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Mechatronics and Intelligent Transportation Systems
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Mechatronics and Intelligent Transportation Systems (MITS)
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ISSN (print): 2958-020X
ISSN (online): 2958-0218
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2024: Vol. 3
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Mechatronics and Intelligent Transportation Systems (MITS) offers an in-depth exploration of the evolving fields of mechatronics and intelligent transportation. This journal uniquely focuses on the fusion of mechanical engineering, electronic control, and intelligent systems, positioning itself at the forefront of technological advancements in transportation. MITS stands out for its commitment to bridging the gap between theoretical research and practical, real-world applications in mechatronics and transportation systems. Targeting both academic researchers and industry professionals, MITS provides a comprehensive platform for disseminating groundbreaking work in smart transportation technologies and mechatronic systems. The journal is characterized by its thorough coverage of topics like autonomous vehicles, robotics in transportation, and innovative control systems. Published quarterly by Acadlore, the journal typically releases its four issues in March, June, September, and December each year.

  • Professional Service - Every article submitted undergoes an intensive yet swift peer review and editing process, adhering to the highest publication standards.

  • Prompt Publication - Thanks to our proficiency in orchestrating the peer-review, editing, and production processes, all accepted articles see rapid publication.

  • Open Access - Every published article is instantly accessible to a global readership, allowing for uninhibited sharing across various platforms at any time.

Editor(s)-in-chief(2)
yougang sun
Tongji University, China
1989yoga@tongji.edu.cn | website
Research interests: Rail Transit; Maglev Vehicle Dynamics; Nonlinear Control and Intelligent Monitoring; Underactuated Robot; Electromechanical System Control
ana vulevic
Institute of Architecture and Urban & Spatial Planning of Serbia (IAUS), Serbia
anavukvu@gmail.com
Research interests: Urban Planning; Transportation Planning; Accessibility; Mobility; Environment Protection

Aims & Scope

Aims

Mechatronics and Intelligent Transportation Systems (MITS) is a cutting-edge journal dedicated to the latest advancements in mechatronics and intelligent transportation systems, along with their synergistic integration. MITS stands out as a platform for global researchers to present their novel and innovative ideas, particularly in areas such as intelligent vehicle control systems, mechanical engineering in transportation, and smart transportation infrastructure. The journal invites diverse forms of original submissions, including reviews, research papers, and short communications, along with Special Issues on specific topics. MITS is particularly keen on research that enhances transportation planning and operations through the application of new mechatronic technologies.

MITS aims to be a premier source for detailed theoretical and experimental research in its field, imposing no limits on paper length to ensure comprehensive and replicable studies. The journal's distinctive features include:

  • Every publication benefits from prominent indexing, ensuring widespread recognition.

  • A distinguished editorial team upholds unparalleled quality and broad appeal.

  • Seamless online discoverability of each article maximizes its global reach.

  • An author-centric and transparent publication process enhances submission experience.

Scope

The scope of MITS encompasses a broad range of topics, setting it apart from other journals with its focus on the intersection of mechatronics and intelligent transportation:

  • Computer Vision in Transportation: Explores advanced computer vision techniques for traffic monitoring, vehicle detection, and autonomous driving systems.

  • Image Processing Technologies: Focuses on the application of image processing in traffic signal recognition, lane detection, and vehicle classification.

  • Intelligent Transportation Infrastructure: Studies the development of smart roads, traffic management systems, and infrastructure that support intelligent transportation solutions.

  • E-Transportation Innovations: Examines electronic transportation advancements, including electric vehicles, charging infrastructure, and e-mobility services.

  • Development of Intelligent Vehicles: Research on designing and developing intelligent vehicles, encompassing aspects of automation, connectivity, and electrification.

  • Control Systems for Intelligent Vehicles: Investigates advanced control algorithms and systems for enhancing the safety and efficiency of autonomous vehicles.

  • Industrial Design in Transportation: Looks at the role of industrial design in vehicle aesthetics, ergonomics, and user experience.

  • Product Modeling and Design: Explores the process of conceptualizing and creating models for transportation products, emphasizing functionality and user interface.

