Search Results (177)

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

In this paper, an assessment of the impact of the electrification of the vehicle fleet in Bosnia and Herzegovina on the total electrical energy consumption is made, for different scenarios of increasing the number of electric vehicles. Based on a statistical analysis of the structure and number of vehicles in Bosnia and Herzegovina in the period from 2010 to 2024, an estimate of the total number of passenger cars, as well as the number of electric vehicles for the period up to 2050, is made. It is estimated that in 2050 the number of electric passenger cars will be around 300,000. For one representative electric passenger car, averaged annual electrical energy consumption is calculated. Based on the calculation and for the estimated number of electric vehicles in use, the total annual consumption of electrical energy for the segment of passenger cars is defined, for different scenarios of increasing the number of electric vehicles. Following the estimated increase in the number of passenger electric cars, an exponential increase in electrical energy consumption is estimated, reaching the annual amount of 635 GWh in 2050, which is 10 times higher than the total electrical energy consumption of the transport sector in 2024. In this way, for the period up to 2050, the additional amount of electrical energy that the electrical power grid should provide, due to the electrification of the vehicle fleet, is estimated. Full article

Although the transition to electric vehicles (EVs) is well underway and expected to continue in global car markets, most vehicles on the world’s roads will be powered by internal combustion engine vehicles (ICEVs) and fossil fuels for the foreseeable future, possibly well past 2050. Thus, good environmental performance and effective emission control of ICE vehicles will continue to be of paramount importance if the world is to achieve the stated air and climate pollution reduction goals. In this study, we review 228 publications and identify four main issues confronting these objectives: (1) cheating by vehicle manufacturers, (2) tampering by vehicle owners, (3) malfunctioning emission control systems, and (4) inadequate in-service emission programs. With progressively more stringent vehicle emission and fuel quality standards being implemented in all major markets, engine designs and emission control systems have become increasingly complex and sophisticated, creating opportunities for cheating and tampering. This is not a new phenomenon, with the first cases reported in the 1970s and continuing to happen today. Cheating appears not to be restricted to specific manufacturers or vehicle types. Suspicious real-world emissions behavior suggests that the use of defeat devices may be widespread. Defeat devices are primarily a concern with diesel vehicles, where emission control deactivation in real-world driving can lower manufacturing costs, improve fuel economy, reduce engine noise, improve vehicle performance, and extend refill intervals for diesel exhaust fluid, if present. Despite the financial penalties, undesired global attention, damage to brand reputation, a temporary drop in sales and stock value, and forced recalls, cheating may continue. Private vehicle owners resort to tampering to (1) improve performance and fuel efficiency; (2) avoid operating costs, including repairs; (3) increase the resale value of the vehicle (i.e., odometer tampering); or (4) simply to rebel against established norms. Tampering and cheating in the commercial freight sector also mean undercutting law-abiding operators, gaining unfair economic advantage, and posing excess harm to the environment and public health. At the individual vehicle level, the impacts of cheating, tampering, or malfunctioning emission control systems can be substantial. The removal or deactivation of emission control systems increases emissions—for instance, typically 70% (NO_x and EGR), a factor of 3 or more (NO_x and SCR), and a factor of 25–100 (PM and DPF). Our analysis shows significant uncertainty and (geographic) variability regarding the occurrence of cheating and tampering by vehicle owners. The available evidence suggests that fleet-wide impacts of cheating and tampering on emissions are undeniable, substantial, and cannot be ignored. The presence of a relatively small fraction of high-emitters, due to either cheating, tampering, or malfunctioning, causes excess pollution that must be tackled by environmental authorities around the world, in particular in emerging economies, where millions of used ICE vehicles from the US and EU end up. Modernized in-service emission programs designed to efficiently identify and fix large faults are needed to ensure that the benefits of modern vehicle technologies are not lost. Effective programs should address malfunctions, engine problems, incorrect repairs, a lack of servicing and maintenance, poorly retrofitted fuel and emission control systems, the use of improper or low-quality fuels and tampering. Periodic Test and Repair (PTR) is a common in-service program. We estimate that PTR generally reduces emissions by 11% (8–14%), 11% (7–15%), and 4% (−1–10%) for carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen (NO_x), respectively. This is based on the grand mean effect and the associated 95% confidence interval. PTR effectiveness could be significantly higher, but we find that it critically depends on various design factors, including (1) comprehensive fleet coverage, (2) a suitable test procedure, (3) compliance and enforcement, (4) proper technician training, (5) quality control and quality assurance, (6) periodic program evaluation, and (7) minimization of waivers and exemptions. Now that both particulate matter (PM, i.e., DPF) and NO_x (i.e., SCR) emission controls are common in all modern new diesel vehicles, and commonly the focus of cheating and tampering, robust measurement approaches for assessing in-use emissions performance are urgently needed to modernize PTR programs. To increase (cost) effectiveness, a modern approach could include screening methods, such as remote sensing and plume chasing. We conclude this study with recommendations and suggestions for future improvements and research, listing a range of potential solutions for the issues identified in new and in-service vehicles. Full article

