Engine Vehicles Research Articles

Development of Student Worksheets based on the Discovery Learning Approach in the Automotive Light Vehicle Engineering Program at Vocational High Schools

This research aims to develop student worksheets based on the discovery learning approach. Designing Student Worksheets based on the discovery learning approach for class x automotive light vehicle engineering at state vocational high school 1 Seunuddon. Vocational Education Study Program in Mechanical Engineering fakultas keguruan dan ilmu pendidikan Malikussaleh University, 2024. This research aims to describe the design, feasibility and learning outcomes of class automotive. This research uses a mix methods approach. This research used a sample consisting of 8 class X students majoring in automotive engineering. Data collection uses questionnaires, documentation, interviews and observations, data analysis techniques use percentage qualitative analysis through data collection and drawing conclusions. The research results show student learning outcomes. The media I results gave an overall score with a percentage of 89.28% and media experts II gave an overall score with a percentage of 89.28%. The average percentage of the two validators shows the "very feasible" category. Thus, this student worksheet is very suitable for usefor learning by teachers and students. Where the value obtained has passed the learning objective achievement criteria value which has been determined as a reference for the success of the learning process at 75 with a percentage of 78.75%, thus it can be concluded that the value obtained by the students is categorized as complete. Thus, this student worksheet is suitable for use by teachers and students for a more effective learning process.

The Food, Energy, and Water Nexus through the Lens of Electric Vehicle Adoption and Ethanol Consumption in the United States

The US produces a large share of global biofuels but is unique in using a relatively inefficient biofuel pathway involving corn (maize) for ethanol production. The Renewable Fuel Standards that enshrine this feedstock were intended as a greenhouse gas emissions reduction measure but have had the effect of coupling the food, energy, and, to a lesser extent, water systems. This paper looks at the food–energy–water (FEW) nexus as exemplified by the growth in corn agriculture for internal combustion engine vehicle fuel and how that will likely change as vehicle electrification proceeds and accelerates. Starting with scenarios in which there is a rapid uptake in electric vehicles by 2030 and beyond, we examine the implications for the switch from liquid fuels for transportation in the United States toward electric vehicles (EVs). We find that scenarios in which EV penetration grows rapidly will clearly decrease demand for corn ethanol. Our analysis shows that, with judicious planning, the decrease in corn ethanol demand can have potential positive co-benefits. These co-benefits include reducing stressors on depleting aquifers and nutrient runoff to waterways. Substituting a small fraction of displaced industrial corn–ethanol cropland with large-scale solar photovoltaic (PV) capacity can supply a large fraction of the additional electricity needed for EVs. Finally, solar PV generation can ameliorate or even increase income and create more jobs than those lost to the decreased ethanol demand.

Recent Progress in Creep-Resistant Aluminum Alloys for Diesel Engine Applications: A Review.

Diesel engines in heavy-duty vehicles are predicted to maintain a stable presence in the future due to the difficulty of electrifying heavy trucks, mine equipment, and railway cars. This trend encourages the effort to develop new aluminum alloy systems with improved performance at diesel engine conditions of elevated temperature and stress combinations to reduce vehicle weight and, consequently, CO2 emissions. Aluminum alloys need to provide adequate creep resistance at ~300 °C and room-temperature tensile properties better than the current commercial aluminum alloys used for powertrain applications. The studies for improving creep resistance for aluminum casting alloys indicate that their high-temperature stability depends on the formation of high-density uniform dispersoids with low solid solubility and low diffusivity in aluminum. This review summarizes three generations of diesel engine aluminum alloys and focuses on recent work on the third-generation dispersoid-strengthened alloys. Additionally, new trends in developing creep resistance through the development of alloy systems other than Al-Si-based alloys, the optimization of manufacturing processes, and the use of thermal barrier coatings and composites are discussed. New progress on concepts regarding the thermal stability of rapidly solidified and nano-structured alloys and on creep-resistant alloy design via machine learning-based algorithms is also presented.

