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This study aims at proposing a method to estimate the deflection angle (rotation) and deflection of bridges using acceleration measurements without integrating acceleration in the time domain. It applies multiple measurements, filtering, and fitting techniques for a constrained local optimum rather than integrating acceleration in the time domain. First, the acceleration of the bridge is filtered to obtain the time history of the deflection angle at each measurement point. Then, the Karush–Kuhn–Tucker (KKT) algorithm as a constrained least squares (CLS) method is used to estimate the shape curve of the deflection angle and deflection of the bridge. The validity of the proposed acceleration-based deflection angle and deflection identification method is verified through an in-house moving vehicle experiment on a model bridge. The method develops a data-driven model based on a small amount of observational data, enabling accurate predictions of the angular rotation and deflection of beam structures. [ABSTRACT FROM AUTHOR]

Damage detection of bridges using vibrations from a passing vehicle has received a lot of interest recently. Though non-modal parameter-based methods (e.g., data-driven approaches) have shown promising results in this context, their advancement towards a comprehensive and rigorous monitoring system is hampered by their overreliance on machine learning techniques. On this background, this paper proposes a novel automatic physics-guided diagnosis framework for bridge health monitoring utilizing only raw vehicle accelerations. First, numerical studies are conducted to investigate the relationship between vehicle time-domain signals and bridge damage, based on which a new damage index is proposed. At the same time, it also explores the identification of damage locations and proposes a location index. Second, a damage diagnosis framework, which consists of a data processing method and a physics-guided model, is designed to overcome deficiencies from a drive-by measurement and to automate the damage detection process. The proposed framework was validated using datasets acquired from laboratory experiments employing a scale vehicle model and a steel beam. The results affirmed the method's efficacy in damage indication, quantification, and localization. Moreover, the superiority of the proposed damage index and the rationale for the proposed physics-guided approach were also demonstrated through comparisons with machine learning-based methods. [ABSTRACT FROM AUTHOR]

• An optimized bidirectional long short-term memory (BiLSTM) using genetic algorithm (GA), called GA-BiLSTM model, is proposed to evaluate vehicle interior sound quality. • Raw noise is converted to log-mel spectrum containing time–frequency information in line with human auditory perception. It is more suitable as the input feature representation than waveform. • GA-BiLSTM model perform better than BiLSTM model without GA. At the training, validation and testing stages, the former has higher evaluation accuracy and lower loss than the latter. • The evaluation obtained by the proposed model is in good agreement with human subjective evaluation. Interior sound quality strongly affects passengers' physiological and psychological perceptions. Therefore, it is important to evaluate vehicle interior sound quality. Compared with assessing by humans, the artificial intelligent-based evaluation model can acquire an evaluation efficiently. However, this type of models determines initial learnable parameters at random before training, which is easy to cause final trained model to trap in local optima. This paper proposes an evaluation model based on an optimized bidirectional long short-term memory using genetic algorithm. Firstly, interior noise measurement and subjective evaluation are completed. Secondly, to obtain the time–frequency information in line with human auditory perception, log-mel spectrum is used to preprocess the noise. Thirdly, the evaluation model is constructed, which consists of two bidirectional long short-term memory layers, two fully connected layers and one Softmax output unit. Next, to avoid model trapping in local optima, initial learnable parameters are optimized using genetic algorithm. After optimization, average fitness and best fitness decreased by 6.5136% and 1.4415%, respectively. The training accuracy is 95.79%. The validation accuracy is 93.15%. The testing accuracy is 93.33%. Only two samples are misclassified in the confusion matrix of testing stage. These suggest that genetic algorithm can greatly enhance the model's performance by optimizing initial learnable parameters. The evaluation obtained by the optimized model is very close to human subjective evaluation. [ABSTRACT FROM AUTHOR]

Electrical analogues of linear mechanical systems have traditionally been obtained using a set of rules that can potentially be tedious when applied to complex systems. In addition, such rules do not cover all the possibilities of mechanical systems including rotational and translational generalized coordinates. The present work establishes a comprehensive and systematic methodology for obtaining the electrical analogue of any mechanical system modelled by rigid bodies, springs, and dampers. The method is simple and straightforward. First, the dynamic equations of the mechanical system are derived into matrix form. These equations are then translated to the electrical domain by means of the electromechanical analogy and, finally, the electrical circuit is obtained by direct inspection of the matrices. In addition, the method can deal with a combination of translational and rotational coordinates in the definition of the mechanical model. To support the proposed method, and beyond the planar vehicle models found in the literature, the electrical analogue of a linear 3D 7-DOF full car model is presented in this work. [Display omitted] • A comprehensive framework for electrical analogues of mechanical systems is presented. • The method is simple and straightforward for complex linear mechanical systems. • Combination of translational and rotational coordinates can be used. • An electric analogue of a 3D 7-DOF car model is detailed • The electrical and mechanical analogues are numerically validated. [ABSTRACT FROM AUTHOR]

• Parametric topology optimization of an electrostatic gyroscope proposed. • Gyro is modeled using a parameterized 3D physical model and a control model. • The optimization algorithm is carried out considering automotive application. • The double-lane-change (DLC) maneuver performed on a coupled vehicle-gyro model. • The DLC-maneuver yaw rate error is improved significantly considering 14,000 cases. Automotive MEMS gyroscopes are used for various purposes, such as rollover prevention and dynamic stability. Although, employment of gyroscopes for automotive applications is reported, what is not discussed is the gyro topology design and optimization for these applications. This article reports parametric topology size optimization of a MEMS gyroscope proper for automotive applications. The structure optimization of a translational dual-mass Coriolis vibratory gyroscope (CVG) with electrostatically sense/excitation mechanism is presented for automotive applications. Fabrication considerations conform to the X-FAB procedures, and application uncertainty is inherent in the vehicle lateral model. The proposed design approach takes into account the inaccessible levels of application uncertainty to optimization, which provides results that are more reliable, compared with the ideal models. More than thirteen thousand virtual cases are studied, in which the gyroscope performance specifications including scale factor, linearity, etc. are calculated. The double-lane-change (DLC) maneuver based on ISO 3888-1 is performed on the optimal candidates in order to apply the vehicle application constrains. The suggested optimal solution is the one with the least yaw rate error with respect to the target yaw rate instructed by the maneuver, which suits best for the automotive application. The results show that the yaw rate error of DLC maneuver improved from about 40% to about 1.4% using the optimization algorithm, which shows the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

