Journal Articles - SAE Mobilus
SAE journals provide rigorously peer-reviewed, archival research by subject matter experts--basic and applied research that is valuable to both academia and industry.
In order to meet the driving characteristics and needs of different types of drivers and to improve driving comfort and safety, this article designs personalized variable transmission ratio schemes based on the classification results of drivers’ steering characteristics and proposes a switching strategy for selecting variable transmission ratio schemes in response to changes in driver types. First, data collected from driving simulator experiments are used to classify drivers into three categories using the fuzzy C-means clustering algorithm, and the steering characteristics of each category are analyzed. Subsequently, based on the steering characteristics of each type of driver, suitable speed ranges, steering wheel travel, and yaw rate gain values are selected to design the variable transmission ratio, forming personalized variable transmission ratio schemes. Then, a switching strategy for variable transmission ratio schemes is designed, using a support vector machine to build a
This study explores the effectiveness of two machine learning models, namely multilayer perceptron neural networks (MLP-NN) and adaptive neuro-fuzzy inference systems (ANFIS), in advancing maintenance management based on engine oil analysis. Data obtained from a Mercedes Benz 2628 diesel engine were utilized to both train and assess the MLP-NN and ANFIS models. Six indices—Fe, Pb, Al, Cr, Si, and PQ—were employed as inputs to predict and classify engine conditions. Remarkably, both models exhibited high accuracy, achieving an average precision of 94%. While the radial basis function (RBF) model, as presented in a referenced article, surpassed ANFIS, this comparison underscored the transformative potential of artificial intelligence (AI) tools in the realm of maintenance management. Serving as a proof-of-concept for AI applications in maintenance management, this study encourages industry stakeholders to explore analogous methodologies. Highlights Two machine learning models, multilayer
Vibration comfort is a critical factor in assessing the overall performance of engineering machinery, with significant implications for operator health and safety. However, current evaluation methods lack specificity for construction machinery, impeding accurate prediction of vibration comfort and hindering the optimization of noise, vibration, and harshness (NVH) performance. To address this challenge, this article proposes a model that combines a random forest with a genetic algorithm (GA-RF) to enable rapid and accurate prediction of vibration comfort in construction machinery cabins. The approach begins with an improved objective evaluation methodology for extracting key features from vibration signals at five measurement points: seat, floor, back, and left and right armrests. Additionally, subjective evaluation technology, combining semantic differential and rating scales, is employed to capture operators’ personal comfort perceptions. The implementation of the GA-RF model
Modern diesel engines temporarily use a very late post-injection in the combustion cycle to either generate heat for a diesel particulate filter regeneration or purge a lean NOx trap. In some configurations, unburned fuel is left at the cylinder walls and is transported via the piston rings toward the lower crankcase region, where fuel may dilute the oil. Reduced oil lubrication shortens the oil service intervals and increases friction. Beside diesel fuel, this problem may also occur for other types of liquid fuels such as alcohols and e-fuels. The exact transport mechanism of the unburned fuel via the piston ring pack grooves and cylinder wall is hard to measure experimentally, motivating numerical flow simulation in early design stages for an in-depth understanding of the involved processes. A new CFD simulation methodology has been developed to investigate the transient, compressible, multiphase flow around the piston ring pack, through the gap between piston and liner, and its
Toward the goal of “dual carbon economy” development, new energy hybrid commercial vehicles have become the main vehicles to meet the future fuel consumption and emission targets. In order to meet the high requirements of commercial vehicles on power and to minimize the influence of ambient temperature on the power of the vehicle, this study proposes a composite energy storage system (CESS) incorporating ultracapacitors. To further understand the impact of ultracapacitor on the dynamic performance of the vehicle, this study compares the dynamics of series range-extended hybrid pickup trucks with and without ultracapacitor at ambient and low temperatures, as well as the effect of ultracapacitor on the service life of lithium-ion batteries, by means of simulation. The results show that at room temperature (25°C), the addition of ultracapacitor shortens the 0–100 km/h acceleration time of the whole vehicle by 24.4% and improves the off-road climbing performance by 11.7%; at low
This article proposes a new model for a cooperative and distributed decision-making mechanism for an ad hoc network of automated vehicles (AVs). The goal of the model is to ensure safety and reduce energy consumption. The use of centralized computation resource is not suitable for scalable cooperative applications, so the proposed solution takes advantage of the onboard computing resources of the vehicle in an intelligent transportation system (ITS). This leads to the introduction of a distributed decision-making mechanism for connected AVs. The proposed mechanism utilizes a novel implementation of the resource-aware and distributed–vector evaluated genetic algorithm (RAD-VEGA) in the vehicular ad hoc network of connected AVs as a solver to collaborative decision-making problems. In the first step, a collaborative decision-making problem is formulated for connected AVs as a multi-objective optimization problem (MOOP), with a focus on energy consumption and collision risk reduction as
