Car System Research Articles

Influence of coconut and castor oil coating on engine intake non-woven filter performance

PurposeThis research focuses on the development and characterization of oil-wetted spun-bonded polypropylene (PP) non-woven filters for improved air intake systems in automobiles. The study aims to enhance engine performance, durability, fuel economy and emission reduction by addressing key aspects such as contaminants filtration efficiency, loading capacity, pressure drop, temperature performance and longevity.Design/methodology/approachThe research methodology involves the utilization of textile fabrics, particularly oil-wetted spun-bonded PP non-woven filters, renowned for their effective particle collection capability from intake air. Experiments were conducted using a Box–Behnken design with three variables – oil concentration, areal density and dust quantity – each at three different levels to establish correlations with the filter’s dust holding capacity (DHC) and pressure drop.FindingsThe findings indicate that immersing particles in oil-coated medium significantly enhances the filter’s DHC. Notably, castor oil as a coating demonstrates remarkable results, with a 97.53% increase in DHC and a high particulate matter filtration efficiency of 94.12%.Originality/valueThis study contributes to the originality of research by emphasizing the importance of oil density in determining the filter’s DHC and filtration efficiency. Furthermore, it highlights the superiority of castor oil over coconut oil-coated filter media, advancing air intake and/or filter systems for automotive engines.

Stochastic analysis of six wheeler automobile system model with a spare wheel

Stochastic analysis of six wheeler automobile system model with a spare wheel

Multibody Dynamics and Terramechanics-based modeling and simulation of Quarter-Car with Suspension

Abstract While tires only compress when acted upon by a load in on-road scenarios, in off-road conditions, on the other hand, the tires not only undergo compression but also sinkage into the ground, in a manner that depends on the terrain. The tire-ground contact, in turn, affects the forces acting on the suspension system. Hence, it is necessary to study the vertical dynamic response of a system in off-road conditions by accounting for the terramechanics phenomenon (tire-terrain interface). The forces acting at the tire and terrain interface in off-road conditions have been studied in terramechanics separately; however, their integration with multibody systems is relatively less explored. In this paper, a multibody dynamic-based modeling and simulation of a quarter car with a linear spring-damper suspension system for off-road vehicles is presented. The generalized external forces are obtained using the Bekker-Wong based soil pressure-sinkage approach for two different kind of multibody systems interacting with sandy loam terrain. This model is integrated with the Tangent Space Ordinary Differential Equations of the quarter car and suspension system and solved using numerical integration techniques. Thus, by accounting for the terramechanics in the multibody system, the technique provides a more realistic response of the system in off-road scenarios.

Theoretical and experimental considerations regarding the airbag system operation

Abstract The concept of Passive Safety aims to reduce the harmful effects on vehicle occupants and pedestrians in the event of an accident. Intelligent car bodies and restraint systems have been designed. Thus, side, front, knee and head airbag systems have been introduced, as well as pre-strained seat belts and force limiters. The objectives of car manufacturers are not only to reduce injuries that can cause death, but also a significant reduction of those that can result in permanent damage such as a whiplash. Advanced airbag systems are currently introduced, which offer protection to the occupants at the knees, torso, hips and abdomen. The topic proposed in the paper concerns both theoretical research on the operation of passive security systems, respectively the airbag system, and experimental research. Regarding the theoretical research, this paper proposes a virtual prototyping method by means of the LS Dyna software package of the airbag system functioning in order to protect the driver in the event of a frontal collision. For the analysis of the airbag system functioning, a complex assembly which contains of a dummy, seat, steering wheel, passive safety systems was used. For the experimental research a stand was designed for the analysis and testing of the airbag system, which has the role to protect the driver in the event of a frontal collision of the vehicle. In order to carry out the experimental tests, it was proposed to create a sledge type stand, which simulates frontal collisions.

Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.

It is estimated that 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of driving recorders in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement, vehicle position on the road, or driver monitoring. Driver monitoring is a technology that has been exploited little so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based on the recording of facial expressions, gaze direction, and head position and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the driver's state by monitoring their facial expressions, such as the frequency of blinking, yawning, eye-opening, gaze direction, and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows the creation of a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The results show that the proposed multi-index methodology performs better in detecting drowsiness than algorithms based on one-index detection.

Agricultural Field Monitoring Robot: Using IoT

Abstract: The agricultural field monitoring robot is a big step forward in precision farming. It uses technology like path memorization and smart IoT to help farmers manage their fields better. The robot can move around the farm on its own, remembering where it's been to do tasks more efficiently later. It's built on a platform-Arduino car system, which is controlled via Bluetooth. The smart IoT part comes from a platform called BlynkIOT, this system gathers data in real-time, helping farmers keep track of things like crop health and environmental conditions. With these technologies combined, the robot can do things like planting seeds, watering crops, and checking their health without needing much help from humans. Overall, it's meant to help farmers work smarter, save money on labor, and farm in a more sustainable way.