• Optimal understeer characteristics for energy consumption reduction. • Set of rules for energy-efficient electric vehicle operation. • Novel feedforward-feedback torque-vectoring control algorithm. In electric vehicles with multiple motors, the individual wheel torque control, i.e., the so-called torque-vectoring, significantly enhances the cornering response and active safety. Torque-vectoring can also increase energy efficiency, through the appropriate design of the reference understeer characteristic and the calculation of the wheel torque distribution providing the desired total wheel torque and direct yaw moment. To meet the industrial requirements for real vehicle implementation, the energy-efficiency benefits of torque-vectoring should be achieved via controllers characterised by predictable behaviour, ease of tuning and low computational requirements. This paper discusses a novel energy-efficient torque-vectoring algorithm for an electric vehicle with in-wheel motors, which is based on a set of rules deriving from the combined consideration of: i) the experimentally measured electric powertrain efficiency maps; ii) a set of optimisation results from a non-linear quasi-static vehicle model, including the computation of tyre slip power losses; and iii) drivability requirements for comfortable and safe cornering response. With respect to the same electric vehicle with even wheel torque distribution, the simulation results, based on an experimentally validated vehicle dynamics simulation model, show: a) up to 4% power consumption reduction during straight line operation at constant speed; b) >5% average input power saving in steady-state cornering at lateral accelerations >3.5 m/s2; and c) effective compensation of the yaw rate and sideslip angle oscillations during extreme transient tests. [ABSTRACT FROM AUTHOR]

• A novel design of a HIS-HESA based on a 7-DOF full vehicle model was presented. • Different HIS-HESA working modes including bounce, pitch and roll modes were investigated. • HIS-HESA can harvest a maximum power of 397 W at 72 km/h and class C ISO-road. • The ride comfort and road holding using the HIS-HESA were improved by 33% and 20%. • Controlling the external resistance can realize the trade-off between ride comfort and energy recovery. The suspension system is an important part which can mitigate the influences of vibration caused by road roughness where these vibrations are traditionally dissipated by shock absorbers. From the view of energy recovery, a hydraulic interconnected suspension based on the energy regenerative shock absorbers (HIS-HESA) is proposed to improve ride comfort, road holding ability and energy recovery ability simultaneously. This paper mainly focuses on the modelling and ride analysis of a 7-DOF full vehicle suspension model integrated with the HIS-HESA. The mathematical model of the HIS-HESA is described based on the relationship between the pressure drop and the flow rate of every hydraulic element. The characterization of the HIS-HESA is given based on a parametrical analysis under a sinusoidal excitation. Further, the traditional shock absorber and the single HESA have been compared terms of suspension dynamics and energy recovery. Thereafter, the simulation is implemented to compare the vehicle dynamics (body acceleration, tire dynamic load and power regenerated) using HIS-HESA with that of the traditional suspension. The simulation results suggest that the HIS-HESA can attenuate the vibrations to maintain a good ride comfort besides it can also regenerate energy to power electrical equipment on the vehicle. The analysis also indicates that the influence of external resistance within HIS-HESA provides the possibility regarding the semi-active control of the HIS-HESA which will be the research focus in future development. [ABSTRACT FROM AUTHOR]

The dynamics of roadway bridges crossed by vehicles moving at variable speed has attracted far less attention than that generated by vehicles travelling at constant velocity. Consequently, the role of some parameters and the combination thereof, as well as influence and accuracy of the modelling strategies, are not fully understood yet. Therefore, a large statistical analysis is performed in the present study to provide novel insights into the dynamic vehicle–bridge interaction (VBI) in braking conditions. To this end, an existing mid-span prestressed concrete bridge is selected as case study. First, several numerical simulations are performed considering alternative vehicle models (i.e., single and two degrees-of-freedom models) and different braking scenarios (i.e., soft and hard braking conditions, with both stationary and nonstationary road roughness models in case of soft braking). The statistical appraisal of the obtained results unfolds some effects of the dynamic VBI modelling in braking conditions that have not been reported in previous studies. Additionally, the use of machine learning techniques is explored for the first time to develop surrogate models able to predict the effect of the dynamic VBI in braking conditions efficiently. These surrogate models are then employed to obtain the fragility curve for the selected prestressed concrete bridge, where the attainment of the decompression moment is considered as relevant limit state. Whilst the derivation of the fragility curve using numerical simulations turned out to be almost unpractical using standard computational resources, the proposed approach that exploits surrogate models carried out via machine learning techniques was demonstrated accurate despite the dramatic reduction of the total elaboration time. Finally, the accuracy of the numerical (physics-based and surrogate) models is evaluated on a statistical basis through comparisons with experimental data. [ABSTRACT FROM AUTHOR]