This study aims to elucidate the impact of A-pillar blind spots on drivers’ visibility of pedestrians during left and right turns at an intersection. An experiment was conducted using a sedan and a truck, with a professional test driver participating. The driver was instructed to maintain sole focus on a designated pedestrian model from the moment it was first sighted during each drive. The experimental results revealed how the blind spots caused by A-pillars occur and clarified the relationship between the pedestrian visible trajectory distance and specific vehicle windows. The results indicated that the shortest trajectory distance over which a pedestrian remained visible in the sedan was 17.6 m for a far-side pedestrian model during a right turn, where visibility was exclusively through the windshield. For the truck, this distance was 20.9 m for a near-side pedestrian model during a left turn, with visibility through the windshield of 9.5 m (45.5% of 20.9 m) and through the
In this research, we propose a set of reporting documents to enhance transparency and trust in artificial intelligence (AI) systems for cooperative, connected, and automated mobility (CCAM) applications. By analyzing key documents on ethical guidelines and regulations in AI, such as the Assessment List for Trustworthy AI and the EU AI Act, we extracted considerations regarding transparency requirements. Recognizing the unique characteristics of each AI system and its application sector, we designed a model card tailored for CCAM applications. This was made considering the criteria for achieving trustworthy autonomous vehicles, exposed by the Joint Research Centre (JRC), and including information items that evidence the compliance of the AI system with these ethical aspects and that are also of interest to the different stakeholders. Additionally, we propose an MLOps Card to share information about the infrastructure and tools involved in creating and implementing the AI system
The present work deals with the effects of nano-additives on ternary blend biodiesel fuel added in diesel engine. The ternary blend comprises of mustard oil biodiesel and rice bran oil biodiesel, synthesized by means of transesterification and diesel. Nano-additives used in the current study include carbon nanotubes (CNT) and MgO/MgAl2O4 spinel, which were added in a suitable concentration to the biodiesel. CNTs were procured from the market and MgO/MgAl2O4 spinel was prepared by co-precipitation via ball milling process. The nano-additives were characterized by means of FTIR (Fourier transform infrared spectroscopy), AFM (atomic force microscopy), and DSC (differential scanning calorimetry) analysis. Biodiesel blend samples were prepared such as B20 (20% biodiesel + 80% diesel), B20 + CNT (1000 PPM), B20+MgO/MgAl2O4 spinel (1000 PPM), and B20+CNT+MgO/MgAl2O4 spinel (1000 PPM) were tested against diesel fuel. The maximum increase in brake thermal efficiency (BTE), oxides of nitrogen
The increased use of computational human models in evaluation of safety systems demands greater attention to selected methods in coupling the model to its seated environment. This study assessed the THUMS v4.0.1 in an upright driver posture and a reclined occupant posture. Each posture was gravity settled into an NCAC vehicle model to assess model quality and HBM to seat coupling. HBM to seat contact friction and seat stiffness were varied across a range of potential inputs to evaluate over a range of potential inputs. Gravity settling was also performed with and without constraints on the pelvis to move towards the target H-Point. These combinations resulted in 18 simulations per posture, run for 800 ms. In addition, 5 crash pulse simulations (51.5 km/h delta V) were run to assess the effect of settling time on driver kinematics. HBM mesh quality and HBM to seat coupling metrics were compared at kinetically identical time points during the simulation to an end state where kinetic
The electronic mechanical brake (EMB) system is a critical actuator for achieving brake-by-wire control. This review categorizes and summarizes the literature related to EMB into three sections: actuator, mathematical modeling, and control strategies. In the actuator aspect, this article compares and analyzes motors, motion conversion mechanisms, and self-reinforcing mechanisms. For mathematical modeling, this article reviews modeling methods for EMB systems concerning motors, transmission mechanisms, friction, contact collisions, nonlinear stiffness, and hysteresis characteristics. Regarding control strategies, this article consolidates methods for clamp force control, clamp force estimation, and gap management. Finally, the article discusses potential future research directions in EMB from both hardware structure and software algorithm perspectives
