Search Results (247)

If new technologies can increase production efficiency and reduce the consumption of natural resources, they can also bring new environmental risks. This dynamic is particularly relevant for the automotive industry, since it is one of the sectors that invests most in R&D, but at the same time also contributes a significant portion of greenhouse gas emissions and consumes a large amount of energy. This article aims to analyze the feasibility of meeting the environmental targets in place within 32 European countries in light of the recent technological trajectory of the automotive industry, namely with regard to the adoption of the propulsion model’s alternative to oil and diesel. Using data disaggregated by countries from 2000 up until 2020, in this paper, the estimated regressions aimed to not only verify whether electrical vehicles had a positive impact on CO₂ emissions found in the European market, but to also assess whether they will meet the target set for the next 30 years, with attention to the economy recovery after 2025 and a more robust EV market penetration in replacement of traditional fossil fuels cars. Full article

The hybridization of ADM-Type Rail Service Cars aims to enhance energy efficiency, environmental sustainability, and cost-effectiveness within Uzbekistan’s railway network. Diesel-powered service cars currently contribute to high fuel consumption, elevated emissions, and costly maintenance, necessitating a transition to hybrid technology. This study introduces an innovative “sequence of linear sets–torsion electric motor–wheel pairs” design, optimizing torque distribution and power efficiency for improved operational reliability. Through system modeling, performance simulations, and real-world field trials, the hybrid system demonstrates a 15% reduction in energy consumption, a 25% decrease in CO₂ emissions, and up to 30% lower maintenance costs compared to conventional diesel models. Additionally, the hybrid technology enhances operational flexibility, allowing seamless functionality on both electrified and non-electrified railway lines. From an economic perspective, retrofitting existing service cars instead of full fleet replacement provides a cost-effective alternative, offering an estimated 10-year return on investment (ROI) through fuel savings and reduced downtime. This initiative directly supports Uzbekistan’s Green Development Strategy and railway modernization plans while holding significant commercialization potential in Central Asia and other regions with aging railway infrastructure. By addressing technical scalability, regulatory compliance, and economic feasibility, this study proposes a practical and timely hybrid retrofit solution for sustainable railway operations, aligning current industry needs with long-term environmental and financial benefits. Full article

Due to their improved performance and advantages for the environment, fuel cell hybrid electric cars, or FCEVs, have garnered a lot of attention. Establishing an energy management strategy (EMS) for fuel cell electric vehicles (FCEVs) is essential for optimizing power distribution among various energy sources. This method addresses concerns regarding hydrogen utilization and efficiency. The Arithmetic Optimization Algorithm is employed in the proposed energy management system to enhance the strategy of maximizing external energy, leading to decreased hydrogen consumption and increased system efficiency. The performance of the proposed EMS is evaluated using the Federal Test Procedure (FTP-75) to replicate city driving situations and is compared with existing algorithms through a comparison co-simulation. The co-simulation findings indicate that the suggested EMS surpasses current approaches in reducing fuel consumption, potentially decreasing it by 59.28%. The proposed energy management strategy demonstrates an 8.43% improvement in system efficiency. This enhancement may reduce dependence on fossil fuels and mitigate the adverse environmental effects associated with automobile emissions. To assess the feasibility and effectiveness of the proposed EMS, the system is tested within a Processor-in-the-Loop (PIL) co-simulation environment using the C2000 launchxl-f28379d Digital Signal Processing (DSP) board. Full article

