Vehicle Fuel Research Articles

Advancing combustion technologies and alternative fuels in hybrid electric vehicles: a pathway to high-efficiency, low-emission propulsion systems

Advancing combustion technologies and alternative fuels in hybrid electric vehicles: a pathway to high-efficiency, low-emission propulsion systems

Anti-Inflammatory Effect of Ethanol Extract from Hibiscus cannabinus L. Flower in Diesel Particulate Matter-Stimulated HaCaT Cells

Background/Objectives: Diesel Particulate Matter (DPM) is a very small particulate matter originating from cities, factories, and the use of fossil fuels in diesel vehicles. When DPM permeates the skin, it causes inflammation, leading to severe atopic dermatitis. Hibiscus cannabinus L. (Kenaf) seeds and leaves possess various beneficial properties, including anti-coagulation, antioxidant, and anti-inflammation effects. In this study, we investigated the anti-inflammatory effects of an ethanol extract of Hibiscus cannabinus L. flower (HCFE) in HaCaT cells stimulated with 100 μg/mL of DPM. Methods: The anthocyanin content of HCFE was analyzed, and its antioxidant capacity was investigated using the DPPH assay. After inducing inflammation with 100 ug/mL of DPM, the cytotoxicity of HCFE 25, 50, and 100 ug/mL was measured, and the inhibitory effect of HCFE on inflammatory mediators was evaluated. Results: Anthocyanin and myricetin-3-O-glucoside were present in HCFE and showed high antioxidant capacity. In addition, HCFE decreased the mRNA expression of inflammatory cytokines and chemokines such as IL-1β, IL-4, IL-6, IL-8, IL-13, and MCP-1, and significantly reduced the gene expression of CXCL10, CCL5, CCL17, and CCL22, which are known to increase in atopic dermatitis lesions. Furthermore, HCFE reduced intracellular reactive oxygen species (ROS) production, and down-regulated the activation of NF-κB, MAPKs. Inhibition of the NLRP-3 inflammasome was observed in DPM-stimulated HaCaT cells. In addition, the restoration of filaggrin and involucrin, skin barrier proteins destroyed by DPM exposure, was confirmed. Conclusions: These data suggest that HCFE could be used to prevent and improve skin inflammation and atopic dermatitis through the regulation of inflammatory mediators and the inhibition of skin water loss.

Space–Time Analysis of Refueling Patterns of Alternative Fuel Vehicles Using GPS Trajectory Data and Machine Learning

ABSTRACTThe urgency to combat climate change has led countries worldwide to embrace clean energy solutions across various sectors, including transportation, to reduce greenhouse gas emissions. This shift is evident in the growing popularity and adoption of alternative fuel vehicles (AFVs), such as natural gas vehicles and electric vehicles. AFVs have significantly lower carbon footprints compared to conventional petrol‐powered vehicles with internal combustion engines. Consequently, there arises a need to gain a deeper understanding of AFV refueling demand to optimize the distribution of refueling stations. To address this, our research proposes an innovative space–time method that integrates GPS trajectory data with the support vector machine technique to accurately identify and analyze patterns in AFV refueling behavior. The results highlight distinct space–time patterns, notably the clustering of refueling activities in areas like Shuiguohu, Shouyilu, Houhu, Hanshuiqiao, Zhoutou, and Yongfeng around noon, influenced by taxi drivers' breaks. This underscores the importance of increasing staff levels at refueling stations in these areas during peak refueling periods, forming alliances with local eateries, and coordinating taxi shift hours to evenly distribute refueling demand throughout the day, ultimately reducing congestion during peak refueling periods in these areas. The proposed method by this research is applicable to urban contexts worldwide and equips policymakers and planners with a powerful tool for effective planning of future AFV refueling stations.

Impact of Wildfire Smoke on PM2.5, CO, NO2, and SO2 Levels

Air pollution can negatively impact public and individual health, as well as the environment. This paper investigates the impact of wildfires on levels of different air pollutants in order to determine whether focusing on reducing wildfires can be an efficient way to reduce specific pollutants. By plotting the number of acres burning in wildfires along with the overall concentration of particulate matter less than 2.5 microns in diameter (PM2.5), carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) during a period of time in California, this paper estimates the contribution of wildfire smoke to the level of each pollutant. The results show that PM2.5 levels were the most heavily impacted by wildfires out of the four pollutants studied, and therefore actions to reduce the size and quantity of wildfires can be helpful to reduce the damage done by PM2.5 pollution. However, CO, NO2, and SO2 levels, while they may be correlated with fire size, are likely more strongly influenced by other factors. To minimize the harmful effects of these three pollutants, it may be more effective to focus on investigating other sources of CO, NO2, and SO2, such as the burning of fossil fuels in industrial machinery and motor vehicles.