  • Intelligent Mechatronic Control: Covers the integration of mechatronics in the control of transportation systems, including robotics and automation.

  • Connected Vehicle Technologies: Focuses on vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications for improved traffic flow and safety.

  • Auto Navigation and Driver Assistance: Studies navigation systems and driver assistance technologies such as adaptive cruise control and parking assistance.

  • Electrical Engineering in Transportation: Investigates the role of electrical and electronic engineering in the development of transportation systems, focusing on circuit design, signal processing, and power systems.

  • Lidar and 3D Sensing Technologies: Examines the use of lidar and three-dimensional sensors in vehicle navigation, obstacle detection, and environment mapping.

  • Proximity Sensors in Transportation: Discusses the application of proximity sensors in collision avoidance systems and traffic monitoring.

  • Mechatronic Products and Applications: Exploration of mechatronic products and their applications in transportation.

  • Modeling and Control of Mechatronic Systems: Strategies for modeling and controlling complex mechatronic systems in transportation.

  • Smart Energy Systems in Transportation: Integration of smart energy solutions in future transportation systems.

Articles
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The degradation of road infrastructure presents significant challenges to public safety and maintenance budgets, with cracks serving as critical indicators of structural instability. Despite extensive advancements, existing detection methodologies frequently fail to address complex surface textures, variable illumination, and diverse crack geometries, resulting in inconsistent performance. An adaptive, multi-stage framework has been developed to mitigate these limitations, integrating advanced image processing techniques with fuzzy logic-based analysis. The proposed approach utilises dynamic contrast enhancement and multi-scale feature extraction to ensure accurate detection of both fine and extensive cracks across heterogeneous surfaces. A fuzzy graph-based methodology is employed to evaluate crack connectivity, while an adapted algorithm is applied to assess continuity and severity. The framework incorporates fuzzy wavelet transforms to enhance feature segmentation and employs morphological techniques for precise crack boundary delineation. Dijkstra’s algorithm is integrated to optimise connectivity analysis, facilitating the identification of critical structural deficiencies. The performance of the model has been rigorously validated through extensive experimental testing, achieving an accuracy rate of 94.2%, with high precision and recall metrics. Comparative analysis with conventional techniques reveals a significant reduction in false detection rates and an improved capacity for capturing intricate crack features. The results underscore the practical utility of the proposed model, demonstrating its scalability and reliability across diverse roadway conditions. By enabling early and accurate identification of structural anomalies, the framework enhances roadway safety, minimises maintenance costs, and supports proactive infrastructure management. The findings highlight its potential as a transformative solution for addressing modern challenges in road maintenance, with implications for improved public safety and resource optimisation.

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This study investigates the spatial distribution and potential expansion of electric vehicle (EV) charging infrastructure in Ludhiana, India, with a focus on optimizing site selection to accommodate increasing demand. A multi-criteria framework was employed, incorporating traffic volume, demographic data, and usage patterns of existing charging stations to identify high-priority locations. Central commercial zones, including Ghumar Mandi, Feroze Gandhi Market, ISBT Ludhiana, and Ludhiana Railway Station, were found to exhibit significant traffic density and high EV ownership rates, making them prime candidates for the establishment of new charging stations. Spatial analysis, including heat maps, bar graphs, and pie charts, was used to visualize these key areas, revealing critical patterns in demand and facilitating the strategic targeting of infrastructure expansion. Community engagement was emphasized as an essential component in ensuring that infrastructure development aligns with user needs and preferences. The study further highlighted the importance of accessibility, economic viability, and sustainability as pivotal criteria for site selection. The findings offer valuable insights for urban planners and policymakers, supporting the development of a robust EV charging network that contributes to the advancement of sustainable urban mobility and the reduction of carbon emissions in Ludhiana. These results provide a basis for informed decision-making in the design of EV infrastructure, guiding the city's efforts towards an eco-friendly, future-ready transportation system.

