Car Model Research Articles

Dynamic Behavior Analysis of the Bus with Two-stage Asymmetric Damper Using the Quarter Car Model Subjected to Transient Road Profile

The influences of two-stage asymmetric damper on the dynamic responses under transient road inputs in range of different vehicle velocities were investigated. This study’s purpose is to get full understanding of the dynamic behavior of the bus using the two-stage asymmetric damper. The body’s vibration acceleration, the tire dynamic load coefficient, and the suspension dynamic deflection were all analyzed to determine how much vibration was conveyed to the bus. By evaluating these criteria, this paper shows how the two-stage asymmetric damper affected an inner-city bus’s vibrational behavior. The transient input was employed in accordance with the GB/T 4970-2009 and IRC-99-1988 standards. For the purposes of the investigation, the two degrees of freedom quarter car model has been employed to carry out the simulation. According to the analysis's findings, a two-stage asymmetric damper maintains the better ride comfort level but offers the bus less handling stability and working space than a linear symmetric one.

Challenges and Countermeasures of the Automobile Marketing Model in the New Media Era

With the continuous development of intelligent technology, the technology of "Internet plus" is also increasingly developed. Therefore, it is necessary to innovate and reform the automobile marketing model in the new era to better adapt to the modern society and thus increase the turnover. The article analyzes the advantages of China's automobile marketing model under the new media environment, analyzes the challenges of the automobile marketing model, and discusses the relevant countermeasures to promote the smooth development of automobile marketing, hoping to make automobile marketing more orderly and create more economic benefits for the development of the automobile industry. Comprehensively analyze the current situation and trend of China's automobile development, reform and innovate China's automobile marketing model, analyze the management, marketing, news communication process and communication phenomenon of automobile sales, and propose countermeasures for automobile marketing under the new media environment, with the purpose of improving the competitiveness of China's automobile sales enterprises.

Computational Seated Pedestrian Impact Design of Experiments with Ultralight Wheelchair.

Pedestrians who use wheelchairs (seated pedestrians) report higher mortality rates than standing pedestrians in vehicle-to-pedestrian collisions but the cause of this mortality is poorly understood. This study investigated the cause of seated pedestrian serious injuries (AIS 3+) and the effect of various pre-collision variables using finite element (FE) simulations. An ultralight manual wheelchair model was developed and tested to meet ISO standards. The GHBMC 50th percentile male simplified occupant model and EuroNCAP family car (FCR) and sports utility vehicle (SUV) were used to simulate vehicle collisions. A full factorial design of experiments (n = 54) was run to explore the effect of pedestrian position relative to the vehicle bumper, pedestrian arm posture, and pedestrian orientation angle relative to the vehicle. The largest average injury risks were at the head (FCR: 0.48 SUV: 0.79) and brain (FCR: 0.42 SUV: 0.50). The abdomen (FCR: 0.20 SUV: 0.21), neck (FCR: 0.08 SUV: 0.14), and pelvis (FCR: 0.02 SUV: 0.02) reported smaller risks. 50/54 impacts reported no thorax injury risk, but 3 SUV impacts reported risks ≥ 0.99. Arm (gait) posture and pedestrian orientation angle had larger effects on most injury risks. The most dangerous arm posture examined was when the hand was off the wheelchair handrail after wheel propulsion and the two more dangerous orientations were when the pedestrian faced 90° and 110° away from the vehicle. Pedestrian position relative to the vehicle bumper played little role in injury outcomes. The findings of this study may inform future seated pedestrian safety testing procedures to narrow down the most concerning impact scenarios and design impact tests around them.

An optimal control approach to the computation of g-g diagrams

The so-called g-g diagram is widely employed to assess the performance of road vehicles since it provides the maximum longitudinal and lateral accelerations that can be extracted from the selected vehicle. The different points of the g-g diagram are usually computed independently from one another, e.g. the maximum longitudinal accelerations corresponding to given lateral accelerations are computed sequentially. In this work, the problem of computing the points of the g-g diagram is transformed into an optimal control problem, which allows to avoid one of the most annoying practical problems often encountered when deriving such diagrams, namely the ‘jumps’ between different solutions (i.e. large slip vs. small slip solutions at similar lateral acceleration) in adjacent points along the g-g envelope. In the proposed method, the objective is to maximise the area enclosed within the g-g diagram, which has a polar representation. The rate of change of the radial coordinate is the control input. Additional constraints are included to enforce the continuity between the states of the vehicle model in adjacent points along the envelope. These conditions can hardly be enforced using standard methods based on sequential and independent computation of the g-g points. An example of the application of the method is demonstrated on the classic double-track car model with nonlinear tyres.