Design of Solar Electric Bicycle Based on Pulse Width Modulation on Throttle as DC Motor Speed Regulator

Electric bicycles are designed to use renewable energy to support the government's efforts to reduce carbon emission consumption. Problems in every region or city regarding fueled vehicles certainly increase air pollution. Generally, electric bicycles are designed to be a solution and are used in daily life to carry out daily activities by requiring a bicycle gas throttle control that can assist the driver or person in controlling the speed of the electric bicycle. In this study, designing a solar electric bicycle as environmentally friendly energy based on PWM signals to determine the duty cycle value or duty cycle with an optimized PWM value can affect the speed system (km / h) in Revolutions Per Minute (RPM) to determine the performance of the DC motor. The electric bicycle uses 30Wp solar power, two 12V 7.5 Ah batteries assembled in series, and a 24V 250W DC brushless motor. Based on the design results with a total weight of 37kg electric bicycle plus the weight of 55kg, 65kg, 75kg, and 85kg driver variations, the total controller power value is 163.44 watts. The results of duty cycle testing (duty cycle) were 25%, 50%, 75%, and 98%, with 249.9 as the maximum value of PWM and 9.8 Vout generated against the maximum speed of the electric bicycle of 2648 RPM or the equivalent of an electric bicycle speed of 18-28 km/hour. In this case, the electric bicycle is in a condition suitable for use to carry out daily activities by utilizing clean, cheap and appropriate energy

Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

Distribution and Risk Assessment of VOCs in Road Dust Across Different Urban Areas

ABSTRACT Volatile organic compounds (VOCs) are significant pollutants in urban road dust, but their distribution and associated health risks remain unclear. VOC distribution in road dust from six distinct urban areas in Beijing Daxing District, China was systematically investigated. Eighteen VOCs were detected, with the highest load in commercial areas (7.73 μg/g), followed by industrial parks (6.38 μg/g), residential areas (3.35 μg/g), leisure areas (2.79 μg/g), traffic areas (2.01 μg/g) and urban-rural fringe (1.85 μg/g), much lower than in air and water phases. Typical VOCs in road dust include methyl tertiary butyl ether (MTBE) (0.82–1.24 μg/g), benzene series compounds (0.02–4.19 μg/g) and volatile halogenated alkanes (0.002-0.095 μg/g). Incomplete combustion of road vehicle fuels is the key source of VOCs. Particular attention was given to VOCs in typical sites within the urban-rural fringe, known for their unique pollution characteristics. The VOCs loads were ranked as follows: Gas Stations (2.61–2.62 μg/g) > Intersection (1.41 μg/g) > Main Road (1.34 μg/g) > University Gate (1.27 μg/g). Increased vehicle activity and frequent stalling contribute to higher VOCs accumulation in road dust. Health risk assessment indicates there is no appreciable non-carcinogenic risk associated with exposure to road dust VOCs. Further research should consider VOCs in the broader environmental context, including air and water.

Optimal Production Strategies with Credit Sharing for Automakers under the Dual-Credit Policy

This paper investigates strategic production selections in scenarios of credit sharing between cooperative fuel vehicle (FV) automakers and new energy vehicle (NEV) automakers under the dual-credit policy. Three coopetition production strategies are formulated: the simultaneous production strategy, the FV priority production strategy, and the NEV priority production strategy. On the basis of these three production strategies, this study examines the optimal strategy for both parties in scenarios of no credit sharing, credit sharing dominated by the FV automaker, and credit sharing dominated by the NEV automaker. The simultaneous production strategy is the most conducive to both parties’ coexistence in the vehicle market, and the FV or NEV priority production strategy can be adopted to realize the Pareto optimization of their total profit in certain applicable intervals. Credit sharing will greatly change both parties’ applicable intervals and optimal strategy selections, and credit sharing dominated by FV automakers has been proven to effectively improve their social welfare with a low credit price. Interestingly, a high credit price is sometimes more important for the development of NEVs than the NEV cruising range and substitutability under the dual-credit policy. This study also demonstrates the impact of the credit coefficient, credit equilibrium, and NEV substitutability on both parties’ production decisions and credit sharing. Our study has important managerial implications and can be utilized as strategic guidance for FV/NEV automakers to pursue coopetition under the dual-credit policy.