Traffic congestion is becoming an increasing problem due to the rapid growth of the population. In the current situation, the mode choice of the people has a direct impact on traffic density. For this reason, many studies have been carried out by researchers and planners to reduce the number of vehicles on the road. Various strategies have been proposed, such as incentives for public transport, parking restrictions, parking pricing and car sharing. It is very important that these strategies are implemented by the institutions in order to reduce traffic during the commuting hours, which coincide with the rush hour. Especially in areas such as shipyards and industrial zones, which are far from the city center and relatively difficult to reach but which provide employment opportunities for thousands of people, a shuttle service is one of the most preferred strategies to discourage employees from using private cars. However, in companies with thousands of employees, this situation generates costs that cannot be ignored. The examined case study similarly needs to optimize and reduce operational costs related to fuel consumption, maintenance and tax expenses by optimizing the number of two different types of service vehicles required for employee transportation at the Yalova Shipyard. For this aim, a dynamic linear programming (DLP) model was used to achieve a cost-effective, sustainable and demand-responsive shuttle service. According to the analysis results, it was concluded that the annual fuel cost of the vehicles will be reduced by 33.9%, the maintenance cost by 35.2% and the annual tax cost by 49.3% by disposing of the unneeded vehicles (27%) in the studied Yalova Shipyard. Taking all these positive improvements into account, it is clear that the optimization study significantly reduces the costs incurred by the service. Full article

There is an urgent need for a method of evaluating the real energy performance of vehicles that eliminates the effects of external conditions (topography, altitude, and road conditions) and human factors (driving styles), especially in the case of heavy-duty vehicles. Governmental authorities require results on the energy performance of vehicles to develop strategies that result in reductions in greenhouse gas emissions, while fleet managers require results regarding the energy efficiency of existing vehicle technologies to select the technologies that minimize energy consumption and, therefore, operational costs. Aiming to address this need, we propose a method for evaluating the global energy efficiency of road vehicles by monitoring at 1 Hz the operational variables of a vehicle under normal conditions of use for a long time. The variables monitored are engine RPM and vehicle location, speed, payload, and energy consumption. This method was verified using 49 vehicles, representing 23 vehicle technologies. These vehicles varied in size (light duty and heavy duty), application (cars, buses, and freight), energy sources (gasoline, diesel, and electric), and operational conditions (Chile, Ecuador, Colombia, and México). Testing was conducted across various altitudes (0–3600 masl) and topographies (flat and mountainous regions). The results showed that the energy efficiencies for gasoline-fueled light-duty vehicles ranged from 17 to 30%, those for diesel-fueled heavy-duty vehicles ranged from 25 to 42%, and those for electric heavy-duty vehicles (HDVs) ranged from 70 to 80%. Full article

Plug-in hybrid electric vehicles (PHEVs) typically employ batteries with relatively small capacities due to constraints on chassis space and vehicle cost. Consequently, under conditions such as acceleration and hill climbing, these vehicles often experience high-current battery discharges, which can significantly compromise the battery’s lifespan. To address this issue, this paper focuses on a plug-in hybrid passenger vehicle, introducing supercapacitors to form a hybrid energy storage system (HESS) in conjunction with the PHEV battery, and it proposes a dual-layer energy management strategy for PHEVs. First, a PHEV model is developed, and a rule-based energy management strategy is designed. By conducting simulation comparisons of the CLTC under three control rules with different thresholds, the strategy yielding the lowest fuel consumption is selected, and its battery discharge characteristics are analyzed. Subsequently, the required power parameters of the supercapacitor are calculated, and, taking chassis space constraints into account, the number and specifications of the supercapacitors are determined. Subsequently, a dual-layer energy distribution strategy for PHEVs equipped with supercapacitors is proposed. In the upper layer, an equivalent fuel consumption minimization method is applied to optimize the torque distribution between the engine and the motor, while the lower layer employs a rule-based strategy for power allocation between the battery and the supercapacitor. A proportional feedback factor is introduced for the real-time adjustment of the engine and motor torque distribution, and simulations under the CLTC are conducted to evaluate the PHEV’s torque distribution and fuel consumption. The results indicate that the dual-layer energy management strategy reduces the duration of high-current battery discharge in the supercapacitor-equipped PHEV by 73.61%, decreases the peak current by 30.76%, and lowers the overall vehicle fuel consumption by 5%. Unlike other studies, this paper analyzes and calculates the specifications, dimensions, and quantity of supercapacitors based on the available chassis space of the PHEV passenger car. The results demonstrate that the designed supercapacitor array effectively mitigates the high-current discharge of the PHEV battery, and the proposed dual-layer energy management strategy is capable of reducing the overall fuel consumption of the vehicle. 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 optimization of passenger car efficiency is an important contribution to GHG emissions mitigation. This global warming concern is pushing technological solutions to reduce vehicle fuel consumption and consequently CO₂ emissions. In this work, the impacts of engine lubricants with lower viscosity and friction modifier additive in a light-vehicle with a spark ignition engine were numerically simulated and experimentally validated. The substitution of a baseline 5W40 lubricant by a lower viscosity 5W20 proposal resulted in 2.9% lower fuel consumption in a combined cycle. This fuel consumption improvement is enhanced to 6.1% with a 0W16 lubricant with friction modifier. A 1D simulation model based on lubricant temperature and viscosity impact on engine friction was developed and presented good experimental correlation in combined cycle for 5W20, showing a 7% lower fuel consumption advantage than the experimental results. The numerical simulation advantage was 38% lower than experimental results for 0W16 that contains friction modifier, as the additive impact was not considered in this mathematical model. Full article

