Engine Vehicles Research Articles

Research on the Aerodynamics of Electric Vehicles

A major contributing factor to the growing severity of the global warming issue is greenhouse gas emissions, of which carbon dioxide from moving cars makes up a significant amount. Therefore, electric vehicles have become the focus of countries to replace conventional internal combustion engine vehicles due to their low emission characteristics. However, the weight of electric vehicles is increased by their reinforced structures and batteries, which reduces their range. In the short run, it is challenging to lower the weight of electric vehicles greatly because of the constraints of battery technology. Thus, lowering air resistance has emerged as a key strategy for extending the range of electric cars. This paper first analyses the reasons why electric vehicles need better aerodynamic design to increase range, including increased environmental demands, battery technology limitations and the high weight of electric vehicles. Next, the causes of air resistance are explored. Finally, the paper discusses ways to design active aerodynamic components to increase or decrease drag under different operating conditions. For example, a front air dam design can reduce the amount of airflow into the underbody, reducing lift and increasing grip. Variable air dams can optimize aerodynamics by automatically adjusting their position according to speed and driving conditions. In conclusion, the aim of this paper is to find and discover ways to optimize the aerodynamic performance of electric vehicles in order to solve the range problem. This paper provides an important theoretical and practical basis for aerodynamic optimization of electric vehicles

Research on Battery Electric Vehicles’ DC Fast Charging Noise Emissions: Proposals to Reduce Environmental Noise Caused by Fast Charging Stations

The potential of electric vehicles (EVs) to support the decarbonization of the transportation sector, crucial for meeting greenhouse gas reduction targets under the Paris Agreement, is obvious. Despite their advantages, the adoption of electric vehicles faces limitations, particularly those related to battery range and charging times, which significantly impact the time needed for a trip compared to their combustion engine counterparts. However, recent improvements in fast charging technology have enhanced these aspects, making EVs more suitable for both daily and long-distance trips. EVs can now deal with long trips, with travel times only slightly longer than those of internal combustion engine (ICE) vehicles. Fast charging capabilities and infrastructure, such as 350 kW chargers, are essential for making EV travel times comparable to ICE vehicles, with brief stops every 2–3 h. Additionally, EVs help reduce noise pollution in urban areas, especially in noise-saturated environments, contributing to an overall decrease in urban sound levels. However, this research highlights a downside of DC (Direct Current) fast charging stations: high-frequency noise emissions during fast charging, which can disturb nearby residents, especially in urban and residential areas. This noise, a result of the growing fast charging infrastructure, has led to complaints and even operational restrictions for some charging stations. Noise-related disturbances are a significant urban issue. The World Health Organization identifies noise as a key contributor to health burdens in Europe, even when noise annoyance is subjective, influenced by individual factors like sensitivity, genetics, and lifestyle, as well as by the specific environment. This paper analyzes the sound emission of a broad sample of DC fast charging stations from leading EU market brands. The goal is to provide tools that assist manufacturers, installers, and operators of rapid charging stations in mitigating the aforementioned sound emissions in order to align these infrastructures with Sustainable Development Goals 3 and 11 adopted by all United Nations Member States in 2015.

Design conversion, and Performance Estimation of BLDC Motor in Hybrid Electric Vehicle

The core concept in this paper is to hybridize a conventional engine-powered two-wheeler into a hybrid electric vehicle. The shift in the consumers' mindset from conventional IC Engine vehicles to Electric/hybrid vehicles may be a challenge due to the high initial investment. Hence, as a preliminary work, it is proposed to convert an IC engine vehicle to a hybrid electric vehicle considering factors such as affordability, user control, and flexibility. To make the hybrid vehicle available at an affordable price, lead-acid batteries are used for the study. Also, the BLDC motor used in this vehicle has been designed and developed according to the available design constraints. The moped has been redesigned to operate in either engine or electric mode individually depending on the rider’s desire and can switch over to any mode at any time as intended. This is achieved using a unique switching mechanism constructed using needle roller bearings. The concept of hybridization thus results in reduced emissions, especially in stop-go traffic where electric mode can be used, and the performance of hybrid electric vehicles is estimated.

Impact of On-Road U.S. Vehicle Electrification and Lightweighting on Critical Materials Demand.