Analisis Penambahan Bioethanol dan Oktan Booster pada Bahan Bakar Pertamax terhadap Daya dan Torsi Sepeda Motor 4-Langkah

There are various methods to improve vehicle performance, including by blending fuels. In addition to bioethanol that can increase octane rating, there is the use of additives in vehicle fuels that are known to increase octane rating. With the addition of additives such as octane boosters and bioethanol, it is expected to increase the degree of ignition, reduce knocking, achieve more complete combustion, and automatically increase vehicle power.This study aims to analyze the power and torque of a four-stroke motorcycle by considering the addition of bioethanol and octane boosters to Pertamax fuel. Experimental method was used with variation of bioethanol concentration and octane booster in fuel as test parameters. This study provides insight into the potential for power improvement in the use of Pertamax fuel on four-stroke motorcycles through the addition of bioethanol and octane booster. The implications of these findings can serve as a basis for the development of more efficient and sustainable fuels for four-stroke motorcycles.

Transforming Atmospheric CO2 into Sustainable Vehicle Fuel: A Promising Path Forward

Transforming Atmospheric CO2 into Sustainable Vehicle Fuel: A Promising Path Forward

Evaluating Lean Combustion Cycle Stability and Emissions in Spark Ignition Engines with Gasoline, Ethanol, and Methanol Blends

Abstract Fuels for vehicles account for a large portion of the world’s total energy demand, which in turn leads to increased carbon emissions. Ethanol and methanol are a fuel with a simple carbon chain and OH- bonds. It has similar properties to gasoline, and ethanol can be made from the fermentation of plant carbohydrates, called bioethanol. The advantage of using bioethanol is that it contributes to carbon neutrality. This paper will investigate the use of three manually blended gasoline ethanol and methanol (GEM) fuels in a spark ignition engine to address cycle-to-cycle variation (CCV), knock potential, and emissions with lean blend conditions. In the experiments conducted, the air-fuel ratio was conditioned lean by utilizing an electronic control unit to adjust the injector spray duration. This experiment provides results that there is a potential for mild knocking on the use of alcohol fuel with lean fuel mixture conditions at engine speed 4000 RPM, while at engine speed 6000 RPM and 8000 RPM the use of GEM tends to be stable, but in the CCV results the increase in COV (coefficient of variation) value using GEM fuel tends to be more sloping, especially with the addition of more methanol. Emission results from the use of GEM produce top emission CO2 value obtained by the E5M15 mixture at λ=1.2 and an engine speed of 8000 RPM, with a value of 13.75% and then peak CO2 emissions at a value of λ = 1.2 whereas in the use of pure gasoline peak CO2 is at a value of λ = 1.1.

Study on the Influence of Vertical Baffles on Liquid Sloshing Damping Effect in Vehicle Fuel Tank Under Resonance Conditions

Study on the Influence of Vertical Baffles on Liquid Sloshing Damping Effect in Vehicle Fuel Tank Under Resonance Conditions

The Impact of the Bursting New Energy Vehicle Market on the Traditional Fuel Vehicle Industry - The Case of Audi

The automotive industry is facing an era of change against the backdrop of escalating environmental concerns and a global shift towards sustainable transportation. This thesis focuses on the impact of the emerging New Energy Vehicle (NEV) market on Audi, a stalwart of the traditional fuel vehicle segment. The purpose of this study is to assess how Audi is responding to this transition, with particular emphasis on its strategy of embracing electrification in the face of increased competition and changing consumer preferences. Through a comprehensive literature review, analysis of Audi's financial reports and sales data, this study provides insights into Audi's investment in electrification, the pace of model launches, and sales of hybrid and all-electric vehicles. The data for this study was obtained from official Audi publications, industry reports, and relevant academic articles. The findings show that Audi has made significant progress in adapting to the new energy vehicle market, as evidenced by significant investment in electric vehicles, an expanding portfolio of electric models, and sales growth in key markets.