Highlights • A novel road classification approach for controllable suspension system is proposed. • The proposed algorithm is robust to controller parameters and vehicle velocity. • An experiment is conducted to show the advantage of the established algorithm. Abstract Driven by the increasing requirement for road conditions in the field of the controllable suspension system, this paper presents a novel nonlinear road-excitation classification procedure for arbitrary suspension control strategy. The proposed procedure includes four steps: the definition of controller parameters, selection of insensitive frequency ranges, calculation of superior features, and generation of the classifier. To better illustrate the proposed procedure, the clipped optimal control strategy is taken as an example in the simulation part. Simulation results reveal that the proposed method can accurately estimate road excitation level for various controller parameters, vehicle speeds, and vehicle models. Three contributions have been made in this paper: (1) A road classification procedure that can be used for road adaptive suspension control with any control algorithm is developed; (2) In order to improve classification accuracy, the concept of insensitive index which is based on the time-frequency analysis is proposed; (3) Experimental validation with a quarter vehicle test rig is performed, which has verified the effectiveness of the proposed method for the adaptive controllable suspension system. [ABSTRACT FROM AUTHOR]

• A multi-degree-of-freedom two-axle vehicle is used for scanning bridge mode shapes. • Bridge mode shapes are extracted using a novel two-peak spectrum idealized filter. • The synchronous identification of bridge frequencies and mode shapes is verified. • Laboratory VBI tests reconstruct bridge mode shapes by a two-axle vehicle. Bridge mode shape is an essential indicator in the assessment of structural health of bridges. Previous studies have demonstrated the feasibility of extracting bridge mode shapes from the response of a passing vehicle. However, the majority of these studies modeled the vehicle as a single degree-of-freedom (DOF) spring mass, and the accuracy of the recovered bridge mode shapes was not desirable. Furthermore, limited experimental validations exist for bridge mode shape extraction. In this study, a practical multi-DOF two-axle vehicle for scanning bridge mode shapes was investigated. First, a closed-form solution for the responses of a passing symmetric two-axle vehicle interacting with a simply supported bridge was obtained, in which the bridge mode shapes were extracted from the vehicle responses using a novel two-peak spectrum idealized filter approach. Then, a numerical finite element simulation was performed to verify the proposed approach for synchronously identifying bridge frequencies and mode shapes, and the impacts of vehicle speed, vehicle parameters, damping, road roughness, and environmental noise were parametrically analyzed. Finally, laboratory vehicle-bridge interaction tests were conducted to extract bridge mode shapes from the measured accelerations of a passing two-axle vehicle. The proposed approach only considered the two-peak spectra of the left-shifted and right-shifted bridge frequencies in the frequency spectrum of the vehicle response, which was theoretically derived to be capable of accurately extracting bridge mode shapes. Numerical investigations revealed that the proposed approach can synchronously identify bridge frequencies and mode shapes for high vehicle speeds (e.g., 20 m/s), various vehicle parameters, and the existence of vehicle and bridge damping. Road roughness and environmental noise contaminated the two-peak spectra of bridge frequencies, which negatively impacted the effectiveness of the proposed approach. However, provided that the bridge frequency is visible in the frequency spectrum, the proposed approach is feasible. Results of the laboratory tests demonstrated that bridge frequencies and mode shapes were successfully identified from the accelerations of a passing two-axle vehicle. This study verified that a two-axle passing vehicle can be used to scan bridge mode shapes, which could facilitate wide engineering applications of the vehicle scanning method for indirect monitoring of bridge infrastructures. [ABSTRACT FROM AUTHOR]

Aiming at the vertical elastic vibration reduction of a railway vehicle carbody, the traditional technology mainly focuses on the design of modal frequency, the method of increasing damping and the design of dynamic vibration absorber (DVA), with little attention to the design of modal shape. In this paper, a novel vertical elastic vibration reduction method based on the minimum generalized force principle (MGFP) is proposed, i.e., the vertical elastic vibration of carbody is reduced by adjusting its own parameters (weight distribution and stiffness distribution) to control the first-order vertical bending (FVB) modal shape nodes to the carbody–bogie interface positions. Firstly, to better apply MGFP, a factor called generalized force factor (GFF) that can determine the level of generalized force (GF) is proposed. By improving theoretical model of a railway vehicle carbody, the traditional uniform cross-section Euler–Bernoulli beam (UCEB) is replaced by variable cross-section Euler–Bernoulli beam (VCEB) model, and the influencing factors of modal frequency and modal shape node locations can be investigated by the VCEB model. Then, the vertical vehicle system dynamics model is established based on the VCEB carbody model, and the influence of weight distribution, stiffness distribution and carbody–bogie interface positions on the elastic vibration is analyzed under a sweep frequency signal and track irregularities. Finally, the vibration reduction method is verified by a full-scale roller rig test. The theoretical analysis and experimental results show that: by changing the weight distribution or stiffness distribution of the carbody, the FVB modal shape node locations can be effectively controlled, and then combined with the design of carbody–bogie interface positions, the vertical bending elastic vibration of the carbody can be reduced. The vibration reduction method proposed in this paper can be applied to the design of new carbodies or the layout optimization of carbody subsidiary mass. • A novel vibration reduction method based on MGFP is proposed for railway carbody. • A railway vehicle dynamics model which considering carbody as VCEB is derived. • A generalized force factor is proposed to determine the generalized force level. • The effectiveness of the vibration reduction method is verified by a roller rig test. [ABSTRACT FROM AUTHOR]

