Vehicle Parameters Research Articles

Optimisation Studies of Mesoporous Silica Nanoparticle as a Drug Carrier for Gemcitabine: Enhancing Therapeutic Effectiveness in Pancreatic Cancer

Pancreatic cancer, often referred to as “the silent killer”, presents with minimal or no symptoms in its early stages, leading to late detection when surgical resection is no longer the optimal treatment option. Gemcitabine (GEM), one of the leading chemotherapeutic drug for advanced stages of cancer, is a crucial treatment for pancreatic cancer. However, the low 5-year survival rate of pancreatic cancer patients highlight the limited effectiveness of current treatments. In recent years, mesoporous silica nanoparticles (MSNP) have garnered significant attention in both scholarly and pharmaceutical fields due to their unique combination of properties including stable porous structure and high loading capacities. This research aims to investigate the potential of MSNP as a carrier for anticancer drugs, specifically GEM. MSNP was successfully synthesized in the laboratory using sol-gel method with tetraethyl orthosilicate (TEOS) as silica source and cetyltrimethylammonium bromide (CTAB) as surfactant template. Comprehensive morphological and physical characterizations of the MSNP product were performed through transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), energy dispersive X-ray (EDX) spectroscopy, element mapping, X-ray diffractometry (XRD), and accelerated surface area porosimetry (ASAP). The results demonstrate that MSNP exhibits desirable properties for drug loading, including a stable mesoporous structure with pore size of ~ 4.94 nm, a high surface area of about 278.32 m²/g, and average particle diameter of approximately 85 nm. The effects of incubation time and initial GEM concentrations were studied to determine the optimal drug loading parameters for the MSNP vehicle. The successful loading of up to 24 µg of GEM in 1 mg of MSNP achieved in an optimized incubation time of 2 hour, validates the tremendous potential of MSNP as a potential anticancer drug carrier in pancreatic cancer treatment. These findings provide a valuable reference for future research and investigations in this promising field.

Theory and Empirical Analysis of Bridge Load Limitation Under the Action of Typical Heavy-Duty Vehicles

Abstract With the continuous growth of transportation demands, in-service highway bridges face greater challenges in their long-term operational lifespans, and bridge collapse accidents caused by vehicle overloading occur from time to time. Additionally, under the influence of loads and environmental factors, various wear patterns inevitably lead to degraded reinforced concrete bridges. In view of this problem, it is reasonable and feasible to limit the vehicle loads passing over highway bridges, and the key basis for limitation is determining the load limit value of a bridge. To determine a reasonable load limit value of a bridge, this paper has carried out the following research: Based on a classification of vehicle types, this paper explores the load parameters of several heavy-duty vehicles with large traffic volumes through traffic flow information and summarizes the load spectra of typical heavy-duty vehicles. On the basis of the theory of structural reliability, a method for determining the limit load of bridges is proposed. Given a structural target reliability index, the theoretical load limit value of a bridge can be calculated. To ensure the rationality of the theory of bridge load limit value, by relying on the engineering example, the theoretical load limit value is calculated. By comparing actual bridge load test data with the finite element model results, the rationality of the bridge load limiting theory is verified. Finally, the paper notes that it is safer and more reliable to define the load limit value according to the bending stress state for bridges.

Effect of underwater vehicle wake on sound propagation characteristics in stratified medium

Due to the uneven distribution of seawater temperature and salinity, the density distribution at different depths is different, thus forming a density step layer with a certain gradient. Acquiring the wake internal wave field generated by underwater vehicles passing through this layered structure and mastering its modulation characteristics for the sound field can provide new technical support for the tracking and identification of underwater vehicles. In this paper, the real size model of a certain type of underwater vehicle is taken as the research object to explore the characteristics of its wake internal wave field, and the influence of navigation parameters on the wake field and the change of winning characteristics under the influence of internal waves are simulated and analyzed. The results show that the navigation parameters of underwater vehicles have obvious influence on the wake field, and then have specific influence on the sound field structure in a certain range, especially on the sound signal of the sound field.

