Vehicles

The testing and pilot operations of autonomous vehicles are currently booming in terms of real-world operations. Although the validation and verification methods are not standardized, nor is the legislation, as well as the methodology of data collection on autonomous vehicles’ performance and safety. The safety of autonomous vehicles can be inferred from the collision and disengagement reports provided by manufacturers and operators. This report documents instances when a human driver or operator took control of an autonomous vehicle during testing in detail. Disengagement reports are primarily aimed at safety and performance evaluation of autonomous vehicles, but can they be the basis for determining the readiness of autonomous driving technology and technological progress? This study analyzes disengagement reports to assess their utility in determining autonomous vehicles’ progress and readiness. Our findings indicate a declining trend in reported disengagements, despite increased operational distances, suggesting possible improvements in autonomous vehicle technology. However, disparities in data collection, varying operational design domains, and inconsistent reporting practices among manufacturers limit direct comparability. These factors challenge the reliability of disengagement reports as a definitive measure of technological evolution. The study highlights the need for more standardized and transparent reporting to better assess autonomous vehicle safety and development trends. Full article

The automotive sector plays an essential role in the Romanian economy, making a significant contribution to industrial production and employment. This study conducts a comprehensive bibliometric analysis of scholarly publishing in the Romanian automotive sector. By analyzing publication trends, citation patterns, and collaboration networks, the study maps the evolution of research in this field and highlights key contributions and future directions. The findings reveal a significant increase in research output over the past two decades, with a focus on emerging fields such as artificial intelligence, electric and autonomous vehicles, and sustainable mobility solutions. The analysis also identifies leading researchers and institutions and explores collaboration networks between Romanian and international actors. These insights provide valuable benchmarks for assessing Romania’s position in the global automotive research arena and inform strategies for future research efforts. Full article

Electrification and modularity are emerging as key trends in off-road vehicle development, prompting the need for innovative solutions in steering and modular coupling. This study presents an electromechanical connection and steering joint, conceived to replace traditional hydraulic systems and offer enhanced steering precision, modular adaptability, and system efficiency. By eliminating hydraulic components, the design reduces fluid leakage risks, lowers maintenance requirements, and improves energy integration with the vehicle’s electric drivetrain. The joint enables independent module articulation, including steering and controlled tilting, to optimize vehicle stability across diverse terrains. A prototype was built and tested under real-world conditions, assessing functional reliability, ease of integration, and operational performance. The findings demonstrate that electromechanical steering substantially boosts system flexibility compared to conventional hydraulic setups. Full article

Tires are important for the transmission of forces, good traction of the vehicle, and safety of the passengers. Tires also influence vehicle fuel consumption and cause tire and road wear pollution to the environment in the form of microplastics. In the United States, the Uniform Tire Quality Grading (UTQG) for tread wear is reported on the tire sidewall and is used as an indicator of the expected service life of a tire. In Europe, a similar approach that applies tread depth reduction measurements and projection to the minimum tread depth is under discussion. Tread depth measurements will be carried out in parallel with abrasion measurements over the recently introduced abrasion rate test in the United Nations regulation 117. Testing is carried out with an on-road convoy method accompanied by a vehicle fitted with reference tires to minimize the influence of external parameters. In this brief review, we start with a short historical overview of the methods that have been applied so far for the measurement of tire service life. Based on the limited publicly available data, we calculate the average tread depth reduction per distance driven for summer and winter tires fitted both in the front and rear axles of passenger cars (1–1.2 mm for front wheels and 0.5–0.6 mm for rear wheels per 10,000 km). We theoretically estimate the tread mass loss per mm of tread depth reduction (250 g per 1 mm tread depth reduction, depending on the tire size) and we compare the values to experimental data obtained in recent campaigns. We give estimations of the tire service life as a function of the tread wear UTQG (100 times the indicated tread wear rating). We also discuss the projected service life using tread depth reduction and mass loss. Full article