Life cycle greenhouse gas emissions of retrofit electrification: Assessment for a real case study

Nowadays fleet electrification appears the most concrete opportunity to reduce the environmental burdens of the transportation sector. However, Battery Electric Vehicle penetration has to deal with crucial challenges, such as new BEVs high price, customers’ reluctant and scarcity of materials. In this regard, electric retrofit is emerging as a low-cost and short-term solution to deal with these issues. However, to date only few studies focus on regulatory framework of electric retrofit and fewer quantify the environmental benefits of a converted BEV used in a certain context. This paper analyses a retrofit industrial prototype made by a local start up with all features available for a future homologation and it proposes a comparative Life Cycle Assessment, stressing the significant environmental hotspots to suggests in which geographical context governments should promote retrofit conversion. Two scenarios composed by two time-related parts are evaluated: a vehicle replacement with a new Internal Combustion Engine Vehicle and a retrofit conversion process of a SmartForTwo W450. The production stage of conversion kit is assessed mainly through primary data, while operation consumption is calculated through an analytic model implemented in MATLAB- Simulink. A detailed sensitivity analysis is performed in which different options for electricity grid mix, energy consumption, battery pack maintenance and EoL pathways are combined, for a total of 274 scenario combinations. The results reveal that retrofit conversion allows achieving about 45 % Greenhouse Gases saving compared to new replacement, in particular when urban driving and renewable electricity grid mix are considered.

Electric Vehicle Usage Patterns in Multi-Vehicle Households in the US: A Machine Learning Study

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

Multi-Objective Optimization of Bogie Stability for Minimum Radius Curve of Battery Track Engineering Vehicle

A battery track engineering vehicle faces challenges such as derailment and other safety concerns when navigating an R20m minimum radius curve, primarily owing to its low vertical and horizontal stabilities. To address these issues, a methodology integrating genetic optimization algorithms with a rigid and flexible coupled multi-body dynamics simulation is proposed to optimize the primary suspensions of the bogie of the vehicle. Initially, a multi-objective optimization model combining rigid and flexible coupled multi-body dynamics of battery track engineering vehicles with a genetic optimization algorithm was formulated. Subsequently, the optimal Latin hypercube design was applied to analyze the sensitivity of vertical and horizontal stability to various suspension parameters. Finally, a non-dominated sorting genetic algorithm (NSGA-II) and an archive-based micro genetic algorithm (AMGA) were applied to optimize the primary suspensions to enhance stability. Consequently, a set of optimal suspension parameter combinations was obtained. A notable enhancement was observed in the lateral stability of the optimized battery track engineering vehicles by 23.33% and in the vertical stability by 3.5% when traversing the R20m minimum radius curve, thereby establishing a theoretical foundation for further improving the running safety of railway vehicles and resolving the shortcomings of less research on the smallest radius curve.

Flash thermography to detect and evaluate impacts in polycarbonate parts produced by additive manufacturing

In this work, active thermography has been evaluated for detecting impacts on polycarbonate parts produced by additive manufacturing with different fillers and under various impact energies. Polycarbonate is a material with very good impact resistance, very useful in sectors where security is critical (e.g., aeronautics, vehicle engineering, ballistics, defense, etc.). The impacts were specifically generated on the back face (non-visible) of the part, using a standardized test under controlled energy conditions. Specifically, flash thermography was used, and different processing techniques was applied. The most favorable results were obtained by principal component analysis (PCA). From a qualitative perspective, the proposed method allows to detect and evaluate the impact from the back side of the part (non-impacted side) taking into account the different material fillings. From a quantitative point of view, by using parametric and non-parametric tests, it can be shown that there are statistically significant differences between the image levels in the defect zone and the non-defect zone, and it is even possible to establish a correlation between the medians of the detected levels and the impact energy.

Vehicle Engine Fault Diagnosis Approach Based on a Decision Tree and Knowledge Base

Vehicle Engine Fault Diagnosis Approach Based on a Decision Tree and Knowledge Base

Which state is the cleanest of them all? Pricing long run heterogeneity in carbon abatement costs across America

Electric vehicles (EVs) feature prominently in the United States' efforts to decarbonize the light-duty vehicle fleet and mitigate climate change. To spur EV adoption, public policy offers procurement incentives for individuals willing to purchase an EV instead of an internal combustion engine vehicle (ICEV). How cost-effective are such policies? To what extent does their efficiency vary across states? We address these questions using state-level data on projected electric grid carbon intensity and fuel prices, as well as models of vehicle utilization. Our approach focuses on comparable vehicle models that enable us to jointly estimate the lifecycle emissions and total cost of ownership (TCO) associated with ICEVs, hybrid electric vehicles (HEVs), and EVs. Our findings are fourfold. First, although EVs offer the largest emissions benefit relative to ICEVs, there exists significant state-level heterogeneity. Compared to HEVs' emissions advantage of 20.54 tons of CO2e, EVs' emissions advantage ranges from 10.18 to 58.83 tons of CO2e across states. Second, HEVs offer a more favorable cost-to-emissions advantage nationwide, with an average cost of $59/ton CO2e reduced compared to $192/ton CO2e reduced via EVs. Third, state-level variation in fuel prices and grid carbon intensity enable HEVs to offer emissions reductions at a minimum cost of $3/ton CO2e reduced, while EVs' minimum cost is $76/ton CO2e reduced. Fourth, envisioned grid decarbonization efforts are insufficient for EVs to deliver economically efficient cost-to-emissions advantages due to EVs' relatively high TCO. If EVs produced zero emissions in totality, realizing cost-effective emissions reductions would still necessitate lowering their TCO by an average of at least $3200. We conclude that favorable abatement cost realization necessitates better targeting of procurement incentives at both the federal and state level. We demonstrate that this outcome can be achieved by incentivizing the adoption of HEVs and high-utilization EVs in the short run and by improving EVs’ cost and continuing to invest in EV battery longevity in the long run.

