Vehicle Parameter Identification Research Articles

Combined Identification of Vehicle Parameters and Road Surface Roughness Using Vehicle Responses

Highways, urban roads, and bridges are the important transportation infrastructures for the economic development of modern society. The evaluation of bridge and road quality is crucial to the maintenance and management of the bridge and road industry. Road roughness is a widely accepted indicator in the evaluation of road quality and safety, which is a major input source for vehicles. The vehicle responses-based method of identifying road roughness is efficient and convenient. However, the dynamic characteristics of the vehicle have an important impact on the interaction between the vehicle and the road. When the vehicle parameters are not yet clear, the coupling of unknown parameters and unknown road roughness results in the need for mutual iteration when the existing methods simultaneously identify vehicle parameters and road roughness. To address this issue, this study proposes an effective method for the combined identification of vehicle parameters and road roughness using vehicle responses. The test vehicle is modeled as a four-degree-of-freedom half-vehicle model. In view of the coupling effect between tire stiffness and road roughness, the unknown vehicle physical parameters, except for tire stiffness, are first included in the extended state vector. Based on the extended Kalman filter for unknown excitation (EKF-UI), unknown vehicle physical parameters and unknown forces on the axle are identified. Subsequently, based on the property that the front and rear axles of the vehicle pass through the same road roughness area at a fixed time lag, the tire stiffness is identified by combining the identified unknown forces on the axle. Finally, the road roughness is obtained using the identified vehicle parameters and unknown forces. Numerical studies with different levels of roughness, different noise levels, and different vehicle speeds have verified the accuracy of this method in identifying vehicle parameters and road roughness.

A novel strengthened road roughness estimation framework based on the adaptive unbiased minimum variance with unknown input

The accurate acquisition of the strengthened road roughness is significant to vehicle reliability and durability. However, the existing road roughness estimation methods are inapplicable due to the non-Gaussian characteristic of the strengthened road roughness. To fulfil this gap, this work proposes a novel strengthened road roughness estimation framework based on the adaptive unbiased minimum variance with unknown input (AUMV-UI) to accurately estimate the roughness of strengthened roads. The proposed AUMV-UI framework consists of four sub-models, i.e. the quarter suspension model, the vehicle parameter identification model, the road roughness estimation model, and the estimation error correction model. In the road roughness estimation model, the unbiased minimum variance with unknown input (UMV-UI) method is proposed for the first to estimate the strengthened road roughness, and thus the first three sub-models are also integrated as the UMV-UI framework. In the estimation error correction model, the Sage–Husa method is introduced to further correct the potential errors of the UMV-UI method. The AUMV-UI framework is validated and compared to the UMV-UI framework by simulations and experiments. The results show that the AUMV-UI framework can significantly improve the estimation accuracy, which can serve as an elegant tool to accurately acquire the strengthened road roughness.

Computer Vision-Based Bridge Inspection and Monitoring: A Review.

Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.

Vehicle parameter identification based on vehicle frequency response function

Accurate vehicle parameter information plays an important role in assessing the conditions of roads and bridges, along with the corresponding maintenance. This study considered a vehicle parameter identification method based on a vehicle frequency response function (FRF). First, the vehicle FRF was deduced with respect to the displacements of the vehicle-road contact points, thereby building the relationships among the FRF, vehicle responses, and road profile in the frequency domain. Next, using the responses of vehicles passing over on-road bumps of known size, a direct estimation of the vehicle FRF was described. Then, a combination of Tikhonov regularization and a shape function method was used to update the estimated vehicle FRF by removing the singular data owing to the direct computation of the vehicle FRF. Subsequently, the modifying factors of the vehicle parameters were iteratively identified based on a sensitivity analysis of the estimated FRF to the vehicle parameters. A numerical simulation for vehicle parameter identification was performed to test the effectiveness of the proposed methods, considering a 5% Gaussian noise pollution and the influences of different driving speeds. At last, field tests of a vehicle passing over bumps were performed for the verification of vehicle parameter identification.

Vehicle parameter identification and road roughness estimation using vehicle responses measured in field tests

Accurate information about vehicle parameters and road roughness is of great significance in vehicle dynamic analysis, road driving quality, etc. In this study, a method for estimating vehicle parameters and road roughness was developed using the measured vehicle responses from field tests which is efficient, economical, and accurate. First, the full-vehicle model was introduced. Then, vehicle modal parameters were identified using the consequent free responses of a vehicle passing over bumps. Second, the expression of the vehicle frequency response function (FRF) with respect to the wheel contact point was derived from the vehicle equation of motion, and a road roughness estimation method based on the vehicle FRF was developed. Third, field tests in which the vehicle passes over bumps were performed for vehicle model identification. Finally, field tests for road roughness estimation were carried out using a calibrated vehicle to verify the effectiveness of the proposed methods.

