Search

The application of 'Target Cascading' in the product development process is well known to be effective in various industry fields, and it is a methodology that is already actively applied in various forms in the field of vehicle Ride & Handling (R&H) performance development. This paper proposes a model-based vehicle performance benchmarking methodology and a technology for setting system characteristics to achieve handling performance objectives as a means of utilization for this. Rather than introducing already well-known target deployment techniques, a process is proposed for practical use in the vehicle development phase through the development of interfaces between low degrees of freedom vehicle model and optimization software. The methodology presented in this study is expected to be available in a timely manner as a practical vehicle performance development tool. [ABSTRACT FROM AUTHOR]

We present a general approach to model the motion of a rigid body ground vehicle on a smooth nonplanar road surface. We generalise planar control-oriented models while preserving standard model states, inputs and outputs. We derive our approach from first principles, apply it to existing vehicle modelling approaches and test it through real-world experiments. We demonstrate that our approach improves upon the prediction accuracy of planar models. [ABSTRACT FROM AUTHOR]

In this paper, we compared the linear and nonlinear motion prediction models of a long combination vehicle (LCV). We designed a nonlinear model predictive control (NMPC) for trajectory-following and off-tracking minimisation of the LCV. The used prediction model allowed coupled longitudinal and lateral dynamics together with the possibility of a combined steering, propulsion and braking control of those vehicles in long prediction horizons and in all ranges of forward velocity. For LCVs where the vehicle model is highly nonlinear, we showed that the control actions calculated by a linear time-varying model predictive control (LTV-MPC) are relatively close to those obtained by the NMPC if the guess linearisation trajectory is sufficiently close to the nonlinear solution, in contrast to linearising for specific operating conditions that limit the generality of the designed function. We discussed how those guess trajectories can be obtained allowing off-line fixed time-varying model linearisation that is beneficial for real-time implementation of MPC in LCVs with long prediction horizons. The long prediction horizons are necessary for motion planning and trajectory-following of LCVs to maintain stability and tracking quality, e.g. by optimally reducing the speed prior to reaching a curve, and by generating control actions within the actuators limits. [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]

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]

This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

The introduction of more automation into our vehicles is increasing our ability to avoid or mitigate the effects of collisions. Early systems could brake when a likely collision was detected, while more advanced systems will be able to steer to avoid or reconfigure a collision during the same circumstances. This paper addresses how the post-impact motion of an impacted vehicle could be included in the decision-making process of severity minimisation motion planning. A framework is proposed that builds on previous work by the authors, combining models from motion planning, vehicle dynamics, and accident reconstruction. This framework can be configured for different contexts by adjusting its cost function according to relevant risks. Simulations of the unified system are carried out and analysed from the perspective of vehicle model complexity and collision parameters sensitivity. Additionally, effects are highlighted concerning different modelling decisions, with respect to vehicle dynamics models and collision models, that are important to consider in further research. [ABSTRACT FROM AUTHOR]

Efficient path tracking plays a key role in the overall ride experience of Autonomous Vehicles (AVs). Model Predictive Control (MPC) is one of the most competent techniques which is capable of handling multiple variables and constraints. The performance of this controller heavily relies on the proper choice of the vehicle model used to predict future states. This work proposes a novel MPC framework for the effective adoption of vehicle models to achieve a compromise between MPC's performance and computational cost. To this aim, a Switched MPC (SMPC) using vehicle models with different levels of complexity and fidelity is developed. The SMPC uses a novel supervision scheme to adopt the appropriate vehicle model based on the models' prediction error and MPC's solution time. Two different configurations of SMPC are implemented: (1) A Fuzzy Logic System (FLS), and (2) An adaptive switching supervisor. The outcomes show that both approaches can perform path tracking accurately on roads with varying curvatures for different AV speeds. Moreover, comparisons with the conventional MPCs show that the proposed controllers can perform comparably with selective use of a complex vehicle model. Notably, they benefit from a higher computation efficiency from sidestepping the most complex models during path tracking. [ABSTRACT FROM AUTHOR]

For vehicle state estimation, the conventional Kalman filter performs well under the Gaussian assumption, but in the real non-Gaussian situation (especially when the noise is non-Gaussian heavy-tailed), it shows poor accuracy and robustness. In this paper, an extended Kalman filter (EKF) algorithm based on the maximum correntropy criterion (MCC) is proposed (MCCEKF), and a lateral-longitudinal coupled vehicle model is established, while a state observer containing the yaw rate, vehicle sideslip angle, and longitudinal vehicle speed is designed using the easily available measurement information of on-board sensors. After analyzing the complexity of the proposed algorithm, the new algorithm is verified on the Simulink/CarSim simulation experimental platform by Double Lane Change and Sine Sweep Steer Torque Input maneuver. Experimental results show that the MCC-based EKF algorithm has stronger robustness and better estimation accuracy than the traditional EKF algorithm in the case of non-Gaussian noise, and the MCCEKF is more applicable for vehicle state estimation in practical situations. [ABSTRACT FROM AUTHOR]

Due to the uncertainty of vehicle model parameters, modeling errors and external disturbances, the performance of path tracking control system is poor, especially under high velocity and large curvature extreme conditions. To address this issue, this paper presents a novel gain-scheduled robust control strategy based on linear parameter varying system with model predictive control (MPC) and H∞. Firstly, fully considering the influence of the time-varying characteristics of vehicle velocity and tire cornering stiffness on the path tracking system model, a novel linear parameter varying system model is built for path tracking control of autonomous vehicle. Then, a path tracking robust controller is designed based on gain-scheduled approach, and the linear matrix inequality (LMI) is applied to solve optimization problem, in which MPC and H∞ robust control theory are applied to the controller design process. Finally, the simulation experiments have verified that the proposed novel robust control strategy can improve the path tracking accuracy and ensure the vehicle lateral and roll stability, especially under high velocity and large curvature extreme conditions. Meanwhile, the proposed robust controller shows superiority to suppress the parameters uncertainty, modeling error and external disturbance. [ABSTRACT FROM AUTHOR]

