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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]

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]

• 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]