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The pavement of a road that is subject to deterioration due to vehicle loads needs quantitative and frequent evaluation. The road profile, which determines the ride quality, is an important property, though its estimation is usually costly and thus, infrequent. In this study, a road profile estimation method using an ordinary-vehicle's responses measured by only a smartphone is developed. The algorithm consists of two steps. At first, an ordinary vehicle is modelled as a half car (HC) and a genetic algorithm (GA) identifies its parameters by using the responses of the vehicle passing over a known-size hump. With the estimated vehicle model, an augmented Kalman filter, in which the road profile is included in the state vector, estimates the road profile; Rauch-Tung-Streiber (RTS) smoothing is employed to improve the accuracy. The observation variables and locations are determined based on an observability analysis. A numerical simulation is conducted to investigate the profile estimation performance in terms of drive speeds, model error, and measurement noise. The experiment is carried out on a 13 km road. The profiles estimated by three types of ordinary vehicles are compared with a reference profile obtained by a laser profiler to validate the proposed method. Results from both the simulation and the experiment show that the combination of the vehicle parameter estimation and the profile estimation methods accurately estimates the road profile with a high degree of accuracy and robustness. • Response-based road profile estimation using a smartphone on a vehicle is proposed. • A pitching HC model with 10 unknowns is calibrated when it passes over a hump. • The difference in dynamic properties, sensor locations, and speeds are compensated. • The accuracy of estimation is confirmed using a high-accuracy laser profiler. [ABSTRACT FROM AUTHOR]

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

The 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]

• 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]

• 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]

• 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]