The electrification of the transport sector is crucial for reducing greenhouse gas emissions and the reliance on fossil fuels. Battery electric vehicles (BEVs) depend on critical materials (CMs) for their batteries and electronic components, yet their widespread adoption may face constraints due to the limited availability of CMs. This study assesses the implications of vehicle electrification and lightweighting (material substitution) on the U.S. CM demand for light-duty vehicles (LDVs) and medium- and heavy-duty vehicles (MHDVs). Market sales scenarios of 100% internal combustion engine vehicles (ICEVs), 100% BEVs, and a 50%/50% mix are considered. The findings reveal that LDVs dominate the total CM demand despite MHDVs requiring more CMs per vehicle. Cobalt, graphite, lithium, neodymium, and nickel are critical for the 100% BEV adoption scenario, whereas palladium and rhodium are critical for ICEVs. LDV lightweighting increases total CM quantity per vehicle due to steel replacement with aluminum but reduces the vehicle's mass, operational energy consumption, and reliance on high-concern battery-related CMs. Transitioning from nickel manganese cobalt (NMC622) to lithium iron phosphate (LFP) battery chemistry reduces CM use but increases demand for strategic materials such as copper and phosphorus. This study uniquely evaluates U.S. CM demand for LDVs and MHDVs across conventional and electric powertrains and investigates light-weighting and battery chemistry impacts.

Dynamic analysis and optimization of functionally graded graphene platelet stiffened plate carrying multiple vibration absorbers

The paper investigates the vibration characteristics of functionally graded graphene platelet reinforced composites (FG-GPLRC) stiffened plates in the presence of coupled Dynamic vibration absorbers (DVAs) and the optimization of the parameters of the DVAs. The FG-GPLRC plate is used as a basis for coupling arbitrary numbers of stiffeners at arbitrary angles and positions by imposing a displacement continuity condition supplemented with displacement coordinate transformations. The artificial virtual spring method is used to simulate the various boundary conditions by setting the spring stiffness and coupling the simplified DVAs to a spring-damped system. The unknown displacement coefficients were expanded using spectral geometry method to obtain the dynamic response of the coupled model. The reliability of the current model is confirmed by comparison with literature, the finite element method (FEM), and experiments. Based on the presented model, the different dynamic behaviors of plates with different FG-GPLRC distribution types at different stiffening parameters are analyzed, and it is found that different GPL distribution types are not equally sensitive to changes in the location and size of stiffeners. It will provide greater structural strength and design flexibility for the engineering of significant watercraft and critical vehicles. The vibration control of FG-GPLRC stiffened plates is developed using DVA and the DVA parameters are optimized using Artificial Bee Colony algorithm to minimize the model energy. These results can extend the structural life, which will increase the potential of FG-GPLRC stiffened plates in a wider range of engineering applications.

A Comparative Case Study on Neutrosophic Linear Programming Approach and  -Constraint Method for Fuzzy Multiobjective Solid Cold Transportation Problem with an Improved Preservation Technology

Cold transportation is one among the unquenching needs of people around the globe. Although cost sensitive, refrigerated transportation is preferred globally as it ensures the quality of perishable items in pharmaceutical, food and beverages, chemicals and certain other industries during transportation. However, many refrigerated vehicles fail in offering consistent preservation as most of their cooling units depend on the vehicle’s engine. It is also important to acknowledge that operating a vehicle unceasingly to maintain temperature is impossible in real life. This set up of poor cold logistics and supply chain leads to an increased deterioration of sensitive items. The paper overcomes this complication by adjoining an extra power source that supports freezing during the shutdown time of the vehicle engine by proposing improved mathematical models on Multi-Objective Cold Fuzzy Solid Transportation Problem (MOCFSTP) with an extra time parameter relating to the static and delay condition of the vehicles during various preservation modes (zero, semi, full) and defends them with comparable scrutinizing. The objectives contemplated in the problem are minimizing the cost, time and rate of deterioration. Numerical examples are discussed in detail and solved using reknown methods in LINGO (19.0) to stress on the effectiveness of the models.