Locomotive-track interaction is commonly represented by a complex mechanical system which is modelled in numerical studies. Numerical studies alone are not enough to precisely describe the behaviour of this mechanical system due to the various elements that can only be delivered from field and laboratory experimental programs. One such element is wheel-rail wear that has been found to contribute a considerable portion of the total cost of maintaining heavily used railway systems. Simulation studies are typically used for predicting wear of wheels and rails, but the majority of these do not consider in-train forces nor provide detailed modelling of traction and braking events and often rely on historic wear rates for comparable materials, reducing the accuracy of the wheel-rail volume loss estimation. This paper proposes a methodology for wear rate experimental measurements that allow improving the accuracy of wear analyses using dynamic simulations. The proposed method considers in-train forces by performing longitudinal train simulations whose results are then implemented on a detailed locomotive vehicle dynamic model with a traction mechatronic system co-simulation approach. The wheel-rail contact stress and slip results are then post-processed into a Dynamic Load Spectrum that contains contact stress and wheel slip occurrences. The Dynamic Load Spectrum was used to measure the wear rates for Australian AS60 head hardened rail steel material operating in combination with Class B wheel steel material. The experimental program delivered more realistic wear rates that correspond to actual industry operational scenarios. The measured wear rates were used to estimate the rail material volume loss for a specific train trip. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel optimal design methodology of vehicle mechatronic ISD suspension using the fractional-order electrical network is proposed, to enhance the vibration suppression performance. • The corresponding relationship and analytical expression between the fractional-order mechanical network elements and the fractional-order electrical network elements are obtained. • A quarter car is taken as an example, the dynamic model of the vehicle mechatronic ISD suspension using the fractional-order electrical network and the integer-order network are established. • The advantages of the vehicle mechatronic ISD suspension using the fractional-order electrical network are validated through numerical simulations and experiments under different road input conditions. To further explore the beneficial effects of the vibration isolation performance of a new vehicle ISD (inerter-spring-damper) suspension using a mechatronic inerter, this paper proposes a novel optimal design methodology for a vehicle mechatronic ISD suspension system based on a fractional-order electrical network is proposed. In view of difficulties in the engineering implementation of fractional-order mechanical network elements, this paper first studies the corresponding relationship and analytical expression of fractional-order mechanical and electrical network elements respectively. Under the instruction of fractional calculus, the fractional-order electrical network elements are used to realize equivalent fractional-order mechanical network elements by adopting a ball-screw mechatronic inerter. Then, a quarter car dynamic model of vehicle mechatronic ISD suspension is constructed, and the parameters of the fractional-order electrical network and the integer-order electrical network are obtained by means of particle swarm optimization algorithm. The performance advantages of vehicle mechatronic ISD suspension using a fractional-order electrical network are verified by numerical simulations and experiments. The results show that, compared with the application of an integer-order electrical network, the vibration suppression performance of a vehicle mechatronic ISD suspension can be further enhanced by using a fractional-order electrical network. [ABSTRACT FROM AUTHOR]

• A novel vehicle dual-motor steer-by-wire system is presented. • An angle tracking controller via super-twisting SOSMC is designed to improve the tracking performance. • A mean deviation coupling structure (MDCS) is proposed to enhance the synchronization performance. • A synchronization control strategy based on MDCS and super-twisting SOSMC algorithm is proposed. • The simulation and experimental results verify the effectiveness of the proposed control strategy. This paper proposes a novel vehicle dual-motor steer-by-wire (SBW) system to improve the reliability and safety of traditional SBW system. However, the dual-motor SBW system is a strongly coupled, nonlinear and multivariable system, whose biggest challenge is the tracking and synchronization control. In order to improve the tracking and synchronization control performance of dual-motor SBW system, a tracking and synchronization control strategy of vehicle dual-motor SBW system via super-twisting second-order sliding mode control (SOSMC) and mean deviation coupling structure (MDCS) is proposed. Firstly, the steering motor model, rack and pinion model and vehicle dynamics model are established. Then, the angle tracking controller based on super-twisting SOSMC is designed to ensure the robustness and tracking performance of each motor. Next, the MDCS and the angle synchronization controller based on super-twisting SOSMC are proposed to compensate the synchronization error between dual-motor caused by mismatched driving force, external interference and parameter uncertainty. Finally, the simulation analysis is carried out by using Carsim/Simulink and the hardware-in-the-loop experiment is implemented based on the dual-motor experimental platform. The simulation and experimental results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

• ASRCKF observation is proposed to estimate vehicle states. • The SP algorithm is put forward to calculate the μ max. • ASRCKF can improve the estimation accuracy, especially for vehicle sideslip angle. • Accurate μ max can always be obtained by the proposed algorithm. • Co-simulation and experiments are designed to verify the algorithm validation. It is infeasible to measure vehicle dynamics states (VDS) directly without expensive measurement instruments, especially for the tire-road peak adhesion coefficient (μ max). However, four-wheel-independent-drive-electric-vehicle (4WIDEV) provides convenience for the observation of these dynamic states, because the rotation rate and torque of the in-wheel motor can be acquired directly. Vehicle nonlinear longitudinal-lateral dynamics, the single estimation method for all VDS and the time-varying measurement noise of sensors cause difficulties for the observation. Common the extended-Kalman-filter (EKF) is unsuitable to estimate VDS in strong nonlinear region. This paper propose a longitudinal-lateral cooperative estimation algorithm based on adaptive-square-root-cubature-Kalman-filter (ASRCKF) and partitioned similarity-principle (SP) to estimate the vehicle states and the tire-road peak adhesion coefficient sequentially for 4WIDEV. Firstly, a nonlinear 7-degree-of-freedom (7DOF) vehicle model and magic-formula (MF) tire model are built as the base of the successive estimation scheme. Then, recursive-least-squares (RLS) is adopted to estimate the tire longitudinal force. With the estimated tire longitudinal force, an ASRCKF which can be adjusted adaptively by the feedback dynamics states, is designed for the estimation of vehicle states. Next, the SP algorithm combined with the characteristic of longitudinal-lateral dynamics, which is benefit for μ max estimation when tire dynamics enters the nonlinear region, is proposed. Finally, experiment and simulation results show that excellent performance can be achieved with the proposed estimation method in varying driving conditions. [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]

