Vehicle Model Research Articles

Impact of Mid-to-Low-Ash, Low-Viscosity Lubricants on Aftertreatment Systems after 210,000-Kilometer Real-World Road Endurance Trials

Engine lubricants globally face the challenge of meeting the demands of new engine technologies while enhancing energy efficiency and reducing emissions. Lubricants must enhance their performance and sustainability, improve reliability in complex and harsh environments, and minimize environmental impact and health risks. This study explores the influence of two different formulations of low viscosity lubricants, tested through actual road endurance trials, on a hybrid vehicle’s aftertreatment system performance and overall emission levels. The study includes 120,000 km of endurance testing in four different challenging environments in China, as well as 90,000 km of endurance testing in a typical urban and highway driving cycle in a large city. Results indicate that emissions from the test vehicles during the 120,000 km and 210,000 km durable Worldwide harmonized Light vehicle Test Cycles (WLTCs) meet China’s Stage 6 light-duty vehicle emission standards, with the 210,000 km Real Driving Emission test (RDE) results also conforming to these standards. Relative to fresh TWC, the light-off temperature increased by a mere 60 °C, and the oxygen storage capacity declined by around 19% following endurance testing. Additionally, the GPF exhibited satisfactory performance after 210,000 km of endurance testing, showing lower backpressure values compared to the fresh-coated samples, with no notable ash buildup observed in the substrate. Drawing on the outcomes of actual road endurance testing, this study illustrates that employing low-to-mid-ash-content, low-viscosity lubricants is both compatible and reliable for aftertreatment systems in present or advanced hybrid technologies. Premium lubricants facilitate vehicles in sustaining compliant and stable emission performance, even amid harsh environments and complex operating conditions. Furthermore, the tested lubricants effectively inhibit excessive aging of the aftertreatment system over prolonged mileage. Moreover, this study discusses the feasibility of rapid aging evaluation methods for aftertreatment systems based on engine test benches, juxtaposed with actual road endurance testing. These findings and conclusions offer crucial references and guidance for enhancing lubricant performance and sustainability. Subsequent studies can delve deeper into the correlation between lubricant performance and environmental impact, alongside optimization strategies for lubricants across various vehicle models and usage scenarios.

Numerical benchmark for road bridge damage detection from passing vehicles responses applied to four data-driven methods

Drive-by bridge monitoring utilizes measured responses from passing vehicles to perform damage detection of bridge, a methodology challenged by multiple factors and operational conditions. Recently, data-driven methods have been used to improve the accuracy of drive-by monitoring. This thriving research field requires (but lacks) publicly available datasets to improve and validate its monitoring and damage detection capabilities. To foster data-driven drive-by bridge damage assessment methods, this document presents an openly available dataset consisting of numerically simulated vehicle responses crossing a range of bridge spans with various damage conditions. The dataset includes results for different monitoring scenarios, road profile conditions, vehicle models, vehicle mechanical properties and speeds. The intention is to provide a useful resource to the research community that serves as a reference set of results for testing and benchmarking new developments in the field. In addition, four recently published data-driven drive-by methods have been tested using the same dataset.

Research on Probabilistic Load Modeling and Simulation Model of Electric Vehicle Charging Station

Abstract The stochastic nature of electric vehicle charging station loads poses certain challenges in establishing relevant load models. This paper introduces a probabilistic load modeling approach that considers factors such as the time electric vehicles begin charging, daily travel distance, charging power, and charging duration. By comprehensively studying the influencing factors of charging station load modeling and combining Monte Carlo simulation with probabilistic statistical analysis, a comprehensive probability load model for typical charging stations is developed. Building upon this, a charging load simulation model is constructed using Simulink. This model can generate daily load curves consistent with the probability load model, making a positive contribution to the study of the impact of electric vehicles on the power grid.

Research on ride comfort optimization of the vehicle considering the subframe.