The rapid development of Connected and Autonomous Vehicles (CAVs) presents challenges in managing mixed traffic flows. Previous studies have primarily focused on mixed traffic flow involving CAVs and Human-Driven Vehicles (HDVs), or on the combination of trucks and cars. However, these studies have not fully addressed the heterogeneous mixed traffic flow consisting of CAVs and HDVs, including trucks and cars, influenced by varying human driving styles. Therefore, this study investigates the influences of the market penetration rate (MPR) of CAVs, truck proportion, and driving style on operational characteristics in heterogeneous mixed traffic flow. A total of 1105 events were extracted from highD dataset to analyze four car-following types: car-following-car (CC), car-following-truck (CT), truck-following-car (TC), and truck-following-truck (TT). Principal Component Analysis (PCA) and clustering techniques were employed to categorize distinct driving styles, while the Intelligent Driver Model (IDM) was calibrated to represent the various car-following behaviors. Subsequently, microscopic simulations were conducted using the Simulation of Urban Mobility (SUMO) platform to evaluate the impact of CAVs on sustainable traffic operations, including road capacity, stability, safety, traffic oscillations, fuel consumption, and emissions under various traffic conditions. The results demonstrate that CAVs can significantly enhance road capacity, improve emissions, and stabilize traffic flow at high MPRs. For instance, when the MPR increases from 40% to 80%, the road capacity improves by approximately 25%, while stability enhances by approximately 33%. In contrast, higher truck proportions lead to reduced capacity, increased emissions, and decreased traffic flow stability. In addition, an increased proportion of mild drivers reduces capacity, raises emissions per kilometer, and improves stability and safety. However, a high proportion of mild human drivers (e.g., 100% mild drivers) may negatively impact traffic safety when CAVs are present. This study provides valuable insights into evaluating heterogeneous traffic flows and supports the development of future traffic management strategies for more sustainable transportation systems. Full article

Traffic congestion and the search for parking spaces have become significant challenges in modern urban development. These issues lead to increased fuel consumption and contribute to stressful lifestyles. To address this, efficient parking allocation has emerged as a challenge in urban environments, requiring innovative solutions to optimize the use of available parking spaces. Various approaches have been employed to tackle this problem, including mathematical programming and heuristic procedures, which typically follow a centralized model. Another promising solution involves the use of multi-agent systems (MASs), which adopt a decentralized approach. However, there is limited research on the application of MASs in parking allocation. This paper presents a multi-agent system where intelligent agents represent vehicles and parking spaces to efficiently allocate parking spaces to cars. The paper evaluates four coordination mechanisms based on the Contract Net Protocol for parking allocation. The results show that the concurrent use of the Contract Net Protocol with decommitment capacities is the most effective approach, as it reduces the time required for parking space allocation while optimizing the utilization of available parking spaces. Full article