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The rapid growth of population and vehicular traffic has necessitated effective urban planning strategies to mitigate traffic congestion and enhance roadway efficiency. This study focuses on a critical signalized intersection in Konya, Turkiye’s largest metropolitan area, which is notable for its agricultural, industrial, and educational significance. Strategically positioned at the nexus of major transportation routes linking the Black Sea and Central Anatolia regions to the Mediterranean and Aegean areas, Konya exhibits considerable logistical potential. The Coşandere intersection, located in the Selçuklu district, was selected for analysis due to its four-legged configuration, featuring three lanes on both the south and north approaches and two lanes on the east and west approaches. Additionally, suitable turning islands and U-turn pockets are provided on the south and north approaches. Observational data indicate that the evening peak period poses significant operational challenges. A video surveillance system was employed to monitor vehicle movements, yielding a traffic volume of 1,874 vehicles per hour. The existing geometric design, traffic dynamics, and signalization were modelled using PTV Vissim software to assess the intersection's performance. The analysis revealed an average delay of 44.1 seconds per vehicle, an average of 0.9 stops per vehicle, and an average vehicle speed of 29.6 km/h, resulting in a Level of Service (LOS) classification of D. These findings indicate that the intersection currently accommodates traffic demand to a moderate degree. However, substantial improvements in operational efficiency could be achieved through enhancements to the signalization system, including the potential implementation of an adaptive traffic signal control system. This study provides valuable insights for traffic management authorities and urban planners aiming to optimise intersection performance in rapidly developing urban environments.

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Magnetic levitation (maglev) transportation represents an advanced rail technology that utilizes magnetic forces to lift and propel trains, eliminating direct contact with tracks. This system offers numerous advantages over conventional railways, including higher operational speeds, reduced maintenance requirements, enhanced energy efficiency, and reduced environmental impact. However, the dynamic interaction between maglev trains and railway bridges, particularly curved bridges, presents challenges in terms of potential instability during operation. To better understand the dynamic behavior of maglev trains on curved bridges, an experimental study was conducted on the Fenghuang Maglev Sightseeing Express Line (FMSEL), the world’s first “Maglev + Culture + Tourism” route. The FMSEL employs a unique ‘U’-shaped girder design, marking its first application in such a setting. Field test data were collected to analyze the dynamic characteristics of the vehicle, suspension bogie, curved rail, and ‘U’-shaped bridge across a range of train speeds. The responses of both the train and bridge were examined in both time and frequency domains, revealing that response amplitudes increased with train speed. Notably, the ride quality of the vehicle remained excellent, as indicated by Sperling index values consistently below 2.5. Furthermore, lateral acceleration of the train was observed to be lower than vertical acceleration, while for the track, vertical acceleration was consistently lower than lateral acceleration. These findings offer insights into the dynamic performance of maglev trains on curved infrastructure, highlighting key factors that must be considered to ensure operational stability and passenger comfort.

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The spatial configuration of the pantograph-catenary system (PCS) is significantly altered by the superelevation present in curved railway tracks, leading to deviations in the system’s dynamic behaviour and imposing constraints on operational speeds. In this study, a detailed model of the PCS in curved sections has been developed to evaluate the dynamic performance of a dual PCS under these conditions. It was observed that the contact loss rate of the trailing pantograph increases markedly as train speed rises, with this effect being more pronounced in curved sections compared to straight tracks. This degradation in performance necessitates optimisation strategies to ensure operational efficiency at higher speeds. To address the issue, it is proposed that the static uplift force of the trailing pantograph be increased when trains traverse curved sections. Additionally, optimisation of the catenary system is recommended, involving both a reduction in the span length and an increase in the tension of the contact wire. By implementing these strategies, the dual PCS can sustain the necessary contact and satisfy dynamic performance criteria at speeds of up to 300 km/h in curved sections. These findings provide valuable insights for improving the reliability and safety of high-speed railway operations on complex track geometries.

