Current Vehicle Research Articles

Carbon dioxide life cycle assessment for Brazilian passenger cars fleet towards 2050

Brazil is the second largest producer of ethanol in the world, losing the first position to the United State of America. Nonetheless, several studies have shown that the sustainability of the ethanol produced from the sugarcane is better than their competitor's wheat, corn, and beet in terms of carbon dioxide emissions, energy rate (input/output), productivity, and energy content. Thus, the different incentives from the government since 1975 to the industry allow it to produce technology capable of using any mix of ethanol hydrous and gasoline in the engines, this also brought several developments to sugarcane productivity. So then, there is a new worldwide transition coming to the world with the focus of reducing carbon dioxide emissions. Therefore, this paperwork assesses the main problems of the sustainability transition from current Brazilian Flex-Fuel Vehicles to electric vehicles, in terms of Law 13.755, ROTA2030 and the National Plan of Energy - 2050. The results show that the best option for Brazil as a country, considering socioeconomic and environmental impacts, is to introduce as a priority the Hybrid Electric Vehicles with a flex-fuel engine and then the Plug-in Hybrid Electric Vehicles with a flex-fuel engine. The last priority for the transition of midsize and full-size vehicles should be the Battery Electric Vehicles.

A Novel Squid-Inspired AUV Design: Revolutionizing Coral Reef Preservation Through CFD and Experimental Insights into Drag and Maneuverability

This study focuses on conceptualizing and optimizing an advanced bionic robotic solution to surmount the limitations of current observational vehicles in coral reef conservation. Human divers are traditionally subjected to physiological challenges, while current robotic alternatives grapple with operational impediments, including anthropogenic marine disturbances. Utilizing advanced robotics tailored for marine ecosystems with intricate topologies and fragile corals, this study aims to enhance conservation precision in the face of climatic shifts and marine pollution from anthropogenic activities.This paper develops a squid-inspired Autonomous Underwater Vehicle (AUV), whose behavior is then examined and improved utilizing both experiments and Computational Fluid Dynamics.(CFD). The governing equations considered here are the incompressible Reynolds-Averaged Navier-Stokes (RANS) with an iterative solver for both the pressure-Poisson and momentum equations. Apart from CFD analysis, a prototype was assembled and experimentally tested to validate the CFD outcomes and assess the lift from its bionic rudders. The maneuverability is optimized through careful designs and parameter studies of several features.A head length is determined to balance viscous and pressure drag while achieving the least total drag in the AUV design. Additionally, a disk is introduced at the AUV’s front, which reduces fluid contact, thereby decreasing the viscous drag. While this modification slightly increased pressure drag, it yielded an overall reduction in total drag. Experimental observations confirmed a propulsion speed and underscored the necessity for maximizing rudder extension.The developed AUV’s squid-like design, optimized via CFD, ensures drag reduction by 5.34% and energy efficiency. By mimicking squid dynamics, disturbances to marine biota are minimized. The incorporation of bionic fins, empirically fine-tuned, enhances the AUV’s agility. These attributes establish it as an exceptional prototype, setting a precedent for future coral reef monitoring systems and augmenting conservation strategies.

Contribution to the Objective Evaluation of Combined Longitudinal and Lateral Vehicle Dynamics in Nonlinear Driving Range

<div>Since the complexity of modern vehicles is increasing continuously, car manufacturers are forced to improve the efficiency of their development process to remain profitable. A frequently mentioned measure is the consequent integration of virtual methods. In this regard, objective evaluation criteria are essential for the virtual design of driving dynamics. Therefore, this article aims to identify robust objective evaluation criteria for the nonlinear combined longitudinal and lateral dynamics of a vehicle. The article focuses on the acceleration in a turn maneuver since available objective criteria do not consider all relevant characteristics of vehicle dynamics. For the identification of the objective criteria, a generic method is developed and applied. First, an open-loop test procedure and a set of potential robust objective criteria are defined. Subsequently, suitable criteria are selected for different vehicle dynamics characteristics based on an investigation of Pearson correlations between the objective criteria and established subjective rating criteria. For this purpose, a subjective evaluation study with six specifically selected vehicle variants is conducted. Finally, the applicability of the selected objective criteria for vehicles of different segments is assessed through a benchmark of current vehicles. The results are objective criteria for the vehicle characteristics driving stability, oversteer/understeer, and traction. In contrast to existing objective criteria, the identified criteria shows a high robustness to measurement noise. Furthermore, there is a comprehensible correlation to established subjective rating criteria for each objective criterion. Lastly, the benchmark of current vehicles proves the applicability of the identified criteria.</div>