In this paper, a linear time-varying model predictive controller (LTV-MPC) is proposed for automated vehicle path-following applications. In the field of path following, the application of nonlinear MPCs is becoming more common; however, the major disadvantage of this algorithm is the high computational cost. During this research, the authors propose two methods to reduce the nonlinear terms: one is a novel method to define the path-following problem by transforming the path according to the actual state of the vehicle, while the other one is the application of a successive linearization technique to generate the state–space representation of the vehicle used for state prediction by the MPC. Furthermore, the dynamic effect of the steering system is examined as well by modeling the steering dynamics with a first-order lag. Using the proposed method, the necessary segment of the predefined path is transformed, the linearized model of the vehicle is calculated, and the optimal steering control vector is calculated for a finite horizon at every timestep. The longitudinal dynamics of the vehicle are controlled separately from the lateral dynamics by a PI cruise controller. The performance of the controller is evaluated and the effect of the steering model is examined as well. [ABSTRACT FROM AUTHOR]

In order to identify the road friction coefficient of the left and right sides of the vehicle more accurately, this paper presents an estimation framework based on a novel tyre model and modified square-root cubature Kalman filter (SCKF). To begin with, a novel tyre model is proposed by adaptively calculating the longitudinal and lateral stiffness and the effective friction coefficient. The tyre forces calculated by this model are more accurate than those calculated by the Brush model. Then, to avoid the influence of abnormal measurement noise on the estimation effect, we develop an improved SCKF (ISCKF) algorithm based on the maximum correntropy criterion. The algorithm can update the measurement noise covariance adaptively. Furthermore, a real-time estimation scheme of road friction coefficient is designed by combining the vehicle dynamics model with the proposed novel tyre model and ISCKF algorithm. Finally, the performance of the presented method is verified by the co-simulation of CarSim and MATLAB/Simulink. The results show that the designed estimation scheme not only has excellent robustness in the case of abnormal measurement noise interference but also has good adaptability to the uncertainty of road friction coefficients distribution. [ABSTRACT FROM AUTHOR]

Axles with leaf spring suspension systems are still a popular choice in many commercial vehicles. However, leaf springs are not in perfect conformity to standard multibody vehicle models because they combine guidance and suspension in one single element. Combining standard multibody vehicle models with sophisticated finite element leaf spring models results in rather complex and computing time-consuming solutions. Purely kinematic models, defined by lookup tables or the design kinematics approach, cover only some but not all features of the leaf spring suspension. As shown here, the five-link model, which incorporates a quasi-static solution of the leaf spring compliance, provides a very practical model. It is comparatively lean and provides results of sufficient accuracy in the whole application range. [ABSTRACT FROM AUTHOR]

The aim of this work is to introduce a new concept for a hydraulic semi-active yaw damper (SAYD) for the improvement of the stability of a high-speed rail vehicle. This concept represents a further elaboration of Secondary Yaw Control but envisages the use of semi-active hydraulic dampers instead of full-active electromechanical dampers, simplifying the design of the system and facilitating the design of a safe and fault tolerant device. Two control algorithms are proposed for the semi-active damper: maximum power point tracking and skyhook with Karnopp approximation. A multi-physics model of the SAYD is introduced and used in co-simulation with a multi-body model of a high-speed vehicle. Using these models, numerical simulations are performed to assess the behaviour of the semi-active damper in terms of improving the running stability of the rail vehicle at very high speed, showing that the use of the SAYD in combination with any of the two control strategies considered allows to improve substantially the stability of the vehicle. The results of experimental investigations performed in the project will be reported in a companion paper. [ABSTRACT FROM AUTHOR]

This article deals with the control chassis dynamics of a light electric vehicle. In the frame of Global Chassis Control, it proposes a hierarchical control approach focuses on compensating the chassis dynamics against driver disturbances. It is based on a supervisory structure consists of two main levels, a global controller for regulating chassis variables and a local controller to each suspension system. Both controllers are designed using a fractional-order robust controller, namely CRONE controller, to provide an optimized solution for the performance-robustness tradeoff. The main objective is to improve ride comfort for passengers while respecting road holding and handling criteria. In contrast to other studies, random road disturbances are considered to investigate the limits of the proposed approach. Analysis in frequency and time domain has been done to evaluate the performance and robustness of the designed controller. Simulation results based on a full nonlinear 14 degree of freedom vehicle model show that the proposed strategy can effectively improve the ride quality in the field of interest where a significant performance in driver disturbance rejection is reported. [ABSTRACT FROM AUTHOR]

Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollution. Moreover, unknown inputs bring great challenges to their accurate estimation. Therefore, this paper develops a novel robust unscented M-estimation-based filter (RUMF) for vehicle state estimation with unknown driver steering torque. The nonlinear system model is constructed based on the vehicle dynamics model. Unscented transformation (UT) and statistical linearization are implemented to transform the nonlinear process and measurement function into a linear-like regression form with data redundancy to achieve outlier suppression. Then, an M-estimation-based iterated algorithm is designed to address process uncertainty and innovation and observation outliers for robust vehicle state estimation. The iteratively reweighted least-squares (IRLS) method based on the M-estimation methodology is utilized for unknown input estimation. Simulations and experimental results compared with extended Kalman filter (EKF) and particle filter (PF) have verified the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article focuses on the 3-D guidance strategy for a reusable launch vehicle (RLV) during terminal area energy management (TAEM) phase. Based on sliding-mode and shrinking-horizon techniques, the proposed scheme consists of trajectory generation and correction mechanisms, which can enhance the guidance precision and robustness against disturbances. The RLV guidance model, in the form of a set of highly nonlinear differential equations in the time domain, is recast as an altitude-domain model. By this means, the main characteristics of TAEM gliding motion are extracted. The altitude-domain model is thereby used for trajectory generation. A sliding surface and a guidance law are proposed. Hybrid TAEM constraints can be fully satisfied when the proposed guidance law drives the altitude-domain vehicle model to the designated altitude. Using the proposed guidance law as the input of the altitude-domain model, a constrained TAEM trajectory is generated, leading to TAEM guidance commands simultaneously. The commands are utilized to drive the time-domain model to the terminal target. In an attempt to compensate for model uncertainties and initial deviations, the guidance commands are modified periodically by the shrinking-horizon correction mechanism according to current states. Simulations on different scenarios are provided to demonstrate the performance of the proposed guidance strategy. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose a complete and powerful methodology that allows the optimization of the passive suspension system of vehicles, which simultaneously takes comfort and safety into account and provides a set of optimal solutions through a Pareto-optimal front, in a low computational time. Design/methodology/approach: Unlike papers that consider simple vehicle models (quarter vehicle model or half car model) and/or simplified road profiles (harmonic excitation, for example) and/or perform a single-objective optimization and/or execute the dynamic analysis in the time domain, this paper presents an effective and fast methodology for the multi-objective optimization of the suspension system of a full-car model (including the driver seat) traveling on an irregular road profile, whose dynamic response is determined in the frequency domain, considerably reducing computational time. Findings: The results showed that there was a reduction of 28% in the driver seat vertical acceleration weighted root mean square (RMS) value of the proposed model, which is directly related to comfort, and, simultaneously, an improvement or constancy concerning safety, with low computational cost. Hence, the proposed methodology can be indicated as a successful tool for the optimal design of the suspension systems, considering, simultaneously, comfort and safety. Originality/value: Despite the extensive literature on optimizing vehicle passive suspension systems, papers combining multi-objective optimization presenting a Pareto-optimal front as a set of optimal results, a full-vehicle model (including the driver seat), an irregular road profile and the determination of the dynamic response in the frequency domain are not found. [ABSTRACT FROM AUTHOR]

In the context of automated and connected vehicle technology, this paper proposes an environment-on-board predictive braking control system, regulating the vehicle velocity to a desired value, in order to enhance path-following performance and vehicle stability when a vehicle is driven on a path where the road friction coefficient changes suddenly on a curve. In this study, we assume that the vehicle enters a wet road from a dry road and moves on to a dry road, and that the forward road surface friction condition can be estimated. In such driving situation, the objective of the vehicle dynamics control system is to improve path-following performance and vehicle stability of automated vehicle, even under abnormal road conditions. To achieve this objective, the predictive braking control is designed to track a safe velocity which is determined based on a simplified linear two-degree-of-freedom bicycle and a 2nd order look-ahead driver model. The effectiveness of this control system is verified using a nonlinear four-degree-of-freedom four-wheel model under various conditions, with varying road friction coefficients and curvatures. Finally, the effectiveness is evaluated using a full vehicle model of the IPG CarMaker, which is similar to an actual vehicle. [ABSTRACT FROM AUTHOR]

Large variations load-to-curb weight ratios are linked to significant changes in parameters critical to control design for vehicle stability control system. Unique and highly customised vehicles, such as the lightweight solar car in this paper, are more susceptible to the impact of such variations when developing control methods. The purpose of this study is to study the influence of variation in loading conditions, the effect of ignoring changes in inertial parameters, and develop and compare a number of alternative vehicle stability control methods that can be applied to rear-wheel driven vehicles via in-wheel motors. In this paper a Sliding Mode Control (SMC) both nominal and when including uncertainty, Dynamic Curvature Control (DCC) and a Proportional–Integral Control (PI) strategies are compared to the baseline open-loop control case. Each controller is implemented through co-simulation via MATLAB® Simulink® and Siemens Amesim™ using a 15-DOF non-linear vehicle model. The results show that SMC achieves the best performance, whilst DCC tends to overshoot target conditions prior to settling, indicating that SMC is the preferred control strategy. It is also demonstrated that by ignoring the change in the inertial parameters in simulation environments can produce an incorrect translation of the control performance. [ABSTRACT FROM AUTHOR]

A novel longitudinal, lateral, and vertical integrated stability control system is proposed for distributed electric vehicle in this paper. The integrated stability control system is based on model predictive control and takes advantage of its multi-objective optimization feature to takes manipulability, stability and comfort requirements into consideration at the same time, and realize the optimal longitudinal $/$ lateral $/$ vertical forces distribution with constraints overall considered. The advantage of the proposed integrated control system is that it can solve the control objective conflict of the cooperative motion control subsystems and the interference of the actuators compared to distributed control system. Finally, the simulation and comparison analysis are given in several different driving conditions under Carsim and Matlab $/$ Simulink environment. Results show that an improvement of over 80 $\%$ in the pitch angle and roll angle is realized compared to the simple control strategy. [ABSTRACT FROM AUTHOR]