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Maglev transportation, as an innovative mode of rail transit, is regarded as an ideal future transportation system due to its wide speed range, low noise, and strong climbing ability. However, the maglev control system faces challenges such as significant nonlinearity, open-loop instability, and multi-state coupling, leading to issues like insufficient tuning and susceptibility to environmental influences. This paper addresses these problems by investigating the self-tuning parameters of a maglev control system using Q-learning to achieve optimal parameter tuning and enhanced dynamic system performance. The study focuses on a basic levitation unit modeled after the simplified control system of an electromagnetic suspension (EMS) train. A Q-learning reinforcement learning environment and Q-learning agent were established for the levitation system, with a forward "anti-deadlock" reward function and discretization of the action space designed to facilitate reinforcement learning model training. Finally, a Q-learning-based method for self-tuning the parameters of the maglev control system is proposed. Simulation results in the Python environment demonstrate that this method outperforms the Linear Quadratic Regulator (LQR) control method, offering better control effects, improved robustness, and higher tracking accuracy under system parameter perturbations.

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A comprehensive analysis of vehicle collision dynamics is presented using a two-mass model that simulates the impact of a vehicle against a rigid barrier. The model incorporates dual springs and dampers to examine the influence of spring stiffness and damping on a mass attached to the vehicle. The equations of motion are solved utilizing state variables, while energy principles are employed to establish correlations between vehicle deformation, impact force, and acceleration. Validation is conducted through comparison with crash test data from a 2023 Honda Accord LX 4-Door Sedan. Average deformation values are used to calculate acceleration, followed by a Monte Carlo simulation to analyze acceleration data recorded by the engine sensor, enabling the determination of vehicle speed through integration. Parametric regression is applied to optimize model parameters, resulting in a high degree of concordance between experimental and theoretical values. The model's accuracy is further verified through the analysis of velocity and deceleration profiles and the integration of the deceleration curve. The findings underscore the model's capability to replicate real-world crash dynamics, highlighting its potential to enhance vehicle safety system design. The innovation of this research lies in its simplified yet effective approach to modeling collision dynamics, offering significant insights into the relationship between vehicle deformation and occupant forces. This work advances the understanding of vehicle collision mechanics and provides a robust tool for the development of advanced safety features. The integration of theoretical and empirical data reinforces the model's reliability, contributing substantively to the field of automotive safety engineering.

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The towing limits for self-propelled rail track maintenance equipment (SP-TME) are influenced by a multitude of factors, including the type and weight of the equipment, speed, braking capabilities, track and weather conditions, traction, engine power, driveline performance, coupler/towing link integrity, and safety regulations. This study investigates these variables to determine their impact on the towing limits of SP-TME. Unlike traditional rail vehicles, SP-TME possesses unique operational constraints and specifications, necessitating careful consideration of its independent mobility. An extensive analysis was conducted on the towing usage and overuse of SP-TME during travel mode, examining various scenarios that incorporate different combinations of trailing load, rail track grade, rail curvature, and weather conditions. These scenarios, ranging from normal to worst-case, aim to simulate demanding operational environments. The parameters evaluated include structural strength, traction, engine and driveline performance, wheel rolling and skidding, braking capabilities, trailing load, speed, and track and weather conditions. Results indicate that under normal and moderate conditions, the equipment can tow significantly higher loads than the defined base load. However, in special situations, such as negotiating tighter curves and steeper grades in adverse weather conditions, wheel skidding and locking emerge as limiting factors. Findings related to service and parking brake performance during steep grade descents, particularly when the trailer lacks independent braking capabilities, are also presented. Recommendations and cautions are provided to ensure safe and efficient operation of SP-TME under various conditions.

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Through the deployment of bibliometric techniques and network visualizations, this analysis synthesizes the evolution and trajectories of autonomous driving research from 2002 to May 2024, as captured in the Scopus database encompassing 342 scholarly documents. This study was conducted to delineate the developmental contours, thematic emphases, and the expansive growth trajectory within this field, particularly noting a surge in scholarly outputs since 2017. Such growth has been primarily facilitated by breakthroughs in artificial intelligence and sensor technologies, along with burgeoning interdisciplinary collaborations and escalating academic and industrial investments. A meticulous examination of publication trends, document types, subject areas, and geographic distributions elucidates the multidisciplinary and international nature of this burgeoning field. Key thematic clusters identified—comprising foundational technologies, environmental sustainability, safety measures, regulatory frameworks, user experience, connectivity, and technological innovations—underscore the prevailing research directions and emerging focal areas poised to shape future autonomous mobility solutions. Notably, increased emphasis on environmental sustainability and regulatory frameworks has been observed, highlighting their critical roles in advancing and integrating autonomous driving systems. This study provides pivotal insights for researchers, policymakers, and industry stakeholders, fostering a nuanced understanding of the field’s dynamics and facilitating strategic alignments and innovations in autonomous mobility. The emergent research domains and collaborative networks revealed herein not only map the current landscape but also guide future scholarly endeavors in autonomous driving systems globally.