When the vehicle is in motion, the elastic deformation of the flexible subframe significantly influences ride comfort. Therefore, it is crucial to investigate the impact of flexible subframes on vehicle ride comfort. In order to enhance the reliability and optimization efficiency of our research, this paper incorporates the concept of elastic deformation in the flexible subframe into the investigation of vehicle ride comfort, and proposes a multi-objective optimization approach to enhance the overall vehicle ride comfort. The vibration mathematical model elucidates how flexible subframes affect vehicle ride comfort and establishes a rigid-flexible coupling model for a specific vehicle with a flexible subframe to analyze the impact of its elastic deformation on vehicle ride comfort through simulation experiments. Subsequently, a radial basis function approximation model is established, and the multi-objective particle swarm optimization and non-dominated sorting genetic algorithm II algorithms are employed to conduct multi-objective optimization of the stiffness of the subframe bushing with the aim of enhancing vehicle ride comfort. The findings indicate that the flexible subframe has a significant impact on vehicle ride comfort. Specifically, on bump roads, peak values of vertical and longitudinal seat accelerations decrease while lateral seat acceleration increases. On random roads, peak values of longitudinal and lateral seat accelerations increase while vertical acceleration decreases. Furthermore, the stiffness of the subframe bushing optimized by the non-dominated sorting genetic algorithm II algorithm further enhances vehicle ride comfort and aligns more closely with the optimization requirements in this study.

Study on the sound radiation characteristics of polygonal wear wheels of CRH3 high-speed train

Abstract Due to the increase in train operating speed, the phenomenon of collision vibration between wheels and rails becomes more and more obvious, and the degree of wheel wear increases dramatically. As a result, the wheel-rail noise problem has become more and more complex. The wheel polygonal wear is also widespread on the train. For the wheel in the form of polygonal wear of the wheel-rail vertical force, combined with the principle of wheel-rail noise, we conduct an in-depth study. An aerodynamic mutual coupling model for CRH3 high-speed vehicles is proposed. The role of the amplitude of 0.03 mm, i.e., the twentieth-order polygonal abrasion rate on the wheel-rail force is analyzed at the speed class of 200~350 km/h, and the acoustic radiation characteristics of the wheels are investigated at different speed classes. The results show that under different speed classes, when the wheel polygon order is in the twentieth order, the wheel-rail vertical force rises with the increase of speed, and the wheel sound power is also affected by a great change. There is a significant increase in the sound power from the low frequency of 20 Hz to 100 Hz and a peak in the mid-frequency up to 1, 000 Hz. Secondly, when the wheel radiated sound pressure levels at different speed levels, significant changes also occurred, with the sound pressure reaching a maximum of 110 dB at 350 km/h.

Continuous mapping of deformation over a ship structure model based on pointwise measurements

The development of structural monitoring systems on-board vehicles aims to achieve a full-picture of the vehicle response under operating conditions. The use of different data sources is a key point for many SHM approaches which demand for extensive information and data fusion techniques to address the specific inverse problem in a robust way. Therefore, a procedure to transform pointwise measurements into a continuous representation of the monitored structure is presented in this paper. To achieve the desired monitoring goals, a numerical approach which combines experimental recorded data and modelled responses from a ship structural model is developed. Craig-Bampton truncation technique is applied on the FE vehicle model to reduce elastic dofs and obtain a computationally cost-effective model. The main advantage of Craig-Bampton modes is the capability to keep unchanged the information at the sensor nodes. A state observer is then built to evaluate the elastic deflection field and the loads over the structure in the points where no measurement is available. A natural second-order observer is exploited as it is more suited for the present application than the Kalman filter. To enhance the observer performances, an optimization process is carried out based on a learning phase. Following a previous paper about theoretical aspects of the virtual sensing approach on 1D structures. the developed techniques are here applied to the monitoring of the structural deflections and the ambient excitation relative to an elastically scaled model of a fast catamaran, tested in the towing-tank. Accelerations and strain-gage data are used to reconstruct the elastic deformations of the physical model metallic backbone scaling the reference ship behavior. Hydrodynamic load data relative to the demihull portions and the wetdeck are obtained as well.