Developing a low-carbon hybrid of ammonia fuel cell and internal combustion engine for carbon neutrality

With global warming and an increase in extreme weather, reducing carbon emissions in response to climate change has become a global consensus. Compared with traditional fuel vehicles, hybrid vehicles have the benefits of energy savings, low grid-load, and low emissions. The hybrid vehicles use the internal combustion engine and lithium-ion batteries as the hybrid power source to achieve the best match between the engine and the motor. However, the current commercial hybrid vehicle still has such defects as high dependence on fossil fuels, high carbon emissions, poor safety, etc. As such, it is necessary to explore and develop the next generation low-carbon hybrid vehicles. Herein, we propose a new-type hybrid of ammonia fuel cell and internal combustion engine with high-energy conversion efficiency and low-carbon emissions. Considering the advantages of low-carbon emissions, high mileage, fast refueling, low-cost, high-energy conversion efficiency, and high safety, a green low-carbon concept of the ammonia fuel cell-internal combustion engine hybrid vehicle (AFC-ICE HV) is proposed. This will help improve the scale and efficiency of China's green energy conversion, establish a green, low-carbon and sustainable economic system, provide new impetus for tackling climate change and environmental protection recovery, and help China and the world to achieve the Carbon Peak and Carbon Neutrality goal.

CANAttack: Assessing Vulnerabilities within Controller Area Network.

Current vehicles include electronic features that provide ease and convenience to drivers. These electronic features or nodes rely on in-vehicle communication protocols to ensure functionality. One of the most-widely adopted in-vehicle protocols on the market today is the Controller Area Network, popularly referred to as the CAN bus. The CAN bus is utilized in various modern, sophisticated vehicles. However, as the sophistication levels of vehicles continue to increase, we now see a high rise in attacks against them. These attacks range from simple to more-complex variants, which could have detrimental effects when carried out successfully. Therefore, there is a need to carry out an assessment of the security vulnerabilities that could be exploited within the CAN bus. In this research, we conducted a security vulnerability analysis on the CAN bus protocol by proposing an attack scenario on a CAN bus simulation that exploits the arbitration feature extensively. This feature determines which message is sent via the bus in the event that two or more nodes attempt to send a message at the same time. It achieves this by prioritizing messages with lower identifiers. Our analysis revealed that an attacker can spoof a message ID to gain high priority, continuously injecting messages with the spoofed ID. As a result, this prevents the transmission of legitimate messages, impacting the vehicle's operations. We identified significant risks in the CAN protocol, including spoofing, injection, and Denial of Service. Furthermore, we examined the latency of the CAN-enabled system under attack, finding that the compromised node (the attacker's device) consistently achieved the lowest latency due to message arbitration. This demonstrates the potential for an attacker to take control of the bus, injecting messages without contention, thereby disrupting the normal operations of the vehicle, which could potentially compromise safety.

Influence of Traction Battery Arrangement on Risk of Thermal Runaway and Loads Suffered by Electric Vehicle Occupant during Side Collision

In the current electric vehicles, the purpose of the traction battery is to store energy. When designing this battery, different parameters are considered to arrange the battery/module/cells in the mechanically and thermally safest configuration. Moreover, the battery layout must produce correct dynamic behavior during collisions. In the present study, different battery configurations plus added energy absorbers were analyzed. To achieve this, an internal combustion vehicle modeled with finite elements was applied as the reference model. The structural behavior of the different battery configurations in the event of a side collision was examined. First, the safest arrangement was established with respect to both cabin intrusion and thermal runaway propagation. Second, the safest arrangement that guarantees the safety of the occupants in the event of a side collision was analyzed using MADYMO. This software includes experimentally validated dummies that allow insight into the stresses experienced by occupants. The results of the analysis showed that battery pack inclusion in the vehicle increases the stiffness of the car floor, resulting in fewer intrusions into the passenger compartment. Therefore, safety of the occupants is increased. However, none of the configurations analyzed showed sufficient safety against thermal runaway. This study contains sufficient contributions to the new body of knowledge, since there is no study that analyzes the safest configuration in terms of battery behavior with respect to intrusion into the passenger compartment and the effect of thermal runaway, together with the fact that this is the safest configuration for occupants after analyzing the injuries they experience in a side collision for the different configurations.

