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The automotive industry must deal with significant challenges in the future. Consequently, predicting the Noise‐Vibration‐Harshness (NVH) in the early design and concept phase becomes essential. Especially dynamic seat behavior immediately affects ride comfort and hence customer acceptance. Therefore, it is necessary to have reliable finite element (FE) models for the simulation‐based prediction of the dynamic seat behavior. This study describes different aspects of the experimental‐ and simulation‐based modeling of a vehicle seat. The experimental modal analysis is used to characterize the seat system as well as the subsystem's seat backrest and frame. The structural dynamics of a seat depend on various sensitive parameters. We identified the dominant mode shapes below 100 Hz, which are lateral, fore‐and‐aft, and twisting modes. We could see that the seat backrest has a substantial mass influence. Besides, we showed that the twisting mode can be modified by varying cross‐direction stiffness. Thus, findings are utilized to improve the predictive power of simulation results. [ABSTRACT FROM AUTHOR]

Vehicle and crew scheduling is vital in public transit planning. Conventionally, the issues are handled sequentially as the vehicle scheduling problem (VSP) and crew scheduling problem (CSP). However, integrating these planning steps offers additional flexibility, resulting in improved efficiency compared with sequential planning. Given the ever-growing market share of electric buses, this paper introduces a new model for integrated electric VSP and CSP, called EVCSPM. This model employs the minimum cost flow formulations for electric VSP, set partitioning for CSP, and linking constraints. Due to the nonlinear integer property of EVCSPM, we propose a method that hybrids a matching-based heuristic and integer linear programming solver, GUROBI. The numerical results demonstrate the efficiency of our methodology, and the integrated model outperforms the sequential model in real-life scenarios. [ABSTRACT FROM AUTHOR]

Compared with the design of traditional fuel vehicles, the design of electric vehicles has its uniqueness, consisting mainly in that the body design must be able to adapt to the new power system and its layout. Power battery pack is an important factor affecting the body design of electric vehicles. In order to study the modeling of power battery packs and its impact on body performance, it was proposed to use the finite element method for modeling the power battery pack and analyzing its influence on the performance of the body-in-white. Based on a certain electric vehicle, the basic body-in-white model and the body-in-white model with the mass point addition and the refined model of the power battery pack were established, the static stiffness of the body-in-white simulation results of different models were compared and analyzed, and finally the effectiveness of the simulation model was verified through bench tests. Based on this effective model, the influence of the power battery pack on the modal and strength of the body-in-white was analyzed. The results show that the established refined model of the power battery pack has higher computational accuracy. Besides, the power battery pack can significantly increase the static stiffness of the electric vehicle body-in-white, effectively optimize the first-order torsion frequency and the first-order front cabin yaw frequency of the body-in-white, reduce the first-order bending frequency of the body-in-white, and greatly increase the risk of static strength failure of the body-in-white. In the setting of body performance goals and structural development, the influence of the power battery pack cannot be ignored. [ABSTRACT FROM AUTHOR]

As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle's fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results. [ABSTRACT FROM AUTHOR]

To enhance the lateral stability of a certain type of high-speed train under bogie's active control, characteristics of inertial actuators that generate control force in the active control system are studied, the control delay of the whole Hardware-in-the-Loop (HIL) system is measured, and multi-objective optimisation for feedback parameters with or without the control delay is achieved in this study. A linear model of bogie lateral dynamics, with two inertial actuators, acting on the front and rear end beams, is established for the optimisation. Correspondingly, a nonlinear vehicle model is established in the Simpack platform for both mathematical simulation and HIL simulation to verify the control effects. As the force source of active control, dynamic characteristics of the inertial actuator with different control strategies are examined. Multi-objective optimal control is carried out on the linear active bogie models with and without the delay measured in the experiment to improve the bogie hunting stability. The control method is then evaluated in a nonlinear vehicle model and HIL simulation. Simulation results show that the lateral dynamic performance of the high-speed train can be effectively improved with the active stability system even with a large time delay. [ABSTRACT FROM AUTHOR]

A significant resonance pattern at high frequencies around 400 Hz to 1000 Hz has been well observed from measured rail accelerations. Such resonances are key issues for rail noise and corrugation. Although this pattern has been noted and discussed in various papers in terms of wave reflections among multiple wheels, the aim of this study is to develop novel dynamic model and reveal generation mechanism of this resonance phenomenon with the train-track coupled system. An infinite Timoshenko beam with continuous supports is adopted for modeling the track system, and the point and transfer receptances of the rail for a moving excitation are explicitly deduced by the residue theorem together with a Fourier transform-based method. A frequency-domain method is then established to calculate the power spectral densities (PSD) of the train-track coupled system responses to stochastic irregularities with the moving vehicle model with multiple wheels. It is found that the high-frequency resonances occur when the reflected waves generated from multiple wheels on the rail are exactly in phase with the initial track irregularity. The load speed has negligible influence on the wheel–rail interaction in the lower frequency range and a small influence in the upper frequency range only for very high train speeds. [ABSTRACT FROM AUTHOR]

xComputational representation of real-world systems plays an important role in vehicle analysis. Aiming to improve mobility systems, this article presents the development of a chassis dynamometer and vehicle model describing the vehicle longitudinal dynamics considering the tire-bench contact. The model simulates the longitudinal vehicle-bench tests where the system is powered by the bench's electric motor and the gearbox in the neutral position, considering tire interaction forces, rolling resistance, and system losses. It was implemented in Matlab/Simulink™ and validated by coast-down experiments, starting at 60 km/h and 100 km/h, where the model result curves presented an appropriated representativity for the experimental data. A total movement resistance analysis resulted in a mean error of 6.71% when compared to experimental data, while the literature slip-independent equations presented mean errors between 31.94% and 77.61%. The developed model is a representative approximation of the vehicle-dynamometer set and can predict longitudinal experiments, helping the vehicle evaluation process, and saving time and resources. [ABSTRACT FROM AUTHOR]