Scenario Identification and Classification to Support the Assessment of Advanced Driver Assistance Systems

In recent years, driver assistance systems in cars, buses, and trucks have become more common and powerful. In particular, the introduction of AI methods to sensors, signal fusion, and traffic recognition allows us to step forward from actual level-2 assistance to level-3 Advanced Driver Assistance Systems (ADAS), where driving becomes autonomous and responsibility shifts from the driver to the automobile manufacturers. This, however, requires a high-precision risk assessment of failure, which can only be achieved by extensive data acquisition and statistical analysis of real traffic scenarios (which is impossible to perform by humans). Therefore, critical driving situations have to be identified and classified automatically. This paper develops and compares two different strategies—a traditional rule-based approach derived from deterministic causal considerations, and an AI-based approach trained with idealized cut-in, cut-out, and cut-through maneuvers. Application to a 10-h measurement sequence on a German highway demonstrates that the latter has the higher performance, whereas the former misses some of the safety-relevant events to be identified.

Advanced Driver Assist System for Automobiles

Advanced Driver Assist System for Automobiles

Semiactive Car-Seat System for Rear-End Collisions

This study proposes and simulates a smart system that can be used in production car seats to decrease whiplash risk in rear-end crashes. A sliding seat incorporating a semiactively controlled magnetorheological (MR) damper model positioned under the seat-pan is simulated with a validated biofidelic human body model. Since this is the first study that demonstrates a computer controlled anti-whiplash car seat system to the best of the authors’ knowledge, a benchmark analysis is carried out to compare the proposed semiactive seat with a state-of-the-art passive anti-whiplash car seat using 23 different crash pulses, including the moderate and high severity crash pulses within the European New Car Assessment Program (EuroNCAP) whiplash risk assessment framework. The proposed semiactive design outperforms the passive seat design by further reducing the values of the critical EuroNCAP whiplash criteria, such as NIC and Nkm, together with the loads acting on the upper neck. The semiactive seat lowers the upper-neck shear force by an amount of 4 kg and 7 kg while lessening the NIC by 10% and 21% and Nkm by 9% and 56% for the EuroNCAP crash pulses, having a delta-V of 16 km/h and 24 km/h, respectively. The findings presented in this paper can aid in the design of car seats to further mitigate whiplash risk in rear-end crashes.

Design and optimization of planetary wheel systems for formula racing cars

Design and optimization of planetary wheel systems for formula racing cars

Automobile system for predicting the trajectory of surrounding vehicles

Automobile system for predicting the trajectory of surrounding vehicles

Design of Radial Flux PM Synchronous Motor for EV Applications

Abstract: High-performance electric propulsion systems are becoming increasingly important as the automotive industry rapidly shifts to electric cars (EVs). The electric motor is an essential part of these systems as it determines the power, efficiency, and overall performance of the vehicle.This project uses Altair Flux, a full-featured finite element analysis (FEA) software suite, to build and optimize a radial flux motor (RFM) with permanent magnet synchronous motor (pmsm) . The main goal is to increase the power density and efficiency of the electric propulsion system for electric cars by utilizing Altair Flux's simulation and analytical capabilities.

Voltage inspector: Sender identification for in-vehicle CAN bus using voltage slice

Controller Area Network (CAN) serves as the neural system of modern cars, connecting and coordinating various electronic control units (ECUs) responsible for vehicle operation. However, the inherent features of CAN, such as broadcast communication and lack of authentication, make it increasingly vulnerable to cyberattacks. Although existing intrusion detection systems (IDSs) perform well in detecting malicious attacks, they often lack the ability to accurately locate the senders of these malicious messages. In this paper, we propose an efficient sender identification method called Voltage Inspector, which leverages physical voltage signal slice to accurately identify the source of messages for CAN bus. We start by extracting voltage slices from the raw physical signals of the CAN bus. Next, we leverage clustering technology to infer the ECU mapping information, which is typically considered confidential. This mapping information, combined with a machine learning classifier, is then utilized to construct an identification model capable of accurately identifying the sender of each message. To validate the effectiveness of our proposed method, we conducted extensive experiments using a publicly available voltage dataset collected from ten real vehicles. The experimental results demonstrate the remarkable accuracy of our approach, achieving a minimum identification accuracy of 99%. Furthermore, our method significantly reduces the data volume by half and reduces the identification time by a quarter when compared to state-of-the-art methods. Our research reveals that even a small portion of the voltage signal can be used to uniquely fingerprint an ECU. We emphasize that our method serves as an alternative identification approach and can complement existing works in the field.