Easy Online Parking System

Due to the proliferation in the number of vehicles on the road, traffic problems are bound to exist. This is due to the fact that the current transportation infrastructure and vehicle parking facility developed are unable to cope with the influx of vehicles on the road. To alleviate the aforementioned problems Easy Online Parking System will be developed. The main aim of this project is to provide the user with the parking space for their vehicle irrespective of the fact that weather they know or don’t know the location properly they are currently searching for parking space. There are lots of unused places owned by the people which can be utilized for this purpose. A website application is developed in such a way that it works in any browsers. The website offers the most convenient way to park the vehicles. It provides privilege for the owners to sign in and provide details about the place they own and the users will have privilege to sign in and access the nearest and safest available parking place in their location as per their requirements like distance, cost and time. The users get to contact the owners of the place directly and book the space for their vehicle. This helps in keeping the city clean and organized. Hence the parking problem in cities can be reduced. We will be using html for the front-end design and java script for the back-end design.

How to Obtain More Market Shares of Domestic New Energy Vehicles from Behavioral Economics -- Taking Xiaopeng Automobile as an Example

Due to the continuous development of China's economy and society, the ownership of new energy vehicles will continue to rise steadily. At the same time, people begin to pay more attention to environmental pollution, and consumers' willingness to buy new energy vehicles is also strengthening. The development of a new generation of new energy vehicles in China can promote the transformation and upgrading of China's automobile industry, speed up the technological revolution, and reduce energy consumption and environmental pollution. In order to discuss the current China's new energy vehicle how to gain more market share, this paper explores the new marketing model of China's new energy vehicles, the prospect of behavioral economics model, the heavy anchor effect, mental accounts, by using field investigation, respectively from the macro environment, China's new energy vehicle depot. Based on the comprehensive analysis of the current situation and consumer psychology, taking Xiaopeng Automobile as an example, this paper proposes that the market share can be improved by issuing time-effective coupons for car purchase, improving the brand's good first impression and regularly organizing incentive activities with high consumer participation which will provide a reference for the development of other companies in the automotive industry and open up new ideas to promote the development of new energy vehicles in China.

Vehicle detection in diverse traffic using an ensemble convolutional neural backbone via feature concatenation

ABSTRACT Nowadays, deploying an intelligent vehicle detection system (IVDS) in diverse traffic is a work priority. It provides real-time traffic information with vehicle counts and types of vehicles. IVDS deployment in diverse traffic is challenging because different vehicle classes occlude each other on the road. In recent years, convolutional neural network (CNN) based deep learning (DL) methods have attained incredible progress in implementing IVDS. However, most CNN-based DL methods do not include diverse traffic conditions in Asian countries. Also, due to existing feature extraction backbones, they cannot accurately detect multi-scale vehicles. This work proposes an advanced visual computing deep learning (AVCDL) method with a vast labeled vehicle dataset to detect vehicles in diverse traffic. It includes an ensemble backbone and an improved multi-stage vehicle detection head (MSVDH). An ensemble CNN backbone extracts the vehicle features and combines them on a single channel via a feature concatenation. The final detection is carried out by an improved MSVDH that classifies the target vehicles. The proposed method is examined, tested, and evaluated using traffic statistics. It is contrasted with current cutting-edge vehicle detection techniques. It achieves 86.32% mean average precision (mAP) on self-collected diverse traffic labeled dataset (DTLD) and 86.17% mAP on KITTI. Moreover, the real-time performance is validated with NVIDIA Jetson Tx2 and Nano boards. It achieves 15 frames per second (FPS) on Jetson Tx2 and 7 FPS on Jetson Nano.