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In the field of pedestrian re-identification (ReID), the challenge of matching occluded pedestrian images with holistic images across different camera views is significant. Traditional approaches have predominantly addressed non-pedestrian occlusions, neglecting other prevalent forms such as motion blur resulting from rapid pedestrian movement or camera focus discrepancies. This study introduces the MotionBlur module, a novel data augmentation strategy designed to enhance model performance under these specific conditions. Appropriate regions are selected on the original image for the application of convolutional blurring operations, which are characterized by predetermined lengths and frequencies of displacement. This method effectively simulates the common occurrence of motion blur observed in real-world scenarios. Moreover, the incorporation of multiple directional blurring accounts for a variety of potential situations within the dataset, thereby increasing the robustness of the data augmentation. Experimental evaluations conducted on datasets containing both occluded and holistic pedestrian images have demonstrated that models augmented with the MotionBlur module surpass existing methods in overall performance.

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To address the lack of multi-perspective, real-time monitoring and management of operations and equipment in automated container terminals, a digital twin system targeted at monitoring automated container terminal equipment has been designed and developed. Based on the concept of a five-dimensional model of digital twins, a digital twin framework for monitoring automated container terminal equipment was constructed. The system's maintainability is enhanced through a layered design, which also reduces coupling between different functional modules. A multi-dimensional, multi-scale virtual scene was built and model consistency evaluations were conducted to verify the system. The system's operational efficiency was improved by optimizing model rendering with discrete level of detail (LOD) techniques. A multi-layered distributed solution for the digital twin system was proposed to achieve multi-perspective monitoring. Ultimately, using a specific automated container terminal as a case study, a system prototype was developed, realizing multi-perspective digital monitoring of terminal operations and equipment. This project offers a solution for the application of digital twin technology in the field of automated container terminals and promotes the development of intelligent digital terminals.

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The acceleration of urbanization and the consequent increase in population have exacerbated urban road traffic issues, such as congestion, frequent accidents, and vehicle violations, posing significant challenges to urban development. Traditional manual traffic management methods are proving inadequate in meeting the demands of rapidly evolving urban environments, necessitating an enhancement in the intelligence level of urban road traffic management systems. Recent advancements in computer vision and deep learning technologies have highlighted the potential of image processing and machine learning-based traffic management systems. In this context, the application of object detection and tracking technologies, particularly the YOLOv5 and Deep learning-based Simple Online and Realtime Tracking (DeepSORT) algorithms, has emerged as a pivotal approach for the intelligent management of urban traffic. This study employs these advanced object detection and tracking technologies to identify, classify, track, and measure vehicles on the road through video analysis, thereby providing robust support for urban traffic management decisions and planning. Utilizing digital twin technology, a virtual replica of traffic flow is constructed from camera data, serving as the dataset for training different YOLOv5 algorithm variants (YOLOv5s, YOLOv5m, and YOLOv5l). Upon comparison of training outcomes, the YOLOv5s model is selected for vehicle detection and recognition in video feeds. Subsequently, the DeepSORT algorithm is applied for vehicle tracking and matching, facilitating the calculation of vehicles' average speed based on tracking data and the temporal interval between adjacent frames. Results, stored in Comma-Separated Value (CSV) format for future analysis, indicate that the system is capable of accurately identifying, tracking, and computing the average speed of vehicles across various traffic scenarios, thereby significantly supporting urban traffic management and advancing the intelligent development of urban road traffic. This approach underscores the critical role of integrating cutting-edge object detection and tracking technologies with digital twin models in enhancing urban traffic management systems.