Natural Environmental Contaminants and the Impact of Green Technologies on Climate Change

Environment has been defined as the natural world in which living things dwell and grow. There are certain factors which often pose challenges to the environment and are capable of interrupting capacity building as well as sustainability. This research evaluates such factors as environmental contaminants which include any chemical, biological, or radiological substance or matter that hurts air, water, soil or living organisms. However, with the advancement of green technologies on the environment, there is a revolutionary change with regards to clean energy production, solar power, reduction of emissions of carbon dioxide, use of alternative fuels and other technologies that are less harmful to the environment than fossil fuels. This research focuses on clean energy production such as solar energy and other technologies that serve the best purpose of reducing emissions of carbon dioxide usually generated by vehicles, motorcycles and fuel power generators majorly in the cities, particularly in the industrial environments. These emissions from vehicle engines, power generators, gas flaring and other environmental contaminants are very dangerous to human health. Many, who have ingested these emissions, have serious interference with their bodies’ internal functioning, causing diseases like cancer, itching in the eyes and respiratory disorders like asthma. The pressing need to explore green technologies for sustainability becomes imperative and this gave rise to this research. To achieve this aim, this research adopts the doctrinal research methodology in examining the natural environmental contaminants and the impact of green technologies on climate change. On this premise, this research recommends tremendous exploration of green technologies as a recipe for a sustainable environment.

Comparative assessment of social impacts: conventional vehicles versus battery electric vehicles

This study assesses and compares the social impacts of conventional internal combustion engine vehicles (ICEVs) and battery electric vehicles (BEVs) using the Social Hotspots Database (SHDB) and Social Life Cycle Assessment (SLCA) methodology. The research focuses on the Japanese automotive industry, analyzing three scenarios: business-as-usual (BAU), widespread adoption, and a 2035 ICEV ban. The study examines social risks across various categories, including labor rights, human rights, health and safety, governance, and community access. Results indicate that BEVs present higher social risks than ICEVs due to manufacturing and battery replacement phase. They show lower risks in the well-to-wheel phase compared to ICEVs. The analysis reveals that increased BEV adoption correlates with reduced overall social impact. Key social hotspots identified include issues related to raw material extraction, battery manufacturing, and supply chain transparency. The study highlights the complex challenges in ensuring a sustainable and ethically responsible transition to electric mobility.

A CASE STUDY: FUZZY LOGIC BASED DECISION-MAKING SYSTEM FOR ELECTRIC VEHICLE CHARGING

Recently, the use of environmentally friendly electric vehicles instead of traditional internal combustion engine vehicles continues to be widespread due to threats to world life such as global warming and climate change. However, the biggest disadvantages of this technology are the limited range of electric vehicles, long charging times, low number of charging stations, and different charging costs. There is a need for more studies on the problem of finding charging stations, especially for long-distance traveling with electric vehicles. In this paper, a fuzzy logic based decision making system is designed for electric vehicle users to find the most suitable one among the charging stations on a long travelling route. In this study, a traveling route of 1779 km between Izmir and Van provinces in Turkey is selected. The current charging station locations obtained from different charging station companies on this route were processed on Google Earth, and charging stations that were too far from the route were not taken into consideration. A fuzzy logic model was created for 56 charging stations on the route, which performs weight calculation according to the current charging cost and the distance of the station to the normal route. The fuzzy control system is expected to decide on the most appropriate charging station in accordance with the specified rule table, and the results are evaluated.

Nanotechnology-Enabled Innovations in High-Strength Lightweight Vehicle Design for Enhanced Safety and Energy Efficiency

Global warming and resource depletion are two of the most pressing issues confronting modern society, with the automotive industry being a significant contributor to energy consumption and environmental impact. Additionally, rising fatalities from traffic accidents have highlighted the need for improved vehicle safety. This paper explores the transformative role of nanotechnology in developing high-strength, lightweight materials that enhance both vehicle safety and fuel efficiency. By incorporating nanocomposites, known for their low density, superior strength, heat resistance, and scratch resilience, vehicles can achieve significant weight reductions without compromising performance. These advancements reduce vehicle failure rates, increase passenger survival in accidents, and contribute to energy savings, whether in electric or internal combustion engine vehicles. The paper also discusses the potential of lightweight vehicles in reducing fuel consumption and carbon emissions, making them key players in combating climate change and addressing resource shortages. Through a comprehensive analysis of nanotechnology’s impact on material science, this study highlights its critical role in shaping the future of sustainable vehicle design.