A Multi-Stage Deep-Learning-Based Vehicle and License Plate Recognition System with Real-Time Edge Inference.

Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates' Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models' accuracy by taking advantage of the temporally redundant information of the video stream's frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022.

“Is Energy That Different from Labor?” Similarity in Determinants of Intensity for Auto Assembly Plants

This paper addresses the question “Is energy that different from labor?” from the perspective of efficiency. It presents a novel statistical analysis for the auto assembly industry in North America to examine the determinants of relative energy intensity, and contrasts this with a similar analysis of the determinants of another important factor of production, labor intensity. The data used combine two non-public sources of data previously used to separately study key performance indicators (KPIs) for energy and labor intensity. The study found these two KPIs are statistically correlated (the correlation coefficient is 0.67) and the relationship is one-to-one. The paper identifies 11 factors that may influence both energy and labor intensity KPIs. The study then contrasts which of the empirical factors the two KPIs’ share and how they differ. Two novel statistical methods, Huber estimators and Multiple M-estimators, combined with regularized algorithms, are identified as the preferred methods for robust statistical models to estimate energy intensity. Based on our analysis, the underlying determinants of energy efficiency and labor productivity are quite similar. This implies that strategies to improve energy may have spillover benefits to labor, and vice versa. The study shows vehicle variety, car model types, and launch of a new vehicle penalize both energy and labor intensity, while flexible manufacturing, production volume, and year of production improve both energy and labor intensity. In addition, the study found that the plants that produce small cars are more energy-efficient and productive compared to plants that produce large vehicles. Moreover, in a given functional unit, i.e., on a per-unit basis, Japanese plants are more energy-efficient and productive compared to American plants. Plant managers can use the proposed data-driven approach to make the right decisions about the energy efficiency targets and improve plants’ energy efficiency up to 38% using hybrid regression methods, mathematical modeling, plants’ resources, and constraints.

The effect of a braking maneuver on the occupant’s kinematics of a highly reclined seating position in a frontal crash

Objective This study aims to make the comparison of the kinematics and possible injury values of the occupant in the highly reclined seating position in two cases: the first case is without the braking maneuver prior to the frontal crash, and the second case is considering the braking maneuver prior to the impact. Methods This study used the MADYMO human body model 50th percentile mid-size male seated on the front passenger seat model with the seatback reclined rearward 53° (fully reclined seatback) to simulate the occupant biomechanics and behavior during a frontal collision. To achieve the study aim, two simulation models were created. The first model is called the non-precrash model (VaAM-O). It was set to simulate the occupant kinematics in the fully reclined initial seating posture in the 200 ms of a crash without the braking maneuver before the crash. The second model, called the precrash model (VaAM-I), identified the occupant’s initial position after the car model performed a 1s deceleration of a braking maneuver. VaAM-I has then also simulated the occupant kinematics in 200 ms of a crash. Results This study showed a significant difference in the occupant’s posture at the beginning of the crash phase in the two cases. The upper torso of VaAM-I rotated up and moved forward earlier than VaAM-O prior to the crash phase due to the influence of the braking maneuver, and at the 50 ms, when the airbag deployed completely, the head had no contact with the airbag, and the head in VaAM-I forwarded to the airbag closer than VaAM-O about 24% of the distance to the airbag. Conclusions This study supplements the value data for the study of the out-of-position OOP of the occupant in the frontal crash. The contact force and position contact between the head and the airbag could attract the audience’s attention. On the other hand, a comparison of the seatbelt force and the seatbelt contacts the body between the conventional seatbelt as in this study, and the seat-integrated seatbelt system in the case wherein the occupant seats on the front seat with a highly reclined seatback should be performed.

Drive-by bridge damage detection using Mel-frequency cepstral coefficients and support vector machine

Bridge damage detection using vibration data has been confirmed as a promising approach. Compared to the traditional method that typically needs to install sensors or systems directly on bridges, the drive-by bridge damage detection method has gained increasing attention worldwide since it just needs one or a few sensors instrumented on the passing vehicle. Bridge frequencies extracted from the vehicle’s vibrations can be good references for damage detection. However, extant literature considered mainly low-frequency responses of the vehicle, while the high-frequency responses that also contained the bridge’s damage information were often ignored. To fill this gap, this paper developed a damage detection approach that utilized both low and high-frequency responses of the passing vehicle. Mel-frequency cepstral coefficients (MFCCs) and support vector machine (SVM) were employed to classify damage severity. Firstly, the vehicle’s frequency responses are utilized as input features to train SVM models to identify the bridge’s condition. Then, to reduce dimensions of inputs and improve training efficiency, frequency responses are projected from the Hertz scale into the Mel scale, and two means using MFCCs are used to feed different SVM models. A laboratory experiment with a U-shaped continuous beam and a model car was used to verify the effectiveness of the proposed method. Results showed that high-frequency responses contain much information about the bridge’s conditions, and using MFCCs could apparently improve computational efficiency. The errors of damage detection when a heavy car was employed were within 5%.