Risk assessment of rear-end crashes by incorporating vehicular heterogeneity into Bayesian hierarchical extreme value models

Extreme value theory (EVT) has been extensively used to assess road safety with traffic conflicts. However, most studies used pooled models that do not account for vehicle heterogeneity which is characterised by different static and dynamic vehicle parameters such as size, speed, acceleration, and braking capacity. This study proposes a risk assessment technique for rear-end crashes while incorporating vehicular heterogeneity. Video-based trajectory data were collected at four uncontrolled intersections, and conflicts were estimated using modified time-to-collision (MTTC). The crash risk was derived from the observed conflicts using pooled as well as Bayesian hierarchical EVT models. Unlike the pooled model, the hierarchical model revealed that crash risk varies across leader-follower pairs. Interactions that involve cars or light commercial vehicles with slow-moving vehicles are riskier. This study highlights the importance of incorporating vehicular heterogeneity in crash risk assessment. The proposed methodology can be utilised for more accurate risk assessment in heterogeneous traffic.

State Estimation of Drive-by-Wire Chassis Vehicle Based on Dual Unscented Particle Filter Algorithm

Accurate vehicle dynamic information plays an important role in vehicle driving safety. However, due to the characteristics of high mobility and multiple controllable degrees of freedom of drive-by-wire chassis vehicles, the current mature application of traditional vehicle state estimation algorithms can not meet the requirements of drive-by-wire chassis vehicle state estimation. This paper proposes a state estimation method for drive-by-wire chassis vehicle based on the dual unscented particle filter algorithm, which make full use of the known advantages of the four-wheel drive torque and steer angle parameters of the drive-by-wire chassis vehicle. In the dual unscented particle filter algorithm, two unscented particle filter transfer information to each other, observe the vehicle state information and the tire force parameter information of the four wheels respectively, which reduce the influence of parameter uncertainty and model parameter changes on the estimation accuracy during driving. The performance with the dual unscented particle filter algorithm, which is analyzed in terms of the time-average square error, is superior of the unscented Kalman filter algorithm. The effectiveness of the algorithm is further verified by driving simulator test. In this paper, a vehicle state estimator based on dual unscented particle filter algorithm was proposed for the first time to improve the estimation accuracy of vehicle parameters and states.

Study on the Mechanism of Wheel Asymmetric Wear of Heavy-Haul Freight Vehicles

Wear on the wheel-rail interface has a serious effect on the operation safety and efficiency of heavy-haul freight vehicles. Wheel asymmetric wear (WAW) greatly affects the vehicle’s dynamic performance. This paper explores the causes of WAW in heavy-haul freight vehicles and proposes the limit value of the WAW. A dynamic model of heavy-haul freight vehicle is established and the Jendel wheel wear model is used to analyze the mechanisms and influencing factors of WAW. The study primarily focuses on four factors: uneven loading of freight vehicles, differences in wheel surface hardness, suspension errors of the left and right side, and the distribution characteristics of the line. Next, the wheel wear model is verified by the test data, the flange wear and tread wear are analyzed at the same time. The WAW limits value under the load and empty conditions by considering the elastic wheelset model is investigated. The results showed that differences in wheel surface hardness between the left and right wheels and line distribution are the main causes of WAW. By conducting dynamic calculations, the WAW limit value of the empty and heavy vehicle is set as 2 mm. During the operation, it is necessary to effectively monitor the vehicle and track parameters that affect WAW.

Design of battery shell stamping parameters for vehicles based on fusion of various artificial neural network models

The application of neural network model in engineering prediction is frequent. The BPE shell material was optimized, and the reliability of the new material was verified by modal simulation. The accuracy of finite element modeling was ensured by constrained mode experiments, and all variables were preprocessed by Latin hypercube sampling. The design parameters were determined by Monte Carlo simulation. Four different neural networks, including back propagation (BP), radial basis function (RBF), extreme learning machine (ELM) and wavelet neural network (WNN), are used to train and learn the dataset. The BPE weight reduction ratio was 14.3%, the stress was reduced by 18.6%, deformation displacement was reduced by 14.2%, and the first-order mode was increased by 29.1%.