The collision avoidance capability of autonomous vehicles in extreme traffic conditions remains a focal point of research. This paper introduces an Adaptive Cruise Control (ACC) strategy based on Model Predictive Control (MPC) and Responsibility-Sensitive Safety (RSS) models. Simulations were conducted in the CARLA environment, where the lead vehicle underwent various rapid deceleration scenarios to optimize the following vehicle’s braking strategy. By integrating the multi-step predictive optimization capabilities of MPC with the dynamic safety assessment mechanisms of RSS, the proposed strategy ensures safe following distances while achieving rapid and precise speed adjustments, thereby enhancing the system’s responsiveness and safety. The model also incorporates a secondary optimization to balance comfort and stability, thereby improving the overall performance of autonomous vehicles. The use of multi-dimensional assessment metrics, such as Time to Collision (TTC), Time Exposed TTC (TET), and Time Integrated TTC (TIT), addresses the limitations of using TTC alone, which only reflects instantaneous collision risk. The optimization of the model in this paper aims to improve the safety and comfort of the following vehicle in scenarios with various gap distances, and it has been validated through the SSM multi-indicator approach. Experimental results demonstrate that the improved ACC model significantly enhances vehicle safety and comfort in scenarios involving large gaps and short-distance emergency braking by the lead vehicle, validating the method’s effectiveness in various extreme traffic scenarios. Full article

This article deals with the technical and official possibilities of changing the official data on vehicle fuel consumption in the Slovak Republic. This case study analyzes various methods of measuring fuel consumption, including the use of a fuel flowmeter, OBD devices and calculation based on emission tests. The tests took place in laboratory conditions using the roller dynamometer on the Kia Ceed mildhybrid vehicle. Based on the Real Drive Emission requirements, five 1.5 h cycles were repeated in urban, suburban and highway conditions. Using multi-criteria analysis, the methods used to measure fuel consumption are evaluated from the point of view of efficiency, accuracy, and economy. This study contains a real view of the performance of these exams in the conditions of the Slovak Republic. The fuel consumption measured by the OBD device compared to the volumetric flowmeters was at a relative difference of −4.94%. The fuel consumption calculated through exhaust gas emissions was +2.83% compared to the volumetric flowmeters. Full article

Degraded shock absorbers have a negative effect on the safety critical driving dynamics of passenger cars. Oil and gas loss due to leaks at the shock absorber seals are the most common degradation mechanisms of vehicle shock absorbers. This paper presents degraded twin-tube shock absorber measurement results. Eight different twin-tube shock absorbers of four passenger cars are modified and measured for this purpose. Based on this analysis, a semi-physical phenomenological model is defined which can represent the properties of a twin-tube shock absorber in the event of oil and gas loss. The model is parameterized based on quasi-static and dynamic harmonic measurements and is validated using harmonic and stochastic signals. The data analysis and a simulation study show that an oil loss of just 10% can reduce the damping work performed by the shock absorber to 50% compared to an intact shock absorber. Similarly, an oil loss of 50% can lead to a reduction in the shock absorber work to zero. Oil foaming and cavitation must be taken into account when modeling the shock absorber characteristics in the event of oil and gas loss. It can be summarized that particularly long-lasting excitations at high shock absorber velocities, such as those that occur when driving on uneven roads, lead to a significant loss of damping work. This in turn leads to increased wheel load fluctuations and lower transmittable horizontal tire forces and unsteady driving behavior. Full article

This paper analyses the impact of a front brake light (FBL) on road safety from a pedestrian perspective. In addition to the traditional brake lights mounted at the rear of vehicles, an FBL can provide extra information about the driver’s intention to stop, especially to road users looking at the front of the approaching vehicle. This innovative feature aims to improve road safety by providing additional visual cues, where rear brake lights are not visible. Because pedestrians usually have a better line of sight to the front of a vehicle, the front brake light is more effective in alerting them to an impending stop. Therefore, an FBL could help them feel more confident when crossing the road by helping determine if it is safe to do so. A total of 621 questionnaires were collected from pedestrians who participated in the first real field test of FBL. The test period was conducted from November 2022 to September 2023 in two neighbouring regions of Slovakia. Their feedback allowed us to assess how the presence of an FBL influenced their perception of road safety, particularly when crossing roads. As a statistical result, more than 81% of the participants felt safer when crossing the road due to the presence of an FBL. Notably, the older generation evaluated FBLs very positively, while the youngest generation demonstrated more dangerous behaviour. Furthermore, the survey revealed that a significant proportion of respondents maintained a more reserved attitude towards the benefits of FBLs, largely due to a lack of information. Full article

Design for Additive Manufacturing (DfAM) encompasses two primary strategies: adapting traditional designs for 3D printing and developing designs specifically optimized for additive manufacturing. The latter emphasizes consolidating assemblies and reducing weight, leveraging complex geometries and negative space through advanced techniques such as generative design and topology optimization. Critical considerations in the design phase include printing methods, material selection, support structures, and post-processing requirements. DfAM offers significant advantages over conventional subtractive manufacturing, including enhanced complexity, customization, and optimization, with transformative applications in aerospace, medical devices, and automotive industries. This review focuses on the automotive sector, systematically examining DfAM’s potential to redefine vehicle design, production processes, and industry standards. By conducting a comprehensive analysis of the existing literature and case studies, this research identifies gaps in the integration of additive manufacturing into broader manufacturing frameworks. The study contributes to the literature by providing insights into how 3D printing is currently reshaping automotive production by offering a forward-looking perspective on its future implications for the industry. Full article