Energy and mobility are currently powered by conventional fuels, and for the specific case of spark ignition (SI) engines, gasoline is dominant. Converting these power-units to hydrogen is an efficient and cost-effective choice for achieving zero-carbon emissions. The use of this alternative fuel can be combined with a circular-economy approach that gives new life to the existing fleet of engines and minimizes the need for added components. In this context, the current work scrutinizes specific aspects of converting a small-size passenger car to hydrogen fueling. The approach combined measurements performed with gasoline and predictive 0D/1D models for correctly including fuel chemistry effects; the experimental data were used for calibration purposes. One particular aspect of H₂ is that it results in lower volumetric efficiency compared to gasoline, and therefore boosting requirements can feature significant changes. The results of the 0D/1D simulations show that one of the main conclusions is that only stoichiometric operation would ensure the reference peak power level; lean fueling featured relative air–fuel ratios too low for ensuring the minimum value of 2 that would allow mitigating NO_x formation. Top speed could be instead feasible in lean conditions, with the same gearbox, but with an extension of the engine speed operating range to 7000 rpm compared to the 3700 rpm reference point with gasoline. 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

Urban transportation demands are growing rapidly. Concurrently, the sharing economy continues to expand. These dual trends establish ride-hailing dispatch as a critical research focus for building sustainable smart transportation systems. Current ride-hailing systems only serve passengers. However, they ignore an important opportunity: transporting packages. This limitation causes two issues: (1) wasted vehicle capacity in cities, and (2) extra carbon emissions from cars waiting idle. Our solution combines passenger rides with package delivery in real time. This dual-mode strategy achieves four benefits: (1) better matching of supply and demand, (2) 38% less empty driving, (3) higher vehicle usage rates, and (4) increased earnings for drivers in changing conditions. We built a Dynamic Heterogeneous Demand-aware Ride-hailing Dispatch System (DHDRDS) using deep reinforcement learning. It works by (a) managing both passenger and package requests on one platform and (b) allocating vehicles efficiently to reduce the environmental impact. An empirical validation confirms the developed framework’s superiority over conventional approaches across three critical dimensions: service efficiency, carbon footprint reduction, and driver profits. Specifically, DHDRDS achieves at least a 5.1% increase in driver profits and an 11.2% reduction in vehicle idle time compared to the baselines, while ensuring that the majority of customer waiting times are within the system threshold of 8 min. By minimizing redundant vehicle trips and optimizing fleet utilization, this research provides a novel solution for advancing sustainable urban mobility systems aligned with global carbon neutrality goals. Full article

Rapid urbanization in China is intensifying travel demand while making transport the nation’s third-largest source of carbon emissions. Anticipating continued growth in private-car fleets, this study integrates vehicle-stock forecasting with multi-scenario emission modeling to identify effective decarbonization pathways for Chinese cities. First, Kendall rank and grey relational analyses are combined to screen the key drivers of car ownership, creating a concise input set for prediction. A Lévy-flight-enhanced Sparrow Search Algorithm (LSSA) is then used to optimize the smoothing factor of the Generalized Regression Neural Network (GRNN), producing the Levy flight-improved Sparrow Search Algorithm optimized Generalized Regression Neural Network (LSSA-GRNN) model for annual fleet projections. Second, a three-tier scenario framework—Baseline, Moderate Low-Carbon, and Enhanced Low-Carbon—is constructed in the Long-range Energy Alternatives Planning System (LEAP) platform. Using Ningbo as a case study, the LSSA-GRNN outperforms both the benchmark Sparrow Search Algorithm optimized Generalized Regression Neural Network (SSA-GRNN) and the conventional GRNN across all accuracy metrics. Results indicate that Ningbo’s car fleet will keep expanding to 2030, albeit at a slowing rate. Relative to 2022 levels, the Enhanced Low-Carbon scenario delivers the largest emission reduction, driven primarily by accelerated electrification, whereas public transport optimization exhibits a slower cumulative effect. The methodological framework offers a transferable tool for cities seeking to link fleet dynamics with emission scenarios and to design robust low-carbon transport policies. Full article