The suspension systems in automobile are required to provide optimum ride comfort and vehicle handling. As these two performance parameters are in contradiction with each other in automobiles fitted with regular passive suspension systems, various studies are being conducted to identify an alternative system that can optimise both the parameters. One such alternative is presented in the form of Magnetorheological (MR) damper, a most viable semiactive device. A MR damper comprises of MR fluid that consists of carbonyl iron powder and silicone oil added with additive. The characteristic of this damper has been established experimentally, which in turn is used to identify the parameters of spencer model for MR damper. Using spencer model of MR damper, at 5 different currents, viz.,0A, 0.25A, 0.5A, 0.75A, 1A, the half-car models of vehicle suspension system of a car and a multi axle truck are simulated for analysing the ride comfort and vehicle handling. The ride comfort and vehicle handling performance of each specific vehicle model with passive suspension system are compared with corresponding skyhook, ground hook and hybrid control semi-active suspension systems. The simulation and analysis are carried out using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

In this study, a static output-feedback controller with variable substitution technique is designed to investigate the performance of non-stationary running active suspension. Established non-stationary random road conditions are augmented into the half-car active suspension vehicle system to structure the model as required. Then, the control problem is formulated by properly choosing the controlled and constrained outputs. The vehicle body vertical and angular accelerations are quantified as controlled output, whereas the suspension rattle space, ride safety and actuator limitation are grouped as constrained output. With the aid of variable substitution and linear matrix inequalities (LMIs), the H∞ static output-feedback control (SOFCWVS) is derived. Eventually, the controller will be designed with a special sparsity constraint. The designed controller is applied to the proposed vehicle model and the model responses are compared in the time domain under various controllers. For the time-domain investigation, a non-stationary random road with a class-C profile is utilized. From the simulation results, it is observed that SOFCWVS enhanced the ride comfort more than other control schemes with guaranteed time-domain constrained outputs. [ABSTRACT FROM AUTHOR]

Vehicle load estimation and health monitoring of bridges are of great importance for the health monitoring of bridge structure under vehicle loads. Traditional methods for the estimation of vehicle load require the positions of the vehicles. The vehicle position tracking is generally conducted in offline manner and requires the installation of additional sensors. To resolve these problems, we developed a Bayesian probabilistic approach for the online estimation of vehicle loads, vehicle positions, and structural parameters for bridges. The crux is to model the vehicle load vector as a modulated filtered Gaussian white noise due to the fact that the vehicle-bridge interaction forces are in essence the responses of the vehicle-bridge coupled system under the excitation of the road roughness described by Gaussian random field and the constant vehicle weights. Furthermore, the vehicle speed vector is introduced to track the unknown positions of vehicles. There are three appealing features in this approach. First, it allows the simultaneous estimation of vehicle loads, vehicle positions, and structural parameters in an online manner. Second, this method allows for time-varying vehicle speed tracking. Third, the proposed method is applicable to the case with multiple vehicles. Examples for the case where single/multiple vehicles pass across bridges with uniform speeds/variable speeds are presented to demonstrate the feasibility of the proposed method for vehicle load estimation, vehicle position tracking, and bridge structural identification using only strain measurements. [ABSTRACT FROM AUTHOR]

The thermal management of the batteries in electrical and hybrid vehicles is a major concern for operation safety, battery life, and performance. In the current research, the temperature of the battery is tracked for 10,000 s of operation while using a Ranque–Hilsch vortex tube (RHVT) for the thermal management of the battery. An analytical model is developed and validated to study the impact of the heat generated per unit volume from the battery, the mass ratio for the cold air at the outlet to the total air at the inlet (x), entered air pressure, temperature, and mass flow rate. The peak temperature of the battery can be reduced by increasing the pressure and mass flow rate of the air at the inlet, decreasing the temperature of the air at the inlet, and using four RHVTs instead of a single RHVT. The proposed system significantly decreased the maximum temperature of the battery to 48.12 °C, compared to the battery thermal management method of using direct air, which resulted in a temperature of 75.73 °C. Furthermore, employing four RHVTs to cool the battery further reduced the maximum temperature from 48.12 to 11.23 °C, demonstrating a substantial improvement compared to utilizing a single RHVT. Utilizing the RHVT provides a good solution for the battery thermal management problem because it is cheap, small, and reduces the temperature of the air rapidly. [ABSTRACT FROM AUTHOR]