Analysis of the Relationship between Fuel Prices and Vehicle Numbers in Urban Road Networks

The article presents an analysis of the relationship between the price of fuel and diesel oil and traffic intensity in the city. First, an analysis of fuel prices was prepared over fourteen months and the number of passenger cars and trucks per day was examined at the same time. From the results obtained, the highest, lowest, and average values were obtained. On this basis, it was noticed that when the price of fuel was the highest, there were fewer passenger cars, but when the price dropped, the number of vehicles increased. Another conclusion from the analysis is that when the price of fuel dropped even more than the average, there were no more cars. Based on the analysis, it was noticed that the number of vehicles may vary by up to 8000 passenger vehicles per day within one intersection. Then, a microsimulation model was performed in the PTV Vissim program to check the amount of pollution generated by vehicles in three variants: the highest, lowest, and average traffic intensities. The results show that the average daily CO pollution at the moment of the lowest traffic intensity is 15,000 g lower than the average, so the high price of fuel causes much less pollution for the consumer.

Exploring Mediators of Green Vehicle Fuel Purchase Intentions: The Roles of Environmental Belief, Concern, and Knowledge

Exploring Mediators of Green Vehicle Fuel Purchase Intentions: The Roles of Environmental Belief, Concern, and Knowledge

DETECTION OF LPG VEHICLES IN RISKY AREAS WITH COMPUTER VISION TECHNIQUES

Vehicle use is becoming more widespread day by day due to the world population growth. Within the scope of intelligent transportation systems, the information technologies sector and the transportation sector work in an integrated manner to solve the problems caused by the increasing number of vehicles. Data obtained with sensors and cameras are analyzed with artificial intelligence-based information technologies and used in autonomous vehicles, security, traffic management, navigation and passenger information systems. Computer vision enables machines to extract meaningful patterns and relationships from images by combining image processing and deep learning technologies. Computer vision techniques are applied in many fields such as tourism, health, industry, defense, transportation, service, e-commerce, etc. The applications developed provide solutions to various challenges in the transportation sector. For vehicles using Liquified Petroleum Gas (LPG) fuel, the gases in LPG tanks are flammable and pose a potential explosion hazard, especially in certain areas in cities. Entry of LPG vehicles is prohibited in institutions and organizations such as hospitals, shopping malls, hotels that have indoor parking services. The control method of the ban is carried out by assigning a personnel and checking the vehicle trunks. In this study, LPG fueled vehicles were automatically detected using computer vision techniques. Vehicle image data captured by mobile cameras in different provinces in Turkey were trained and compared with four different deep learning models. As a result of training and performance tests on the models, the YOLOv8 model was more effective than the other models with an accuracy of 0.994 mAP and a speed of 11.6 ms. It has been shown to be a stable model in terms of real-time monitoring in real life. It is envisaged that the developed system can contribute to the applications of computer vision techniques as well as benefit the national economy, public life safety and environmental protection.

Experimental Research on Regulated and Unregulated Emissions from E20-Fuelled Vehicles and Hybrid Electric Vehicles

Ethanol as a renewable fuel has been applied in fuel vehicles (FVs), and it is promising in hybrid electric vehicles (HEVs). This work aims to investigate the emission characteristics of ethanol applied in both FVs and plug-in hybrid electric vehicles (PHEVs). The paper conducted a real-road test of an internal combustion FV and PHEV, respectively, based on the world light vehicle test cycle (WLTC) by using gasoline and regular gasoline under different temperature conditions. The use of E10 and E20 in FVs has been effective in reducing the conventional emissions of the vehicles. At 23 °C, E10 and E20 reduced the conventional emissions including carbon monoxide (CO), total hydrocarbon compound (THC), non-methane hydrocarbon compound (NMHC), particulate matter (PM), and particulate number (PN) by 15.40–31.11% and 11.00–44.13% respectively. At 6 °C, E10 and E20 reduced conventional emissions including THC, CO, and PM by 2.15–8.61% and 11.02–13.34%, respectively. However, nitrogen oxide (NOX) emissions increased to varying degrees. The reduction trend of non-conventional emissions including methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) from FVs fueled with E10 and E20 is not significant for vehicles. Overall, the emission reduction effect of E20 is better than that of E10, and the emission reduction effect of ethanol gasoline on vehicle emissions is reduced at low temperatures. Lower ambient temperatures increase vehicle emissions in the low-speed segment but decrease vehicle emissions in the ultra-high-speed segment. HEV emissions of THC, CO, PN, and PM are reduced by 25.28%, 12.72%, 77.34%, and 64.59%, respectively, for E20 compared to gasoline, and the use of E20 in HEVs contributes to the reduction of overall vehicle emissions.