Road transport represents a major contributor to air pollution, energy consumption, and carbon dioxide emissions in Switzerland. In response, stringent emission regulations, penalties for non-compliance, and incentives for electric vehicles have been introduced. This study investigates how these policies, along with shifting consumer preferences and vehicle design advancements, have influenced the composition of the Swiss new passenger car fleet. Using machine learning techniques, we segment passenger vehicles to analyze trends over time. Our findings reveal a decline in micro and small vehicles, alongside an increase in lower- and upper-middle-class vehicles, sport utility vehicles, and alternative powertrains across all segments. Additionally, steady increases in vehicle width, length, and weight are observed in all classes since 1995. While technological advancements led to reductions in energy consumption and carbon dioxide emissions until 2016, an increase has since been observed, driven by higher engine power, greater vehicle weight, and changes in certification schemes. 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

The application of intelligent and connected technologies, such as vehicle-to-everything (V2X), profoundly influences car-following behavior and traffic flow characteristics. While empirical studies have demonstrated that the car-following behavior is affected by the vehicles in the adjacent lanes, there is no car-following model that comprehensively incorporates the leading and following neighboring vehicles, including those in the adjacent lanes. Under the conditions of intelligent and connected technologies penetration, the information regarding the aforementioned vehicles can be accessed and applied in the car-following process. However, the absence of the corresponding car-following model limits the understanding of traffic flow characteristics under this condition, particularly concerning critical stability characteristics. To address this research gap, a new car-following model is proposed, which integrates the neighboring leading vehicles in the current and adjacent lances, marked as the surrounding leading vehicle (SLV), and the rear vehicle in the current lane. The linear stability analysis and nonlinear analysis of the proposed model, as well as the numerical simulation of the propagation process of disturbance in the vehicle fleet, are conducted. Based on this, the stability and evolution characteristics of the traffic flow are explored. The results of theoretical and simulation analysis consistently suggest that the integration of the motion state information of the SLV and the rear vehicle can effectively stabilize the traffic flow, which means that traffic congestion can be alleviated and transportation efficiency will be improved. This research can provide references for the research fields including traffic flow theory and is of significant importance for alleviating and mitigating traffic congestion under the condition of intelligent and connected vehicle (CAV) penetration. Full article

Atmospheric nanoparticles, due to their tiny size up to 100 nanometres in diameter, have negligible mass and are better characterised by their particle number concentration. Atmospheric nanoparticle numbers are not regulated due to insufficient data availability, which emphasises the importance of this research. In this paper, nanoparticle number emissions are estimated using nanoparticle number emission factors (NPNEF) and road traffic characteristics. Traffic flow and fleet composition were estimated using the Leeds Transport Model, which showed that the road traffic in Leeds consisted of 41% petrol cars, 43% diesel cars, 9% LGV, 2% HGV, and 4.5% buses and coaches. Two approaches were used for emission estimation: (a) a detailed model, which required detailed information on traffic flow and fleet composition and NPNEFs of various vehicle types; and (b) a simple model, which used total traffic flow and a single NPNEF of mixed fleet. The estimations of both models demonstrated a strong correlation with each other using the values of R, RMSE, FAC2, and MB, which were 1, 2.77 × 10¹⁷, 0.95, and −1.92 × 10¹⁷, respectively. Eastern and southern parts of the city experienced higher levels of emissions. Future work will include fine-tuning the road traffic emission inventory and quantifying other emission sources. Full article

This study explores the importance of considering regional aspects and different calculation approaches when assessing the environmental impact of passenger cars in Germany. The transportation sector, in general, needs to improve its transition to comply with national and international goals, and more efficient measures are necessary. To achieve this, the spatial heterogeneity of underlying data, such as vehicle stocks, cubic capacity classes as a proxy for consumption values, and annual mileage, is investigated with respect to regional differences. Using data samples for the year 2017, the average emission values per car and year are calculated as well as Germany’s total emission values from the utilization of passenger cars. Conducting a spatially informed allocation algorithm, battery electric vehicles (BEVs) are added to certain regional fleets, replacing cars with internal combustion engines (ICEs). The results show significant regional differences in the underlying data, with a divergence between rural and urban areas as well as northern and southern regions, while the spread in mileage values is higher than that in consumption values. Comparing the tank-to-wheel (TtW) and well-to-wheel (WtW) approaches reveals different values with an increased spread as more BEVs are introduced to the fleet. Using the presented concept to allocate BEVs, emissions can be reduced by 1.66% to 1.35%, depending on the calculation perspective, compared to the extrapolation of historical values. Furthermore, rural areas benefit more from optimized allocation compared to urban ones. The findings suggest that regional distribution strategies could lead to more efficient reductions in GHG emissions within the transportation sector while incorporating both TtW and WtW approaches, leading to more comparable and precise analyses. Full article