Various statistical data indicate that mobile source pollutants have become a significant contributor to atmospheric environmental pollution, with vehicle tailpipe emissions being the primary contributor to these mobile source pollutants. The motion shadow generated by motor vehicles bears a visual resemblance to emitted black smoke, making this study primarily focused on the interference of motion shadows in the detection of black smoke vehicles. Initially, the YOLOv5s model is used to locate moving objects, including motor vehicles, motion shadows, and black smoke emissions. The extracted images of these moving objects are then processed using simple linear iterative clustering to obtain superpixel images of the three categories for model training. Finally, these superpixel images are fed into a lightweight MobileNetv3 network to build a black smoke vehicle detection model for recognition and classification. This study breaks away from the traditional approach of "detection first, then removal" to overcome shadow interference and instead employs a "segmentation-classification" approach, ingeniously addressing the coexistence of motion shadows and black smoke emissions. Experimental results show that the Y-MobileNetv3 model, which takes motion shadows into account, achieves an accuracy rate of 95.17%, a 4.73% improvement compared with the N-MobileNetv3 model (which does not consider motion shadows). Moreover, the average single-image inference time is only 7.3 ms. The superpixel segmentation algorithm effectively clusters similar pixels, facilitating the detection of trace amounts of black smoke emissions from motor vehicles. The Y-MobileNetv3 model not only improves the accuracy of black smoke vehicle recognition but also meets the real-time detection requirements. [ABSTRACT FROM AUTHOR]

The aim of the work was to develop a multi-body model that can simulate the Impact between the vehicle wheel and a pothole. The model consists of the vehicle wheel together with front McPherson suspension assembly and a potholed road surface. Therefore the kinematics of the suspension when the vehicle hits a pothole can be simulated. The model was first predetermined by an analytical calculation and then successfully validated by an experimental test performed with the same parameters as the virtual model. The results were very similar and therefore allow the model to be used to simulate different scenarios in terms of wheel dynamics and behavior when impacting potholes with different depths. [ABSTRACT FROM AUTHOR]

Pang J, Li Y, Jin X, Xu L. Detection and analysis of typical vibration load of grain harvester based on short-time Fourier method. Yang X, Ji Y, Ke D, Li F. Research on the methods and means of action strength training in aerobics training. Lu S. Scientific research and training satisfaction evaluation analysis of postgraduates in science and engineering colleges based on big data. [Extracted from the article]

The mobility of vehicles in off-road environments is critical for many applications. Predicting the tire-soil interaction is a challenge, especially in non-linear deformable terrain. This paper presents an overview of the bibliographic references on tires–deformable soil interactions after the 2000's, identifying the gaps in the literature. The capabilities and challenges of different modeling frameworks used for mobility (i.e., empirical, semi-empirical, and physics-based) are discussed; special emphasis is given to continuum-based frameworks. A summary of terrain material models used to approximate the behavior of coarse and fine-grained soils is provided with practices used to characterize such materials. A review of tire models for deformable soil navigation and the tire-soil interfaces is provided. Strategies to validate all these models are presented. Finally, the application of these studies for assessing the sensitivity concerning input parameters (e.g., velocity, inflation pressure, and normal load), multi-pass, multi-layered soils, wet soils, and fully integrated multi-body vehicle models are discussed. The final contribution of this review paper is a summary table that synthesizes the extensive bibliographic review. Overall, this work highlights a lack of physics-based trafficability studies in wet and plastic deformable soils. Moreover, studies on contact adhesion, stone picking, multipass, and cornering also need improvement. [ABSTRACT FROM AUTHOR]

Current emission models primarily focus on traditional combustion vehicles and may not accurately represent emissions from the increasingly diverse vehicle fleet. The growing presence of hybrid and electric vehicles requires the development of accurate emission models to measure the emissions and energy consumption of these vehicles. This issue is particularly relevant for low-emission zones within cities, where effective mobility planning relies on simulation models using continuously updated databases. This research presents a two-dimensional emission model for hybrid vehicles, employing artificial neural networks for low-emission zones. The key outcome is the methodology developed to create a CO2 emission model tailored for hybrid vehicles, which can be used to simulate various road solutions. The CO2 emission model achieved an R2 coefficient of 0.73 and an MSE of 0.91, offering valuable information for further advancements in emission modelling. [ABSTRACT FROM AUTHOR]

This work proposes a centralized data-based orderly charging strategy that considers the user's charging choices. Three charging choices for different types of users are described. Then, a scheduling model of electric vehicles based on the time dimension is established. In this strategy, the optimization model not only considers the demand of the grid side and the user side, but also takes the driving data of electric vehicles as the driver. The grid-side optimization involves minimizing the equivalent load fluctuation, and the user-side is optimized to minimize the charging cost and maximize the charging electric quantity. The scheduling capabilities of the three charging strategies are analyzed based on a series of driving data of electric vehicles. The results show that the peak-valley difference and equivalent load fluctuation of the power grid in the data-based orderly charging strategy reduced by 22.2% and 22.7%, respectively, and the charging cost of users also reduced much more than the other two charging strategies. Additionally, the effect of users' charging choices on the charging strategy is analyzed, and the results show that the orderly charging strategy that considers users' charging choices can effectively decrease the scheduling deviation caused by users' charging choices. It greatly improves the security and economy of the grid. [ABSTRACT FROM AUTHOR]