Iterative learning–based model-free adaptive precise heading following of an autonomous underwater vehicle with unknown disturbances

Due to the nonlinearity, strong coupling, and uncertain parameters of autonomous underwater vehicle (AUV), it is difficult to build an accurate dynamic model, which makes precise control of AUV extremely challenging. To handle the precise heading-following problem of AUV, this paper proposes an iterative learning-based redefine model-free adaptive heading control (IL-RMFAC) method for the underactuated AUV with unknown disturbances based on data driven. The control scheme consists of a learning control algorithm, a parameter iterative update algorithm, and a parameter reset algorithm. It is designed using only the input and output (I/O) data of the controlled system and is a data-driven control method. The pseudo partial derivative (PPD) can be iteratively calculated through the parameter iterative update algorithm and reset algorithm to adjust the learning gain, solving the problem of strictly limited initial position of the traditional fixed learning gain iterative learning control (ILC). A linear combination of angle and angular velocity is introduced in the kinematic layer to avoid overshooting of the expected following target, and an iterative learning method is introduced in the dynamics to improve the accuracy. As the number of iterations increases, the steady-state error is gradually decreased. Finally, by comparing traditional proportional–integral–derivative (PID) simulations, the proposed algorithm’s effectiveness and outstanding performance for the AUV heading tracking are confirmed.

Three-Phase 75 kW Brushless Direct Current Motor for Electric Vehicles: Different Power Stage Design, Calculation of Losses, Cooling Techniques, and Comparison

Three-Phase 75 kW Brushless Direct Current Motor for Electric Vehicles: Different Power Stage Design, Calculation of Losses, Cooling Techniques, and Comparison

An Improved Semi-Transient Brake Cooling Simulation Method

<div>In this article, an improved brake cooling simulation method is introduced. By this method, the vehicle parameters, such as weight, height of the center of gravity, wheelbase, and the like can be included to calculate the braking thermal load under different operating conditions. The effect of the brake kinetic energy regeneration (BKER) on the braking thermal load can also be calculated by this method. The calculated braking thermal load is then input to a coupled 3D simulation model to conduct flow and thermal simulation to calculate brake disc temperature. It is demonstrated that by this simulation method, the difference between the brake disc temperatures obtained from simulation and vehicle test can be controlled below 5%.</div>

Frequency Identification of Equal-Span Continuous Girder Bridge Based on Moving Vehicle Responses

The indirect method for bridge modal identification based on the response of moving vehicles has attracted widespread attention in recent years. However, most existing studies only focus on the simply supported bridge, while continuous girder bridges are widely used in practical engineering. Therefore, this study proposes an indirect frequency identification method for continuous girder bridges. First, the mode shape formula of the equal-span continuous beam is deduced using the three-moment equations, and the analytical solution of the vehicle vibration response is deduced via the vehicle–bridge coupled vibration theory. Second, the derived analytical solution is verified through numerical analysis results and field test results. Finally, the effects of bridge and vehicle parameters on the frequency identification accuracy are analyzed. The results show that a reasonable frequency of the trailer should be selected to avoid the resonance between the vehicle and the bridge for better performance. The research findings can provide a reference for the indirect identification of continuous girder bridges based on the response of moving vehicles.

Joint electric vehicle routing and battery health management integrating an explicit state of charge model

Although fleet management has been extensively explored in transportation science, the rise of electromobility imposes several scientific challenges and opportunities. So far, few attempts were made to include battery degradation in the Electric Vehicle Routing Problem (EVRP). To do it realistically, it is necessary to model State of Charge (SoC), however most versions of routing problems use oversimplified SoC models or consider only energy consumption which leads to less robust solutions overall. In this work, a method for estimating battery degradation, which relies on a realistic SoC model is presented and incorporated into a new version of the electric vehicle routing problem. In this version, not only battery degradation is integrated, but also the possibility of limiting different vehicle parameters, such as maximum vehicle speed and acceleration. Due to the extra computational complexity related to the SoC and degradation models, a genetic algorithm capable of solving the aforementioned extended EVRP is presented. Finally, through different numerical experiments, the advantages of the proposed methodology are shown.

Fleet and Vertiport Sizing for an Urban Air Mobility Commuting Service

An understanding of fleet size and vertiport size sensitivity to demand and operational parameters is necessary to quantify the scalability of urban air mobility (UAM) services. In this work, we implement a bilevel rolling window fleet scheduling formulation that includes vertiport area as a secondary objective. We also present a simple vertiport area estimation methodology that leverages the fleet scheduling results and provides a lower bound on vertiport infrastructure area requirements. Lastly, we explore the sensitivity of fleet size and vertiport infrastructure requirements to several vehicle and operational parameters, including geographical demand distribution, daily passenger volume, vehicle passenger capacity, passenger aggregation window, battery charge rate, pad separation, and pad size. We find that, although the fleet size is reasonable for a UAM commuting service scaled to serve 10,000 passengers per day, vertiport area requirements are likely problematic under current sizing guidance from the Federal Aviation Administration, particularly area requirements for vertiports that serve as workplace hubs located in dense urban centers.