The Feasibility of Achieving Objectives of the Euro 7 Standards in the Baltic Countries

Abstract Today, the concept of sustainability covers an increasing number of business areas. The transport sector is one of the largest contributors to air pollution in the world today but still plays a fundamental role in Europe’s economic growth. Finding the optimum balance between achieving sustainability objectives and boosting the performance of the transport sector is essential to avoid undermining Europe’s economic activity and growth prospects. One of the main tools for promoting environmental, social and economic sustainability is the EURO 7 standards, which have the most significant impact on the transport sector, and which aim to minimise carbon emissions by phasing out the production of combustion-engined cars by 2035, set stricter emission standards and introduce various emission control mechanisms. Each EU country has ambitious carbon minimisation goals. However, the current vehicle fleet structure analysis of the EU countries shows negative past and future trends in terms of sustainability factors. The goal of the study is to evaluate the feasibility of achieving the carbon minimisation objectives of the EURO 7 standards in the Baltic countries within the timeframe set. In order to achieve this goal, the structure of the Baltic countries vehicle fleets was analysed in terms of sustainability parameters and the transport sector expert forecasts of achieving the carbon minimisation objectives of the EURO 7 standards in the Baltic countries were collected.

Research on Vehicle Detection and Recognition Technology Based on Artificial Intelligence

The recognition and detection technology of automobiles has attracted more and more attention. In order to improve the intelligent recognition technology of automobiles, this paper proposes vehicle detection and recognition technology based on artificial intelligence. After a brief overview of the current vehicle detection and recognition technology, the paper designs an artificial intelligence vehicle detection and recognition technology based on the support vector machine algorithm. The algorithm has a good learning process, and the accuracy of the algorithm's prediction is increased at any time. Gradually improve, in subsequent comparative experiments, it is proved that the recognition rate of the artificial intelligence-based vehicle detection and recognition technology designed in this paper is the highest in common natural weather such as sunny, cloudy and rainy days. This result has important guiding significance for vehicle identification and detection in the current transportation industry.

Asphalt Pavement Compaction and Vehicle Speed Monitoring Using Intelligent Aggregate

The stable skeletal structure formed by the interlocking mechanism of the spatial movement of aggregate particles during compaction is the mechanism by which asphalt pavements are compacted and shaped. A major problem facing conventional compaction monitoring is that lacking the ability to monitor particle movement and a real-time evaluation method for pavement compaction, which can easily lead to problems such as uneven compaction and over-compaction. In addition, vehicle speed is an important parameter for analysing the dynamic response of a pavement. Current vehicle speed monitoring relies on complex field equipment and the collected vehicle speed parameters are difficult to match with other sensors for fusion analysis. In this paper, a method for monitoring the compaction quality of asphalt pavements and a method for collecting vehicle speed is proposed based on intelligent aggregate and aggregates interaction mechanisms. The experimental results show that with the continuous action of the compaction machinery, the compaction state of the asphalt pavement can be reliably captured and monitored by studying the spatio-temporal movement pattern of the intelligent aggregates. Meanwhile, the analysis of the time nodes of intelligent aggregate attitude change under the action of vehicle load allows accurate acquisition of vehicle speed parameters. Therefore, the method proposed in this study can improve the accuracy of asphalt pavement compaction state control as well as the accurate acquisition of vehicle speed. It can provide a reference for the later analysis of the dynamic response of pavement loads and the intelligent compaction of pavements.

Modeling, Model Calibration, and Characterization of Graphite Anodes from Coal Derived Carbon

Most current production electric vehicles (EVs) contain cells with a battery pack or module in order to maintain electrical conductivity, prevent fatigue due to vibrations, and provide efficient cooling. Various module hardware has to be designed in order to facilitate the efficient rejection of heat and contain volume change due to operation and battery cell aging. As we see a shift towards EVs both globally and domestically, the time from concept development to vehicle production needs to rapidly increase. The use of model-based engineering is critical to driving rapid design and virtual prototyping of electric vehicles to ensure that battery module components meet EV requirements.Currently, empirical data is used to characterize and parameterize battery models that are used to drive design decisions at the battery cell and battery module level. Typical vehicle level use profiles such as the Hybrid Pulse Power Characterization (HPPC) profile, constant rate discharging, constant rate charging, and US06 drive cycles are traditionally used to parameterize battery models. In this work, we discuss select cell level testing profiles, evaluate the electrochemical transport and reaction parameters that are estimated using a porous electrode model, and provide insight and recommendations based on data from coal derived graphite for battery applications.