This paper presents a novel estimation and identification approach for lateral vehicle dynamics. The algorithm leverages on a Linear Fraction Transform (LFT) reformulation of vehicle and tyre models, allowing for a simple and computationally efficient inclusion of complex and nonlinear dynamic models, like, for example, two-wheels, four-wheels or single-track as vehicle model, and Pacejka, brush or Fiala as tyre model. As a result, this technique can be easily adopted in the development of an online identification system, able to run on a standard embedded device, implementing a flexible identification procedure that can handle different driving conditions, up to the limits of handling, different vehicle modelling approaches, and different input measurements. Experimental results demonstrate the effectiveness of the proposal, either in a persistent excitation and in a non-persistent excitation scenario. [ABSTRACT FROM AUTHOR]

This research presents a data-driven Neural Network (NN)-based Virtual Sensor (VS) that estimates vehicles' Unsprung Mass (UM) vertical velocity in real-time. UM vertical velocity is an input parameter used to control a vehicle's semi-active suspension. The extensive simulation-based dataset covering 95 scenarios was created and used to obtain training, validation and testing data for Deep Neural Network (DNN). The simulations have been performed with an experimentally validated full vehicle model using software for advanced vehicle dynamics simulation. VS was developed and tested, taking into account the Root Mean Square (RMS) of Sprung Mass (SM) acceleration as a comfort metric. The RMS was calculated for two cases: using actual UM velocity and estimations from the VS as input to the suspension controller. The comparison shows that RMS change is less than the difference threshold that vehicle occupants could perceive. The achieved result indicates the great potential of using the proposed VS in place of the physical sensor in vehicles. [ABSTRACT FROM AUTHOR]

The parking environment for drivers is now increasingly complex, and parking has become a driving phenomenon with high incidence of accidents. To solve the problem, a method of path planning for autonomous parking was proposed based on improved β-spline theory. The vehicle kinematical model was established, and considering the vehicle kinematics and electric power steering(EPS) performance requirements in parking environment, the path collision avoidance constraints and the vehicle steering wheel angle and angular velocity constraints were established. The simulation model was built by Matlab/Simulink. According to the actual parking scenario, three autonomous parallel parking conditions were designed, and the path planning was simulated. The results show that the proposed algorithm can realize autonomous parking for different sizes of parking lot, and the planned path curvature is continuous with slowly changed curvature. The vehicle kinematics and EPS performance requirements can be met with high parking efficiency. [ABSTRACT FROM AUTHOR]

Path planning is a critical part for improving the driving safety and driver comfort of autonomous vehicles (AVs), especially in complex maneuvering conditions. In addition, different drivers have different preferences for AVs, thus, how to provide personalized trajectories for different drivers is a vital issue for AVs. The collision-free path planning problem in conditions with large road curvatures is investigated in this paper, with the consideration of environmental safety constraints, drivers’ comfort, vehicle actuator constraints, etc. Firstly, a Driver-Vehicle-Road (DVR) system is established based on the combination of the kinematic vehicle model and the two-point visual preview driver model, such that the driver's individual handling characteristics can be considered in the controller. The kinematic vehicle model is modified to have the similar understeering characteristics with those of the nonlinear full car models, and then the proposed DVR system can satisfy different groups of drivers and cars. Secondly, for environmental constraints, a new artificial potential field (APF) method is proposed, which can form a banana-shaped 3-D dangerous imaginary mountain and a lane boundary cliff suitable for arbitrary curvature roads to generate a collision-free evasive path. Finally, the Linear-Time-Varying (LTV) model predictive control (MPC) method is adopted to design the path planner. The CarSim-Simulink joint simulation illustrates that with the proposed planner, the host vehicle is capable of avoiding obstacles with a safer and more comfortable maneuver on large curvature roads. And the proposed path planner can provide individually safe trajectories for different drivers with good maneuverability. [ABSTRACT FROM AUTHOR]

Study of vehicle dynamics aggregates possibilities to enhance performance, safety and reliability, such as the integration of control systems, usually requiring knowledge on vehicle's states and parameters. However, some critical values are difficult to measure or are not disclosed. For this reason, dynamics and stability analysis of six-wheeled vehicles are compromised, and available information on this matter is limited. In this context, this paper proposes the estimation of the cornering stiffness of a 6x6 vehicle by an inverse problem approach applying the Levenberg–Marquardt (LM) method. The algorithm required data from field experiments and from simulations of a vehicle model during a double-lane change manoeuvre developed using S i m u l i n k ® / M A T L A B ® . Experimental and theoretical values for the vehicle yaw rate were combined through LM method for cornering stiffness estimation. The excellent agreement between measured and simulated yaw rate indicates that the proposed model and the estimated parameters properly represent the vehicle dynamics response. [ABSTRACT FROM AUTHOR]

In this work, a novel single-level adaptive trajectory planner and tracking controller is developed for off-road autonomous vehicles operating on deformable terrains. Trajectory planning and tracking algorithms often rely on a simplified vehicle model to predict future vehicle states based upon control inputs, hence requiring accurate modeling and parameterization. On off-road deformable terrains this is a challenging task due to unknown terrain parameters and the complex interactions at tire-terrain interfaces, which pose issues in continuous differentiability, operating conditions, and computational time. To address these difficulties, in this paper, a neural network deformable terrain terramechanics model and its implementation within a terrain adaptive model predictive control algorithm is presented to improve vehicle safety and performance through more accurate prediction of the plant response. It is shown in simulations that the neural network is able to predict the lateral tire forces accurately and efficiently compared to the Soil Contact Model as a state-of-the-art model and is able to yield accurate bicycle model predictions. It is demonstrated that the implementation of the neural network within model predictive control can outperform both a baseline Pacejka-based and a rapidly exploring random tree controller by improving performance and allowing for more severe maneuvers to be completed that otherwise lead to failure when terrain deformations are not explicitly taken into account. The improved performance achieved through estimating terrain parameters online in an adaptive controller is highlighted against the nonadaptive realization. Finally, it is shown the algorithm is conducive to real-time implementation. [ABSTRACT FROM AUTHOR]

