOPs), including Brown–Conrady distortion. Building on this model, we design an error-decoupled calibration path that exploits (i) odd/even symmetry under inner-axis scans, (ii) basis switching via outer-axis waypoints, and (iii) frequency tagging through rate-limited triangular motions. A piecewise-constant system (PWCS)/SVD analysis quantifies segment-wise observability and guides trajectory tuning. Simulation and hardware-in-the-loop results show that all parameter groups converge primarily within the segments that excite them; the final relative errors are typically ≤5% in simulation and 6–$16\%$ with real IMU/gimbal data and catalog-based star pixels. Full article

The rapid development of China’s high-speed railways (HSRs) and the implementation of revenue management policies have promoted the marketization of railway passenger transport, which is mainly reflected in the gradual transformation from a seller’s market dominated by operating companies to a buyer’s market dominated by passenger demand. Passenger travel needs are becoming increasingly diverse. In order to improve the quality of HSR services and attract more passengers, this paper starts from passenger satisfaction and considers the heterogeneity of travel preferences of passengers with different travel distances. Based on the passenger travel data of the Nanning-Guangzhou (NG) HSR line, the K-means clustering method is used to classify passengers into three categories: short-distance, medium-distance, and long-distance travel. A structural equation modeling–multinomial logit (SEM-MNL) model integrating both explicit and latent variables was constructed to analyze passenger travel origin-destination (OD) choices. Stata software was used to estimate passenger preferences for perceived satisfaction functions across different travel distances. Finally, considering constraints such as load factor, departure capacity, and spatiotemporal passenger flow demand, a line planning optimization model was constructed with the goal of minimizing train operating costs and maximizing passenger travel satisfaction. An improved subtraction optimizer algorithm was designed for the solution. Using the NG Line as a case study, the proposed method achieved a reduction in train operating costs while enhancing overall passenger satisfaction. Full article

This paper proposes an integrated numerical–experimental methodology for the durability assessment and optimisation of a passenger-car rear axle. A dedicated rear-suspension durability test bench was designed to impose a controlled cyclic vertical excitation on a dependent axle, reproducing service-like translational and rotational amplitudes of the beam and stabiliser bar. A detailed flexible multibody model of the bench–axle system was developed in MSC ADAMS 2023 and used to tune the kinematic excitation and determine an equivalent design load at the wheel spindles, consistent with the stiffness of the suspension assembly. Experimental strain measurements at nine locations on the axle, acquired with strain-gauge instrumentation on the bench, were converted into stresses and used to validate an explicit dynamic finite element model in ANSYS. The FE predictions agree with the experiments within about 10% at the beam mid-span and correctly identify a critical region at the junction between the side plate and the arm, where peak von Mises stresses of about 104 MPa occur. The validated model then supports a response-surface-based optimisation of the safety-critical wheel spindle, yielding an optimised geometry in which spindle-fillet stresses remain around 180–185 MPa under a severe loading case corresponding to the maximum admissible wheel load at the bearings, while the associated increase in mass is modest and compatible with practical design constraints. Full article

Autonomous vehicles (AVs) are expected to operate safely and efficiently in complex urban environments characterized by dynamic and uncertain elements such as pedestrians, cyclists and adverse weather. Although current neural network-based decision-making algorithms, fuzzy logic and reinforcement learning have shown promise, they often struggle to handle ambiguous situations, such as partially hidden road signs or unpredictable human behavior. This paper proposes a new hybrid decision-making framework combining multi-agent reinforcement learning (MARL) and explainable artificial intelligence (XAI) to improve robustness, adaptability and transparency. Each agent of the MARL architecture is specialized in a specific sub-task (e.g., obstacle avoidance, trajectory planning, intention prediction), enabling modular and cooperative learning. XAI techniques are integrated to provide interpretable rationales for decisions, facilitating human understanding and regulatory compliance. The proposed system will be validated using CARLA simulator, combined with reference data, to demonstrate improved performance in safety-critical and ambiguous driving scenarios. Full article