In 2023, the EU set the target to reduce greenhouse gas (GHG) emissions by 55% until 2030 compared to 1990. The European Transport Policy sees battery–electric vehicles as a key technology to decarbonize the transport sector, so governments support the adoption through dedicated funding programs. Battery–electric trucks hold great potential to decarbonize the transport sector, especially for high-impact, heavy-duty trucks. Theoretical life-cycle assessments (LCA) predict a lower CO₂e emission impact from battery–electric trucks compared to conventional diesel trucks. Yet, one concern repeatedly mentioned by potential users is the doubt about the ecological advantage of battery–electric vehicles. This is rooted in the problem of a much higher CO₂e impact of the lithium-ion batteries production process. As heavy-duty trucks have a much larger battery, the hypothec in the construction phase of the vehicle is significantly higher, which must be regained during the use phase. Although theoretical assessments exist, CO₂e evaluations using real-world application data are almost nonexistent, as the technology is at the very start of the adoption curve. Exemplary is the fact that there were only 72 registered battery–electric heavy-duty tractor trucks throughout the whole of Germany at the start of 2023. This paper aims to deliver one of the first real-world quantifications using operational data for the actual reduction impact of battery–electric heavy-duty trucks compared to diesel trucks. This study uses the methodology of the life-cycle assessment approach according to ISO 14040/14044 to gain a systematic and holistic technology comparison. For this LCA, the system boundaries are considered from cradle to cradle. This includes the production of raw materials and energy, the manufacturing of the trucks, the use phase, and the recycling afterward. The research objects of this study are battery–electric and diesel Volvo FM trucks, which have been in use by the German freight company Nord-Spedition GmbH since May 2023. The GREET^® database is used to assess the emission impact of the material production and manufacturing process. The Volvo tractor trucks resemble a critical case, as the vehicles have a battery size of 540 kWh—around 11 times larger than a usual passenger car. The operation data is directly provided by the logistics company to observe fuel/electricity consumption. Other factors are assessed through company interviews as well as a wide literature research. Finally, a large question mark concerning total emissions lies in the cradle-to-cradle capabilities of large-scale lithium-ion batteries and the electricity grid mix. Different scenarios are being considered to assess potential disposal or recycling paths as well as different electricity grid developments and their impact on the overall balance. The findings estimate the total emissions reduction potential to range between 34% and 69%, varying with assumptions on the electricity grid transition and recycling opportunities. This study displays one of the first successful early-stage integrations of battery–electric heavy-duty trucks into the daily operation of a freight company and can be used to showcase the ecological advantage of the technology. Full article

Energy demand is continuously increasing owing to rapid technological developments and population growth. Additionally, it has been shown that the consumption of fossil fuels contributes to the emission of gases that increase the greenhouse effect. An alternative for addressing the problems of greenhouse gas emissions and dependence on oil is to replace fossil fuels with biofuels. This article presents the combustion gas emissions and performance assessment of a used car using gasoline–bioethanol blends at concentrations free of mechanical risk to contribute information for energy transition. The tests were carried out using the mixtures E0, E5, and E10 at speeds of 1500, 2500, and 4500 rpm for the evaluation of emissions. Meanwhile, for the performance assessment, the speed was varied from 2500 rpm to 8000 rpm. The vehicle was analyzed under functional operating conditions, and no mechanical modifications were made to the alcohol mixtures. Testing was performed using a gas analyzer with non-dispersive infrared (NDIR) electroluminescence and electrochemical cells to measure the emissions, and a computerized chassis dynamometer was used to measure the torque and speed. From the results shown here, it can be concluded that the use of bioethanol at low concentrations in the range without mechanical risk, such as E0, E5, and E10, can be utilized in used cars and in functional operating conditions, improving the thermal efficiency of the engine by 2% and 1.2% with the E5 and E10 mixtures. The specific consumption increased up to 3% with the E10 mixture owing to the lower energy capacity of the mixture. Meanwhile, HC polluting emissions decreased by up to 8.44%, 20%, and 100 at speeds of 1500 rpm, 2500 rpm, and 4500 rpm, respectively. The nitrogen oxide emissions decreased by up to 5% for mixtures E5 and E10. The results presented in this article may be useful for decision making in the use of biofuels in automobiles used in the energy transition. In addition, our study can be taken as a reference for studies on cars that are more than 20 years old. Full article