Bridge frequency (BF) identification using the vehicle scanning method has attracted considerable attention during the last two decades. However, most previous studies have adopted unrealistic vehicle models, thus finding limited practical applications. This study proposes a smartphone-based BF identification method that uses the contact-point acceleration response of a four degree-of-freedom vehicle model. The said response can be inferred from the vehicle body response measured by a smartphone. For realizing practical applications, this method is incorporated into a self-developed smartphone app to obtain data smoothly and identify BFs in a timely manner. Numerical and experimental investigations are performed to verify the effectiveness of the proposed method. In particular, the robustness of this method is investigated numerically against various factors, including the vehicle speed, bridge span, road roughness, and bridge type. Furthermore, laboratory calibration tests are performed to investigate the accuracy of the smartphone gyroscope in measuring the angular velocity, where anomalous data are detected and eliminated. Laboratory experiment results for a simply supported bridge indicate that the proposed method can be used to identify the first two BFs with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Model-based approaches to navigation, control, and fault detection that utilize precise nonlinear models of vehicle plant dynamics will enable more accurate control and navigation, assured autonomy, and more complex missions for such vehicles. This paper reports novel theoretical and experimental results addressing the problem of parameter estimation of plant and actuator models for underactuated underwater vehicles operating in 6 degrees-of-freedom (DOF) whose dynamics are modeled by finite-dimensional Newton-Euler equations. This paper reports the first theoretical approach and experimental validation to identify simultaneously plant-model parameters (parameters such as mass, added mass, hydrodynamic drag, and buoyancy) and control-actuator parameters (control-surface models and thruster models) in 6-DOF. Most previously reported studies on parameter identification assume that the control-actuator parameters are known a priori. Moreover, this paper reports the first proof of convergence of the parameter estimates to the true set of parameters for this class of vehicles under a persistence of excitation condition. The reported adaptive identification (AID) algorithm does not require instrumentation of 6-DOF vehicle acceleration, which is required by conventional approaches to parameter estimation such as least squares. Additionally, the reported AID algorithm is applicable under any arbitrary open-loop or closed-loop control law. We report simulation and experimental results for identifying the plant-model and control-actuator parameters for an L3 OceanServer Iver3 autonomous underwater vehicle. We believe this general approach to AID could be extended to apply to other classes of machines and other classes of marine, land, aerial, and space vehicles. [ABSTRACT FROM AUTHOR]

Semi-active suspensions are seen as a promising technology to improve ride quality in high-speed railway vehicles as they provide substantial benefits not achievable through an optimisation of the passive suspension while avoiding some drawbacks of full-active control. Previous works showed that semi-active primary suspensions (SAPS) offer significant improvement of ride quality. However, published implementations of this concept did not consider the coupling effects between car-body and bogie vibrations, which play a decisive role in the success of control strategies for SAPS. This paper provides a comprehensive analysis of SAPS to improve vehicle ride comfort, with special attention to the mitigation of car-body bending vibration. A vehicle model considering the coupling of car-body bending and bogie pitch is proposed and used for the synthesis of an LQG controller. The performance of the semi-active suspension and controller is assessed using a detailed flexible multibody system of a high-speed vehicle. The proposed control scheme is shown to be highly effective in reducing car-body vibration in the entire frequency range of interest from 1 Hz to20Hz. A remarkable ride comfort improvement is found in the entire speed range from 150 km/h to 350 km/h. [ABSTRACT FROM AUTHOR]

Braking control of urban rail vehicles with multiple carriages is critical to ensure the safe operation of urban rails. However, existing decentralized braking control methods lead to the inconsistent speed and excessive coupler force among carriages, which compromises the operation safety of urban rails. To address this issue, a cooperative braking strategy is proposed for urban rail vehicles based on Koopman model predictive control method. First, a cyber‐physical model is established, where the physical layer characterizes the dynamic model of multiple carriages using the Koopman operator. The cyber layer represents the communication topology of carriages with graph theory. Second, a cooperative braking controller is designed with the distributed model predictive control method. An optimisation problem is formulated to minimize the speed inconsistency and relative displacement difference among carriages. Third, extensive simulations under various scenarios, including normal and extreme operating conditions, are conducted to verify the effectiveness of the proposed method. Simulation results show that, compared with the classical decentralized method, the proposed braking method reduces the average relative displacement by 78% while reducing the speed error by 29%. [ABSTRACT FROM AUTHOR]

In emergency scenarios, lane changing can provide a considerable advantage over braking by aiding in the prevention of rear-end collisions. However, executing lane changes on horizontal curves might lead to rollover collisions. This study proposes a systematic methodology for quantifying the rollover safety margin during lane-changing maneuvers by encompassing the complex characteristics of vehicle-road interactions. Specifically, an enhanced six-degree-of-freedom vehicle dynamics model was developed for a tractor-semitrailer and integrates road superelevation. Using this model, the rollover safety margin reduction rate (fS) was calculated. The fS represents the ratio of the difference between the lateral load transfer ratio margins under both reference state and emergency lane change conditions to the lateral load transfer ratio margin in the reference state. The reference state corresponds to vehicles maintaining 80 km·h-1 on a 270 m radius curve, while the emergency condition is defined as lane change durations of less than 4 seconds. The results reveal that emergency lane change maneuvers and roadway alignment significantly affect rollover safety margin. Shorter lane change duration, higher speed, and smaller radius worsen the rollover safety margin; these effects are further amplified when the lane change direction is opposite to the curve's bending direction. When the tractor-semitrailer performs a lane change at 60 km·h-1 within a 4-second duration on a 600 m radius curve, the fS exceeds 100%, indicating an imminent rollover. Consequently, this study contributes valuable evidence to the development of more reliable and secure lane-change strategies. [ABSTRACT FROM AUTHOR]