Effect of differential control and sizing on multi-FCS architectures for heavy-duty fuel cell vehicles

The current trend towards a zero-emission transport sector has increased the interest of the scientific community and the industry in fuel cell (FC) technologies in the past few years. Previous studies have focused on passenger car analyses to differentiate them from the current battery electric vehicle (BEV) alternative. However, deploying these technologies may be even more critical for the transportation-produced global emissions if they are used in different applications, such as heavy-duty commercial vehicles. This study uses a differential control strategy to find the best fuel-cell performance for a heavy-duty vehicle application. In addition, and as a differentiation point from other studies in the literature, this article exploits the modularity of the heavy-duty truck sector to implement a design with optimal fuel cell system (FCS) sizing and control dynamics distribution in terms of durability and H2 consumption. Low dynamics could increase 471% in durability just for a 3.8% increase in H2 consumption. When using a multi-FCS with non-equal power FCS, a high dynamics behavior of the small FCS significantly improves the durability for a small consumption penalty (less than 0.7%). The obtained data has proven that the combination of these two design strategies shows an improved vehicle performance that could lead to environmental impact and cost reduction, which is significant in the current development stage of fuel cell vehicle (FCV) technologies.

IoT-Based Smart Battery Management and Monitoring System for Electric Vehicles

The growing popularity of electric vehicles on a worldwide scale leads to further research to monitor their performance. The use of Internet of Things (IoT) technology will make it easier to integrate the automated real-time monitoring system with the current electric vehicle technology. The great majority of electric vehicles use rechargeable lithium-ion batteries. Use of lithium-ion batteries creates an overcharging situation in the battery, which significantly decreases battery life. It also increases the possibility of disastrous safety risks due to fire. This paper develops an IoT-based battery management system to minimize hazardous situations. The battery monitoring system (BMS) notifies the user about the condition of the battery in real time.

Enhancing Lane-Tracking Performance in Challenging Driving Environments through Parameter Optimization and a Restriction System

The autonomous driving market has experienced rapid growth in recent times. From systems that assist drivers in keeping within their lanes to systems that recognize obstacles using sensors and then handle those obstacles, there are various types of systems in autonomous driving. The sensors used in autonomous driving systems include infrared detection devices, lidar, ultrasonic sensors, and cameras. Among these sensors, cameras are widely used. This paper proposes a method for stable lane detection from images captured by camera sensors in diverse environments. First, the system utilizes a bilateral filter and multiscale retinex (MSR) with experimentally optimized set parameters to suppress image noise while increasing contrast. Subsequently, the Canny edge detector is employed to detect the edges of the lane candidates, followed by utilizing the Hough transform to make straight lines from the land candidate images. Then, using a proposed restriction system, only the two lines that the current vehicle is actively driving within are detected from the candidate lines. Furthermore, the lane position information from the previous frame is combined with the lane information from the current frame to correct the current lane position. The Kalman filter is then used to predict the lane position in the next frame. The proposed lane-detection method was evaluated in various scenarios, including rainy conditions, low-light nighttime environments with minimal street lighting, scenarios with interfering guidelines within the lane area, and scenarios with significant noise caused by water droplets on the camera. Both qualitative and quantitative experimental results demonstrate that the lane-detection method presented in this paper effectively suppresses noise and accurately detects the two active lanes during driving.

Examining the effect of moderating variables on autonomous public van acceptance model (APVAM).