This article presents the results of simulation-based research aimed at assessing the impact of tyre damage on vehicle travel safety. The analysis takes into account various influencing factors, including vehicle speed, load conditions, and road surface condition (dry or wet asphalt). Particular emphasis was placed on the dynamic analysis of the vehicle during collision scenarios, including post-impact vehicle positioning, changes in kinetic energy, and the magnitude of the generated impact force. Simulation results indicate that tyre damage significantly compromises vehicle trajectory stability and, in certain cases, makes vehicle control impossible. The conclusions highlight the critical importance of maintaining proper tyre condition in mitigating the consequences of road collisions and emphasise the need for regular tyre inspections as part of routine vehicle maintenance. Full article

This paper proposes the integration of the phase-field method (PFM) with deep neural networks (DNNs) for UAV navigation in smart city environments. Using the proposed approach, simulations of an intelligent navigation and obstacle avoidance framework for drones in complex urban environments have been presented. Within the unified PFM-DNN model, phase-field modeling provides a continuous spatial representation, allowing for highly accurate characterization of boundaries between free space and obstacles. In parallel, the deep neural network component offers semantic perception and intelligent classification of environmental features. The proposed model was tested using the 3DCity dataset, which comprises 50,000 urban scenes under diverse environmental conditions, including fog, low light, and motion blur. The results demonstrated that the proposed system achieves high performance in classification and segmentation tasks, outperforming modern models such as DeepLabV3+, Mask R-CNN, and HRNet, while exhibiting high robustness to sensor noise and partial obstructions. The framework was evaluated within a simulated environment, and no real-world UAV drone tests were performed. This framework proves its effectiveness as a promising solution for intelligent drone navigation in future cities thanks to its ability to adapt and respond in dynamic environments. Full article

Highway traffic incident management is a dynamic and time-dependent process, and rapidly and accurately predicting its complete sequence of actions and corresponding time schedule is essential for improving the refinement and intelligence of traffic control systems. To address the limitations of existing studies that predominantly focus on predicting the total duration while lacking fine-grained modeling of the response procedure, this study proposed a multi-task sequence-to-sequence (Seq2Seq) framework based on a BERT encoder and Transformer decoder to jointly predict incident response steps and their associated time offsets. The model first leveraged a pretrained BERT to encode the incident type and alarm description text, followed by an autoregressive Transformer decoder that generated a sequence of response actions. An action-aware temporal prediction module was incorporated to predict the time offset of each step in parallel, and an adaptive weighted multitask loss was adopted to optimize both action classification and time regression tasks. Experiments based on 4128 real records of highway incident handling in Yunnan Province demonstrated that the proposed model achieved improved performance in duration prediction, outperforming baseline approaches in RMSE (18.05), MAE (14.69), MAPE (37.13%), MedAE (13.23), and SMAPE (33.55%). In addition, the model attained BLEU-4 and ROUGE-L scores of 62.33% and 82.04% in procedure text generation, which confirmed its capability to effectively learn procedural logic and temporal patterns from textual data and offered an interpretable decision-support approach for traffic incident duration prediction. The findings of this study could further support intelligent traffic management systems by enhancing incident response planning, real-time control strategies, and resource allocation for expressway operations. Full article

Road anomaly detection is essential for intelligent transportation systems and road maintenance. This work presents a MATLAB-native hybrid Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN–BiLSTM) framework for context-aware road event detection using multiaxial acceleration and vibration signals. The proposed architecture integrates short-term feature extraction via one-dimensional convolutional layers with bidirectional LSTM-based temporal modeling, enabling simultaneous capture of instantaneous signal morphology and long-range dependencies across driving trajectories. Multiaxial data were acquired at 50 Hz using an AQ-1 On-Board Diagnostics II (OBDII) Data Logger during urban and suburban routes in San Andrés Cholula, Puebla, Mexico. Our hybrid CNN–BiLSTM model achieved a global accuracy of 95.91% and a macro F1-score of 0.959. Per-class F1-scores ranged from 0.932 (none) to 0.981 (pothole), with specificity values above 0.98 for all event categories. Qualitative analysis demonstrates that this architecture outperforms previous CNN-only vibration-based models by approximately 2–3% in macro F1-score while maintaining balanced precision and recall across all event types. Visualization of BiLSTM activations highlights enhanced interpretability and contextual discrimination, particularly for events with similar short-term signatures. Further, the proposed framework’s low computational overhead and compatibility with MATLAB Graphics Processing Unit (GPU) Coder support its feasibility for real-time embedded deployment. These results demonstrate the effectiveness and robustness of our hybrid CNN–BiLSTM approach for road anomaly detection using only acceleration and vibration signals, establishing a validated continuation of previous CNN-based research. Beyond the experimental validation, the proposed framework provides a practical foundation for real-time pavement monitoring systems and can support intelligent transportation applications such as preventive road maintenance, driver assistance, and large-scale deployment on low-power embedded platforms. Full article