Energy storage devices such as batteries and supercapacitors are arousing a growing interest in many areas of industrial applications since they allow improving the dynamic response and efficiency. In this sense, the availability of electric models of batteries and supercapacitors is necessary for the correct sizing, control, and energy management of power systems. In order to identify the parameters of the equivalent electric model, the identification current profile is often a pulse of charge or discharge. Subsequently, these models are validated with a current verification profile. However, the verification profile compared to the identification profile is more dynamic in amplitude and frequency, producing validation errors due to the identification signal's limited bandwidth. This paper proposes a novel Pulse – Pseudo Random Binary Sequence (Pulse – PRBS) identification current profile, with similar spectral characteristics to the verification profile, in order to improve the modeling accuracy. The parameters of a Lithium-ion battery and a supercapacitor are obtained with this new identification current profile and the Optimization Toolbox of Matlab, using the electrical models in Simulink or Simscape. Finally, the models are validated with the Urban Driving Cycle ECE-15 and the Hybrid Pulse Power Characterization (HPPC) for electric vehicle applications. [ABSTRACT FROM AUTHOR]

In vehicle trajectory prediction, the difficulty in modeling the interaction relationship between vehicles lies in constructing the interaction structure between the vehicles in the traffic scene. Majority of existing models only focus on the interaction between the historical trajectory of the vehicle and the surrounding vehicles in the spatial domain, and do not pay attention to the interaction between the vehicle and the non-Euclidean correlation structure (graph structure) that exists in the environment. In order to overcome the deficiencies in the existing models, this paper proposes the Environment-Attention Network model (EA-Net) to obtain the full interactive information between the vehicle and its driving environment. In the proposed model, a new type of parallel structure consisting of Graph Attention network (GAT) and Convolutional social pooling containing Squeeze-and-Extraction mechanism (SE-CS), is constructed as the environmental feature extraction module and embedded in LSTM encoder-decoder. This structure solves the limitation of the dimension and structure influence when constructing the interaction model between the vehicle and the surrounding environment, making the extracted feature information comprehensive and effective. The prediction accuracy of the model with the RMSE loss function is tested on two public datasets– NGSIM and highD, and compared with several state-of-the-art trajectory prediction algorithm models. The results show that the prediction accuracy of the proposed Environment-Attention Network in the two datasets is more than 20% higher than that of the single structure model, which indicates that the proposed model proposed has superior performance and better adaptability to different traffic environments compared with the existing models. [ABSTRACT FROM AUTHOR]

This article is motivated by an interesting recent result on antilock braking system (ABS) design using two-time scale (TTS)-based estimation. In this article, the basic method of TTS is extended to estimate not only the disturbances but also their derivatives, improving the accuracy of estimation by an order of magnitude. Further, the problem of ABS is extended to an eight-DOF vehicle model to include the effects of load transfer, steering dynamics, and actuator dynamics. Since analysis showed that the use of basic TTS to ABS design results in a poor transient in the estimation error dynamics, this article proposes a new formulation of the TTS method specifically for ABS. Two new ABS-specific sliding surfaces are proposed to compensate the actuator dynamics where the second sliding surface is proposed as an improvement over the first one. The stability, and convergence of estimation, and tracking errors are analyzed. The proposed methods are validated on MATLAB/Simulink, and CarSim platforms for a D-class sedan car. The results are also validated on an experimental ABS platform in the laboratory. [ABSTRACT FROM AUTHOR]

The efficiency of public transport is fundamental for the city traffic. Taking this as a goal and linking with the financial and structural situations of each market, new technologies regarding city buses have emerged in recent years and one of them is studied in a comparative way in the present work. A new city bus which has 15 meters length, front-engine and three-axles. The great difference of this vehicle is the positioning of the third axle near to the front axle of the vehicle instead of the traditional configuration whereupon this axle is located near the rear axle. With the aid of MATLAB/Simulink, the two mentioned vehicle models were generated with eighteen degrees of freedom and were tested in two different manoeuvrers, passing through a bump and then a sequence of turns. At the end of the work, it was possible to conclude that the new city bus presents less oscillations in terms of roll and pitch angles in addition to smaller vertical displacements for the same proposed manoeuvre, which leads to greater passenger comfort. [ABSTRACT FROM AUTHOR]

Autonomous vehicles are the future of automotive engineering and understanding how this systems work is critical. In these vehicles, controller models are usually needed to generate signals that would normally be imposed by the driver e.g., steering angles, acceleration inputs and braking commands. Intuitively, each control method utilized has its peculiarities and presents different behaviours. In such situation, this paper aims to develop an error comparison between a car displacement and its reference path due the use of two different predictive driver controllers: The proportionalintegrative and the MacAdam model. For this purpose, a 14 degrees of freedom vehicle model is used with the aid of MATLAB/Simulink, whereas simulations were made using the double-lane change manoeuvre, a commonly used manoeuvre to analyse the vehicle dynamics performance. At the end of this paper, lateral acceleration, displacement and steering wheel angle analysis led the conclusion that the vehicle behaviour is smoother with the use of the proportionalintegrative control regardless of longitudinal velocity. Nevertheless, the trajectory error is smaller for MacAdam model than PI controller is and therefore it is easier to follow the reference path with this one, although in aggressive maneuverers it can cause more discomfort and increase the risk of rolling when compared to the PI controller in a vehicle with the same body stiffness. [ABSTRACT FROM AUTHOR]