Toward Indirect Real-Time Prediction of Bridge Vibration Responses Under Traffic Flow Through a Population of Connected Sensing Vehicles

Condition monitoring of bridge structures as the lifelines of smart cities is high of importance. While indirect vehicle scanning techniques have shown promising results and have less cost compared to mounting fixed sensors on the bridge, they have limitations in predicting response under traffic flow since the crossing time of one vehicle is very short. This paper presents a novel crowsensing-based framework for predicting bridge acceleration responses and identifying the modal characteristics. This method utilizes smartphone data from a diverse population of sensing vehicles as they traverse the bridge to predict bridge acceleration response at various virtual sensing locations. Subsequently, the predicted acceleration is used to identify the mode shapes and natural frequencies of the bridge. The principal innovation in this practical and cost-effective monitoring solution is the utilization of a randomly selected set of sensing vehicles at each timestamp. These selections may differ from one timestamp to the next, reflecting the real-world conditions where certain vehicles may intermittently lose or disconnect their internet connectivity. By consistently updating the set of sensing agents while other vehicles cross the bridge, the proposed framework overcomes the data length limitations of conventional vehicle-based methods by leveraging multiple vehicles and continuous data collection. Comprehensive numerical studies are conducted to evaluate the performance of the method. In the numerical investigations, a three-span bridge is subjected to the continuous passing of a large number of half-car vehicle models with random speeds and initial locations. Vehicle-bridge interaction is considered in the analysis. Utilizing a single randomly selected sensing agent at each timestamp, the results demonstrate the effectiveness of the framework in predicting bridge acceleration response with a relative error of less than 5%. Additionally, the method achieves an accuracy level of 95% in identifying the bridge's initial three mode shapes.

A centralized fleet management system for electrified transportation

Abstract Hybrid Electric Vehicles are a promising alternative to Conventional and Electric Vehicles, as they offer better fuel economy, lower emissions, and long drive range without requiring extensive charging infrastructure. Limited driving range and battery degradation in electric vehicles are the main challenges in electrified fleets working in large metropolitan. For these challenges, the hybrid electric vehicle fleet management system is an effective solution to optimize power consumption and mitigate electric vehicle and conventional vehicle challenges. This paper proposes a hybrid electric vehicle fleet management system for public transportation, to analyze and investigate the performance and energy consumption rate of conventional, electric, and hybrid electric vehicles on different routes using representative driving cycles. The hybrid electric vehicle fleet management system enhances operational efficiency for the fleet management system and reduces power consumption in transportation. The proposed work comprises a hybrid electric vehicle model that is based on object-oriented programming. The proposed work offers the ability to add the input of the initial state of charge, power split ratio, and route data for each bus, and the hybrid electric vehicle model computes the state of charge and fuel consumption rate along the road. Finally, The proposed work shows how a hybrid electric vehicle fleet management system has a significant potential to be a solution for public transportation in Egyptian urban areas at this time compared with conventional vehicle and electric vehicle fleet management systems.

Design of a Radar Signature Measurement Model of an Unmanned Aerial Vehicle with Low Radar Signature

Existing databases of RCS benchmarks lack a complex, low-observable target. This paper describes the design of such a complex and low-observable measurement model. Starting point of the design is the so-called Muldicon model, developed by the NATO/STO/AVT panel. Hot spots of the original model are identified and treated with radar-absorbing materials. Simulations on the treated model demonstrate that the model is indeed low observable. The effect of the manufacturing process of 3D-printing and separable parts is assessed experimentally on a cone-sphere; the effect is found to be negligible. These results give confidence that the model, when built, satisfies the requirements of being complex and low observable; and that artefacts of the manufacturing process will not impair its signature.

Design and modeling of the case of unmanned aerial vehicle, in the technical facility control system

This article presents the design and modeling of a case with a combat kit for an army unmanned aerial vehicle with an autonomous launch system. The scientific problem solved by the authors in this article is the design and modeling of a new medium-class combat vehicle, according to the current concept of modern design and produc-tion «Industry 4.0», which implies the creation of new dual-use equipment and technologies. The purpose of scien-tific research is the design and modeling of a case with a combat kit for an army unmanned aerial vehicle. The following methods of scientific research were used in the scientific article: theoretical and empirical. The authors associate the prospects of their research with the design and modeling of an innovative control system for a two-case army unmanned aerial vehicle with an autonomous launch system, which has mounts for installation on the chassis of a combat vehicle.