This paper introduces a novel strategy for an intelligent plug-in hybrid electric vehicle (PHEV) energy optimization strategy based on machine learning (ML) prediction of the upcoming journey, without recourse to navigation or other external data, which underpins many of the existing approaches. This study, based on extended real-world data (journeys history from 10 vehicles over 12 months), shows that trip patterns can be learnt quite effectively using classic ML classification algorithms. In particular, the RusBoosted ensemble classifier performed consistently well across the heterogeneous dataset (volume of data for training and variable imbalance in the datasets, reflecting the natural variability in the vehicle usage profiles), providing sufficiently accurate predictions for the proposed EMS strategy. Performance evaluation experiments were carried out using a model-in-the-loop (MIL) simulation set-up developed in this research. The results demonstrated that the proposed strategy has the potential to deliver significant reductions in engine running time (up to 76% on routine short journeys), with associated benefits in CO₂ consumption and tailpipe emissions, as well as enhanced engine reliability. The broader importance of this study is that it demonstrates the great potential of using predictive insights from computation-efficient and robust ML to learn vehicle usage patterns to optimize the control strategies without reliance on uncertain external inputs. Full article

The current product and assembly processes of system development in the vehicle industry are characterised by a multitude of different model formats, a relatively low level of data integration, and an unsatisfactory management of information. This article presents an integrated design and assembly planning process which applies several model-to-model (M2M) transformations in order to ensure a seamless transition from product requirements to an assembly system layout and design. The digital process employs a framework based on graph-based design languages (GBDLs) and achieves an integration in a model-based systems engineering (MBSE) industrial context. The underlying hypothesis that this seamless transition is possible is tested on the basis of the product and assembly system development of a sports vehicle. In this article, a skateboard is used for detailing and explaining the different modelling perspectives throughout the engineering and assembly process of this product. Due to a conscious application of GBDLs in an MBSE framework, it is possible to achieve a continuous sequence of M2M transformations which guarantees a maximum level of information integrity. These two aspects are cornerstones for a future integrated design automation of a product and its assembly system. It is important to note that the presented approach is universal and can be used in the production of components for the automotive industry, entire vehicles, and their assembly. Full article

Lithium-ion batteries (LIBs) play a critical role in electric vehicles (EVs) and hybrid electric vehicles (HEVs) and degradation of LIBs influences lifetime, reliability, safety and dependability. The ability to assess and quantify degradation enables assessment of LIB’s true state of health. This paper investigates LIB degradation using a pseudo two-dimensional (P2D) model, particularly focusing on the changes to Electrochemical Impedance spectroscopy (EIS) results due to degradation. Three key degradation mechanism are considered and the impact of State-of-Charge (SoC) and temperature on EIS results are discussed. This paper also identifies the need for a more realistic approach to assess degradation. Simulations are conducted considering four repetitive standard drive cycles (viz., HTDDT, HWFET, US06 and OCTBC) for a vehicle travel distance of 150,000 km for each case. The cycle counting method is used to convert partial SoC variations during a drive cycle to an equivalent full cycle count which is then used within the degradation model to modify the parameters to represent the P2D model. This study demonstrates a robust process for analyzing degradation dynamics. The methodology presented here can guide future researchers with experimental data, enabling validation and refinement of model parameters to advance LIB degradation analysis and improve battery life predictions under operational scenarios. Full article

Driving at night is riskier in terms of crash involvement than it is during the day. Fortunately, it is clearly established that illumination on roadways can reduce the number and severity of nighttime crashes. However, state and municipal departments of transportation (DOTs) lack the available illumination data. Therefore, the objective of this research is threefold, as follows: (i) to develop machine learning models that use readily available roadway characteristic data to predict the severity of nighttime crashes; (ii) determine the effect that illumination has on crash severity; and (iii) develop a tool to assist DOT decision makers in collecting illumination data. To accomplish this objective, we have extracted data from the Texas Department of Transportation (TxDOT) Crash Record Information System (CRIS) database, which was then further split into a training and a test dataset. Then, seven machine learning techniques, namely binary logistic regression, k-nearest neighbors, naïve Bayes, random forest, artificial neural network, Extreme Gradient Boosting (XGBoost), and a Long Short-Term Memory (LSTM) model, were all applied to the unseen test data. The random forest model produced the most promising results by predicting severe crashes with 97.6% accuracy. In addition, we conducted a pilot study to test the collection of illumination data using a light meter. In the future, we aim to complete the development of a smartphone application, which can be used in conjunction with the random forest model presented in this paper, to collect crowdsourced illumination data and predict nighttime crash hotspots. This may assist DOT decision makers to prioritize funding for illumination at the hot spots. Full article