Vehicle make and model recognition (VMMR) is an important aspect of intelligent transportation systems (ITS). In VMMR systems, surveillance cameras capture vehicle images for real-time vehicle detection and recognition. These captured images pose challenges, including shadows, reflections, changes in weather and illumination, occlusions, and perspective distortion. Another significant challenge in VMMR is the multiclass classification. This scenario has two main categories: (a) multiplicity and (b) ambiguity. Multiplicity concerns the issue of different forms among car models manufactured by the same company, while the ambiguity problem arises when multiple models from the same manufacturer have visually similar appearances or when vehicle models of different makes have visually comparable rear/front views. This paper introduces a novel and robust VMMR model that can address the above-mentioned issues with accuracy comparable to state-of-the-art methods. Our proposed hybrid CNN model selects the best descriptive fine-grained features with the help of Fisher Discriminative Least Squares Regression (FDLSR). These features are extracted from a deep CNN model fine-tuned on the fine-grained vehicle datasets Stanford-196 and BoxCars21k. Using ResNet-152 features, our proposed model outperformed the SVM and FC layers in accuracy by 0.5% and 4% on Stanford-196 and 0.4 and 1% on BoxCars21k, respectively. Moreover, this model is well-suited for small-scale fine-grained vehicle datasets. [ABSTRACT FROM AUTHOR]

The subject of electric vehicles (EVs) is constantly relevant from the perspective of climate change and sustainability. Multi-Criteria Decision Analysis (MCDA) methods can be successfully used to evaluate models of such vehicles. In many cases, the MCDA methods are modified to account for uncertainty in the data. There are many ways to express uncertainty, including more advanced ones, such as fuzzy sets, for example, but expressing attributes in terms of interval numbers remains a popular method because it is an easy-to-implement and easy-to-understand technique. This study focuses on interval extensions of the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. It aims to compare the most popular extension proposed by Jahanshahloo and the proposed new modification, which returns the result in an interval form. Certain inconsistencies of the Jahanshahloo extension are discussed, and it is explained how the new extension avoids them. Both extensions are applied to an EV evaluation problem taken from the literature as an example for sustainable assessment. The results are then analyzed, and the question of whether the input data of the interval should receive an evaluation in the form of interval results is addressed. [ABSTRACT FROM AUTHOR]

Track irregularity is the main factor that affects the safety and comfort of high-speed railway. It is important to study the influence of the track irregularity on the safety service of high speed train–track coupling system. In this paper, a vehicle–track spatial nonlinear coupled system dynamics model is developed with FEM. The model includes vehicle subsystem and track subsystem, which are coupled by wheel–rail interface. The vehicle subsystem is a 31-DOF spatial vehicle model, and the track subsystem is a three-dimensional slab track model composed of the rail, rail pad and track slab. Considering wheel tread profile and rail head profile, the wheel–rail spatial contact geometry relationship is established, and the "Trace method" and the "Minimum distance method" are used to search for wheel–rail contact points. Considering the nonlinear contact between the wheel and the rail, a numerical approach is presented, where the "Trace method" is embedded into the algorithm for solving the vehicle–track nonlinear coupling dynamics equation by cross iteration, realizing searching the wheel–rail contact points and solving the vehicle–track nonlinear coupling equation simultaneously, which greatly improves the efficiency of numerical analysis. As application examples, the dynamic responses of a high speed train–track spatial nonlinear coupled system excited by the track V-shaped local profile irregularity and the track torsional irregularity are analyzed. The results show that the algorithm is effective in solving the vehicle–track spatial nonlinear coupled system dynamics problem, and the track irregularity has significant influence on the dynamic response of vehicle and track structure. [ABSTRACT FROM AUTHOR]

Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends. [ABSTRACT FROM AUTHOR]

The control barrier function (CBF) provides a good tool to design supervisory controllers for safety‐critical systems. However, constructing a CBF for a general nonlinear system is a challenging task. This paper presents a general CBF computational synthesis approach for nonlinear affine systems with state constraints and input constraints using sum‐of‐squares (SOS) programming. System dynamics and state constraints are approximated using polynomial functions, and the input constraints are approximated using linear functions. The CBF synthesis algorithm is presented step‐by‐step, and tricks to deal with potential numerical issues during the SOS optimizations are discussed. The proposed algorithm is applied to an automotive lane keeping application to synthesize a CBF, and a supervisory control based on this CBF is designed. The effectiveness and resilience of this CBF‐based supervisory control against disturbance are illustrated in simulation with a 14‐degree‐of‐freedom vehicle dynamics model. Results show that this CBF‐based supervisory control can improve the resilience of nominal controllers in automotive lane keeping applications. [ABSTRACT FROM AUTHOR]