Dyna algorithm-based reinforcement learning energy management for fuel cell hybrid engineering vehicles

Fuel cell hybrid engineering vehicles utilize green energy with high energy conversion efficiency. Optimizing the utilization of hybrid energy storage systems is crucial for improving energy efficiency and promoting energy conservation. This study introduces an innovative energy management framework that employs reinforcement learning to regulate a fuel cell/battery hybrid engineering vehicle. This novel framework facilitates the system achieve optimal energy management, even when confronted with demanding and ever-changing operational scenarios. The proposed strategy integrates the concepts of the Dyna algorithm and the deep Q-network (DQN) algorithm. A working conditions predictive model is developed using the gated recurrent unit (GRU) neural network. The aim of this predictive model is to generate a virtual depiction of the environment and accelerate the process of model updating. The results from simulations and hardware-in-the-loop test illustrate that the predictive model exhibits effective prediction capabilities, enabling the development of an accurate environmental model even when limited sample data is available. The proposed strategy exhibits superior performance compared to the DQN and Q-Learning algorithms regarding adaptability, real-time optimization, training efficiency, convergence speed, and energy conservation. When interacting with the environment, our proposed learning algorithm exhibits a faster convergence rate. Moreover, this strategy achieves a fuel economy of 95 %, as determined by the dynamic programming-based method. The model-based deep reinforcement learning approach is practical and does not require extensive training. An added benefit of this energy management architecture is its use of a more accurate world model.

Operational Risk Assessment of Engineering Vehicles Considering Driver Characteristics

As vehicles with high accident and casualty rates within the road transportation system, engineering vehicles have been receiving much attention and emphasis in terms of safety. Accurate analyses and evaluations of risk factors in vehicle operation are imperative for enhancing the management level of engineering vehicles. This study explores the differences between various types of drivers by analyzing the driving characteristics of professional drivers. The evaluation index system is developed and quantified by integrating factors related to engineering vehicle drivers, road environment, and industry management. Additionally, the risk assessment model is developed using the error backpropagation algorithm. The optimal model is determined by comparing the number of nodes in different hidden layers, the activation function, and regularization optimization. The prediction accuracy of this model’s coefficient of determination is 0.912, indicating that the model has validity. This study is conducive to improving the safety level of engineering vehicle operation in order to reduce the rate of vehicle traffic accidents, the severity of accidents, and the consequences of losses. It also has practical application value in safeguarding social security.

Experimental Study on Performance and Emission of an Electric Turbocharged Hydrogen Direct Injection Engine.

Hydrogen is emerging as a promising zero-carbon emission energy source for vehicle engines. However, the full potential of hydrogen engines and their economic viability remain uncertain. Additionally, comprehensive data on ultralean combustion performance and emissions as well as strategies for improving HC and CO emissions are lacking. This study addresses these gaps by thoroughly examining hydrogen combustion and emissions in a 1.5 L engine. Incorporating a hydrogen direct injection (HDI) system and an electric supercharger, we investigated emissions under various piston ring tensions during lean limit operations. Our findings reveal that hydrogen exhibits a fast combustion speed in the engine within 20 °CA, along with excellent ultralean burn performance, expanding the lambda limit to 3. As the load increases, the thermal efficiency significantly improves, reaching a maximum of 40.8%. NOx emissions peak at a lambda of 1.1 but decrease notably above 2.7, with levels below 10 ppm. At a lambda near 3, NOx emissions hover between 0 and 5 ppm, approaching zero emissions. Additionally, lubricating oil consumption during engine operation yields a minor HC and CO emissions. Higher tension piston rings result in lower HC and CO emissions, typically below 5 ppm, nearing zero emission levels.