Intersection eco-driving for automated vehicles: SMPC-based strategies for handling leading vehicle starting-up uncertainties

Driving at intersections often differs from regular car-following scenarios, particularly in terms of start-up speed uncertainties. Extracting useful information from these uncertainties is crucial for improving traffic and energy efficiency, ultimately achieving eco-driving goals for automated vehicles. However, traditional car-following models are inadequate for describing intersection driving behaviors, while current eco-driving strategies often lack a thorough consideration of intersection traffic uncertainties. To this end, a new eco-driving strategy based on stochastic model predictive control (SMPC) is proposed, specifically addressing uncertainties in the starting-up behavior of leading vehicle (LV). To begin, a naturalistic driving dataset is constructed for a queuing scenario at a signalized intersection, enabling the establishment of probabilistic models for LV starting-up behavior. Next, an SMPC-based motion planning algorithm is introduced to facilitate optimal control of the ego vehicle (EV), directly incorporating the LV’s uncertainties. Specifically, a phantom LV model is introduced to depict LV’s starting-up probability in its initial preparing stage, and then in the accelerating stage a gaussian process regression (GPR) approach is employed for more accurate prediction of LV acceleration. Then the algorithm’s effectiveness is validated through stochastic and playback simulations in SUMO and Matlab. The playback simulations using conditions from naturalistic driving demonstrate that the algorithm significantly enhances energy efficiency, achieving savings of 21% and 37% compared to electric and fueled vehicles in real-world scenarios, respectively. Surprisingly, despite expectations, the algorithm can also improve traffic efficiency by an average of 17.9% and 13.6% for electric and fueled vehicles, respectively. Finally, the road experiment validates that our algorithm can achieve total savings of 15.8% energy in our experimental electric vehicle compared to real drivers. This algorithm has the potential to serve as an effective eco-driving solution for automated vehicle longitudinal planning and control in urban settings.

Low carbon strategy and coordination for a two-echelon automotive supply chain considering dual credit policy

The dual-credit policy currently plays a crucial role in the automobile industry and significantly contributes to low-carbon operations in the automotive supply chain. This paper employs the game-theoretic approach to build a supply chain comprising a supplier (leader) and an automaker (follower), considers the impact of dual-credit policy, and incorporates vertical spillovers into the supply chain abatement. Both parties lower carbon emissions through vertical spillovers while maximizing profits, thereby studying the low-carbon strategy of the automobile supply chain. Our findings reveal that (1) increased vertical spillover rate reduces the prices of fuel vehicles (FVs) and new energy vehicles (NEVs), raises the low-carbon levels and profits of automakers and suppliers. (2) An increase in the NEV credit ratio under the centralized system raises both price and demand for NEVs, while the low-carbon levels of automotive enterprises are reduced. (3) The carbon emissions of each vehicle under the centralized system will be lower than those of the decentralized system, and the order of total carbon emission levels under both systems is closely related to the credit trading price and credits per NEV. Moreover, we explore the automotive supply chain coordination and find that the Nash bargaining model fully coordinates the supply chain, while the cost-sharing and revenue-sharing contracts do not. Also, numerical analysis shows that the optimal system profit under revenue-sharing is greater than cost-sharing.

Competition between New Energy and Fuel Vehicles with Behavior-Based Pricing Strategies When Considering Environmental Concerns and Green Innovation

Environmental sustainability is an important issue in supply chain management (SCM). New energy vehicles (NEVs) have significant environmental value when compared to traditional fuel vehicles (FVs). Currently, there is intense competition between fuel and new energy vehicles, owing to differentiated pricing strategies. This paper focuses on behavior-based pricing (BBP) strategies between energy vehicles and fuel vehicles in a two-echelon supply chain wherein consumers are environmentally conscious. A two-period game-theoretic model is built to examine the effect of consumers’ environmental concerns on competition between fuel and energy vehicles, behavior-based pricing strategies, supply chain efficiency, and social welfare. The analytical results indicate that consumers’ environmental concerns facilitate the market share competition by new energy vehicle firms in the second period. If consumers care more about the environment, supply chain efficiency is improved in cases of retail as well as wholesale-and-retail behavior-based pricing strategies. Wholesale-and-retail behavior-based pricing strategies benefit all members of the supply chain, but this is not the case for retail behavior-based pricing strategies. If consumers are sufficiently concerned about the environment and new energy vehicle firms are more efficient, a win–win–win scenario for firms, consumers, and social welfare occurs in the two behavior-based pricing strategies. Counterintuitively, green innovation improves new energy vehicle, fuel vehicle as well as overall supply chain efficiency, in three cases.