Developing nations like Bangladesh have yet to adopt hybrid (HEVs) or electric vehicles (EVs) for goods carrying, whereas environmental pollution and fuel costs are hitting hard. The electrically powered cars and trucks market promises an excellent opportunity for environmentally friendly transportation. However, these countries’ inadequate infrastructure, substantial initial expenses, and insufficient policies impeding widespread acceptance hold market growth back. This study examines the current status of the electric car market in low- and middle-income developing nations like Bangladesh, focusing on the infrastructure and regulatory framework-related barriers and the aspects of growth promotion. To promote an expanding hybrid and EV ecosystem, this article outlines recent studies and identifies critical regions where support for policy and infrastructural developments is needed. It discusses how developing nations may adapt successful international practices to suit their specific needs. At the same time, the research adopted system dynamics and case study methods to assess the transportation fleet (142 vehicles) of a livestock farm and find the feasibility of adopting HEVs and EVs. Several instances are improving infrastructures for recharging, providing incentives for lowering the adoption process cost, and creating appropriate regulatory structures that promote corporate and consumer involvement. Findings highlight how crucial it is for governments, businesses, customers, and international bodies to collaborate to build an affordable and sustainable EV network. The investigation concludes with recommendations for more research and appropriate regulations that may accelerate the adoption of EVs, reduce their adverse impacts on the environment, and promote economic growth. Full article

Connected autonomous vehicles (CAVs) face constraints from multiple traffic elements, such as the vehicle, road, and environmental factors. Accurately quantifying the vehicle’s operational status and driving risk level in complex traffic scenarios is crucial for enhancing the efficiency and safety of connected autonomous driving. To continuously and dynamically quantify the driving risks faced by CAVs in the road environment—arising from the front, rear, and lateral directions—this study focused s on the self-driving particle characteristics that enable CAVs to perceive their surrounding environment and make driving decisions. The vehicle-to-vehicle interaction behavior was analogized to the inter-molecular interaction relationship, and a molecular Morse potential model was applied, coupled with the vehicle dynamics theory. This approach considers the safety margin and the specificity of driving styles. A multi-layer decoder–encoder long short-term memory (LSTM) network was employed to predict vehicle trajectories and establish a risk quantification model for vehicle-to-vehicle interaction behavior. Using SUMO software (win64-1.11.0), three typical driving behavior scenarios—car-following, lane-changing, and yielding—were modeled. A comparative analysis was conducted between the risk field quantification method and existing risk quantification indicators such as post-encroachment time (PET), deceleration rate to avoid crash (DRAC), modified time to collision (MTTC), and safety potential fields (SPFs). The evaluation results demonstrate that the risk field quantification method has the advantage of continuously quantifying risk, addressing the limitations of traditional risk indicators, which may yield discontinuous results when conflict points disappear. Furthermore, when the half-life parameter is reasonably set, the method exhibits more stable evaluation performance. This research provides a theoretical basis for the dynamic equilibrium control of driving risks in connected autonomous vehicle fleets within mixed-traffic environments, offering insights and references for collision avoidance design. Full article

Since private cars and vans accounted for more than 25% of global oil consumption and about 10% of energy-related CO₂ emissions in 2022, increasing the share of electric vehicle (EV) ownership is considered an important solution for reducing CO₂ emissions. At the same time, reducing emissions entails certain economic losses for those countries whose exports are largely covered by the oil trade. The explosive growth of the EV segment over the past 15 years has given rise to overly optimistic forecasts for global EV penetration by 2050. One of the major obstacles to such a development scenario is the limited availability of resources, especially critical materials. This paper proposes a mathematical model to predict the global EV fleet based on the limited availability of critical materials such as lithium, one of the key elements for battery production. The proposed model has three distinctive features. First, it shows that the classical logistic function, due to the specificity of its structure, cannot correctly describe market saturation in the case of using resources with limited serves. Second, even the use of a special multiplier that describes the market saturation process taking into account the depletion (finiteness) of the used resource does not obtain satisfactory economic results because of the “high speed” depletion of this resource. Third, the analytical solution of the final model indicates the point in time at which changes in saturation rate occur. The latter situation allows us to determine the tracking of market saturation, which is more similar to the process that is actually occurring. We believe that this model can also be validated to estimate the production of wind turbines that use rare earth elements such as neodymium and dysprosium (for the production of powerful and permanent magnets for wind turbines). These results also suggest the need for oil-exporting countries to technologically diversify their economies to minimize losses in the transition to a low-carbon economy. Full article