In recent years, since the unique advantages in automotive structures, the vehicle active suspension systems have received widespread attentions. A good active suspension system can reduce the vibration and improve the overall performance of the vehicle. Therefore, the design of the controller for the active suspension system to perform autonomous adjustment plays a vital role in vehicle comfort and safety. For the active suspension of the seven-DOF sport utility vehicle (SUV) model, this paper takes the vehicle body acceleration, tire dynamic load and suspension dynamic travel as the indicators to evaluate the performance, and the proportional-integral-derivative (PID) controller is designed to improve the performance of the vehicle active suspension system. Based on the software of MATLAB/Simulink and Carsim, a closed-loop co-simulation model diagram is established, which includes a PID controller module. Meanwhile, the random road input model and the whole vehicle model are constructed in Carsim. Finally, at the speeds of 70, 90, and 120 km/h, the active suspension system under the designed PID controller is simulated and compared with the passive suspension system. The simulation results show that the active suspension system based on PID controller can effectively improve the overall performance of the vehicle, and then the comfort and safety of the vehicle can be further enhanced. [ABSTRACT FROM AUTHOR]

This paper aims at developing a novel detection and identification method against malicious attacks in intelligent transportation. Due to the development and applications of communication and advanced sensor technologies, intelligent transportation has faced new safety risks. In particular, the emerging malicious attacks, such as false data injection attack, can mask the destruction of physical dynamic by tampering with information in layer to fool the current detection methods. Because of this reason, an adaptive unknown input observer-based detection and identification method is developed. Firstly, a physical dynamics model of vehicle networking system is established by considering the actual physical state. Considering the spoofing characteristics of false data injection attack, an unknown input observer-based detection method is proposed. Through the design of adaptive unknown input observer parameters, the detection performance, can be improved by cutting down the state estimation error. Compared with the UIO-based detection method, simulations demonstrate that the false positive rate can be reduced 0.1%. Based on the feature of state residuals that is not sensitive to the attacked i th residual, but sensitive to other residuals, a novel identification criterion is developed. At last, simulation experiments on the Matlab verify the performance of the proposed detection and identification algorithm in intelligent transportation system. [ABSTRACT FROM AUTHOR]

The highlight model is widely used as a simple and convenient method in the radar field but its accuracy is not high. Based on the traditional highlight model, the algorithm has been improved to address the acoustic scattering problems of underwater vehicles with more complex line shapes. The basic idea is to partition the model into micro-bodies to calculate the scattered sound pressure, consider the phase interference of each part, and then synthesise the scattered sound pressure to approximate the target's actual shape. A computational model of the wedge-shaped convex structure on the back of the underwater vehicles is developed using a highlight model of a trapezoidal plate. The results of the calculations using the highlight model approach are consistent with those of the planar element method. Utilising the modified highlight model method, the accuracy of acoustic scattering characteristics calculations for the stern and overall structures of underwater vehicles has proven satisfactory. Finally, fast acoustic scattering prediction software is developed for underwater vehicles, enabling the calculation of the acoustic scattering characteristics for individual structures, combined structures, and coated silent tiles. This software provides algorithmic support for the fast prediction of the acoustic stealth performance of underwater vehicles. [ABSTRACT FROM AUTHOR]

Polymer nanocomposites (PNCs) usually have superior properties than pristine polymers. Understanding the dynamics properties in PNC system is crucial to reveal the mechanism of property change unpon the addition of nanoparticles (NPs), and therefore for a better design of the material properties. In this short perspective, we summarize recent advances mainly from theoretical and simulation studies of dynamics properties in polymer nanocomposite system. One is the "vehicle model" which reveals that diffusion dynamics of sticky NP is coupled to surrounding chain segments. Similarly, recent simulations demonstrate that such coupling also exists in all-polymer nanocomposite wich is composed of linear polymer chains and single-chain nanoparticles (SCNPs). These SCNPs have almost the same chemical composition as the matrix chain. Therefore, it is assumed that such all-polymer nanocomposite can act as a model system where there are no enthalpic interactions between NPs and polymer chains. Although the above dynamic coupling was found in the above two different systems containing inorganic NPs or relatively small organic SCNPs, it was found that the length scale of such dynamic coupling (the thickness of the matrix/NP interface) is comparable to the NP size, which is surprisingly consistent in the above two different systems. In addition, a chain-length dependence of the NP influence on the chain dynamics reported from a recent joint simulaiton and experimental study of all-polymer nanocomposite system, and a theoretical model developed for such phenomena are also reviewed. At the end, we give an outlook of this field, especially for possible chain-length dependence of complex dynamics in sticky-NP systems. [ABSTRACT FROM AUTHOR]

• Derive new closed-form solution for multi contact responses from a six-DOF vehicle model. • Identify track modulus from the first "plus" frequency of the dual-beam with a simple formula. • Detect rail damage from the instantaneous amplitude squared generated from the driving component of the contact response. • Contact-point spectrum performs better since it is free of the vehicle frequencies. • Parametric study for factors affecting the technique. The vibration data recorded for the test vehicle cannot be directly used to detect the rail and bridge frequencies due to the overshadowing effect of vehicle's frequencies. To overcome this problem, the vehicle-rail contact response may be used instead. However, current techniques on this are available only for simple test vehicles with single or two degrees of freedom (DOFs). Normally, a train car contains a car body and two bogies (each of two wheelsets), and should be modeled by a multi- DOF system. The objective of this paper is first to solve the four wheelset-rail contact responses from the six measured vehicle responses. Then the contact responses are employed to retrieve the track modulus , while detecting the rail damages, for their salient feature of being free of the vehicle's frequencies. The railway track is modeled by a dual-beam system, with the upper (rail) and lower (bridge) beams connected by a spring-dashpot layer. The track modulus is retrieved through identification of the first "plus" frequency of the dual-beam, and the rail damage is detected from the instantaneous amplitude squared (IAS) generated from the driving component of the contact response. Through comparison with results by the finite element method (FEM), it is confirmed that: (1) the proposed procedure for computing the multi contact responses from the multi-DOF vehicle responses is reliable; (2) track irregularity presents a limited influence even for high disturbance; (3) to trade-off between efficiency and accuracy, a medium vehicle speed is suggested for the field measurement; (4) for the dual-beam with sufficient length, the use of simply supported condition is acceptable for rails; and (5) the effect of track damping is small and can be ignored. [ABSTRACT FROM AUTHOR]