With the influx of artificial intelligence (AI) models aiding the development of autonomous driving (AD), it has become increasingly important to analyze and categorize aspects of their operation. In conjunction with the high predictive power innate to AI solutions, due to the safety requirements inherent to automotive systems and the demands for transparency imposed by legislature, there is a natural demand for explainable and predictable models. In this work, we explore the various strategies that reveal the inner workings of these models at various component levels, focusing on those adapted at the modeling stage. Specifically, we highlight and review the use of explainability in state-of-the-art AI-based scenario understanding and motion prediction methods, which represent an integral part of any AD system. We break the discussion down across three key axes that are inherent to any AI solution: the data, the model architecture, and the loss optimization. For each of the axes, we
Accurate estimation of vehicle energy consumption plays an important role in developing advanced energy-saving connected automated vehicle technologies such as Eco Approach and Departure, PHEV mode blending, and Eco-route planning. The present study developed a reduced-order energy model with second-order response surfaces and torque estimation to estimate the energy consumption while just relying on the drive cycle information. The model is developed for fully electric Chevrolet Bolt using chassis dynamometer data. The dyno test data encompasses the various EPA test cycles, real-world, and aggressive maneuvers to capture most powertrain operating conditions. The developed model predicts energy consumption using vehicle speed and road-grade inputs for a drive cycle. The accuracy of the model is validated by comparing the prediction results against track and road test data. The developed model was able to accurately predict the energy consumption for track drive cycles within the error
The advent of neck braces for the helmeted motorcycle rider has introduced a pertinent research question: To what extent do they reduce measures related to the major mechanism of neck injury in unrestrained torso accidents, i.e., compression flexion (CF)? This question requires a suitable method of testing and evaluating the measures for a load case resulting in the required mechanism. This study proposes a weighted swinging anvil striking the helmeted head of a supine HIII ATD by means of a near vertex impact with a low degree of anterior head impact eccentricity to induce CF of the neck. The applied impact was chosen for the baseline (no neck brace) so that the upper and lower neck axial forces approached injury assessment reference values (IARV). The head impact point evaluated represents those typically associated with high-energy burst fractures occurring within the first 20 ms, with possible secondary disruption of posterior ligaments. The proposed test can be used to evaluate
With the development of automotive intelligence and networking, the communication architecture of automotive network is evolving toward Ethernet. To improve the real-time performance and reliability of data transmission in traditional Ethernet, time-sensitive network (TSN) has become the development direction of next-generation of automotive networks. The real-time advantage of TSN is based on accurate time synchronization. Therefore, a reliable time synchronization mechanism has become one of the key technologies for the application of automotive Ethernet technology. The protocol used to achieve accurate time synchronization in TSN is IEEE 802.1AS. This protocol defines a time synchronization mechanism suitable for automotive Ethernet. Through the master clock selection algorithm, peer link delay measurement, and clock synchronization and calibration mechanism, the time of each node in the vehicle network is synchronized to a reference master clock. In addition, the protocol clearly
Ammonia-fired reciprocating engines have emerged as a promising technology in the maritime and power generation sector at medium-to-large scale (1–80 MW). The use of “on-the-fly” partial ammonia decomposition to produce a relatively small amount of hydrogen that can be used as combustion promoter, replacing fossil fuels in this function, enables this technology to provide carbon-free propulsion and power generation. In this context, it is envisioned that a hydrogen-fired prechamber ignition strategy offers significant advantages by accelerating the ammonia ignition and complete combustion process, increasing its reliability and robustness while still aiming to achieve low NO x , N2O, and NH3 emissions. This study exploits an OpenFOAM-based Large Eddy Simulation (LES) numerical modeling framework to investigate the ignition and combustion behavior of an ammonia main charge ignited by a hydrogen-fired prechamber. First, a conventional port-injection premixed configuration for the ammonia
Vehicle path tracking and stability management are critical technologies for intelligent driving. However, their controls are mutually constrained. This article proposes a cooperative control strategy for intelligent vehicle path tracking and stability, based on the stable domain. First, using the vehicle’s two-degrees-of-freedom (DOF) model and the Dugoff tire model, a phase plane representation is constructed for the vehicle’s sideslip angle and sideslip angular velocity. An enhanced method utilizing five eigenvalues is employed to partition the vehicle stability domain. Second, by employing the divided vehicle stable domain, the design of a fuzzy controller utilizes the Takagi–Sugeno (TS) methodology to determine the weight matrix gain for path tracking and stability control. Subsequently, a fuzzy model predictive control (TS-MPC) cooperative control strategy is designed, which takes into account both the precision of path tracking and the stability of the vehicle. Finally, a