• NG-NRMM software tools will integrate state-of-the-art 3D large soil deformation/flow terramechanics models and vehicle multibody dynamnic models in order to predict ground vehicle mobility on any given terrain. • The requirements for NG-NRMM macro-scale complex terramechanics models are presented. • A prototype software tool which satisfies the NG-NRMM requirements is presented. In part 2 of this paper, the Complex Terramechanics (CT) software tools requirements recommended by the NATO research task group RTG-248 are presented along with example simulations from a CT prototype software tool which attempts to satisfy the requirements. [ABSTRACT FROM AUTHOR]

• An overview of the spectrum of vehicle mobility terramechanics models is presented. • A literature review of macro-scale vehicle mobility terramechanics models is presented. • The requirements for NG-NRMM macro-scale terramechanics models are presented. • A prototype software tool which satisfies the NG-NRMM requirements is presented. The US army along with NATO member and partner nations' militaries need an accurate software tool for predicting ground vehicle mobility (such as speed-made-good and fuel-consumption) on world-wide terrains where military vehicles may be required to operate. Currently, the NATO Reference Mobility Model (NRMM) is the only NATO recognized tool for assessing ground vehicle mobility. NRMM was developed from the 1960s to the 1980s and relies on steady-state empirical formulas which may not be accurate for new military ground vehicles. A NATO research task group (RTG-248) was established from 2016 to 2018 to develop the NG-NRMM (next-generation NRMM) software tool requirements and an NG-NRMM prototype which uses high-fidelity "simple" or "complex" terramechanics models for the terrain/soil along with modern 3D multibody dynamics software tools for modeling the vehicle. NG-NRMM Complex Terramechanics (CT) models are those that utilize full 3D soil models capable of predicting the 3D soil reaction forces on the vehicle surfaces (including tires, tracks, legs, and under body) and the 3D flow and deformation of the soil including both elastic and plastic deformation under any 3D loading condition. In Part 1 of this paper, an overview of the full spectrum of terramechanics models from the highest fidelity to the lowest fidelity is presented along with a literature review of CT ground vehicle mobility models. [ABSTRACT FROM AUTHOR]

The complex slip characteristics between the tracks and the terrain make it difficult to build an accurate model for intelligent tracked vehicles. Both the path planning and lateral control, however, are highly dependent on the accurate tracked vehicle model. To overcome the issue, a slip model based on the instantaneous centers of rotation (ICRs) of tracks and a dual-layer adaptive unscented Kalman filter (DAUKF) are used to estimate the ICR locations in real-time without requiring prior knowledge of terrain parameters. First, the historical trajectory information is used to estimate ICR locations by the upper-layer AUKF estimator preliminarily. The ICR locations estimated in the upper-layer and the current vehicle state are then imported into the model to predict the future trajectory which can be used to estimate the offset as compensation of preliminarily estimated ICR locations by the lower-layer AUKF estimator. The proposed DAUKF is verified by simulations on MATLAB/Simulink. In order to further verify the effectiveness and practicability of the algorithm, a large number of experiments under different terrains and road conditions are implemented on the electric-drive tracked vehicle. The simulation and experimental results illustrate that the proposed DAUKF can estimate the ICR locations efficiently and accurately, which can improve the accuracy of the tracked vehicle model compared with those of extended Kalman filter (EKF), UKF, and AUKF. [ABSTRACT FROM AUTHOR]

Accurate predictions of future pedestrian trajectory could prevent a considerable number of traffic injuries and improve pedestrian safety. It involves multiple sources of information and real-time interactions, e.g., vehicle speed and ego-motion, pedestrian intention and historical locations. Existing methods directly apply a simple concatenation operation to combine multiple cues while their dynamics over time are less studied. In this paper, we propose a novel Long Short-Term Memory (LSTM), namely, to incorporate multiple sources of information from pedestrians and vehicles adaptively. Different from LSTM, our considers mutual interactions and explores intrinsic relations among multiple cues. First, we introduce extra memory cells to improve the transferability of LSTMs in modeling future variations. These extra memory cells include a speed cell to explicitly model vehicle speed dynamics, an intention cell to dynamically analyze pedestrian crossing intentions and a correlation cell to exploit correlations among temporal frames. These three individual cells uncover the future movement of vehicles, pedestrians and global scenes. Second, we propose a gated shifting operation to learn the movement of pedestrians. The intention of crossing the road or not would significantly affect pedestrian’s spatial locations. To this end, global scene dynamics and pedestrian intention information are leveraged to model the spatial shifts. Third, we integrate the speed variations to the output gate and dynamically reweight the output channels via the scaling of vehicle speed. The movement of the vehicle would alter the scale of the predicted pedestrian bounding box: as the vehicle gets closer to the pedestrian, the bounding box is enlarging. Our rescaling process captures the relative movement and updates the size of pedestrian bounding boxes accordingly. Experiments conducted on three pedestrian trajectory forecasting benchmarks show that our achieves state-of-the-art performance. [ABSTRACT FROM AUTHOR]