A Comparative Study of Integrated Vehicle–Seat–Human Models for the Evaluation of Ride Comfort

In the literature the value of the driver’s head acceleration has been widely used as an objective function for the modification of the suspension and/or the seat characteristics in order to optimize the ride comfort of a vehicle. For these optimization procedures various lumped parameter Vehicle–Seat–Human models are proposed. In the present paper a Quarter Car model is integrated with three Seat–Human models with different levels of detail. The level of detail corresponds to the number of degrees of freedom used to describe the Seat–Human system. Firstly, the performance of the Quarter Car model, used as a basis, is analyzed in six excitations with different characteristics. Then, the performance of the three lumped parameter Vehicle–Seat–Human models are monitored in the same excitations. The results indicated that in the case of single disturbance excitations the Quarter Car model provided 50–75% higher values of acceleration compared with the eight degrees of freedom model. As far as the periodic excitation is concerned, the Vehicle–Seat–Human models provided values of acceleration up to eight times those of the Quarter Car model. On the other hand, in stochastic excitations the Vehicle–Seat–Human model with three degrees of freedom produced the closest results to the Quarter Car model followed by the eight degrees of freedom model. Finally, with respect to the computational efficiency it was found that an increase in the degrees of freedom of the Vehicle–Seat–Human model by one caused an increase in the CPU time from 2.1 to 2.6%, while increasing the number of the degrees of freedom by five increased the CPU time from 7.4 to 11.5% depending on the excitation.

Statistical Study of Eco-Efficiency in Compact and Average Cars (Chevrolet, Ford, VW, Fiat, Renault) in Brazil Based on the Metro Table in 2019

This article aimed to identify the most efficient and ecological car models. Based on the analysis of cars classified as “light vehicles” in 2019 by INMETRO data in Brazil. A program in C helped analyze the frequency distribution of the variables of CO2 emission and mileage per liter, allowing us to evaluate the efficiency of the compact and medium models of the “light” class. At the end of the study, there was an emphasis on the eco-efficiency of the vehicle assemblers Fiat and Renault.

Is the Transition to Electric Passenger Cars Sustainable? A Life Cycle Perspective

Compared to conventional passenger cars, the hybrid and electric alternatives include electric motors and large batteries; the use of clean energy, reduced operation emissions, and decreasing purchase prices can represent solid reasons for their market adoption. The feasibility of the transition to electric cars is analyzed herein in terms of the costs, main pollutants, and energy consumption of compact to large-sized cars. In this regard, the proposed life cycle assessment methodology evaluates the pollution and energetic impacts of the current passenger car models with a weight varying from 1.6 to 1.8 tons, depending on the car type, for a complete life cycle. The life cycle emissions and energy consumption are also determined through simulation in order to validate the estimated values for the considered powertrains. This study has shown that a transition to current full-electric passenger cars, based on a European and United States energy mix, is not currently sustainable in terms of energy consumption. The complete life cycle values are similar for the tested conventional and full-electric passenger cars, ranging from 5 to 5.2 MJ/km. By comparison, the hybrid alternatives and full-electric cars based solely on renewable energy present lower energy consumption, ranging from 3.32 to 4.62 MJ/km. At the same time, the hybrid alternatives and conventional cars provide relevant benefits in life cycle costs: 20–25% lower than full-electric cars. In terms of life cycle emissions, the tested full-electric cars based on renewables show a noticeable reduction in greenhouse gases and in other relevant pollutants: 37% and 62%, respectively, lower than that of conventional cars.

Can a nonlinear quasi-zero-stiffness spring improve the ride quality of a vehicle?

The paper examines the possibility of using a nonlinear Quasi-Zero-Stiffness (QZS) spring in a vehicle suspension. The response of a Single Degree of Freedom (SDOF) model to harmonic base excitations is obtained which may be unstable and unbounded depending on the excitation level and the damping ratio. This is followed by obtaining the response of the SDOF model to harmonic base excitation with an amplitude that is varying according to a road profile spectrum. Such dependency of the excitation amplitude to the frequency changes the qualitative behaviour of the nonlinear system and the responses would be always bounded. The QZS spring also improves the isolation of the system. Transient responses of a quarter car model with a QZS suspension to a road hump and random road profile are investigated. The maximum acceleration of the vehicle with the QZS suspension passing over the speed hump is considerably lower than a vehicle with a conventional linear suspension. Wb weighted RMS of acceleration (BS 6841-1987) is also lower by as much as 14% for a vehicle with the QZS suspension travelling at 30 km/h on a class E road compared to its linear counterpart.