Fuel-saving-oriented collaborative driving is a highly promising yet challenging endeavor that requires satisfying the driver's operational intentions while surpassing the driver's fuel-saving performance. In light of this challenge, the paper introduces an innovative collaborative driving strategy tailored to the objective of fuel conservation in the context of commercial vehicles. An enhancement to this strategy involves the development of a network prediction model for vehicle speed, leveraging insights from driver style recognition. Employing the predicted speed as a reference, a model-predictive-control-based optimal controller is designed to track the reference while optimizing fuel consumption. Furthermore, a straightforward yet effective collaborative rule is proposed to ensure alignment with the driver's intention. Subsequently, the proposed control scheme is validated through simulation and real-world driving data, revealing that the human–machine cooperative driving controller saves 4% more fuel than human drivers. [ABSTRACT FROM AUTHOR]

A growing number of studies have demonstrated the effectiveness and superiority of reverse engineering in using axle box vibration acceleration signals to deduce the operation state of wheelsets. However, there is a lack of discussion and summary on the overall mapping relationship between wheel excitation and axle box response, and there has been no detailed investigation on resonance characteristics of axle box. To address the deficiency, a novel resonant excitation loading method is proposed, which is applied to the vehicle model with flexible wheelset and axle box. Amplitude frequency response (AFR) curve of the resonance characteristics of axle box is obtained. AFR is used to characterise the characteristics of various excitation states of wheel, including wheel polygon, wheel tread flat, and wheel-rail separation state, and the corresponding evolution law is revealed. Finally, through two case studies, the guiding role of axle box resonance characteristics on wheel operation status detection is discussed in detail. Analyses show that mastering the resonance characteristics of axle box is an effective way to deduce the operation status of wheel based on the complex axle box vibration response signal, and it will provide a basis for the quantitative evaluation of wheel local damage. [ABSTRACT FROM AUTHOR]

Vehicle active safety system is the hotspot of current research and development, and the road adhesion coefficient is one of the essential parameters in the development of automobile active safety system, which plays a pivotal role in automobile safety. To improve the real-time performance and accuracy of the road adhesion coefficient, this paper proposes different control methods and compares and analyzes them. To accurately estimate the road adhesion coefficient in real-time, an eight-degree-of-freedom vehicle model was adopted in this paper. According to the tire torque and wheel speed, the adhesion coefficient was estimated based on the second-order nonlinear extended state observer. The experimental data, such as slip rate, using the improved tire model, is based on the recursive least squares method with forgetting factor and fuzzy logic algorithm real-time of tire-road friction coefficient estimation. The results show that the observer can effectively observe utilisation adhesion coefficient, and both control algorithms can estimate the road adhesion coefficient in real-time, effectively and quickly. On different roads, the use of the recursive least squares algorithm resulted in higher estimation accuracy, better robustness, and better control effect than using a fuzzy logic algorithm. [ABSTRACT FROM AUTHOR]

Based on the cognitive characteristics of drivers in L2-L3 level intelligent vehicles under the condition of human-vehicle co-driving, a stress steering model for drivers was established under the emergency conditions of human-vehicle co-driving intelligent vehicles. The steering characteristics of drivers in the overstressed state were reflected by the model. The degree of driver stress was characterized by the fitting relationship between steering wheel angle amplitude and steering frequency in the stress steering curve. Considering the body roll under high speed and large turning angles, a nonlinear tire model was introduced to establish three-degree-of-freedom nonlinear vehicle dynamics model. The driver stress steering model was verified by tracking the simulation of lateral acceleration. The results indicate that drivers in the state of excessive stress can cause excessive front wheel turning of vehicle. The model output results are well consistent with the actual vehicle test results, and the model can describe the driver stress steering characteristics. [ABSTRACT FROM AUTHOR]

Relocating the electric motor to a position inside the wheel offers a variety of benefits, such as reduced overall vehicle weight, more efficient production of vehicles, more flexible design and better road handling because of the accurate torque control on each wheel. However, the motor also increases the unsprung mass, which can impair the ride comfort of the vehicle. In this paper, we propose and investigate a control strategy using the vertical component of the driving force provided by an in-wheel motor for better ride comfort and handling dynamics. In the next stage, a mathematical model of the vehicle with coupled longitudinal, lateral, and vertical dynamics was introduced. The parameters of the vehicle were measured and collected from an actual car equipped with in-wheel motors. To analyze the performance of the controller, a model of the vehicle was tested in a series of simulations. The straight-line acceleration test indicated an improvement to the ride comfort of 31.8% in terms of the RMS vertical vibrations of the sprung mass. The control is achieved without any deterioration of the tire load and the vehicle's longitudinal dynamics. The stable lateral dynamics of the controlled vehicle was confirmed using a sine-sweep steering excitation, conducted on a perfectly smooth road surface with a constant longitudinal acceleration. [ABSTRACT FROM AUTHOR]