The study investigates the combined effects of material selection, manufacturing constraints, and a dynamic stress constraint function on the resulting material distribution achieved through a structural optimization process, while ensuring full compliance with the relevant European assessment standards for railway bogie. A high-fidelity finite element model of the complete bogie system was developed to accurately reproduce the operational loads and the structural interactions between the motor support and its surrounding components. The proposed methodology integrates topology optimization within a manufacturability-oriented framework, enabling a systematic evaluation of the influence of material properties, draw direction, and minimum feature size on the optimized configuration. In this context, an adaptive stress coefficient, derived from the performance of the original component, was introduced and proved effective in improving both the material distribution and the resulting stress levels of the optimized design. The results demonstrate that the combined consideration of material selection, manufacturing constraints, and adaptive stress control leads to a structurally efficient and production-feasible design. Three different materials were tested, showing consistent stress distributions and mass savings across all cases. The innovative optimized configuration achieved over 16% mass reduction while maintaining admissible stress levels. The proposed approach provides a generalizable and standard-compliant framework for future applications of topology optimization in railway engineering. Full article

The one-step hybrid forming process is a novel process to fabricate a metal fiber-reinforced plastic (FRP) structure with reduced cycle time and cost compared to classical multi-step methods. It is realized by a combined forming tool for both sheet metal and FRP forming to create a hybrid part in only one step. During the forming process, sheet metal pre-coated with an adhesion promoter is joined with the FRP simultaneously. In this work, the crashworthiness and lightweight potential of a hybrid crash management system manufactured with a hybrid forming process were investigated. It includes the experimental behaviors and finite element analysis of glass mat thermoplastics (GMT), as well as aluminum–GMT hybrid structures, under dynamic axial crushing loadings. Beginning with the original geometry of a series aluminum crash management system, the design was optimized for a hybrid forming process, where an aluminum sheet metal part is reinforced by a GMT structure with a ground layer and additional ribs. The forming behavior and fiber filling of the GMT crash box were determined and analyzed as well. Finite element method optimization was used to obtain the optimal geometry of the hybrid crash box with the highest possible specific energy absorption and the utmost homogeneous force level over displacement. A hybrid bumper beam was also developed, along with other necessary connection parts, to join the beam with the crash box and the entire crash management system (CMS) to the vehicle body. The joining technique was determined to be a key factor restricting the lightweight potential of the hybrid CMS. Full article

The electric vehicle (EV) battery supply chain plays a critical role in promoting sustainable transportation and tackling scarce resources, environmental costs, and supply chain vulnerabilities. The current study aims to conduct an extensive literature review of the EV battery supply chain given its importance for developing sustainable and efficient EVs. Using keyword co-occurrence and article co-citation analyses, this study analyses more than 681 publications from 2005 to 2024 and sourced from the Scopus database. Findings show that the number of articles increased considerably after 2020, which can be attributed to the global focus on decarbonization, electromobility, and circular economy practices. The review identifies important themes such as sustainability challenges, critical materials management, reverse logistics, and policy-driven frameworks for closed-loop supply chains. The findings from this study highlight a multidimensional approach where the integration of technologies, innovative policies, and collaborative actions can contribute to the resilience and sustainability of EV battery supply chains. It offers practical insights for stakeholders, strategic directions to maximize EV battery lifecycle management, and outlines the pathways to reach carbon neutrality in the transportation sector. By identifying the intellectual structure of this emerging field, the study contributes to academic discourse and informs the formulation of practical strategies to advance sustainable mobility. Full article