Thermo-Mechanical Coupled Analysis of Electric Vehicle Drive Shafts

With the growing concerns over global warming and abnormal weather patterns, the development of eco-friendly technologies has emerged as a critical research area in the transportation industry. In particular, the global automotive market, one of the most widely used sectors, has witnessed a surge in research on electric vehicles (EVs) in line with these trends. Compared to traditional internal combustion engine vehicles, EVs require components with high strength and durability to achieve optimal performance. This study focuses on the development of a constant velocity (CV) joint, a critical component for reliably transmitting the maximum output of an electric vehicle motor. Unlike conventional numerical methods, the proposed thermo-mechanical coupled analysis simultaneously accounts for thermal and mechanical interactions, providing more realistic operational performance predictions. This analysis, conducted using the thermal modules of Ls-Dyna and ANSYS Mechanical, effectively simulated field operation scenarios. Prototype testing under simulated conditions showed a 6% discrepancy compared to numerical predictions, validating the high accuracy and reliability of the proposed method. This robust thermo-mechanical coupled analysis is expected to improve the durability and reliability of CV joint designs, advancing electric vehicle component development.

Research into a Two-Stage Filtration System of Inlet Air to the Internal Combustion Engine of a Motor Vehicle

Research into a Two-Stage Filtration System of Inlet Air to the Internal Combustion Engine of a Motor Vehicle

Justification of the Method of Vehicle Engine Radiator Ultrasonic Cleaning

Justification of the Method of Vehicle Engine Radiator Ultrasonic Cleaning

Analyzing the Dynamics and Friction Forces on Spheres in Fluid Flows: Applications Across Engineering and Sports Science

This study explores the dynamics and friction forces of spherical objects in fluid flows, emphasizing their applications across engineering and sports science. By examining key theoretical concepts such as drag coefficient, Reynolds number, and fluid dynamics equations, the research highlights how these principles influence the motion of spheres in various fluid environments. The analysis encompasses computational fluid dynamics (CFD) and experimental approaches, addressing applications in ocean, aerospace, and vehicle engineering, as well as sports science. The findings demonstrate the critical role of understanding drag and friction characteristics in optimizing designs, improving performance, and driving technological innovation. Challenges such as multi-scale phenomena, nonlinear dynamics, and measurement techniques are identified, suggesting future research directions to refine theoretical models and enhance practical applications. The study not only advances the theoretical understanding of fluid dynamics but also provides essential insights for engineering optimization and sports performance enhancement.

Vehicle Mass Estimation via Practical Supervisory Artificial Neural Networks Using Perturbed Engine Torque and Acceleration Inputs

Various model-based mass estimation approaches have been discussed for a long time. However, estimation performance often deteriorates in some driving situations and, in particular, slow convergence and excessive overshoot of estimates are a major issue for model-based approaches. Meanwhile, mass estimation approaches using ANN models have recently emerged to propose better solutions, but their usefulness has not been fully investigated. Therefore, this paper presents a vehicle mass estimation strategy using practical supervisory artificial neural networks to achieve more accurate results with better convergence. Here, the perturbed engine torque and vehicle longitudinal acceleration are selected as the inputs of the ANN (instead of the original engine torque and vehicle acceleration), which allows for the faster convergence of estimates with high accuracy; these inputs are existing sensor values already available in the vehicle system. The effectiveness of the proposed ANN approach was verified using simulation and software-in-the-loop simulation (SILS) with field test data, and it was found that the convergence speed of the proposed ANN is almost twice as fast as that of the model-based approach, the accuracy is much better, and the estimation quality is constantly stable without any excessive transient responses. This study will provide further insights into mass estimation using the ANN approach.

Ensuring Straight-Line Motion of the Hexacopter in Case of One Engine Failure

It has been observed that the number of accidents due to technical malfunctions in the engines of drone-type unmanned aerial vehicles during mass production has risen significantly. When one of the aircraft’s engines fails, the remaining engines must compensate to maintain the mission. This research focuses on the challenges of controlling hexacopter-type unmanned aerial vehicles with six engines in the event of a single engine failure. The paper outlines the mathematical principles for managing the hexacopter’s engines so that it can continue operating as it did before the malfunction. The findings indicate that the limited power of the remaining engines may not be enough to sustain the mission without adjustments.