Motor temperature plays a critical role in controlling pump speed and regulating coolant flow to prevent overheating during motor operation. Presently, negative temperature coefficient (NTC) sensors are commonly used for motor temperature measurement, typically installed at the motor winding’s end for ease of installation. However, in oil spray-cooled motors, the temperature distribution is uneven due to the spray pipe, leading to lower temperatures near the pipe compared to other areas. This results in a challenge where relying solely on NTC measurements at the winding end may not meet the motor’s cooling requirements. To address this issue and improve temperature signal accuracy, a novel approach has been developed that utilizes four signals derived from the motor controller: motor speed, motor torque, along with oil pump speed, oil temperature. Employing the lumped parameter method, a model established in Simulink aims to estimate the average temperature in the motor’s high
The construction of urban transportation infrastructures on the supply side is severely limited due to the extensive development of central urban land. Therefore, optimizing the traffic structure with limited resources is particularly important. The work used the optimum capacity of the road network as one of the constraints. Multi-objective linear programming was used to establish the traffic structure model. The total travel volume, energy consumption, travel quality, and social cost were selected as the optimization objectives of the urban transportation structure. The influencing factors of infrastructure capacity (e.g., total travel demand, optimal capacity of road network, slow traffic capacity, and parking lot capacity) were selected as the constraint conditions in optimizing urban transportation structure. The objective was to develop an optimization model considering the constraints of urban infrastructure. Finally, the optimal traffic structure was compared with the actual
Methanol, as a renewable fuel, is an attractive option for internal combustion engines. The dual direct injection method is one of the most promising strategies for applying methanol fuel in diesel engines as the flexible injection control enables combustion mode switching. In this study, a 1-L single-cylinder common-rail diesel engine with a compression ratio of 17.4 is retrofitted by installing an additional methanol direct injector with 35 MPa injection pressure. The engine is operated at 1400 rpm, intermediate load, and fixed midpoint combustion phasing of 10 °CA aTDC with a fixed total amount of energy while applying an energy substitution principle with up to 70% energy supplied by methanol. From the experiments, three distinct combustion modes were identified. When early methanol injection timings were selected in the range of 180–60 °CA bTDC, the primary combustion mode was premixed burn. Late injection timings of 10 °CA bTDC to TDC led to heat release rate shapes of the
Many cities are built around rivers in the world, and the river-crossing corridors are often their traffic bottlenecks, leading to severe congestions. Changsha is a city divided into two parts by a river with eight river-crossing corridors in China. Aiming at this issue, take Changsha as an example, this study explores developing a precise traffic restriction policy on those river-crossing corridors. First, an investigation is conducted to collect traffic flow data of those corridors. It is found that those corridors generally have serious congestion at peak hours, but their congestion levels vary greatly by corridor and direction. Then, two Greenberg models are developed for the 4-lane and 6 & 8-lane corridors, respectively, to figure out their traffic flow features. Third, a precise traffic restriction policy that balances traffic flows in different corridors is proposed. It would restrict 10% of motor vehicles on those most congested corridors, and the restricted vehicles are
In the highly competitive landscape of the automotive industry, enhancing ride comfort has become a paramount challenge for automakers. To address this challenge, a novel double damper suspension system has been investigated. This system, featuring two single dampers operating collaboratively as an integrated unit, is analyzed with a dual focus: a comprehensive comparison of various control algorithms to identify the one offering superior comfort and the experimental validation of these findings. The modeling process, executed in Simulink, encompasses the representation of pressure, discharge, and force equations, along with the development and testing of multiple control algorithms. The study employs a shock dynamometer, utilizing both the double damper and a single semi-active damper as test subjects in a pseudo-quarter-car test bed setup. Throughout the experimental phase, solenoid actuation in the dampers is guided by specific control logic, utilizing acceleration data for the
This article aims to conduct a comprehensive performance analysis of various propeller configurations and motors for uncrewed aerial vehicles. The experimental method is used for this study through the performance analysis of the motors and propellers at various conditions. In this study, the test rig has been manufactured specially to test the propeller and motor configuration as per the standard to obtain the thrust at various supplied voltage. This study proved that the increase in the size of propeller leads to increase in the thrust, as well as it can be used for specific applications of the drone like racing drone. It reveals that the maximum diameter of a propeller is 14 inches, which produces the thrust in the range of 2400 g to 361 g depending on motor capacity compared to the other size of the propellers. The novelty of the work is to analyze the performance of propellers and motors for optimization and application of drones through experimental methods. This method can be