Vehicle vibration is one of the most critical issues hindering the supply of attractive automobiles to customers. This study focuses on drivability related to longitudinal vehicle body vibration excited by driver input at tip-in acceleration. To understand and design the vibration, a physical model with necessary and sufficient fidelity for its reproduction is required. Moreover, estimation of the excitation input (control input) is also vital for this reproduction. However, achieving both the accurate measurement and modeling of the control input is difficult and labor intensive. Therefore, a novel low-order physical modeling approach with control input estimation using operational output-only measurement is proposed here. The proposed approach requires some assumptions, such as inertia information, but requires no input measurement, no preliminary testing, no limitation to a linear model, and no assumptions in estimating the control input. The basic concept is to regard the vehicle system as a relationship (called a vibration transfer structure) between rigid-body motions and restoring forces and moments (called transfer forces). The proposed approach is implemented in five steps: First, the operational output is measured during tip-in acceleration. Second, important motions are extracted from the output using tensor decomposition. Third, a simplified transfer structure is determined using the important motions. Fourth, the control input and the transfer forces are estimated as time-series data using the structure. Finally, by characterizing the transfer forces, a low-order physical dynamic model is obtained. The effectiveness of this approach is demonstrated using two sets of operational tip-in acceleration data. • This paper proposes a novel modeling approach for automotive vehicle vibrations. • It simplifies modeling of various vehicles by only requiring operational output data. • It constructs a low-order physical model, which is not limited to linearity. • It estimates operational control input signal required for reproducing vehicle vibration. • It incorporates PARAFAC decomposition and a model order selection method is proposed. [ABSTRACT FROM AUTHOR]

This study deals with the enhancement of reliability of vehicle dynamic model for the stability controller design by estimating the effect of uncertainties in both tire forces and vehicle dynamics. In this respect, any selected initial kinetic model of vehicle is updated at each instant by adding some complementary terms using a novel prediction approach. The proposed estimation method uses the information fusion between the inertial measurement unit (IMU) and the aided systems including the global navigation satellite system (GNSS) and the calibrated compass system. The bias of acceleration sensors and the drift of gyroscope are compensated using a novel adaptive algorithm by which the estimator weights are tuned automatically. Therefore, the vehicle longitudinal and lateral velocities together with the yaw rate are accurately estimated with a high frequency. The proposed estimation algorithm is mathematically analyzed for the stochastic stability and experimentally evaluated through real-world vehicular tests. Accordingly, a nonlinear controller for vehicle directional stability is designed based on the updated dynamic model within the CarSim software environment as a virtual vehicle experiment platform. The obtained results reveal that, the designed data fusion algorithm remarkably improves the estimation performance in the presence of system and measurement uncertainties. Also, the controller designed by the estimated vehicle dynamic model will be reliable and robust in the presence of uncertainties. • Novel estimation of reliable kinetic model for vehicle dynamics in the presence of uncertainties. • Stochastic stability analysis of the proposed estimation algorithm. • Automatically tuning the estimator weights by a novel data fusion algorithm for various driving conditions. • Experimental verification of the proposed estimator in several vehicular tests. • Design of vehicle stability controller using the reliable estimated model. [ABSTRACT FROM AUTHOR]

• The inverse system is proposed to eliminate the mutual influence between the AFS and HPS subsystems. • PID and GRNN inverse system are combined to realize the decoupling control of the vehicle chassis system. • Real vehicle tests are conducted to demonstrate the effectiveness of the decoupling control strategy. Aiming at eliminating the mutual interference and coupling between the active front steering (AFS) and hydro-pneumatic suspension (HPS) subsystems of the emergency rescue vehicle, this paper presents a neural network inverse system based decoupling control strategy, which consists of the neural network inverse system and PID feedback control strategy. At first, a three-degrees of freedom dynamics model of the vehicle is constructed, and the reversibility of the vehicle chassis system is analyzed by the Interactor algorithm. Then, the generalized regression neural network (GRNN) model is used to identify the vehicle chassis inverse system, which decouples the multivariable vehicle chassis system into two independent single input and single output systems. Next, this paper proposes a closed-loop compound controller for the single input and single output systems, which is designed based on the PID control strategy and GRNN inverse system to realize the feedback control of the vehicle chassis system, so that the vehicle can track the desired yaw rate and roll angle well. Simulations and experiments are conducted on the self-developed fire rescue prototype vehicle, and the results prove that the proposed decoupling control strategy can not only eliminate coupling between AFS and HPS subsystems of the vehicle chassis, but also make the vehicle have better handling stability than controlled by the integrated control. [ABSTRACT FROM AUTHOR]

• Single-axle test vehicle's rocking motion is firstly used to scan bridge frequencies. • New formulas are derived for calculating wheels' contact point (CP) responses. • Wheels' CP responses are experimentally verified to be free of the masking by vehicle's frequencies. • The present model enables us to look into the mechanism of the wheel-bridge contact for uneven surface roughness. • Temporary parking of the vehicle on bridge can help sharpen the frequencies identified. A self-designed single-axle vehicle is used to detect the bridge frequencies from its rocking motion, caused by wheels moving over an uneven surface. To include both the rocking and vertical motions, the single-axle vehicle is modeled as a two degree-of-freedom (DOF) system, unlike the previous single-DOF model. The rocking frequency, an inborn property of the vehicle, may pollute the frequency extraction of bridges in the test. The sensors mounted near the wheels (called the wheel sensors) for catching the rocking are of higher fidelity than the central sensor for the vertical motion. Both the vertical and rocking frequencies of the vehicle will be eliminated in the newly derived responses of the wheel-bridge contact points (CPs). In the flat road test, the vertical frequency of the test vehicle is identified from the average of the two wheel sensors responses, and rocking frequency from the angular response calculated. From the field test conducted on a two-span girder bridge, it is concluded that: (1) the new formulas for the CPs are reliable for scanning the bridge frequencies, while removing vehicle's frequencies; (2) the present model enables us to look into the mechanism of the wheel-bridge contact for uneven surface roughness; and (3) temporary stop of the test vehicle on the bridge can help sharpen the bridge frequencies identified. [ABSTRACT FROM AUTHOR]