A comparative well-to-wheel analysis of renewable energy pathways for hydrogen and battery electric vehicles

Renewable hydrogen and batteries are pivotal not only for promoting renewable energy adoption but also for serving as key components in electric vehicles. While multiple hydrogen-based and battery-based pathways exist for linking renewable energy to automotive powertrains, comprehensive evaluations of them are lacking, especially in the context of long-distance energy transmission in China. This study aims to fill this gap by offering an in-depth well-to-wheel (WTW) analysis of costs and CO2 emissions across various pathways. The pathways involve two energy storage mediums - hydrogen and battery, five transport options, and three automotive powertrains - Internal Combustion Engine Vehicle (ICEV), Fuel Cell Electric Vehicle (FCEV), and Battery Electric Vehicle (BEV). How the electricity price, storage duration, and transport distance influence these pathways is also examined. The results show that the battery-based pathway offers the lowest WTW cost and CO2 emissions in BEV applications. Hydrogen-based pathways are not economically feasible for ICEVs. Importantly, the indirect use of hydrogen in BEVs - H2 for power generation - outperforms its direct use in FCEVs and ICEVs in both cost and emissions. The battery-based pathway is significantly affected by storage duration and becomes less feasible for durations exceeding one day. The most cost-efficient hydrogen transport methods are identified for different transport distances and electricity prices. This research may provide valuable insights for the planning of the hydrogen industry, automotive decarbonization and renewable energy development in China.

Energy mix-driven dynamic life cycle assessment on greenhouse gas emissions of passenger cars in China

Amidst increasingly stringent global emission regulations, China's energy mix is undergoing profound transformation. To more precisely evaluate vehicle emissions, this study aimed to explore the dynamic responses on life cycle greenhouse gas (GHG) emissions of passenger cars in China, considering the change of energy mix. An energy mix-driven life cycle assessment model was established and inventory data were obtained. The electricity generation mix data in China were predicted, and the results in life cycle GHG emissions of internal combustion engine vehicle (ICEV), battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) from 2020 to 2050 were analyzed. Uncertainty, sensitivity and breakeven distance analyses were performed to explore the impact of various parameters. The results showed that the average emission factor of electricity generation mix is projected to decrease by 87.11% in 2050 with the cleanliness of energy mix. The life cycle GHG emissions of ICEV, BEV and PHEV produced in 2020 were 37.16, 28.04 and 29.84 tCO2eq respectively. Based on the linear models, it is expected that the emissions from these vehicles produced in 2050 would be reduced by 11.48%, 61.31%, and 53.39% respectively compared to their levels in 2020. Policies and measures for emission reduction were proposed based on the sensitive parameters and calculation results.

Identifying Effect of Car Fire Blankets on Chosen Fire Parameter Using Large-Scale Fire Tests of Internal Combustion Engine Vehicle and High-Voltage Traction Battery—Comparative Slovak Case Study

Firefighting units in the Slovak Republic are well prepared for extinguishing fires of vehicles with internal combustion engines. However, the expansion of electromobility is also coming to Slovakia. It is essential to pay attention to this topic from the point of view of repression. This article deals with car fire blankets. The main objective is to verify and compare the effectiveness of extinguishing internal combustion engine vehicle (ICEV) fires and high-voltage traction battery (HTB) fires using car fire blankets. The effectiveness of a car fire blanket was determined based on the analysis of temperature drops during large-scale fire tests of ICEVs and HTBs. The temperature was recorded by four thermocouples. Two thermocouples were placed at 0.7 m from the ICEV and HTB; one thermocouple was placed in the interior of the ICEV and inside the HTB; one thermocouple was placed on the roof of the ICEV and the surface of the HTB. Subsequently, the results were compared based on temperature–time curves obtained from experimental measurements. Applying the car fire blanket to the ICEV fire caused a drop in temperature on all thermocouples. The most significant temperature drop was recorded in the interior of the vehicle. Specifically, the temperature dropped from 724 °C to 140 °C. However, the application of the car fire blanket had a different effect on the HTB fire. There was a minimal temperature change in the thermocouple on the right side at a 0.7 distance. The other thermocouples identified a slight increase in temperature.

Comparative Life Cycle Assessment of Electric and Internal Combustion Engine Vehicles

This article is devoted to the ecological comparison of electric and internal combustion engine vehicles throughout their entire life cycle, from mining to recycling. A scientifically based approach to a comprehensive environmental assessment of the impact of vehicles on the environment has been developed. To analyze the impact on the environmental situation, aspects such as the consumption of natural resources, waste generation, electricity consumption, emission of harmful substances into the atmosphere, water consumption, and greenhouse gas emissions are taken into consideration. As a result of comparing the environmental impacts of vehicles, it was found that natural resources consumption and production of industrial waste from electric vehicles (EV) is 6 times higher than from internal combustion engine vehicles (ICEV). Harmful substance emissions and greenhouse gas emissions from EV production are 1.65 and 1.5 times higher, respectively. The EV total electricity consumption is 1.4 times higher than that of ICEVs. At the same time, it was revealed that during operation, EVs have higher energy consumption and emit more harmful substances into the atmosphere, but EVs produce less greenhouse gas emissions. It means that at different life cycle stages, EVs have a much higher negative impact on the environment compared to gasoline engine vehicles.