For the vibration durability bench test of commercial vehicle batteries, it is essential to have accurate test specifications that exhibit high robustness and reasonable acceleration characteristics. This study evaluates the impact of different battery frame systems on the vibration response of the battery body, as determined by road load spectrum test results of a commercial vehicle battery system. It also confirms the variations in the external environmental load. Utilizing the response spectrum theory, a comprehensive calculation method for the fatigue damage spectrum (FDS) of batteries is developed. The time domain direct accumulation method, frequency domain direct accumulation method, and frequency domain envelope accumulation method are all compared. Analysis of kurtosis and skewness reveals that when the load follows the super-Gaussian distribution characteristics, the time domain direct accumulation method should be used to calculate the fatigue damage spectrum to minimize
Dual-fuel (DF) engines enable efficient utilization of a low reactivity fuel (LRF), usually port-injected, and a high reactivity fuel (HRF) provided directly into the cylinder. Ethanol and Camelina sativa oil can be ecologically effective but not fully recognized alternatives for energy production using modern CI engines equipped with a common rail system and adopted for dual fueling. The high efficiency of the process depends on the organization of the combustion. The article describes the premixed dual-fuel combustion (PDFC) realized by dividing the Camelina sativa dose and adjusting its injection timing to the energetic share of ethanol in the DF mixture. The injection strategy of HRF is crucial to confine knock, which limits DF engine operation, but the influence of EGR is also important. The research AVL engine’s dual-fueling tests focused on combustion process modification by the proposed injection strategy and cooled EGR at different substitution rates. For all examined points
There are examples in aerodynamics that take advantage of electric-to-aerodynamic analogies, like the law of Biot–Savart, which is used in aerodynamic theory to calculate the velocity induced by a vortex line. This article introduces an electric-to-aerodynamic analogy that models the lift, drag, and thrust of an airplane, a helicopter, a propeller, and a flapping bird. This model is intended to complement the recently published aerodynamic equation of state for lift, drag, and thrust of an engineered or a biological flyer by means of an analogy between this equation and Ohm’s law. This model, as well as the aerodynamic equation of state, are both intended to include the familiar and time-proven parameters of pressure, work, and energy, analytical tools that are ubiquitous in all fields of science but absent in an aerodynamicists’ day-to-day tasks. Illustrated by various examples, this modeling approach, as treated in this article, is limited to subsonic flight
Morphology, nanostructure, and composition of soot extracted from the oil sump of different heavy-duty engines operated under dynamometer and field conditions were investigated. Soot characteristics were then compared to a carbon black sample. Soot was extracted from used oil for transmission electron microscopy (TEM) analysis. Energy-dispersive X-ray (EDX) and X-ray photoelectron spectroscopy (XPS) analyses were also performed to assess soot composition. Two soot classes, I and II, can be identified based on their appearance under the TEM. Carbon black and class I particles have graphitic structures, while class II samples have a more sludge-like appearance. Similar aggregate sizes were observed among the samples. In all samples, the primary particle size distribution ranges from 16 nm to 22 nm in terms of mean diameter. Differences in the length and tortuosity of the graphitic fringes between the samples were observed. The findings suggest a greater degree of interaction between
This article investigates the deformation mechanics of cast iron and its implications for notch analysis, particularly in the automotive industry. Cast iron’s extensive use stems from its cost-effectiveness, durability, and adaptability to various mechanical demands. Gray, nodular, and compacted graphite cast irons are the primary types, each offering unique advantages in different applications. The presence of graphite, microcracks, and internal porosity significantly influences cast iron’s stress–strain behavior. Gray and compacted cast iron display an asymmetrical curve, emphasizing low tensile strength and superior compression performance due to graphite flakes and crack closures. Nodular cast iron exhibits a symmetrical curve, indicating balanced mechanical properties under tension and compression. The proposed simplified macrostructural approach, based on monotonic stress–strain, aims to efficiently capture graphite and crack closure effects, enhancing compressive strength and
This article introduces an advanced state-of-charge (SOC) estimation method customized for 28 V LiFePO4 (LFP) helicopter batteries. The battery usage profile is characterized by four consecutive current pulses, each corresponding to distinct operational phases on the helicopter: instrument check, key-on, recharge, and emergency power output stages. To establish a precise battery model for LFP cells, the parameters of a second-order equivalent-circuit model are identified as a function of C-rate, SOC, and temperature. Furthermore, the observability of the battery model is assessed using extended Lie derivatives. The signal-to-noise ratio (SNR) of the open-circuit voltage (OCV)–SOC relation is analyzed and employed to evaluate the estimator’s resilience against OCV flatness. The extended Kalman filter (EKF) and the unscented Kalman filter (UKF) are utilized for SOC estimation. The results emphasize the significance of meticulously choosing process and sensor noise covariance matrices to