Road excitation is the basic input of vehicle vibration, which greatly impacts on vehicle ride comfort. In addition, it can cause vertical vibration of the vehicle, leading to deformation of the powertrain mount. Similarly, the longitudinal movement of the powertrain can also cause deformation of the powertrain mount. Due to the effect of the powertrain mount, there is a mutual coupling between the vehicle in the longitudinal and vertical directions, which has a great influence on the smoothness and comfort of the vehicle. In order to reveal the impact of random road excitation on the vehicle dynamic characteristics of a dual clutch transmission (DCT) vehicle under starting and shifting conditions, a longitudinal-vertical coupled dynamic model of the DCT vehicle considering the powertrain mount was established. This model includes dynamic torque model of the engine, powertrain mount model, transmission system model, tire model and road excitation model. The influence of random road excitation on the vehicle's dynamic characteristics is analyzed under starting, shifting, and different starting and shifting intentions. The simulation results of the longitudinal-vertical coupling dynamic DCT vehicle model considering road excitation are compared with vehicle test results, the model is found to accurately reflect the influence of road excitation on the dynamic performance of DCT vehicles under starting and shifting conditions, thus verifying the correctness of the model. [ABSTRACT FROM AUTHOR]

In this paper, we study the role of disruptions in the multi-period vehicle routing problem (VRP), which naturally arises in humanitarian logistics and military applications. We assume that at any time during the delivery phase, each vehicle could have chance to be disrupted. When a disruption happens, vehicles will be unable to continue their journeys and supplies will be unable to be delivered. We model the occurrence of disruption as a given probability and consider the multi-period expected delivery. Our objective is to either minimize the total travel cost or maximize the demand fulfillment, depending on the supply quantity. This problem is denoted as the multi-period vehicle routing problem with disruption (VRPMD). VRPMD does not deal with disruptions in real-time and is more focused on the long-term performance of a single routing plan. We first prove that the proposed VRPMD problems are NP-hard. We then present some analytical properties related to the optimal solutions to these problems. We show that Dror and Trudeau's property does not apply in our problem setting. Nevertheless, a generalization of Dror and Trudeau's property holds. Finally, we present efficient heuristic algorithms to solve these problems and show the effectiveness of the proposed models and algorithms through numerical studies. [ABSTRACT FROM AUTHOR]

Accurate prediction of vehicle acceleration has significant practical applications. Deep learning, as one of the methods for acceleration prediction, has shown promising applications in acceleration prediction. However, due to the influence of multiple factors on acceleration, a single data model may not be suitable for various driving scenarios. Therefore, this paper proposes a hybrid approach for vehicle acceleration prediction by combining clustering and deep learning techniques. Based on historical data of vehicle speed, acceleration, and distance to the preceding vehicle, the proposed method first clusters the acceleration patterns of vehicles. Subsequently, different prediction models and parameters are applied to each cluster, aiming to improve the prediction accuracy. By considering the unique characteristics of each cluster, the proposed method can effectively capture the diverse acceleration patterns. Experimental results demonstrate the superiority of the proposed approach in terms of prediction accuracy compared to benchmarks. This paper contributes to the advancement of sensor data processing and artificial intelligence techniques in the field of vehicle acceleration prediction. The proposed hybrid method has the potential to enhance the accuracy and reliability of acceleration prediction, enabling applications in various domains, such as autonomous driving, traffic management, and vehicle control. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of path following and dynamic obstacle avoidance for an underwater biomimetic vehicle-manipulator system (UBVMS). Firstly, the general kinematic and dynamic models of underwater vehicles are presented; then, a nonlinear model predictive control (NMPC) scheme is employed to track a reference path and collision avoidance simultaneously. Moreover, to minimize the tracking error and for a higher degree of robustness, improved extended state observers are used to estimate model uncertainties and disturbances to be fed into the NMPC prediction model. On top of this, the proposed method also considers the uncertainty of the state estimator, while combining a priori estimation of the Kalman filter to reasonably predict the position of dynamic obstacles during short periods. Finally, simulations and experimental results are carried out to assess the validity of the proposed method in case of disturbances and constraints. [ABSTRACT FROM AUTHOR]

The focus of car-following models is to analyze the microscopic characteristics of traffic flows, with particular attention given to the interaction between adjacent vehicles. This paper presents a systematic review of existing studies on car-following models, with an emphasis on the behavior of both human-driven vehicles (HDVs) and autonomous vehicles (AVs). We classify car-following models based on their applicable background and structure. By considering driving behavior and specific model parameters, we identify the advantages and limitations of each microscopic simulation model in terms of accuracy and continuity. We also discuss model calibration methods, stability analysis, and the impact of complex traffic environments on the car-following process. Finally, we present detailed discussions of each model's features and provide recommendations based on reviewed works and development trends for future research, including mixed traffic flow composed of AVs and HDVs. [ABSTRACT FROM AUTHOR]