THE USE OF VIDEO YOUTUBE TO IMPROVE STUDENTS ENGLISH VOCABULARY AT SMK MITRA NUSANTARA KOTA BEKASI IN ACADEMIC 2023/2024

English is a global language that serves as a link between different parts of the world. Rich vocabulary and mastery of sentence structure are two of the many important elements in learning English. This study aims to identify the effectiveness of using YouTube videos in improving students' vocabulary and learning motivation in learning English. The method used is Classroom Action Research (PTK) involving cycles of planning, action, observation, and reflection. The research subjects were 28 students of class XI of Light Vehicle Engineering at SMK Mitra Nusantara in the academic year 2023/2024. Data were collected through observation, interviews, and initial and final tests. Data analysis was carried out qualitatively and quantitatively. The results showed that there was an increase in students' active participation and an increase in vocabulary scores. The pre-test average score of 59.64 increased to 70.07 in the cycle 1 post-test, and to 80.39 in the cycle 2 post-test. The number of students who met the Minimum Completion Criteria (KKM) increased from 10 students in the pre-test, to 18 students in cycle 1, and 24 students in cycle 2. The findings indicate that the use of YouTube videos as learning media is more effective than conventional methods in improving students' English vocabulary. The researcher suggests that teachers consider the integration of audio-visual media such as YouTube videos in English language learning to improve students' learning outcomes.

A Control System Design for an Intelligent Unmanned Automotive

Article A Control System Design for an Intelligent Unmanned Automotive Yundi Yang †, Xin Gao †, Jinwen You, Dengbo Zhang, Zhuo Zhang and Yuanmei Song * School of Mechanical & Vehicle Engineering, Linyi University, Linyi 276000, China * Correspondence: ymsong321@163.com † These authors contributed equally to this work. Received: 1 August 2024; Revised: 18 September 2024; Accepted: 28 November 2024; Published: 4 December 2024 Abstract: This study is dedicated to developing a control system for an intelligent unmanned delivery vehicle with rear wheel independent drive. This design includes kinematic analysis based on AT89C51 microcontroller, control software development, arc path interpolation technology and control system circuit design. Typically, a high-precision digital encoder was designed to be installed on the rotating shaft of the motor to produce two pulse signals with a phase difference of 90 degrees, which is essential for determining the motion state of the motor. And the detection of the motor’s rotation direction was achieved through a cleverly designed forward circuit. This system will continuously monitor the driving trajectory concluding from the initially predetermined route and the actual trajectory. As a result, this miniature vehicle is engineered to autonomously navigate and meticulously control its movement trajectory with high precision and efficiency, effortlessly navigating both straight lines and intricate arcs, which propelled a significant enhancement in both the overall level of production automation and the efficiency of manufacturing processes.

Contributions on the Use of Dynamic Absorbers in a Vehicle Engine

In the study presented in the paper, a theoretical calculation will be made regarding engine vibration dampers. These will be validated with the experimental model. These validations bring results in static and dynamic balance through the reduction of torsional vibrations in the engine. Torsional vibrations are caused by various factors as well as engine operating conditions. The paper focuses on the reduction of torsional vibrations from a theoretical and experimental point of view. Therefore, in this study, a dynamic absorber was considered which was integrated into the system considered by the paper and was analyzed both from the point of view of its static and dynamic behavior.

Estimation of the RUL of Alkaline Batteries in EV by using Machine Learning Algorithm

The attention on battery longevity and performance has increased due to the quick uptake of electric cars (EVs) as a sustainable substitute for conventional internal combustion engine vehicles. The foundation of EV technology, batteries control the vehicle's dependability, efficiency, and range. With their distinct chemical characteristics, alkaline. Batteries are one of the various battery varieties that may be advantageous for specific EV applications. However, a variety of variables, including charge-discharge cycles, temperature fluctuations, and usage patterns, affect their life cycle and efficiency. To maximize performance, lower maintenance costs, and guarantee user safety, it is crucial to precisely calculate these batteries' Remaining Useful Life (RUL). Conventional approaches to RUL prediction frequently depend on statistical methods or empirical models, which might not take into consideration the intricate relationships between the factors influencing battery deterioration. For a potential solution to this issue machine learning (ML) algorithms, which can examine big datasets and reveal hidden patterns. This study intends to provide a reliable framework for forecasting the RUL of alkaline batteries in EVs by utilizing cutting-edge machine learning models. This will ultimately help to enhance battery management systems and encourage the wider use of EV technology.