• The developed tire lateral force considers yaw, lateral and longitudinal dynamics. • The vehicle yaw and lateral motions are related to the acceleration as a disturbance. • Balancing multiple objectives/constraints to improve vehicle overall stability. In extreme driving situations, the coupling between the longitudinal and lateral vehicle motion becomes significant with the highly nonlinear tire forces and influences vehicle overall stability. To address the above problem, a model predictive controller is proposed for four wheels independent motor-drive electric vehicles. Firstly, a LuGre combined-slip tire model is developed to describe the coupling nonlinearity of the tire. The variation of longitudinal velocity is considered a disturbance term in the vehicle dynamics model to consider its effect on vehicle stability. Then, the multiple objectives, including tracking the reference values of the yaw rate and lateral velocity, suppressing the tire slip ratios, and reducing the torque energy consumptions, are balanced by the additional torque generated by the designed model-based controller. The safety and actuator constraints are considered concurrently. Finally, the proposed controller is tested via co-simulation with CarSim and MATLAB/Simulink, and a hardware-in-loop system with extreme maneuvers. The results show that the handling performance, longitudinal and lateral stability are effectively improved under extreme conditions. [ABSTRACT FROM AUTHOR]

• A parallel hydraulic hybrid vehicle model is built to research its powertrain dynamics. • Power on gear shift control strategy is designed to avoid torque interruption. • LQR is used to compromise vehicle vibration, driving comfort and dynamic performance. • Extended Kalman filter (EKF) is adopted to estimate the powertrain parameters. • Torque compensation capability is studied with different accumulator charging state. The power on gear shift control of a parallel hydraulic hybrid vehicle (PHHV) is researched in this paper. A multibody dynamic model is built to capture the main vibration characteristics of PHHV powertrain, based on which the power on gear shift control strategy is designed and verified. To avoid the vehicle driving torque interruption during gear shift, power on gear shift control strategy is designed. In the control strategy, hydraulic pump/motor (HPM) compensates the engine torque when the engine clutch is disengaged for gear shift. The engine clutch engagement process is carefully controlled by LQR control strategy to mitigate the vehicle powertrain vibration and improve the vehicle driving comfort. Extended Kalman filter (EKF) is adopted to estimate the PHHV parameters required by LQR control. The available HPM torque depends on its working pressure which varies a lot with different accumulator state of charge. So the HPM torque compensation capability is investigated by analysing the traction force requirement during gear shift. To obtain the gear shift traction force demand, a gear shift schedule considering both vehicle fuel economy and dynamic performance is designed. The simulation results show that the HPM could satisfy most of the torque compensation requirements during gear shifts with the designed gear shift schedule in typical driving cycles. [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]

In the high-speed railway industry, the pantograph-catenary system is responsible to provide continuous electric energy for the high-speed train. The pantograph-catenary system suffers multiple impacts from the complex work environment. The vehicle-track excitation is one of the normal disturbances to the pantograph-catenary interaction. Previous studies only consider the vertical effect of the vehicle-track vibration on the pantograph-catenary interaction. To address this deficiency, both of the pantograph-catenary and vehicle-track models are constructed in this paper. The validations of both models are verified by the experimental test and the world benchmark, respectively. The pantograph base follows the translations and rotations of the car-body caused by random rail irregularities. In combination with a spatial contact model between the contact wire and the pantograph strip, the spatial vibration of the carbody can be fully considered in the pantograph-catenary interaction. The statistical analysis, stochastic analysis and frequency analysis are performed to make sense of the effect of the random track irregularities on the pantograph-catenary interaction. The deviation of the contact point away from the strip centre caused by the carbody vibration is also analysed. The results show that the reliability of the pantograph-catenary system shows a continuous decrease in the degradation of rail quality. The carbody vibration may cause the de-wirement of the pantograph in extreme conditions. Finally, an application example is given to evaluate the dynamic performance of the pantograph-catenary system running on the China high-speed network with realistic rail irregularities. [ABSTRACT FROM AUTHOR]

• A robust SMO is introduced to improve the capability when uncertainty are involved in the vehicle dynamic model. • The coupling effect between sensors drift and vehicle motion is studied, and the measurement error is compensated by the modified ENN. • The extended SCKF employs measurement from different sensors by a fusion strategy to deal with the severe driving motions. • Simulation and field tests with different maneuvers are implemented. To improve the safety and stability of land vehicles, this paper explores the estimation problem for vehicle states, including lateral velocity and attitude. First, a robust sliding mode observer is introduced to improve the adaptability for uncertain inputs, especially for the varying parameters in the vehicle dynamic model and longitudinal velocity. Furthermore, theoretical studies are performed to enhance the capability of the observer. In order to mitigate errors with the integrated navigation system, sensor drift model is primarily established based on a modified Elman neural network, so as to investigate the coupling between driving motion and errors. In addition, an extended square-root cubature Kalman filter is proposed to combine measurements from different sensors, utilizing a fusion strategy, to deal with severe driving motion and state estimation problems. Finally, simulation and field tests are carried out under a variety of maneuvers and conditions. The approach is compared with existing methods and evaluated experimentally, which indicates its effectiveness in improving the accuracy of vehicle state estimation. [ABSTRACT FROM AUTHOR]

• Wedge tests provide high-quality response data for railway vehicle identification. • Moving vehicle can be an effective tool in the bridge free response excitation. • Real parameter-based modelling increase reliability of numerical simulation results. • Bridge accelerations assessment requires consideration of sprung models of loads. This paper addresses the methodology of the bridge-vehicle dynamic model definition based on the free response measurements of an existing train and existing bridge spans. In the case of the railway vehicle, the methodology uses the impulse excitations of a single car by means of the wedge method. In the case of the bridge spans, free responses are collected after the passages of trains. The global modal parameters (frequencies and modal damping) of both structures are identified using two modal identification techniques, i.e., the eigensystem realization algorithm (ERA) and the peak picking method (PP). A simplified single-suspension model of the vehicle with 11 global dof's is proposed that can be useful in the bridge-vehicle interaction analysis. Two steel spans having non-ballasted tracks are the bridge structures. Field tests are conducted to identify the modal parameters and validate the numerical models. Finally, numerical simulations of the train passing over the bridges are compared with the in situ measurements performed under operating conditions. [ABSTRACT FROM AUTHOR]