Modeling and Implementation of PID Controller in Vehicle Ride Comfort Improvement Using Bond Graph

The proportional integral derivative (PID) controller method is a famous and influential approach used in a variety of engineering domain systems. This paper presents Bond Graph modelling and implementation of PID controller in the active quarter-vehicle suspension system for the purpose of achieving better ride comfort. Bond graph is a better tool for modelling multi-domain energy systems with control hardware associated with it. SYMBOL SHAKTI software is used to simulate the active vehicle suspension system, which is theoretically represented as a 4-DOF human Bio-dynamic model combined with a 3-DOF quarter automobile system. The suggested PID Controller's performance is contrasted with that of the passive model. To measure the effectiveness of the suggested PID controller suspension system, time domain evaluations of systems performance criteria are conducted. The findings show that a significant improvement in ride comfort is offered by the suggested Bond graph PID controller model of the active vehicle suspension.

Influence of pneumatic tire enveloping behavior characteristics on the performance of a half car suspension system using multi-objective optimization algorithms

The interaction between a vehicle's tire and the road surface is pivotal for a driver's control over the vehicle's movements. It serves as the fundamental link between the vehicle and the road. The modeling of tires holds significant importance in contemporary vehicle design, playing a critical role in assessing aspects such as vehicle handling, ride comfort, and road load analysis. This study focuses on investigating the impact of the enveloping behavior characteristics of a pneumatic tire on the performance of a suspension system. The analysis of the vehicle's ride comfort utilizes a half-car model. Unlike a previous model with a single point of contact with the road, the presented suspension system, coupled with a four-degree-of-freedom rigid ring tire model, offers a more precise estimation of both ride comfort and road holding. The primary emphasis of this research lies in the modeling and evaluation of the proposed suspension system's performance. A comprehensive computer model of the entire system is developed using MATLAB software. This work enhances the existing framework by incorporating both a Multi-Objective Genetic Algorithm (MOGA) and a Multi-Objective Particle Swarm Optimization (MOPSO) to optimize the damping and stiffness coefficients of the passive suspension. This approach allows for a detailed comparison of the optimization capabilities and effectiveness of both algorithms in refining vehicle ride comfort. The results from MATLAB simulations highlight performance improvements, and the comparative analysis of MOGA and MOPSO provides insights into the selection of optimization techniques for suspension system design.

Aggregation operators based on Einstein averaging under q-spherical fuzzy rough sets and their applications in navigation systems for automatic cars

This study introduces innovative operational laws, Einstein operations, and novel aggregation algorithms tailored for handling q-spherical fuzzy rough data. The research article presents three newly designed arithmetic averaging operators: q-spherical fuzzy rough Einstein weighted averaging, q-spherical fuzzy rough Einstein ordered weighted averaging, and q-spherical fuzzy rough Einstein hybrid weighted averaging. These operators are meticulously crafted to enhance precision and accuracy in arithmetic averaging. By thoroughly examining their characteristics and interrelations with existing aggregate operators, the article uncovers their distinct advantages and innovative contributions to the field. Furthermore, the study illustrates the actual implementation of these newly constructed operators in a variety of attribute decision-making scenarios employing q-SFR data, yielding useful insights. Our suite of decision-making tools, including these operators, is specifically designed to address complex and uncertain data. To validate our approach, this study offers a numerical example showcasing the real-world applicability of the proposed operators. The results not only corroborate the efficacy of the proposed method but also underscore its potential significance in practical decision-making processes dealing with intricate and ambiguous data. Additionally, comparative and sensitivity analyses are presented to assess the effectiveness and robustness of our proposed work relative to other approaches. The acquired knowledge enriches the current understanding and opens new avenues for future research.

Heuristic power management of hybrid source electric vehicle based on PV-battery-PEMFC

Renewable energy sources are now being focused on for usage in electric vehicles. Although majority of electric vehicles run solely on batteries, they have eliminated local emissions but not pollution. To tackle this issue, this research looks into the performance and power management aspects of an electric vehicle's hybrid power system, with a particular emphasis on the interactions between fuel cells, batteries, and photovoltaic (PV) cells. Stable operation is ensured via a bi-directional buck-boost converter, which maintains the voltage from all sources around reference 430 V. Initially, the fuel cell supplies electricity under low light conditions. The PV system and battery take over when the irradiance surpasses 100 W/m2, with the battery first draining and then charging in tandem with the increase in irradiance. 1750 RPM is the constant motor speed that is reached after making the required torque adjustments. A model full electric vehicle with multiple sources has been proposed with an efficient power management algorithm. When the battery is in stationary mode and the state of charge (SOC) is less than 100%, extra solar power is used to charge the battery. The model also takes into consideration using the car for entertainment or gadget charging when the ignition is off. The biogas output can be regulated using an inferential control technique using artificial neural network (ANN) based on the fuel demand or consumption. The system also includes anaerobic digestion of organic waste to provide hydrogen for the fuel cell sustainably from biogas. Power, current, SOC profiles, motor performance graphs, and other validations show that the extensive simulations exhibit efficient power management and a strong model for hybrid electric car power systems. The model has been simulated in the MATLAB Simulink environment and has been tested under realistic conditions.