The traditional approach to applying safety limits in electromechanical systems across various industries, including automated vehicles, robotics, and aerospace, involves hard-coding control and safety limits into production firmware, which remains fixed throughout the product life cycle. However, with the evolving needs of automated systems such as automated vehicles and robots, this approach falls short in addressing all use cases and scenarios to ensure safe operation. Particularly for data-driven machine learning applications that continuously evolve, there is a need for a more flexible and adaptable safety limits application strategy based on different operational design domains (ODDs) and scenarios. The ITSC conference paper [1] introduced the dynamic control limits application (DCLA) strategy, supporting the flexible application of diverse limits profiles based on dynamic scenario parameters across different layers of the Autonomy software stack. This article extends the DCLA
Test cycle simulation is an essential part of the vehicle-in-the-loop test, and the deep reinforcement learning algorithm model is able to accurately control the drastic change of speed during the simulated vehicle driving process. In order to conduct a simulated cycle test of the vehicle, a vehicle model including driver, battery, motor, transmission system, and vehicle dynamics is established in MATLAB/Simulink. Additionally, a bench load simulation system based on the speed-tracking algorithm of the forward model is established. Taking the driver model action as input and the vehicle gas/brake pedal opening as the action space, the deep deterministic policy gradient (DDPG) algorithm is used to update the entire model. This process yields the dynamic response of the output end of the bench model, ultimately producing the optimal intelligent driver model to simulate the vehicle’s completion of the World Light Vehicle Test Cycle (WLTC) on the bench. The results indicate that the
In order to improve the speed control performance of permanent magnet synchronous motor (PMSM) under disturbance, an adaptive reaching law sliding mode control (ASMC) is proposed. The objectives are to accelerate the control stabilization time and reduce chattering in speed control. Based on Lyapunov stability theory, the effectiveness of the scheme is proven. Based on the traditional index reaching law (T_SMRL), the adaptive sliding mode reaching law (ASMRL) introduces the adaptive adjustment terms of chattering, system state, and reaching speed, and uses hyperbolic tangent function instead of sign function. The effectiveness of the ASMRL is proved by theoretical analysis and numerical simulation. Compared with the T_SMRL and improved sliding mode control (I_SMC), the convergence is 33% faster and the chattering is 30% less. In addition, based on the ASMRL, the motor speed control system is established. An extended state observer (ESO) is designed in the surface PMSM (SPMSM) control
The rise of AI models across diverse domains includes promising advancements, but also poses critical challenges. In particular, establishing trust in AI-based systems for mission-critical applications is challenging for most domains. For the automotive domain, embedded systems are operating in real-time and undertaking mission-critical tasks. Ensuring dependability attributes, especially safety, of these systems remains a predominant challenge. This article focuses on the application of AI-based systems in safety-critical contexts within automotive domains. Drawing from current standardization methodologies and established patterns for safe application, this work offers a reflective analysis, emphasizing overlaps and potential avenues to put AI-based systems into practice within the automotive landscape. The core focus lies in incorporating pattern concepts, fostering the safe integration of AI in automotive systems, with requirements described in standardization and topics discussed
This article presents an optimization scheme for LoRaWAN-based electric vehicle batteries monitoring system located in warehouses by utilizing techniques to optimize packet delivery and power settings. Utilizing simulations, we identify that system optimization largely depends on network traffic, influenced by active users and the adoption of the pure ALOHA protocol. We define a reward metric based on the packet delivery rate and power efficiency, aiming for settings that yield the maximum reward. Our approach includes duty cycle management to minimize network traffic and maximize throughput, especially critical when handling urgent data from batteries. Traffic management based on the number of critical batteries in the warehouse also plays a crucial role. Predictive modeling of future traffic further refines power settings for optimal performance. The proposed system, tested through simulations, shows an average of 31% higher reward compared to traditional methods without duty cycle
The shape and energy distribution characteristics of exhaust pulse of an asymmetric twin-scroll turbocharged engine have a significant impact on the matching between asymmetric twin-scroll turbines and engines, as well as the matching between asymmetric twin scrolls and turbine wheels. In this article, the exhaust pulse characteristics of an asymmetric twin-scroll turbocharged engine was studied. Experiments were conducted on a turbine test rig and an engine performance stand to determine the operation rules of exhaust pulse strength, turbine flow parameters, turbine isentropic energy, and turbine efficiency. The results showed that the exhaust pulse strength at the inlets of both the small and large scrolls continuously decreased with the increase of engine speed. And the flow parameters at the inlets of the small and large scrolls exhibited a “ring” or “butterfly” shape with the change of expansion ratio depending on the pressure deviation of the extreme points at the troughs on both