Global adoption of electric vehicles (EVs) faces many challenges such as range anxiety, high cost of EVs, and inadequate charging infrastructure. EV-sharing platforms resolve such concerns by setting up an optimal configuration for charging infrastructure and optimizing the charging decisions for depleted EVs. These platforms manage the vehicles' flow to different charging stations and decide when and to what energy level the depleted vehicles should be recharged. Station-based platforms are one of the mainstream vehicle sharing systems where the customer picks-up and drops-off the vehicle at the designated stations. If a vehicle's battery energy level falls below a threshold after completing the customer trip, it is charged either partially or fully at the charging station. This study addresses various operational and strategic decisions (such as the number of chargers, vehicle repositioning, and partial charging policy) for a one-way station-based EV-sharing platform using a stylized three-stage analytical framework. We use vehicle dynamics to model the EV powertrain and regenerative braking under different traffic conditions and simulate them using AVL CRUISE™. We model the platform operations using an open queuing network and provide a mixed-integer non-linear optimization program using inputs from the queuing network and vehicle dynamics simulation. We also provide a bound-based heuristic to solve this NP-hard optimization problem. We generate various managerial insights for an efficient implementation of the partial charging policy for EV-sharing platforms. The increase in the partial charging probability (the fraction of depleted vehicles charged partially) reduces the effective charging demand, resulting in fewer chargers and a higher profit. On the other hand, if we increase the target battery energy level for partial charging, the platform's profit decreases due to higher effective charging demand dominating the benefits of lower charging frequency of vehicles. • Assess performance of EV-sharing platforms by considering vehicle dynamics. • Understand the effect of partial charging policy on platform profits. • Analyze the effect of distance among stations and parking capacity on profits. • Determine the optimal number of chargers and repositioning flows in the network. [ABSTRACT FROM AUTHOR]

The demand of accurate and real-time dynamics model has recently become critical for implementing effective vibration control in vehicles. The multibody dynamics method provides an appealing alternative for accurate modelling of closed-loop vehicles. In this work, vibration-suppression strategies for a vehicle traversing a bumpy road are proposed, which use a semi-recursive multibody model and a series of control algorithms. First, a 17-degree-of-freedom vehicle is modelled using a semi-recursive multibody method to perform real-time simulation. The vehicle multibody model is verified by using the results from a commercial software package. Second, the PID, fuzzy, and optimal control algorithms are tailored using the obtained vehicle states for vibration suppression. By applying different initial speeds and driving torques, the simulations of the vehicle traversing the bumpy road are performed. The results show that the controllers effectively suppress the vehicle's vibration, and the optimal controller has the best performance. Furthermore, the effect of mass uncertainty on vibration-suppression is discussed. Numerical experiments are performed to verify the effectiveness of the optimal control strategy. The vibration-suppression method based on the vehicle multibody model and the optimal control algorithm can be used to improve the ride comfort in worse road conditions. • An accurate semi-recursive multibody model is developed and verified for vehicle dynamics. • Three control strategies are tailored based on the multibody model to suppress the vibration. • The vibration-suppression method is studied in different speed and road conditions. • The effect of mass uncertainty on vibration-suppression is discussed. [ABSTRACT FROM AUTHOR]

• The mode-decoupling capability of the HIIRS was analyzed and validated. • The damping range and the coupling effects between the damping and the stiffness of the HIIRS were investigated. • A piecewise-linearizing method was proposed to model the accumulator and thus to determine the analytical solution of the roll and pitch stiffness of the HIIRS. • The HIIRS could reduce the roll angle and pitch angle by 34.42% and 28.91%, while harvesting power as 87.69 W and 147.86 W, respectively. An innovative hydraulic integrated interconnected regenerative suspension system (HIIRS), which can simultaneously harvest suspension vibration energy and enhance the vehicle handling performance, is studied in this paper. One big challenge for the HIIRS is the mode-decoupling problem among the four vehicle vibration modes, namely bounce, roll, pitch, and warp modes. This paper comprehensively studied why and how the HIIRS could decouple the four modes. Specifically, the nonlinear fluidic model of the HIIRS was built and integrated with a 14DOF nonlinear vehicle model. Based on the model, the damping range and the coupling effects between the damping and the stiffness of the HIIRS were investigated. The roll and pitch stiffness of the HIIRS were calculated, where a novel piecewise-linearizing method was proposed to determine the analytic expression of the stiffness. Numerical experiments were conducted to demonstrate the mode-decoupling capability of the HIIRS and the influence of its parameters on the vehicle dynamics and the energy harvesting performances. In addition, the anti-roll, anti-pitch, and energy harvesting performances of the HIIRS were also studied and compared with a conventional off-road vehicle. The roll angle in a double lance change scenario and the pitch angle in a straight-line braking scenario could be respectively reduced by 34.42% and 28.91% when the initial system pressure in the HIIRS was 40 bar and the load resistance was 10 Ω. The corresponding regenerative power was 87.69 W and 147.86 W for the two cases. [ABSTRACT FROM AUTHOR]

The paper presents a unique application of artificial neural networks (ANN) for replicating the predictions of Persson's flash-temperature viscoelastic friction model. This allows the efficient calculation of the coefficient of friction between a tyre and the road surface, given the operating conditions, the tread material properties, and the power spectral density of road roughness. This is important because it allows the update of friction in a computationally efficient manner and therefore presents an opportunity for carrying out vehicle simulations on several road surfaces, without the requirement for tyre testing on every surface. To this end, a method is also proposed for integrating the most successful ANN configuration with a tyre model that had its baseline parameters identified by flat-track testing on a sandpaper surface. It is shown that the ANN-enhanced tyre model operates several times faster than real-time, predicting reduced peak and asymptotic tyre forces, as expected in most cases when moving from sandpaper to regular asphalt. The enhanced tyre model is further integrated with a vehicle model to illustrate the significant effect of reduced friction in stopping distance and handling dynamics. [ABSTRACT FROM AUTHOR]