Autonomous vehicles (AVs) will soon become the primary means of transportation for millions of people. However, the introduction of these vehicles may lead to increased traffic and changes in people's travel patterns. To address this issue, one solution is to promote the use of AVs in a shared and public manner. However, the success of this approach depends on public acceptance of public AVs. If the promoters of public AVs are unaware of how people perceive this technology, it is possible that these vehicles will face failure in the market. Therefore, this paper aims to identify the factors that influence the willingness to use public AVs. To achieve this, an autonomous public van acceptance model (APVAM) was developed by adapting the unified theory of acceptance and use of technology (UTAUT2) through structural equations modeling. In this study, 824 citizens of Tehran participated in a field study and completed an online questionnaire. The research results indicated that the variable of effort expectancy indirectly affects the use of autonomous public vans (APVs), while the variables of performance expectancy, facilitating condition, hedonic motivation, and perceived PUnTrust directly affect their use. The research also found that various factors such as gender, level of education, individualism/collectivism, travel purpose, the dominant travel mode, marital status, occupation, age, household wealth, number of vehicles owned, and the price of the current vehicle of the household have a moderating effect on the APVAM. Furthermore, it was revealed that individuals who primarily use personal or public transport for their daily trips are more influenced by the perceived enjoyment of APVs in increasing their willingness to use these vehicles, compared to those whose predominant mode of travel is walking or cycling.

Alternative Methods of Replacing Electric Batteries in Public Transport Vehicles

Current electric vehicle solutions offer the possibility of a fully electrified bus fleet, although due to financial constraints, most cities cannot afford it. Therefore, the possibility of battery replacement is a needed alternative to the electrification process of a city’s bus fleet. The aim of this study is to investigate the needs of cities and present the concept of battery replacement in an electric bus. The research was based on two groups of selected Polish cities: (1) up to 150,000 inhabitants, and (2) up to 1 million inhabitants. The research part includes an analysis of the means of transport in provincial cities in Poland, an analysis of the kilometers covered by the city fleet, the average distances covered by buses per day, and an estimate of the number of battery replacements. The concept is based on current technological solutions. The description of the concept includes the proposed battery and the technology used, the placement of the battery in the vehicle, and the replacement scheme. Research indicates that the concept can be used with existing technology but will be more justifiable for a larger city due to the higher fleet load. The paper shows the importance of researching bus electrification solutions and that modern solutions can improve existing urban networks in cities.

Are californians willing to use shared automated vehicles (SAV) & renounce existing vehicles? An empirical analysis of factors determining SAV use & household vehicle ownership

Future smart transportation systems are anticipated to integrate automation and sharing of vehicles. Responding to the expected changes in shared mobility services, this study used representative data from over 4100 households in California to examine consumers' affinity to use shared automated vehicles (SAV) and their willingness to renounce existing vehicle(s) in the presence of SAVs - two interlinked factors determining the long-term success of SAVs. Results of the study showed that around 50 % of the households reported willingness to use SAVs but unwillingness to renounce their current vehicle(s). As innovators, another 9.1 % stated their affinity to use SAVs and renounce existing vehicle(s) but with heterogeneity across vehicle ownership levels. Random parameter logit models revealed that households using existing vehicles to work for ride hailing companies were significantly less likely to use SAVs and renounce their current vehicle(s). Households who had high awareness about self-driving cars, possessed full electric vehicles, used sustainable work travel modes (bike, e-bike, bikeshare), had greater number of leased vehicles and ridesharing trips were more likely to use SAVs and renounce current vehicle(s). Significant unobserved preference heterogeneity was recorded in the effects of behavioral and sociodemographic correlates. The study contributes by shedding new light on the behavioral determinants of the vehicle ownership impacts of SAVs. We discuss policy implications of the key findings.

Optimal design of three-dimensional thermal protection structure considering orthotropic properties of woven composites based on Micro-CT image

The optimal design of a three-dimensional thermal protection structure is urgent for the current hypersonic vehicles. A designed gradient woven material composed of fabric and resin is proposed, thus meeting the lightweight requirements of an advanced thermal protection system. A prediction method based on micro-CT image is established for obtaining the anisotropic thermophysical properties. After validation of the improved models, the deterministic optimization with a kriging surrogate model for the lightweight of the thermal protection structure is implemented. The results show that the prediction method based on micro-CT can accurately provide the anisotropic properties of woven material in simulation for response of thermal protection structure. The heatshield made of woven material with anisotropic thermophysical properties offers better thermal insulation than that made of isotropic material. The optimized thermal protection structure shows a mass reduction of 14.1% at the stagnation point, 14.8% at the flank region, and 15.3% at the edge.