This research aims at understanding how the driver interacts with the steering wheel, in order to detect driving strategies. Such driving strategies will allow in the future to derive accurate holistic driver models for enhancing both safety and comfort of vehicles. The use of an original instrumented steering wheel (ISW) allows to measure at each hand, three forces, three moments, and the grip force. Experiments have been performed with 10 nonprofessional drivers in a high-end dynamic driving simulator. Three aspects of driving strategy were analyzed, namely the amplitudes of the forces and moments applied to the steering wheel, the correlations among the different signals of forces and moments, and the order of activation of the forces and moments. The results obtained on a road test have been compared with the ones coming from a driving simulator, with satisfactory results. Two different strategies for actuating the steering wheel have been identified. In the first strategy, the
Driving safety in the mixed traffic state of autonomous vehicles and conventional vehicles has always been an important research topic, especially on highways where autonomous driving technology is being more widely adopted. The merging scenario at highway ramps poses high risks with frequent vehicle conflicts, often stemming from misperceived intentions [1]. This study focuses on autonomous and conventional vehicles in merging scenarios, where timely recognition of lane-changing intentions can enhance merging efficiency and reduce accidents. First, trajectory data of merging vehicles and their conflicting vehicles were extracted from the NGSIM open-source database in the I-80 section. The segmented cubic polynomial interpolation method and Savitzky–Golay filtering are utilized for data outlier removal and noise reduction. Second, the processed trajectory data were used as input to a hybrid Gaussian hidden Markov (GMM-HMM) model for driving intention classification, specifically lane
This research systematically explores the significant impact of geometrical dimensions within fused deposition modeling (FDM), with a focus on the influence of raster angle and interior fill percentage. Through meticulous experimentation and the application of response surface modeling (RSM), the influence on critical parameters such as weight, length, width at ends, width at neck, thickness, maximum load, and elongation at tensile strength is thoroughly analyzed. The study, supported by ANOVA, highlights the notable effects of raster angle and interior fill percentage, particularly on width at ends, width at neck, and thickness. During the optimization phase, specific parameters—precisely, a raster angle of 31.68 and an interior fill percentage of 27.15—are identified, resulting in an exceptional desirability score of 0.504. These insights, substantiated by robust statistical data, fill a critical gap in the understanding of 3D-printed parts, offering practical recommendations for
Minimizing vibration transmitted from the exhaust system to the vehicle’s passenger compartment is the primary goal of this article. With the introduction of regulatory norms on NVH behavior and emissions targets, it has become necessary to address these issues scientifically. Stringent emissions regulations increased the complexity of the exhaust system resulting in increased size and weight. Exhaust system vibration attenuation is essential not only from the vehicle NVH aspects but also for the optimized functionality of the subsystems installed on it. Based on earlier studies, this work adopts a more thorough strategy to reduce vehicle vibration caused by the exhaust system by adjusting it to actual operating conditions. To achieve this, a complete vehicle model of 22 DOF is considered, which consists of a powertrain, exhaust system, chassis frame, and suspension system. A method for evaluating static and dynamic vibration response is proposed. Through the use of the vehicle’s rigid
This article aims to address the challenge of recognizing driving styles, a task that has become increasingly complex due to the high dimensionality of driving data. To tackle this problem, a novel method for driver style clustering, which leverages the principal component analysis (PCA) for dimensionality reduction and an improved GA-K-means algorithm for clustering, is proposed. In order to distill low-dimensional features from the original dataset, PCA algorithm is employed for feature extraction and dimensionality reduction. Subsequently, an enhanced GA-K-means algorithm is utilized to cluster the extracted driving features. The incorporation of the genetic algorithm circumvents the issue of the model falling into local optima, thereby facilitating effective driver style recognition. The clustering results are evaluated using the silhouette coefficient, Calinski–Harabasz (CH) index, and GAP value, demonstrating that this method yields more stable classification results compared to