We provide an ice friction model for vehicle dynamics of a two-man bobsled which can be used for driver evaluation and in a driver-in-the-loop simulator. Longitudinal friction is modeled by combining experimental results with finite element simulations to yield a correlation between contact pressure and friction. To model lateral friction, we collect data from 44 bobsleigh runs using special sensors. Non-linear regression is used to fit a bob-specific one-track vehicle dynamics model to the data. It is applied in driving simulation and enables a novel method for bob driver evaluation. Bob drivers with various levels of experience are investigated. It shows that a similar performance of the top drivers results from different driving styles. • Development of a bob-suitable vehicle dynamics ice friction model for driving simulation. • Analysis and modeling of lateral ice friction which seems to be a research gap in the area of ice friction for wintersports. • A novel method for bob driver evaluation using real world measurement data and the proposed ice friction model. • Application of the driver evaluation method on a data set containing different drivers showing that a similar overall performance results from different driving styles. [ABSTRACT FROM AUTHOR]

A fast calculation algorithm for the wear evolution of wheel profiles is proposed and results presented with a high-speed train as an example. The dynamics simulation includes the calculation of the vehicle running on a short straight track with random irregularities and the vehicle passing various curves without track excitation. The penetration curves of wheel/rail contact are assumed as the shape of wear distribution in a contact patch, which are calculated after the update of wheel profiles and interpolated in each step of wear calculation. The total wheel wear is the sum of the wear caused by track irregularities and the wear under smooth tracks, such as the straight and curved lines. After each dynamic simulation, several loops for wear calculation and profile update are carried out to smooth the wear curve. Simulation results show that the calculated distribution and average of the wear depth and the equivalent conicity in a reprofile cycle are in good agreement with the measured results. The predicted and measured wear shapes agree reasonably well in the area near the tape circle. Moreover, the proposed method is at least 15 times faster than the traditional wear calculation method and it can consider the influences of the stochastic parameters of the wheel/rail contact. • A fast algorithm for the wheel wear evolution is proposed using the wheel/rail penetration curves as wear distribution. • The nonlinear vehicle model and simplified dynamic simulation method are used to improve the speed of wear prediction. • The stochastic wheel/rail interactions are considered as the vital part in the wheel wear prediction. • There are good agreements in the wear evaluation between simulated and measured results with a travel distance in 300k km. [ABSTRACT FROM AUTHOR]

A levitation system based on sidewall electrodynamic suspension (EDS) is considered for a capsule vehicle, which is a next-generation high-speed transportation system currently being studied. This levitation system does not require controlling of the gap between the guideway and the vehicle on which the superconducting electromagnet is mounted. However, when the vehicle is operated in a levitated state, the ride comfort is worse than that of the levitation system based on electromagnetic suspension (EMS), making it necessary to develop methods that can ensure good riding comfort. In addition, because the EDS system is complex and nonlinear with a combination of electromagnetics and mechanical dynamics, it is complicated to analyze the dynamic characteristics of the capsule vehicle, and the corresponding numerical analysis is time-consuming. Therefore, to easily understand the running dynamics of a capsule vehicle in the sidewall EMS system, the magnetic suspension characteristics corresponding to the primary suspension are simply modeled by considering the levitation stiffness in the vertical direction and the guidance stiffness in the lateral direction, similar to that in the case of the mechanical suspension. In this study, mathematical models of the levitation and guidance stiffnesses with respect to the speed and position of a vehicle body running at high speeds in a levitated state in the sidewall EDS system were derived for three design proposals of the levitation coil. The dynamic behavior of the vehicle based on the three design proposals was investigated by simulating a capsule vehicle model with 15 degrees of freedom. [ABSTRACT FROM AUTHOR]

The vehicle dynamics simulation and preview control require a dedicated vehicle model, such as multibody dynamics model. However, the multibody model has higher computational complexity which affects the response time of the vehicle controller. This issue can be alleviated by using a data-driven vehicle dynamics model due to its effective generalization and computational speed. In this work, we propose a data-driven modeling approach based on deep neural networks (DNNs) for computing and predicting the vehicle characteristics. The high-fidelity simulations of a validated vehicle multibody model are performed for data acquisition. This data is then used for training and testing the proposed model. The DNN inputs comprise the initial speed of the vehicle and the torque applied on front wheels to imitate vehicle acceleration and deceleration. The DNN outputs comprise the driving distance and the longitudinal velocity of the vehicle. The dynamics characteristics resulting from both the data-driven model and the multibody model are investigated and compared. Furthermore, the accuracy of the data-driven model is analyzed in terms of various error functions. The data-driven model is verified by using the results obtained from a commercial software package. The simulation results show that the data-driven vehicle model predicts the accurate velocity and driving distance in real-time. The data-driven model can be used for real-time simulation and preview control in autonomous vehicles. • A semi-recursive multibody formulation is used for vehicle dynamics and data acquisition. • A DNN-based approach is proposed to predict the vehicle characteristics for preview control. • Different initial speeds and torques in a large range are used for a robust DNN model. • The DNN model is verified by the results obtained from a commercial software package. [ABSTRACT FROM AUTHOR]

The aim of this work is to present a novel accurate estimator for vehicle dynamics. Following the multibody dynamics approach, a vehicle can be modeled with a high level of detail including non-linear dynamics. As a consequence, a rich simulation data-set is available for engineering analysis, richer than with vehicle analytical models. The proposed novel estimator is a new form of a dual Kalman filter. The first filter uses an indirect extended Kalman filter (i.e. the errorEKF) incorporating force estimation and using a vehicle multibody model. The second filter is an unscented Kalman filter (UKF) used to increase the accuracy of the errorEKF by estimating uncertain modeling parameters such as the mass of the vehicle and the tire-road friction coefficient. The performance of the proposed state-parameter-input (SPI) observer is tested in a simulation environment. The performance of the observer is demonstrated using two maneuvers, out of which one covers aggressive driving conditions. The results show that the new observer estimates with high accuracy the variables of interest for vehicle dynamics, such as the tire forces. [ABSTRACT FROM AUTHOR]