• The real-time vehicle body domain motion-mode identification method is developed. • The developed method requires less inputs and the inputs are easily approachable. • The developed method drives practical application of the conventional MEM theory. • Mode energy ratio percentage error of the developed method is under 5%. Vehicle body dominant motion-modes, including bounce, roll, and pitch, are highly related to vehicle ride comfort and handling performance, while are generally coupled. Real-time identification of dominant motion-mode with the three motion-modes benefits control on active suspensions. The conventional (7-DOF vehicle model based) motion-mode energy method (MEM) for motion-mode identification needs 14 vehicular states and 4 road information, blocking its real vehicle application. This study simplifies the MEM on a 3-DOF body model to identify the dominant motion-mode real-timely. This method requires less and easily accessible inputs compared with conventional methods in real time. This consequently enables practical application of instant dominant motion-mode identification. The proposed method gives a new way to decouple suspension deflection into each body dominant motion-mode, and only uses easily accessible suspension deflection, vehicle body angle, angle rate and vertical motion of a vehicle. Moreover, the relative error of mode energy ratio between the developed method and the traditional method is under 5% in simulation, which indicates high accuracy of the developed method. The real-time ability and reliability of the developed method are experimentally proved under different driving conditions. The experiment results confirm that the proposed method is effective regarding identification of the dominant vehicle motion-mode on typical driving scenarios. This method helps design of vehicle dynamic control strategy, and therefore improves active suspension performance. [ABSTRACT FROM AUTHOR]

• The 13 DOFs vehicle dynamic model with semi-active HDS is built. • The experiment results show that the acceleration of the seat rail in all directions are reduced. • The active side acceleration of torque strut calculated is basically consistent with the measured value. • The 13 vehicle dynamic model and the evaluation indexes are feasible in this study. • The semi-active HDS introduced in this paper can improve the vehicle NVH to a great extent. On the basis of previous research, this paper takes a more comprehensive study on the vibration of a vehicle with automatic transmission when engine start-stop and automatic start-stop. A 13 degrees of freedom (DOFs) vehicle dynamic model is established, and the dynamic response evaluation method is put forward. Three methods to reduce the vibration of powertrain and vehicle are analyzed, and the results show that increasing the damping of the mounting system can effectively reduce the vibration in these three methods. A novel semi-active hydraulic damping strut (HDS) as the fourth mount is designed and developed, the installation position and its outer channel size are optimized and calculated respectively based on its damping force calculation method and the design method of the powertrain mounting system (PMS). The calculation results of evaluation indexes with engine start-stop are analyzed in-depth. The optimized parameters of the semi-active HDS are taken into the 13 DOFs dynamic model to analyze the dynamic response of the engine automatic start-stop process, and the values of the evaluation indexes are decreased. To validate the effectiveness of the design method of the semi-active HDS, the experiment of the vehicle with and without the semi-active HDS are carried out, and the test results show that the seat rail acceleration are reduced with semi-active HDS introduced. The active side acceleration of the torque strut calculated by the 13 DOFs model is consistent well with the measured value with and without the semi-active HDS, which proves that the model is reasonable. [ABSTRACT FROM AUTHOR]

• Robust and accurate road profile estimation method using smartphone data. • HC vehicle model identification through the automatic processing of smartphone data. • The influences of drive speed differences and changes are small. • The influences of differences in the left and the right paths are clarified. • The differences in drive speeds and vehicle dynamics are accurately compensated. This paper proposes a robust road profile estimation method and vehicle parameter identification method through an optimization with an objective function and constraint conditions on estimated profiles. The methods require only vehicle response measurements, enabling easy and inexpensive, yet effective, road condition monitoring through the automated processing of smartphone data. A half-car (HC) model, representing both bouncing and pitching motions, is employed for the profile estimation. The road profiles at the front and rear tire locations are included as state variables in the augmented state vector and are estimated by combining the augmented Kalman filter (AKF), Robbins–Monro (RM) algorithm, and Rauch–Tung–Striebel (RTS) smoothing. The two independent state variables, however, correspond to a single physical profile, while their distance coordinates differ by the wheelbase. Therefore, the vehicle parameters are optimized through the minimization of the difference between the identified road profiles at the front and rear tire locations using a genetic algorithm. Three objective functions and three constraint conditions are proposed to automatically select the best vehicle parameters. With this HC model, the road profile is subsequently estimated by combining the AKF, RM, and RTS methods. Through numerical simulations, the accuracy of the profile estimation and validity of the parameter identification are clarified. The influences of different drive speeds and difference between the left and the right profiles are numerically investigated. Drive tests with three different vehicles and a reference laser profiler show that the algorithm can automatically compensate for differences among vehicles with different drive speeds and estimate profiles accurately. [ABSTRACT FROM AUTHOR]