А comprehensive approach to managing robot group formation

Subject matter: Research and development of methods for controlling swarms of unmanned aerial vehicles (UAVs) based on the "master – slave" model. This includes examining existing classifications and interactions between unmanned aerial vehicles in various formations such as groups, flocks, associations, and swarms, with the goal of creating an effective management system. Goal To improve the quality of interaction between unmanned aerial vehicles based on the "master – slave" model during flight missions through constant control between objects. Ensuring reliable execution of flight missions by implementing new management methods that account for different modes of interaction between devices. Tasks: Analyze the classification of existing UAVs; analyze the parameters and model of interaction of unmanned aerial vehicles in existing groups, flocks, associations, swarms; create a scenario of interaction between two UAVs based on the "master – slave" model; develop a program for visualizing the flight of unmanned aerial vehicles based on the "master – slave" model; conduct flight testing according to the proposed model on stages with various geospatial objects. Methods: Simulation method for developing a UAV flight visualization subsystem; graphical modeling method for creating an aircraft-type unmanned aerial vehicle model; methods of algorithm theory for developing a scenario of interaction between two UAVs. Utilization of specialized software tools for visualization and simulation of UAV behavior in real-time conditions. Results: Developed a classification of unmanned aerial vehicles; created a graphical model of the Mini-Flight-M aircraft; developed a scheme for the interaction of two UAVs in "teacher" or "mentor" modes; created a program for visualizing the flight of UAVs based on the "master – slave" model; conducted flight testing according to the proposed model on stages with various geospatial objects. The results confirmed the effectiveness of the developed model and demonstrated its applicability in various fields, including environmental monitoring, rescue operations, and other autonomous missions. Conclusions: The proposed approach to controlling a UAV swarm based on the "master – slave" model improves the quality of interaction between the devices and ensures reliable execution of flight missions. Further research should focus on optimizing energy consumption and ensuring reliable communication between swarm agents. It is also important to develop methods for protecting UAV swarms from cyberattacks and other threats to enhance their resilience and reliability during complex missions.

Engine & vehicle modeling for fuel assessment under local driving conditions

Automotive biofuels offer a promising alternative to traditional fossil fuels. Accurate evaluation of combustion and emissions in IC engines and vehicles is crucial. This research aimed at developing and validating an engine and vehicle simulation methodology to assess the fuel effect on vehicle consumption and emissions considering different driving cycles and the road slope (barely evaluated for fuels widely used in emerging markets). Two blends were tested: 20 % biodiesel (B20) and 20 % hydrotreated vegetable oil (HVO20) with Ultra-Low-Sulfur Diesel (ULSD). A light-duty diesel vehicle model was developed in GTSuite®, using emission maps from a calibrated steady-state engine model. Good agreement with experiments was found. Road slope in local DC significantly increased fuel consumption and CO, CO2, NOx, and PN emissions, reducing HC. Compared to ULSD, B20 reduced PN and HC by 27–35 % and 12–22.5 %, respectively. HVO20 had a smaller effect on PN but reduced HC emissions by up to 19.5 %. Neither blend significantly affected CO and CO2. B20 slightly increased NOx and fuel consumption, while HVO20 had no significant impact on these.

Parameter identification of an open-frame underwater vehicle based on numerical simulation and quantum particle swarm optimization

Accurate parameter identification of underwater vehicles is of great significance for their controller design and fault diagnosis. Some studies adopt numerical simulation methods to obtain the model parameters of underwater vehicles, but usually only conduct decoupled single-degree-of-freedom steady-state numerical simulations to identify resistance parameters. In this paper, the velocity response is solved by applying a force (or torque) to the underwater vehicle based on the overset grid and Dynamic Fluid-Body Interaction model of STAR-CCM+, solving for the velocity response of an underwater vehicle in all directions in response to propulsive force (or moment) inputs. Based on the data from numerical simulations, a parameter identification method using quantum particle swarm optimization is proposed to simultaneously identify inertia and resistance parameters. By comparing the forward velocity response curves obtained from pool experiments, the identified vehicle model’s mean square error of forward velocity is less than 0.20%, which is superior to the steady-state simulation method and particle swarm optimization and genetic algorithm approaches.