Observational study on the identification of left atrial tachycardia circuits using Local Activation Time Histogram.

Mapping of atypical atrial arrythmias arising in the left atrium is often challenging. The Local Activation Time (LAT) Histogram, a new function of the 3D color mapping system CARTO version 7, may help improve identification of atrial tachycardia circuits. We aimed to assess the effectiveness of the LAT Histogram for identification of left atrial tachycardia circuits. This retrospective study compared 25 consecutive cases of left atrial tachycardia that were treated before use of LAT Histogram (unused group) and 25consecutive cases that were treated after introduction of LAT Histogram (used group) at Nagano Chuo Hospital. We evaluated whether we could identify the circuit of left atrial tachycardia from the electrophysiology lab data during ablation and the CARTO system data and whether we could perform effective ablation. Door-to-door time, skin-to-skin time, and fluoroscopy time (p≤.011) were all shorter in the LAT Histogram used group versus unused group, while mapping analysis times were longer in the used group (p ≤.019). A significantly greater number of cases in the LAT Histogram used compared with the unused group had ablation for entrance or exit points (19vs. 10 cases; p=.001 for first map). Ablation resulted in a return to sinus rhythm and changed cycle length at the first mapping in 20 cases (80%) in the LAT Histogram unused group and in 24 cases (96%) in the used group. LAT Histogram may provide a simple and effective method to identify entrance and exit locations in left atrial tachycardia.

Evaluation of comfort zone boundary based automated emergency braking algorithms for car‐to‐powered‐two‐wheeler crashes in China

AbstractCrashes between cars and powered two‐wheelers (PTWs: motorcycles, scooters, and e‐bikes) are a safety concern; as a result, developing car safety systems that protect PTW riders is essential. While the pre‐crash protection system automated emergency braking (AEB) has been shown to avoid and mitigate injuries for car‐to‐car, car‐to‐cyclist, and car‐to‐pedestrian crashes, much is still unknown about its effectiveness in car‐to‐PTW crashes. Further, the characteristics of the crashes that remain after the introduction of such systems in traffic are also largely unknown. This study estimates the crash avoidance and injury risk reduction performance of six different PTW‐AEB algorithms that were virtually applied to reconstructed car‐to‐PTW pre‐crash kinematics extracted from a Chinese in‐depth crash database. Five of the algorithms include combinations of drivers’ and PTW riders’ comfort zone boundaries for braking and steering, while the sixth is a traditional AEB. Results show that the average safety performance of the algorithms using only the driver's comfort zone boundaries is higher than that of the traditional AEB algorithm. All algorithms resulted in similar distributions of impact speed and impact locations, which means that in‐crash protection systems likely can be made less complex, not having to consider differences in AEB algorithm design among car manufacturers.

Lane Line and Object Detection Using Yolo v3

In the contemporary age, creating autonomous vehi- cles is a crucial starting point for the advancement of intelligent transportation systems that rely on sophisticated telecommu- nications network infrastructure, including the upcoming 6g networks. The paper discusses two significant issues, namely, lane detection and obstacle detection (such as road signs, traffic lights, vehicles ahead, etc.) using image processing algorithms. To address issues like low accuracy in traditional image processing methods and slow real-time performance of deep learning-based methods, barriers for smart traffic lane and object detection algorithms are proposed. We initially rectify the distorted image resulting from the camera and then employ a threshold algorithm for the lane detection algorithm. The image is obtained by extracting a specific region of interest and applying an inverse perspective transform to obtain a top-down view. Finally, we apply the sliding window technique to identify pixels that belong to each lane and modify it to fit a quadratic equation. The Yolo algorithm is well-suited for identifying various types of obstacles, making it a valuable tool for solving identification problems. Finally, we utilize real-time videos and a straightforward dataset to conduct simulations for the proposed algorithm. The simula- tion outcomes indicate that the accuracy of the proposed method for lane detection is 97.91% and the processing time is 0.0021 seconds. The proposal for detecting obstacles has an accuracy rate of 81.90% and takes only 0.022 seconds to process. Compared to the conventional image processing technique, the proposed method achieves an average accuracy of 89.90% and execution time of 0.024 seconds, demonstrating a robust capability against noise. The findings demonstrate that the suggested algorithm can be implemented in self-driving car systems, allowing for efficient and fast processing of the advanced network.