The usage of the inerter and its studies has greatly developed in recent years as it offers better performance compared to passive systems and has lower cost and power consumption than active and semi-active systems. This article focuses on studying a half-vehicle model to obtain the optimal layout of the mechatronic inerter, spring, and damper suspension system (ISD) for comfort enhancement with the aid of the structure-immittance approach, ensuring structural simplicity. The mechatronic inerter, which consists of a single capacitance, resistance, and inductance, is added to a half-vehicle model composed of an inerter, spring, and damper. All possible layouts are studied to achieve the optimal design layout. Evaluation criteria such as the performance index, system peak-to-peak value, and settling time are utilized to assess body acceleration, thereby improving passenger comfort. Furthermore, the system’s impact on dynamic tire load and suspension working space under diverse road
To identify the influences of various built environment factors on ridership at urban rail transit stations, a case study was conducted on the Changsha Metro. First, spatial and temporal distributions of the station-level AM peak and PM peak boarding ridership are analyzed. The Moran’s I test indicates that both of them show significant spatial correlations. Then, the pedestrian catchment area of each metro station is delineated using the Thiessen polygon method with an 800-m radius. The built environment factors within each pedestrian catchment area, involving population and employment, land use, accessibility, and station attributes, are collected. Finally, the mixed geographically weighted regression models are constructed to quantitatively identify the effects of these built environment factors on the AM and PM peak ridership, respectively. The estimation results indicate that population density and employment density have significant but opposite influences on the AM and PM peak
Autonomous vehicles (AVs) provide an effective solution for enhancing traffic safety. In the last few years, there have been significant efforts and progress in the development of AVs. However, the public acceptance has not fully kept up with technological advancements. Public acceptance can restrict the growth of AVs. This study focuses on investigating the acceptance and takeover behavior of drivers when interacting with AVs of different styles in various scenarios. Manual and autonomous driving experiments were designed based on the driving simulation platform. To avoid subjective bias, principal component analysis (PCA) and the Gaussian mixture model (GMM) were used to classify driving styles. A total of 34 young participants (male-dominated) were recruited for this study. And they were classified into three driving styles (aggressive, moderate, and conservative). And AV styles were designed into three corresponding categories according to the different driving behavior
The present study explores the performance of high-density polyethylene (HDPE) pyrooil and ethanol blends with gasoline in SI engine using statistical modeling and analysis using response surface methodology (RSM) and the Anderson–Darling (AD) residual test. The pyrooil was extracted from HDPE through pyrolysis at 450°C and then distilled to separate the liquid fraction. Two blends were prepared by combining pyrooil and gasoline, and pyrooil–ethanol mixture (volume ratio of 9:1) and gasoline, both at volumetric concentrations ranging from 2% to 8% to evaluate brake thermal efficiency (BTE) and specific fuel consumption (SFC) in a SI engine. An experimental matrix containing speed, torque, and blend ratio as independent variables for both blends were designed, analyzed, and optimized using the RSM. The results show that a 4% blend of pyrooil with gasoline (P4) and a 6% blend of pyrooil–ethanol mixture with gasoline (P6E) were optimum for an SI engine. Also, the experimental findings
The current research elucidates the application of response surface methodology to optimize the collective impact of methanol–isobutanol–gasoline blends and nanolubricants on the operational parameters of a spark-ignition engine. Diverse alcohol blends in conjunction with gasoline are employed in engine trials at 2500 rpm across varying engine loads. The alcohol blends exhibit notable enhancements in brake thermal efficiency, peak in-cylinder pressure, and heat release rate. At 2500 rpm and 75% load, the break thermal efficiency of iBM15 surpasses that of gasoline by 33.5%. Alcohol blends significantly reduce hydrocarbon and carbon monoxide emissions compared to gasoline. The iBM15 demonstrates a reduction of 25.2% and 51.12% in vibration along the Z and Y axes, respectively, relative to gasoline. As per the response surface methodology analysis, the optimal parameters are identified: an alcohol content of 29.99%, an engine load of 99.06%, and a nanolubricant concentration of 0.1%. It
The controller area network (CAN) bus, the prevailing standard for in-vehicle networking (IVN), has been used for more than four decades, despite its simple architecture, to establish communications between electronic control units (ECUs). Weight, maintenance overheads, improved flexibility, and wiring complexity escalate as the quantity of ECUs rises, especially for high-demand autonomous vehicles (AVs). The primary objective of this study is to examine and discuss the significant challenges that arise during the migration from a wired CAN to a wireless CAN (WCAN). Suggested remedies include changing the configuration of the conventional ECU, creating a hidden wireless communication domain for each AV, and developing a plan to counteract the jamming signals. The simulation of the proposed WCAN was done using MATLAB and validated using OPNET analysis. The results showed that the packet loss of the eavesdropping electronic control unit ranged from 63% to 100%. Anti-jamming results show
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