Bayesian modelling of phosphorus content in wheat grain using hyperspectral reflectance data

BackgroundAs a result of the technological progress, the use of sensors for crop survey has substantially increased, generating valuable information for modelling agricultural data. Plant spectroscopy jointly with statistical modeling can potentially help to assess certain chemical components of interest present in plants, which may be laborious and expensive to obtain by direct measurements. In this research, the phosphorus content in wheat grain is modeled using reflectance information measured by a hyperspectral sensor at different wavelengths. A Bayesian procedure for selecting variables was used to identify the set of the most important spectral bands. Additionally, three different models were evaluated: the first model assumes that the observations are independent, the other two models assume that the observations are spatially correlated: one of the proposed models, assumes spatial dependence using a Conditionally Autoregressive Model (CAR), and the other through an exponential correlogram. The goodness of fit of the models was evaluated by means of the Deviance Information Criterion, and the predictive power is evaluated using cross validation.ResultsWe have found that CAR was the model that best fits and predicts the data. Additionally, the selection variable procedure in the CAR model reveals which wavelengths in the range of 500–690 nm are the most important. Comparing the vegetative indices with the CAR model, it was observed that the average correlation of the CAR model exceeded that of the vegetative indices by 23.26%, − 1.2% and 22.78% for the year 2010, 2011 and 2012 respectively; therefore, the use of the proposed methodology outperformed the vegetative indices in prediction.ConclusionsThe proposal to predict the phosphorus content in wheat grain using Bayesian approach, reflect with the results as a good alternative.

An investigation of a bus’s ride comfort by using the quarter car model with linear asymmetric damper

The linear asymmetric damper’s effects on the ride comfort, safety, and handling control were investigated to get full understanding of the suspension system design processes of an inner-city bus. The dynamic responses were analyzed for both two cases of linear symmetric and linear asymmetric dampers using the quarter car model with two degrees of freedom (2 DOFs) subjected to random road profiles and single bump cases of triangular and sine-squared bumps. The obtained results show that the linear symmetric damper performs the same as that of the linear asymmetric one in terms of comprehensive performance when the vehicle is subjected to a random road profile. The ride comfort, the working space have been significantly improved in specific range of velocity in case of the linear asymmetric damper. However, in general, slightly better performance has been obtained in the linear symmetric damper case.

Parametric study of rider’s comfort in a vehicle with semi-active suspension system under transient road conditions

Considering that many Nigerian roads are untarred, the effect of frequent plying of these untarred roads on passengers and the expected performance of suspension system of vehicles are important for health and safety reasons. This is significant because the transmission of vibration associated with suspension systems are dependent on the frequency spectrum of the road input, and the nature of the suspension system and the vehicle seating arrangement that is producing the vibration. Thus, this study focuses on the discomfort experienced by passengers based on parametric study of vehicle with semi-active suspension system under transient road conditions. The modeling of the of semi-active vehicle suspension system properties are contrived on the mass- spring damper system for 4 degree-of–freedom half- car model integrated with 3 degrees of freedom human-seat arrangement. Using vehicle parameters, the severity of ride discomfort experienced by the passenger as the vehicle traversed transient road conditions (i.e., traversing obstruction) was evaluated in terms of the vibration dose value (VDV). Results of simulation based on the parametric studies are presented and the vibration dose values evaluated to show the dependence of vehicle ride comfort on the characteristics of the various elements of the vehicle suspension such as stiffness and damping characteristics. The result showed that the variation of sprung mass and suspension stiffness of the vehicle had more significant effects on passenger discomfort than the variation of the unsprung mass. The parametric study also revealed that suspension stiffness affects the suspension working space as the vehicle traversed transient road condition.

Data-Driven Fault Early Warning Model of Automobile Engines Based on Soft Classification

Since automobile engine fault is the main factor leading to a vehicle breaking down, engine fault diagnosis has captured a lot of attention. Fault diagnosis identifies fault types to facilitate maintenance. However, the method of the warning before the fault occurs is more attractive to users and is more challenging. Therefore, this study would like to explore the feasibility of implementing automobile engine fault early warning based on the fault diagnosis model. First, the theoretical method of a fault domain is established, and the state of the engine is regarded as a point in n-dimensional space. The normal or fault of the engine will correspond to different state domains in this space. Second, to diagnose multiple fault types at the same time, an ensemble model based on multiple machine learning methods is established. The probability outputs by the ensemble model measure the distance between the point and each fault domain in the space. Finally, considering the temporal factor, an early warning threshold is established based on the probability, and a fault warning model is established by using the dual probability structure. Comparative experiments show that the proposed method can greatly reduce the calculation time based on ensuring the accuracy of early warning and is suitable for real-time early warning of multiple faults.