Peer-to-peer car-sharing systems are an evolving branch of urban mobility, aligning with global trends focused on sustainable development and reducing congestion in cities. A research gap has been identified concerning the specific vehicle attributes that would encourage the public to potentially use these services. Addressing this gap, and in the context of launching a new peer-to-peer car-sharing service in Katowice, Poland, this article investigates the technical features influencing the choice of vehicles in peer-to-peer car-sharing systems, particularly from the perspective of individuals who currently do not use such platforms. The study employs Social Network Analysis (SNA) to examine the interrelationships between vehicle attributes. The analysis reveals that key factors influencing users’ decisions include fuel/energy consumption, safety features, and technological advancement, with a particular emphasis on driver assistance systems, including autonomous driving capabilities. The network structure, characterized by a relatively low density (0.2536) and a short average path length (1.872), suggests that a few central vehicle features dominate user decisions, and improvements in these key areas can quickly propagate through the decision-making process, enhancing overall user satisfaction. To validate the findings, a Gradient Boosting Regression (GBR) analysis was conducted, confirming the significance of the key factors identified by the SNA, such as fuel efficiency, battery capacity, and safety systems, thus strengthening the reliability of the results. This study underscores the growing importance of sustainability and technological innovation in the automotive industry, particularly in the context of the sharing economy. It suggests that car-sharing platforms and vehicle manufacturers should prioritize these features to meet user expectations and preferences. These findings provide valuable insights for the strategic and operational management of peer-to-peer car-sharing services, emphasizing the importance of targeted vehicle selection and user-centered innovations to improve platform performance and scalability. Full article

Population growth and rising mobility demands have significantly increased traffic congestion and extended travel times. To address these challenges, traffic flow can be optimized by organizing vehicles into clusters, known as vehicle platoons, where cars travel closely together in a co-ordinated manner. Although the concept of vehicle platoon control holds great promise for improving traffic efficiency and reducing fuel consumption, its practical implementation faces several issues. Variations in vehicle specifications, such as differences in mass, can destabilize platoons and negatively impact overall performance. This paper introduces a novel method to maintain stable vehicle co-ordination despite such uncertainties. The proposed method utilizes a static output feedback control strategy, which simplifies the communication architecture within the platoon, as only partial state information from each vehicle is required. The simulation results demonstrate that this method effectively minimizes spacing errors and ensures platoon stability. This approach not only enhances safety but also improves traffic flow, making it a viable strategy for future intelligent transportation systems. Full article

CO₂ emissions from gas-powered cars have a large impact on global warming. The aim of this paper is to develop an accurate estimation method of CO₂ emissions from individual private passenger cars by using actual driving data obtained by telematics. CO₂ emissions from gas-powered cars vary depending on various factors such as car models and driving behavior. The developed approach uses actual monthly driving data from telematics and vehicle features based on drag force. Machine learning based on random forest regression enables better estimation performance of CO₂ emissions compared to conventional multiple linear regression. CO₂ emissions from individual private passenger cars in 24 car models are estimated by the machine learning model based on random forest regression using data from telematics, and the coefficient of determination for all 24 car models is ${\mathrm{R}}^2=0.981$. The estimation performance for interpolation and extrapolation of car models is also evaluated, and it keeps enough estimation accuracy with slight performance degradation. The case study with actual telematics data is conducted to analyze the relationship between driving behavior and monthly CO₂ emissions in similar driving record conditions. The result shows the possibility of reducing CO₂ emissions by eco-driving. The accurate estimation of the reduced amount of CO₂ estimated by the machine learning model enables valuing it as carbon credits to motivate the eco-driving of individual drivers. Full article