The electrified environments encountered in electric vehicles (EVs) in terms of parasitic currents present significant challenges for the performance of EV bearings and their lubricants. This study investigates the effectiveness of various concentrations (0.1 wt.%, 0.2 wt.%, 0.3 wt.%, and 0.4 wt.%) of multi-walled carbon nanotubes (MWCNT) and alumina (Al₂O₃) as two different nanoparticles incorporated into lithium grease, specifically focusing on their ability to mitigate the bearing surface damage caused by varying magnitudes of bearing DC discharges. A specialized test rig was developed to evaluate the electrical discharge characteristics, vibration response, and extent of surface wear on bearings lubricated with both lithium grease without additives and when infused with each nano-additive. Microscopic examination was employed to qualitatively and quantitatively evaluate the surface degradation of each test bearing. The results of this study demonstrate that the addition of nano-additives into the lubricating grease of bearings subjected to electrical loads resulted in a reduction in electric discharge voltage thresholds and levels. This reflected on the mitigation of surface damage in terms of surface roughness and vibration amplitudes by up to 70.67% and 65.19% in the case of MWCNTs. In contrast, alumina nanoparticles yielded a reduction in vibration amplitude and surface wear by 44.89% and 37.5%, respectively. Full article

When a railway train runs along a curved track with braking, the dynamic behaviors of the vehicle are extremely complex and difficult to accurately reveal due to the coupling effects between the wheel–rail interactions and the disc–pad frictions. Therefore, a rigid–flexible coupled trailer car dynamics model of a railway train is established. In this model, the brake systems and vehicle system are dynamically coupled via the frictions within the braking interface, wheel–rail relationships and suspension systems. Furthermore, the effectiveness of the established model is validated by a comparison with the field test data. Based on this, the dynamic response characteristics of vehicle under curve and straight braking conditions are analyzed and compared, and the influence of the curve geometric parameters on vehicle vibration and operation safety is explored. The results show that braking on a curve track directly affects the vibration characteristics of the vehicle and reduces its operation safety. When the vehicle is braking on a curve track, the lateral vibration of the bogie frame significantly increases compared to the vehicle braking on a straight track, and the vibration intensifies as the curve radius decreases. When the curved track maintains equilibrium superelevation, the differences in primary suspension force, wheel–rail vertical force, and wheel axle lateral force between the inner and outer sides of the first and second wheelsets are relatively minor under both straight and curved braking conditions. Additionally, under these circumstances, the derailment coefficient is minimized. However, when the curve radius is 7000 m, with a superelevation of 40 mm, the maximum dynamic wheel load reduction rate of the inner wheel of the second wheelset is 0.54, which reaches 90% of the allowable limit value of 0.6 for the safety index, and impacts the vehicle running safety. Therefore, it is necessary to focus on the operation safety of railway trains when braking on curved tracks. Full article

Given the considerable length and weight of freight trains, their operation can be quite challenging. Improper operation may lead to train decoupling and derailment. Driver Advisory Systems (DASs) are used in some countries to assist train drivers by providing the speed curves, which are desired to be easy to track. Multi-mass train model is a good choice to depict the in-train forces in train speed curve generating, but its application is often hindered by the computation time. A single mass train model is considered as another choice to simplify the computation. To exploit the advantages of the multi-mass and single-mass models, this paper proposes a Two-step Optimization Method to generate the optimal speed curves for the freight trains. In the first step, the Rolling Optimization Algorithm (ROA) is proposed to optimize the speed curve on the basis of the single-mass model, taking the train energy consumption and punctuality as the optimization objectives. In order to assist the driver in operating the train smoothly, the speed curve generated by the ROA was tested on DAS, but it could not be followed accurately in the actual operation. To solve this problem, a Model Predictive Control (MPC) algorithm based on a multi-mass model is adopted as the second optimization step, which takes the output speed curve of the ROA as the reference speed curve. The MPC algorithm will generate a new speed curve, taking in-train forces, energy consumption and punctuality as the optimization indices. Simulations are carried out using the data from the Dalailong railway in China to evaluate the proposed method. The simulation results show that the speed curves generated by the Two-step Optimization Method are smoother than that of the ROA, and the throttle sequences are more conducive for the driver to follow in practical operation. The simulation results show that the energy consumption is reduced by 17.1% compared to that of the ROA simulation. The speed curve also can be integrated into the onboard DAS or the Automatic Train Operation (ATO) system, aiming to obtain a smooth and energy-efficient train operation. Full article