Road profile estimation and half-car model identification through the automated processing of smartphone data

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.

Estimation of gross weight, suspension stiffness and damping of a loaded truck from bridge measurements

Particle filter method (PFM) based on Bayesian inference gives a reliable estimate of hidden parameters from the noisy measured signal. A new method of vehicle parameter identification based on measured bridge response has been proposed using PFM. An uncoupled iterative technique is utilised for solving bridge vehicle interaction problem which has been used as a forward solution of the PFM. A field test under moving truck has been conducted on an existing pre-stressed concrete bridge to collect response data at different locations. Based on the extracted bridge natural frequencies and measured peak acceleration responses at five sensor locations, finite element model of the tested bridge has been updated using response surface-based model updating technique. In addition to estimation of test truck parameters using measured bridge response, dynamic wheel load induced in the bridge has been determined. Excellent agreement has been found between the measured and reconstructed bridge response using estimated parameters.

Vehicle Parameter Identification through Particle Filter using Bridge Responses and Estimated Profile

The weight of vehicles driving over bridges needs to be evaluated because the vehicle load potentially causes problems such as fatigue and large vibration. Bridge Weigh-In-Motion systems to evaluate vehicle load by measuring strain response of bridges have been proposed. However, the installation of strain gauges at bridge members are often costly and time consuming, limiting practical applications. This paper investigates the identification of vehicle weight using bridge acceleration response data at different sensor locations through the numerical simulation of vehicle-bridge interaction system. The identified parameters are not limited to the vehicle weight; suspension stiffness and damping coefficients are also identified. A data assimilation technique known as particle filter based on the Bayesian theory is employed to identify the parameters. The time history of the profile is estimated using the same particle filter technique from the dynamic response of a probe vehicle equipped with sensors. The proposed method is shown to have robustness against noises for the mass parameter identification.

Investigation into on-road vehicle parameter identification based on subspace methods

The randomness of road–tyre excitations can excite the low frequency ride vibrations of bounce, pitch and roll modes of an on-road vehicle. In this paper, modal parameters and mass moments of inertia of an on-road vehicle are estimated with an acceptable accuracy only by measuring accelerations of vehicle sprung mass and unsprung masses, which is based on subspace identification methods. The vehicle bounce, pitch and roll modes are characterized by their large damping (damping ratio 0.2–0.3). Two kinds of subspace identification methods, one that uses input/output data and the other that uses output data only, are compared for the highly damped modes. It is shown that, when the same data length is given, larger error of modal identification results can be clearly observed for the method using output data only; while additional use of input data will significantly reduce estimation variance. Instead of using tyre forces as inputs, which are difficult to be measured or estimated, vertical accelerations of unsprung masses are used as inputs. Theoretical analysis and Monte Carlo experiments show that, when the vehicle speed is not very high, subspace identification method using accelerations of unsprung masses as inputs can give more accurate results compared with the method using road–tyre forces as inputs. After the modal parameters are identified, and if vehicle mass and its center of gravity are pre-determined, roll and pitch moments of inertia of an on-road vehicle can be directly computed using the identified frequencies only, without requiring accurate estimation of mode shape vectors and multi-variable optimization algorithms.

A Switching Rollover Controller Coupled With Closed-Loop Adaptive Vehicle Parameter Identification

This paper presents a real-time adaptive switching controller to mitigate rollover accidents without reducing the performance of the vehicle. The proposed controller relies on adaptive identification of vehicle lateral and vertical dynamics parameters, including the center-of-gravity height that has a major role in rollover. Least squares and Kalman filtering techniques are employed to propose two novel identification algorithms that are robust against speed variations, which can be further coupled effectively with a switching rollover controller while parameter identification is in progress. Extensive simulations are carried out to demonstrate the superior performance of the proposed method.

2205 走行時における車両のコーナリングスティフネスとヨー慣性モメントの同定(OS3-1 自動車の制御,OS3 交通・物流システムの制御,オーガナイズド・セッション(OS))

Accurate parameters are necessary to accomplish precise driving control of vehicle. Identification of vehicle parameters is important to get accurate parameters because vehicle control is affected by the vehicle parameters. An identification method is proposed using Dual Kalman filter algorithm and GPS sensor for the simultaneous estimation of vehicle parameters, i.e. yaw moment of inertia and cornering stiffness. Performance of the proposed identification method is evaluated through examination. Possibility of the proposed identification method is confirmed by examining the estimated results.