Adaptive Control for Suspension System of In-Wheel Motor Vehicle with Magnetorheological Damper

This study proposes two adaptive controllers and applies them to the vibration control of an in-wheel motor vehicle’s (electric vehicle) suspension system, in which a semi-active magnetorheological (MR) damper is installed as an actuator. As a suspension model, a nonlinear quarter car is used, providing greater practical feasibility than linear models. In the synthesis of the controller design, the values of the sprung mass, damping coefficient and suspension stiffness are treated as bounded uncertainties. To take into account the uncertainties, both direct and indirect adaptive sliding mode controllers are designed, in which the principal control parameters for the adaptation law are updated using the auto-tune method. To reflect the practical implementation of the proposed controller, only two accelerometers are used, and the rest of the state values are estimated using a Kalman observer. The designed controller is applied to a quarter car suspension model of an in-wheel motor vehicle featuring an MR damper, followed by a performance evaluation considering factors such as ride comfort and road holding. It is demonstrated in this comparative work that the proposed adaptive controllers show superior control performance to the conventional proportional–integral–derivative (PID) controller by reducing the vibration magnitude by 50% and 70% for the first and second modes, respectively. In addition, it is identified that the second mode (wheel mode) of the in-wheel motor vehicle is more sensitive than the first body mode depending on the mass ratio between the sprung and unsprung mass.

Enhancing smart charging in electric vehicles by addressing paused and delayed charging problems

Smart charging of electric vehicles can alleviate grid congestion and reduce charging costs. However, various electric vehicle models currently lack the technical capabilities to effectively implement smart charging since they cannot handle charging pauses or delays. These models enter sleep mode when charging is interrupted, preventing resumption afterwards. To avoid this, they should be continuously charged with their minimum charging power, even when a charging pause would be desirable, for instance with high electricity prices. This research examines this problem to inform various stakeholders, including policymakers and manufacturers, and stimulates the adoption of proactive measures that address this problem. Here, we demonstrate through technical charging tests that around one-third of tested car models suffer from this issue. Through model simulations we indicate that eliminating paused and delayed charging problems would double the smart charging potential for all applications. Lastly, we propose concrete legal and practical solutions to eliminate these problems.

Quadcopter Modeling Using a System for UAV Parameters Measurement

This article deals with quadcopter modeling using a system for the measurement of unmanned aerial vehicle (UAV) parameters. UAVs are often equipped with various measurement devices and equipment for measurement, which significantly affects their weight. The currently available technical solutions and inventions do not allow corrections to be made to the on-board control electronics settings without the need to perform a test flight, or without the need to create complex and time-consuming mathematical models of the unmanned aerial vehicle; therefore, it is desirable to create a new method for modeling the characteristics of an UAV based on static laboratory measurements. The goal of this paper is to create a dynamic model of a quadcopter that will be adapted to a system for measuring UAV parameters, specifically the thrust of individual motors, which will be the next step to creating a new method for modeling UAV characteristics. This method can be used in the future for tuning flight control algorithms, based on static laboratory measurements.

Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model

This paper addresses the issue of trajectory tracking control for an autonomous underwater vehicle in the presence of parameter perturbations and disturbances in three-dimensional space. The control scheme is based on a combination of the backstepping method, the adaptive integral sliding mode control scheme, and velocity transformation resulting from the decomposition of the inertia matrix, which is symmetric. In addition, adaptive laws were applied to eliminate the effects of parameter perturbations and external disturbances. The main feature of the proposed approach is that the vehicle model is not fully symmetric but contains quantities due to the shift of the center of mass. Another important feature of the control scheme is the ability to detect some of the consequences caused by reducing the vehicle model by neglecting dynamic couplings. Numerical results on the five degrees of freedom (DOF) vehicle model show the efficiency, effectiveness, and robustness of the developed controller.