In the global context, transportation contributes 26% of the total CO₂ emissions, with land transport responsible for 92% of the emissions within the sector. Given this significant contribution to climate change, it is crucial to quantify vehicular impacts to implement effective mitigation strategies. This study introduces an innovative method for predicting fuel consumption and emissions of carbon monoxide, hydrocarbons, and nitrogen oxides in vehicles, based on instantaneous vehicle-specific power (VSP) and mean accumulated power. VSP is a parameter that measures a vehicle’s power in relation to its mass, providing an indicator of the efficiency with which the vehicle converts fuel into motion. This indicator is particularly useful for assessing how vehicles utilize their energy under different driving conditions and how this affects their fuel consumption and emissions. Using data collected from 10 vehicles over 2000 h and covering altitudes from 0 to 4000 m above sea level in Ecuador, the method not only improved the accuracy of consumption predictions, reducing the margin of error by up to 10% at high altitudes, but also provided a detailed understanding of how altitude affects both consumption and emissions. The precision of the new method was notable, with a standard deviation of only 0.25 L per 100 km, allowing for reliable estimates under various operational conditions. Interestingly, the study revealed an average increase in fuel consumption of 0.43 L per 1000 m of altitude gain, while CO₂ emissions showed a significant reduction from 260.93 g/km to 215.90 g/km when ascending from 500 m to 4000 m. These findings underscore the relevance of considering altitude in route planning, especially in mountainous terrains, to optimize performance and environmental sustainability. However, the study also indicated an increase in CO and NOx emissions with altitude, a challenge that highlights the need for integrated strategies addressing both fuel consumption and air quality. Collectively, the results emphasized the complex interplay between altitude, energy efficiency, and vehicular emissions, underscoring the importance of a holistic approach to transportation management, to minimize adverse environmental impacts and promote sustainability. Full article

Induction motor (IM) drives are considered one of the important technologies in modern industry. Several industrial applications, such as material handling and food and beverage applications, are driven and operated by modern AC drives. Moreover, modern electric transportation systems such as EVs and e-trucks are based on AC drives. Recently, high-efficiency IM drive systems have been studied as a major opportunity to reduce energy and fuel consumption. This article addresses the recent trends and advancement in high-efficiency IM drives during a particular period (2017–2024), including the development of high-efficiency motors, the utilization of efficient wide bandgap (WBG) semiconductor devices for inverter topology, and commonly used control strategies to achieve high-performance drives. Moreover, the article addresses several manufacturers of industrial IM drives and the corresponding adopted control techniques in their products. A comparison of these control techniques, including their pros and cons, has been conducted as well. Full article

Sliding Mode Control (SMC) has gained significant attention due to its simplicity, robustness, and rapid response in ensuring system stability, particularly with the Lyapunov approach. Despite its advantages, SMC faces challenges such as chattering near equilibrium, sensitivity to parameter variations, and delayed convergence. To address these issues, advanced techniques like Terminal Sliding Mode Control (TSMC) and Integral Terminal Sliding Mode Control (ITSMC) have been proposed. TSMC ensures finite-time convergence while mitigating chattering, while ITSMC further handles singularities and disturbances. Additionally, Adaptive Switching Control (ASC) based on Particle Swarm Optimization (PSO) is applied to achieve faster convergence, suppress chattering, and enhance system robustness. The adaptive control law, utilizing a Lyapunov-based approach, is employed to estimate and compensate for external disturbances, further improving system performance under uncertainties. Gain tuning, essential for optimizing system performance and reducing tracking errors, is achieved using the efficient Teaching–Learning-Based Optimization (TLBO) algorithm. This study applies TSMC, ITSMC, and ASC-based PSO to an Anti-Lock Braking System (ABS), aiming to enhance robustness, stability, and finite-time convergence while reducing chattering. Stability is analyzed through the Lyapunov theory, ensuring rigorous validation. MATLAB simulations demonstrate the effectiveness of the proposed methods in improving ABS performance, offering a valuable contribution to robust control techniques for systems operating under dynamic and uncertain conditions. Full article