A Scoping Review of Energy-Efficient Driving Behaviors and Applied State-of-the-Art AI Methods

The transportation sector remains a major contributor to greenhouse gas emissions. The understanding of energy-efficient driving behaviors and utilization of energy-efficient driving strategies are essential to reduce vehicles’ fuel consumption. However, there is no comprehensive investigation into energy-efficient driving behaviors and strategies. Furthermore, many state-of-the-art AI models have been applied for the analysis of eco-friendly driving styles, but no overview is available. To fill the gap, this paper conducts a thorough literature review on ecological driving behaviors and styles, and analyzes the driving factors influencing energy consumption and state-of-the-art methodologies. With a thorough scoping review process, thirty-seven articles with full text were assessed, and the methodological and related data are compared. The results show that the factors that impact driving behaviors can be summarized into eleven features including speed, acceleration, deceleration, pedal, steering, gear, engine, distance, weather, traffic signal, and road parameters. This paper finds that supervised/unsupervised learning algorithms and reinforcement learning frameworks have been popularly used to model the vehicle’s energy consumption with multi-dimensional data. Furthermore, the literature shows that the driving data are collected from either simulators or real-world experiments, and the real-world data are mainly stored and transmitted by meters, controller area networks, onboard data services, smartphones, and additional sensors installed in the vehicle. Based on driving behavior factors, driver characteristics, and safety rules, this paper recommends nine energy-efficient driving styles including four guidelines for the drivers’ selection and adjustment of the vehicle parameters, three recommendations for the energy-efficient driving styles in different driving scenarios, and two subjective suggestions for different types of drivers and employers.

Connecting the Dots: A Comprehensive Modeling and Evaluation Approach to Assess the Performance and Robustness of Charging Networks for Battery Electric Trucks and Its Application to Germany

The successful introduction of battery electric trucks heavily depends on public charging infrastructure. But even as the first trucks capable of long-haul transportation are being built, no coherent fast-charging networks are yet available. This paper presents a methodology for assessing fast charging networks for electric trucks in Germany from the literature. It aims to establish a quantitative understanding of the networks’ performance and robustness to deviations from idealized system parameters and identify crucial charging sites from a transportation planning perspective. Additionally, the study explores the quantification of adaptation effects displayed by agents in response to charging site outages. To achieve these objectives, a comprehensive methodology incorporating infrastructure, vehicle and operational strategy modeling, simulation, and subsequent evaluation is presented. Factors such as charging station locations, C-rates, mandatory rest periods, and vehicle parameters are taken into account, along with the distribution of traffic according to publicly available data. The study aims to offer a comprehensive understanding of charging networks’ performance and resilience. This will be applied in a case study on two proposed networks and newly created derivatives. The proposed network offers over 99% coverage for long-haul transport but leads to a time loss of approximately 7% under reference conditions. This study advances the understanding of the performance and resilience of proposed charging networks, providing a solid foundation for the design and implementation of robust and efficient charging infrastructure for electric trucks.

ADAS Simulation Result Dataset Processing Based on Improved BP Neural Network

The autonomous driving simulation field lacks evaluation and forecasting systems for simulation results. The data obtained from the simulation of target algorithms and vehicle models cannot be reasonably estimated. This problem affects subsequent vehicle improvement and parameter calibration. The authors relied on the simulation results of the AEB algorithm. We selected the BP Neural Network as the basis and improved it with a genetic algorithm optimized via a roulette algorithm. The regression evaluation indicators of the prediction results show that the GA-BP neural network has better prediction accuracy and generalization ability than the original BP neural network and other optimized BP neural networks. This GA-BP neural network also fills the Gap in Evaluation and Prediction Systems.