Open Access
Research article
Evaluating the Road Environment Through the Lens of Professional Drivers: A Traffic Safety Perspective
aleksandar trifunović ,
aleksandar senić ,
svetlana čičević ,
tijana ivanišević ,
vedran vukšić ,
sreten simović
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Available online: 03-03-2024

Abstract

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In the context of traffic safety, the interplay between the road environment and the human factor emerges as a critical determinant of the severity of road crash consequences. This study was designed to explore the perceptions of professional drivers regarding the road environment, with a particular focus on the elements that either contribute to or mitigate safety risks. A comprehensive survey was conducted, wherein 118 professional drivers from the Republic of Serbia were asked to rate photographs depicting various road environments in terms of safety. The investigation aimed to elucidate the extent to which these drivers recognize and assess road hazards, as well as to examine potential variations in their evaluations based on demographic characteristics. The findings underscore the significant impact of the road environment on traffic safety, particularly highlighting the role of solid obstacles such as trees, pillars, and masonry objects. When vehicles veer off the road, collisions with these obstacles frequently result in exacerbated outcomes of road crashes. The methodology employed in this research involved a quantitative analysis of the survey responses, ensuring a systematic evaluation of the drivers' perceptions. The study contributes to the existing body of knowledge by offering insights into the evaluative processes of professional drivers concerning the road environment, thereby informing strategies aimed at enhancing driver safety.

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In the face of the increasingly demanding of goods transportation and storage, the orchestration of cold chain logistics emerges as a critical and multifaceted endeavor. This study, addressing a notable gap in literature, establishes a comprehensive framework for temperature monitoring within cold chain logistics, focusing particularly on transportation and warehousing aspects. The complexity of managing temperature-sensitive goods is amplified by the burgeoning number of entities involved in this sector, underscoring the need for a robust monitoring approach. Recent global challenges have precipitated a series of disruptive events, further complicating the reliable transport of temperature-sensitive commodities. In light of these challenges, the necessity for meticulous temperature control during both transportation and warehousing phases is paramount; lapses in this regard could lead to grave consequences. A thorough analysis of existing cold chain delivery systems was conducted, alongside an examination of various temperature monitoring devices utilized in vehicle cargo compartments and storage facilities. The study not only scrutinizes current trends but also introduces novel solutions for effective monitoring. By exploring and evaluating these elements, the research contributes significantly to both theoretical and practical spheres, offering a solid foundation for future investigations and guidance for practitioners and decision-makers in the field. This exploration revealed the imperative for advanced sensor technologies and integrated data management systems, capable of providing real-time, accurate temperature readings throughout the entire cold chain process. The integration of smart transportation solutions, leveraging Internet of Things (IoT) technology, emerges as a pivotal factor in enhancing the reliability and efficiency of temperature monitoring. Additionally, the study underscores the importance of standardized protocols and practices across the industry to ensure consistency and reliability in temperature management. In conclusion, the framework proposed in this study not only addresses existing challenges in cold chain logistics but also paves the way for innovative approaches in temperature monitoring, fostering enhanced quality control and safety in the transportation and storage of temperature-sensitive goods.

Open Access
Research article
Cause Analysis of Whole Vehicle NVH Performance Degradation under Idle Conditions
haiping lai ,
huaguang xu ,
nian liu ,
jieliang guo ,
ruiqiang zhang ,
haigang wei
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Available online: 01-16-2024

Abstract

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The NVH (noise, vibration, harshness) performance of automobiles is a key issue in enhancing user comfort. However, car manufacturers and original equipment manufacturers often invest more research and development effort into the new car performance at the initial design stage, neglecting the study of whole vehicle NVH durability and reliability, and this can significantly affect the user's riding experience. This paper focuses on the phenomenon of NVH performance degradation under idle conditions. Using LMS data acquisition equipment and software, vibration acceleration and frequency at 17 points on the vehicle, including the steering wheel, seat rail, and engine mount, were collected and analyzed. By conducting comparative experiments, the causes of NVH performance degradation after long mileage were explored. This aims to provide new ideas for improving the durability and reliability of whole vehicle NVH in future research and production.
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