The left to right wheel-path coherence function was proven to be nonstationary. The variations in the coherence could be described using the distribution of a single arbirary exponent of an exponential curve-fit. The distribution of the expoent was shown to be independent of overall road roughness. • Analysed left–right wheel-path coherence function of 400 km of asphalted roads. • Assessed all published coherence models to identify the most appropriate. • Developed new approach to apply coherence model to nonstationary roads. • Showed that wheel path coherence function is independent of road roughness. • Established a new method for statistically describing road roughness distributions. The ability to accurately simulate the vibratory motion of transport vehicles is of great importance when designing vehicle components and product containment systems. Direct measurement and analysis of the vibrations is not always practical and laboratory testing using synthesized road elevation data is a common alternative, as is numerical simulation. However, no technique exists to generate realistic nonstationary dual track road elevation data. This research focuses on uncovering statistical distributions that describe the nonstationary relationships between the left and right wheel-paths. Analysis of the short-time (nonstationary) coherence functions and instantaneous International Roughness Index (IRI) of measured road profile data provided distributions which describe variations in left to right wheel-path correlation and roughness variations for both tracks. The resulting distributions can be described with a three-parameter Weibull distribution and can be adopted to generate nonstationary dual wheel-path profile data that can be used to excite numerical vehicle models and physical vehicles via multi-axis simulators. [ABSTRACT FROM AUTHOR]

• Hydraulic controllers are modelled and coupling multi-axle vehicle models are established based on co-simulation. • Optimum matching controller is obtained by analyzing the stability of vehicle. • A fuzzy logic controller as the second auxiliary control is designed to further improve performance of vehicle suspension. The control of suspension has been extensively studied and practically utilized in numerous fields of the automotive industry. However, few studies have been conducted on the suspension performance of multi-axle vehicles with an anti-roll hydraulically interconnected system control. In order to solve this problem, the present study is focused on the roll-resistance performance and optimum matching of articulated vehicles equipped with anti-roll hydraulic interconnected control suspension. Three hydraulic interconnected control systems for single-axle, dual-axle and tri-axle suspension of semitrailers are firstly modelled and verified in AMESim, and then seven coupling vehicle models are established by co-simulation method in AMESim, MATLAB/Simulink and Trucksim. Dynamic simulation analysis shows that D23 and D123 have great application value as the optimum strategy considering the engineering cost and anti-roll capacity. Subsequently, a fuzzy controller is proposed for the problem of insufficient roll resistance of D23. The results show that designed controller can further effectively improve the handling stability of vehicle. [ABSTRACT FROM AUTHOR]

• Developing a complex vehicle model including tire, suspension, and firing system. • Uncertainty definition for significant parameters selected by analysis of variance. • Constructing the surrogate models using an effective adaptive sampling method. • Prediction of uncertainty propagation by using surrogate model-based Monte Carlo simulation. • Obtaining a joint probability of correctness for integrated vehicle performance. The design uncertainties of vehicles cause variation of the vehicle performance. This variation increases with the complexity of the vehicle; e.g., it is greater for heavy-duty vehicles than for passenger cars. This paper presents an efficient uncertainty quantification method based on uncertainty definition, propagation, and certification, with regard to the integrated performance of a heavy-duty vehicle. For the uncertainty definition of the design parameters, an analysis of variance is performed to select the parameters with the greatest effect on the performance, and various probability density functions are employed for these parameters. To predict the precise uncertainty propagation of the vehicle performance and reflect the design uncertainty in the real-world, a full vehicle model is constructed. Additionally, a Monte Carlo simulation (MCS) with surrogate models is performed to assess the efficiency and accuracy of the performance estimation. To efficiently develop the surrogate models, an adaptive-sampling method is used to reduce the required amount of sampling data. For certification of the required performance, the joint probability of correctness for the integrated performance is suggested for practical application, and a comparison of the probability results between the surrogate and dynamic vehicle models indicates the accuracy of MCS with a surrogate model. [ABSTRACT FROM AUTHOR]

• An all-aluminum vehicle body was used as an example and the finite element model of the body-in-white was established in ANSA. • The bending stiffness, torsional stiffness and modal characteristics of the body were calculated, analyzed and optimized, based on the sensitivity analysis of thickness of the vehicle body structure. • The body structure was optimized according to the body structure optimization, the quality was reduced by 9.2% to achieve lightweight goal. The intensity analysis was proposed for a certain all-aluminum vehicle body-in-white, and an optimistic method of the vehicle body was carried out based on lightweight target. The finite element model of vehicle body-in-white was established, and the flexural rigidity and torsional rigidity of the vehicle body are calculated according to the actual operating conditions of the vehicle. The flexural rigidity and torsional rigidity of the vehicle body are calculated, and the main opening deformation of the vehicle body under these two working conditions was studied. Finally, based on the thickness of the body panel, the structural sensitivity analysis of the vehicle body was carried out. According to the sensitivity analysis data, the optimization variable is determined with lightweight as the target, and the vehicle body structure is optimized. The results showed that the static and dynamic performance indexes of the white body were higher than the standards of similar motorcycle type at home and abroad, the bending stiffness of the structurally improved basic model after sensitivity optimization was reduced by 2.1% compared with before optimization, but still much higher than the target reference value (122,000 N/mm). And the quality of the vehicle body was reduced, which met the lightweight requirements of optimized design after the optimization of the thickness of the improved basic model. [ABSTRACT FROM AUTHOR]

• A comfort braking dynamics model for brake-by-wire vehicle is established. • A robust comfort braking controller is proposed to synthetically regulate the brake and pitch movement. • A reduced-order extended state observer (ESO) is employed to estimate the uncertainties for the braking subsystem. For conventional fuel vehicles, anti-lock braking system (ABS) and active suspension system (ASS) are designed separately to enhance the braking safety and ride comfort respectively. However, these two systems sometimes interfere with each other. Hence, it is necessary to explore the two systems synthetically, namely one comfort braking controller is synthesized instead of independent ABS and ASS controllers. On the other hand, the special X-by-wire systems for electric vehicles make it possible. Accordingly in this paper, the comfort braking dynamics model is established for brake-by-wire vehicles, and subsequently an observer-based robust controller is proposed to simultaneously address braking safety and ride comfort problems. To verify the effectiveness of the proposed control scheme, the numerical simulation is implemented. [ABSTRACT FROM AUTHOR]