Validation of high-fidelity simulation-based safe operating envelopes for articulated heavy vehicles using real test data

The electrification of towing and trailing units creates new torque allocation alternatives among different units of articulated heavy vehicles. To increase the power and energy efficiency, control algorithms can request propulsion or regenerative braking from a single unit while keeping the other units unbraked or unpropelled. However, this may lead to safety problems, such as jackknifing or trailer swing. This paper uses a high-fidelity simulation tool to formulate safe operating envelopes for a tractor and semitrailer combination for braking-in-turn cases. The effects of different vehicle and environment parameters on the safe operating envelope are studied. The safe operating envelope obtained is then validated using real vehicle tests and can be used with any control algorithm to avoid requesting unsafe unit force combinations.

Estimation of Lateral Velocity and Cornering Stiffness in Vehicle Dynamics Based on Multi-Source Information Fusion

<div>To address the challenge of directly measuring essential dynamic parameters of vehicles, this article introduces a multi-source information fusion estimation method. Using the intelligent front camera (IFC) sensor to analyze lane line polynomial information and a kinematic model, the vehicle’s lateral velocity and sideslip angle can be determined without extra sensor expenses. After evaluating the strengths and weaknesses of the two aforementioned lateral velocity estimation techniques, a fusion estimation approach for lateral velocity is proposed. This approach extracts the vehicle’s lateral dynamic characteristics to calculate the fusion allocation coefficient. Subsequently, the outcomes from the two lateral velocity estimation techniques are merged, ensuring rapid convergence under steady-state conditions and precise tracking in dynamic scenarios. In addition, we introduce a tire parameter online adaptive module (TPOAM) to continually update essential tire parameters such as cornering stiffnesses, with its effectiveness demonstrated through DLC and slalom simulation tests. Using a dual extended Kalman filter (DEKF) observer, the article allows for joint estimation of vehicle states and tire parameters. Ultimately, we offer a cost-effective estimation method of vital dynamic vehicle parameters to support the motion control module in autonomous driving.</div>

Determination of control parameters based on the characteristics of a calibrated vehicle

The calculation of control parameters when using traffic light control systems is carried out taking into account the indicators of the design car, fixed in methodological and regulatory documents. Over the past decades, the technical and dynamic parameters of motor vehicles have changed significantly. This change is primarily an improvement in dynamic indicators, which affects the process of traffic flows, which requires mandatory consideration when organizing traffic, in particular when using traffic light control systems and determining the parameters of their operation. The heterogeneity of the composition of the fleet of passenger cars in the urban traffic flow is determined and the calculation of the dynamic parameters of cars prevailing in the flow is carried out. The indicator "calibrated car" is introduced as a conditional transport unit having averaged indicators of technical and dynamic parameters of modern vehicles, prevailing in traffic flows. This indicator allows you to expand the previously used indicator of the design car in terms of dynamic parameters, in relation to the process of managing urban traffic flows when using traffic light regulation. Keywords urban traffic flow, calibrated car, calculated car, traffic light regulation, dynamic parameters, transport delay

Vehicle Parameter and Electric Powertrain Efficiency Analysis Using Real-Driving Data

With the conversion from Internal Combustion Engine Vehicles (ICEV) to Battery Electric Vehicles (BEV) mainly promoted by CO2 emission targets, innovative powertrain concepts arose in the automotive industry. Original Equipment Manufacturers (OEMs) practice the socalled benchmarking to identify technological potentials in their competitor’s concepts and reduce their development costs by focusing on the best-performing technologies in the electric vehicle market. In contrast, these analyses mean significant expenses in terms of time and cost. Especially on vehicle level, preparing the vehicles for dynamometer tests and performing multiple test series on these test benches require high personnel and time capacities. In this work, we present a methodology that reduces the effort of benchmarking analyses on vehicle level by substituting dynamometer tests. This methodology describes the identification of vehicle parameters and the analysis of the electric powertrain’s efficiency. With no manipulation of the vehicle’s structure and low-cost test equipment, data is recorded on public roads during real-driving scenarios, demonstrating our procedure's simplicity and universal application. With the obtained vehicle parameters (i.e. Road Load Coefficients (RLCs), rolling and air resistance) and the electric powertrain’s efficiency map, we enable the parametrization of simulation models for further analyses. We validate our methodology based on tests performed on closed test tracks and a vehicle dynamometer.