Height Control Strategy Design and Simulation of Electronic Control Air Suspension for Trucks

To address the large height error and attitude destabilization phenomenon in regulating the frame height of trucks with electronic control air suspension (ECAS), a height control strategy was designed. Firstly, the fundamental principles of height control were elucidated based on the single degree-of-freedom (DOF) vehicle model. The limitations of the classic non-linear mathematical model for the air spring were also highlighted. Thus, a dynamic model was constructed, consisting of an AEMSim model for the ECAS and a Simulink model for the truck. A frame height fuzzy controller was designed based on the fuzzy control theory to improve the height control accuracy and to solve the control conflict problem of the solenoid valves. Additionally, three typical control modes of the height and corresponding control strategies were proposed based on the practical requirements of usage scenarios for trucks. Finally, dynamic simulations were conducted under different modes. The results show that, compared to the existing switching control method, the proposed control approach can reduce height control errors by an order of magnitude and decrease the pitch angle by over 30%. The steady-state error remains nearly unchanged under the 30% variation of the sprung mass. The proposed control approach exhibits the superior control performance and robustness. It effectively reduces height errors and avoids the posture instability during the adjustment of the ECAS.

Unsteady Surface Pressure Characterization on Launch Vehicle in Liftoff Configuration

Acquisition of unsteady pressure using remote scanners can provide practical advantages over making the same measurements directly using flush-mounted pressure transducers. However, pneumatic distortion of the signal is introduced by the tubing between the sensing module and surface port. This typically limits the usefulness of such measurements to averaged, steady data only, restricting the application of pressure scanners for unsteady pressure measurements in wind-tunnel and flight-testing applications. In this work, unsteady pressure on the surface of a 1.75%-scale model of the NASA Space Launch System Block 1B Cargo vehicle was remotely measured using an electronic scanning pressure module at the NASA Langley Research Center 14-by 22-Foot Subsonic Tunnel. Subsequent reconstruction using an empirically tuned, Wiener-filtered inverse transfer function produced excellent agreement compared to nearby surface-mounted transducers within a usable bandwidth of approximately 500 Hz. Interactions between the launch vehicle and the support tower produced strong oscillations that were absent in the vehicle-alone configuration, and it is likely that the wake interaction leads to a resonant flow response when the launch tower is immediately downstream of the vehicle.

E-Heater Performance for Aftertreatment Warm-Up in a 48V Mild-Hybrid Heavy-Duty Truck over Real Driving Cycles

High-efficiency and low-emissions heavy-duty (HD) internal combustion engines (ICEs) offer significant GHG reduction potential. Mild hybridization via regenerative braking and enabling the use of an electric heater component (EHC) for the aftertreatment system (ATS) warm-up extends these benefits, which can mitigate tailpipe GHG and NOx emissions simultaneously. Understanding such integrated hybrid powertrains is essential for the system optimization of real-world driving conditions. In the present work, the potential of a low engine-out NOx (1.5–2.5 g/kWh range) ‘Low-NOx’ HD diesel engine and EHCs were analyzed in a 48V P1 mild-hybrid system for a class 8 commercial vehicle concept and compared with those in an EPA-2010-certified HD diesel truck as a baseline under real-world driving cycles, including those from the US, Europe, India, China, as well as the world harmonized vehicle cycle (WHVC). For analysis, an integrated 1-D vehicle model was utilized that consisted of models of the ‘Low-NOx’ HD engine, the stock ATS, and a production EHC. For the real driving cycles, ‘GT-RealDrive’-based vehicle speed profiles were generated for busy trucking routes for different markets. For each cycle, the effects of the Low-NOx and EHC performances were quantified in terms of the ATS warm-up time, engine-out NOx emissions, and net fuel consumption. Depending on the driving route, the regenerative braking fully or partly neutralized the EHC power penalty without a significant impact on the ATS thermal performance. For a two-EHC system, the fueling penalty associated with every second reduction in the warm-up time FCEHC (g/s) was several-fold higher for the real driving routes compared with the WHVC. Overall, while a multi-EHC setup accelerated the ATS warm-up, a single EHC integrated at the SCR inlet showed minimized EHC heating power, leading to a minimized fueling penalty. Finally, for the India and China routes, being highly transient, the P1 hybridization proved inadequate for GHG reduction due to the limited energy recuperation. A stronger hybridization was desirable for such driving cycles.