Evaluation of image quality metrics designed for DRI tasks with automotive cameras

Driving assistance is increasingly used in new car models. Most driving assistance systems are based on automotive cameras and computer vision. Computer Vision, regardless of the underlying algorithms and technology, requires the images to have good image quality, defined according to the task. This notion of good image quality is still to be defined in the case of computer vision as it has very different criteria than human vision: humans have a better contrast detection ability than image chains. The aim of this article is to compare three different metrics designed for detection of objects with computer vision: the Contrast Detection Probability (CDP) [1, 2, 3, 4], the Contrast Signal to Noise Ratio (CSNR) [5] and the Frequency of Correct Resolution (FCR) [6]. For this purpose, the computer vision task of reading the characters on a license plate will be used as a benchmark. The objective is to check the correlation between the objective metric and the ability of a neural network to perform this task. Thus, a protocol to test these metrics and compare them to the output of the neural network has been designed and the pros and cons of each of these three metrics have been noted.

Factors Affecting Customer Satisfaction in Purchasing Car

Globalization of the markets coupled with economic downturn had changes the pattern of customer behavior and consumption patterns. The customer buying behavior is a complex topic as many internal and external factors have impact on the level of satisfaction of the customer. From the past decade, previous researchers had attempted to understand how customers’ needs their responses and feedbacks. In 2017, the number of Honda Civic 2017 Model car owners has reached 109,511 units in Malaysia and the Malaysia’s southern region occupied 34% of Honda’s total sales compared to others car model (Honda, 2018; Lye, 2018). Seeing that the demand for this model is high, it is crucial to study the car owners’ satisfaction align with the automobile and organizational standards, especially through effective customer satisfaction measurement model. The objective of the research is to study the relevant factors that affecting customers’ satisfaction in purchasing Honda Civic car (model 2017). This research studied the factors (price, customers’ services, brand image and quality) in influencing the customers’ satisfaction. The results show that customers’ services and quality have significant relationship towards customers’ satisfaction. The findings would be useful for academicians to further study on factors related with this area or to find out whether similar to apply this to other industry as well.

Car subscription services: Automakers' shift towards servitized and sustainable business models

Servitized and sustainable business models are growing in the automotive industry, where car manufacturers are shifting towards providing new mobility solutions. Car subscription business models are among these new solutions. However, they are scarcely explored in servitization and sustainability studies regarding their implementations and sustainable benefits. Thus, this article aims to investigate the process of implementing car subscription business models from the automaker's perspective. Case-based research was carried out in a Brazilian subsidiary of a multinational automaker. Our findings highlight implementation practices and prospects for expanding the car subscription offerings. We also identify the internal and external challenges faced by the automaker to implement the car subscription business model. Lastly, we explore its sustainable benefits, such as the expanded revenue and customer base, the improved use of resources, and the customer's well-being. Negative externalities generated by the car subscription business model are also discussed. This study contributes to the theory by shedding light on implementing car subscription business models and their sustainable outcomes. The article also contributes by assisting managers in implementing car subscription business models.

Dynamic Vehicle Modelling and Controlling Techniques for Autonomous Vehicle Systems

The driving scenario of an automated vehicle is the crucial technology in the design of autonomous cars. This suggested approach aims to address the shortcomings of autonomous cars, such as their poor real- time performance and low control precision. The process for building a virtual simulation environment for autonomous vehicle testing and validation is described in this study. Model Predictive Control and Proportional Integral and Derivative Control are used in MATLAB simulation to build three car models. These are related to the 2D and 3D animation used in collision detection and visualization. The virtual engine visualization is included throughout the model. A variety of test circumstances are used to validate the simulation model, and the model’s performance is assessed in the presence of various barriers. The simulation's findings demonstrate that the autonomous vehicle has a strong potential for self-adaptation even in challenging and complex working environments. No instances of car sideslip or track departure have been noted. It is discovered that this autonomous car performs remarkably well overall when compared to other autonomous vehicles. The suggested approach is essential for enhancing autonomous vehicle driving safety, maintaining vehicle control in challenging situations, and improving the advancement of intelligent vehicle driving assistance.