Challenges and Solutions to Meet the Euro 7 NOx Emission Requirements for Diesel Light-Duty Commercial Vehicles

AbstractThe improvement of air quality requires a further reduction of pollutant emissions, especially in urban areas. The Euro 7 regulations aim at the development of a new generation of internal combustion engine vehicles capable of achieving ultra-low pollutant emissions under demanding, real-world operating conditions. They introduce new technical challenges in the holistic design of a vehicle’s powertrain and emission control system. To identify these, four real-world Euro 7 driving scenarios are investigated, covering demanding urban, highway and mountain driving situations. Technical solutions are then presented to address these challenges and ensure compliance with the Euro 7 emission requirements as set out in the latest regulation proposal of the European Commission. The study focuses on the NOx emissions of an N1 Class III light commercial vehicle with 3.5 t mass and a P2 diesel mild-hybrid powertrain. To ensure emission compliance, a Euro 6e exhaust gas aftertreatment system with enlarged catalysts is combined with NOx raw emission improvements. For low-load cold starts, a 4-kW electric heater in the exhaust system is considered in addition to a 2-l DOC and a 6-l DPF with SCR coating. For high-load cycles with high raw emissions, a 10-l underfloor SCR is considered to ensure the necessary deNOx performance.

Virtual Reality Sebagai Media Pembelajaran Teknik Kendaraan Ringan Otomotif Dalam Era Pendidikan Digital

During the aftermath of the Covid-19 pandemic, educational establishments encounter difficulties in providing comprehensive learning experiences due to the unpredictable circumstances caused by the pandemic. Hence, it becomes crucial for these institutions to prioritize the adoption of digitalized education that allows for accessibility at any time and from any location. However, the field of light vehicle engineering faces its own set of challenges when it comes to delivering intricate materials, primarily due to the size and intricacy of the learning resources. To tackle this issue, a novel approach has been introduced in the form of Virtual Reality (VR)-based learning methods, which prove to be invaluable tools in enhancing comprehension of the subject matter.

Faktor-Faktor Keterlaksanaan Merdeka Belajar Pada Program Studi TKR SMK N di Blitar

The implementation of Merdeka Belajar (MB) brings fundamental changes to Vocational High Schools (VHS). The MB curriculum that is implemented needs to be approached with a deep understanding of the concepts and processes. Thus, many factors influence its implementation. This research aims to analyze the factors in implementing independent learning in vocational schools. This research uses a qualitative approach with a multi-case study at two Public Vocational High Schools (PVHS) in Blitar through in-depth interviews supported by observation and documentation. The research results show that the independent curriculum implemented in PVHS has obstacles in the form of teacher misconceptions and difficulties in synchronizing the curriculum in Light Vehicle Engineering (LVE) with industry. The learning model used in LVE subjects to train students to work independently is PjBL. Meanwhile, guest teachers from industries are invited to provide training and insight to teachers and students. Furthermore, internships are carried out in LVE workshops and industry. Meanwhile, student competency tests are carried out involving the career center and school certification. LVE learning technology updates and graduate absorption also influence this. In conclusion, the implementation of the MB curriculum at VHS is determined by teacher awareness and leadership strategy choices.

Vehicles of Transportation Internal Combustion Engine Press the Details of the Institution Inspection

In this article, the engine in the operation of vehicles details, including cylinder-piston group details, and changes in engine performance, piston rod and cylinders in the details of the cylinder when the body presses the flame-touch surface of the cylinder head redistribution of heat flow occurs, their temperature and the change of grooves in the piston-sleeve connection was analyzed. The study aims to improve the reliable performance of internal combustion engine vehicles by focusing on the maintenance and condition of the cylinder-piston group. The research includes a literature review and experimental analysis of the effects of dust and contaminants on the engine's internal components. Comparative tests were conducted on engines before and after cleaning the cylinder-piston group. Findings show that dust particles significantly impact engine performance, causing a 4-6% reduction in power and a 3-4% increase in fuel consumption. Cleaning the cylinder-piston group resulted in a 5.2% increase in engine power and a 4.0% decrease in fuel consumption. The progressive accumulation of soot was found to degrade engine efficiency and cause knocking. Regular maintenance and cleaning of the cylinder-piston group are essential to enhance engine efficiency and longevity. Implementing these practices in vehicle maintenance systems can significantly improve engine performance and reliability.