Vehicle Dynamic Prediction Systems with On-Line Identification of Vehicle Parameters and Road Conditions

This paper presents a vehicle dynamics prediction system, which consists of a sensor fusion system and a vehicle parameter identification system. This sensor fusion system can obtain the six degree-of-freedom vehicle dynamics and two road angles without using a vehicle model. The vehicle parameter identification system uses the vehicle dynamics from the sensor fusion system to identify ten vehicle parameters in real time, including vehicle mass, moment of inertial, and road friction coefficients. With above two systems, the future vehicle dynamics is predicted by using a vehicle dynamics model, obtained from the parameter identification system, to propagate with time the current vehicle state values, obtained from the sensor fusion system. Comparing with most existing literatures in this field, the proposed approach improves the prediction accuracy both by incorporating more vehicle dynamics to the prediction system and by on-line identification to minimize the vehicle modeling errors. Simulation results show that the proposed method successfully predicts the vehicle dynamics in a left-hand turn event and a rollover event. The prediction inaccuracy is 0.51% in a left-hand turn event and 27.3% in a rollover event.

A New Method of Inductive Sensor Impedance Measurement Applied to the Identification of Vehicle Parameters

A New Method of Inductive Sensor Impedance Measurement Applied to the Identification of Vehicle Parameters Inductive loop sensors are widely used for detection of presence, measurement of parameters as well as classification of vehicles. Vehicle classification may be performed based on their magnetic profiles. The magnetic profile is a signal which is proportional to the resultant of an impedance change of the sensor, caused by the measured object (the changes are minor - of the order of 1%). Generator and bridge circuits are most commonly used as conditioning circuits for such sensors. As a result we can obtain one output signal proportional to total changes of sensor parameters (R and L). In this paper, a modified bridge circuit that allows independent measurement of the components (R and L) of the sensor's impedance, has been proposed. With that provided, it is possible to receive broader information on the object, which allows higher classification resolution. This paper provides the concept of a circuit, model testing results, processing algorithms used and the test results of a real circuit.

Identification of vehicles moving on continuous bridges with rough surface

This paper describes the parameter identification of vehicles moving on multi-span continuous bridges taking into account the surface roughness. Each moving vehicle is modelled as a two-degree-of-freedom system that comprises five components: a lower mass and an upper mass, which are connected together by a damper and a spring, together with another spring to represent the contact stiffness between the tyres and the bridge deck. The corresponding parameters of these five components, namely, the equivalent values of the two masses, the damping coefficient, and the two spring stiffnesses together with the roughness parameters are identified based on dynamic simulation of the vehicle–bridge system. In the study, the accelerations at selected measurement stations are simulated from the dynamic analysis of a continuous beam under moving vehicles taking into account randomly generated bridge surface roughness, together with the addition of artificially generated measurement noise. The identification is realized through a robust multi-stage optimization scheme based on genetic algorithms, which searches for the best estimates of parameters by minimizing the errors between the measured accelerations and the reconstructed accelerations from the identified parameters. Starting from the very wide initial variable domains, this multi-stage optimization scheme reduces the variable search domains stage by stage using the identified results of the previous stage. A few test cases are carried out to verify the efficiency of the multi-stage optimization procedure. The identified parameters are also used to estimate the time-varying contact forces between the vehicles and the bridge.

Modeling to Predict Rollover Threat of Tractor-Semitrailers

Summary A predictive model to determine a rollover threat index associated with tractor-semitrailers is proposed. The purpose of this model is to predict the rollover threat sufficiently in advance of the actual event so as to enable the driver to react accordingly. The predictive model is established using simple roll-plane models of the vehicle sprung and unsprung masses in conjunction with online vehicle parameter identification. Using this predictive model, the predicted Load Transfer Ratio (LTR) for the trailer axle can be determined as the rollover threat index. The proposed predictive model and the associated parameter-identification algorithm are verified using a 12-degree-of-freedom vehicle model. It is shown that the identified parameter values are close to the actual ones used in detailed simulation study. Similarly it is shown that the predicted values of the LTR are close to the simulated ones, and hence the proposed approach is potentially suitable as the basis for application in rollover threat assessment.

Parameter identification of vehicles moving on continuous bridges

This paper describes a method for the identification of parameters of vehicles moving on multi-span continuous bridges. Each moving vehicle is modelled as a 2-degree-of-freedom system that comprises four components: an unsprung mass and a sprung mass, which are connected together by a damper and a spring. The corresponding parameters of these four components, namely, the equivalent unsprung mass and sprung mass, the damping coefficient and the spring stiffness are identified based on dynamic simulation of the vehicle–bridge system. The identification process makes use of acceleration measurements at selected stations on the bridge. In the study, the acceleration measurements are simulated from the solution to the forward problem of a continuous beam under moving vehicles, together with the addition of artificially generated measurement noise. The identification is carried out through a robust multi-stage optimization scheme based on genetic algorithms, which searches for the best estimates of parameters by minimizing the errors between the measured accelerations and the reconstructed accelerations from the moving vehicles. This multi-stage optimization scheme reduces the variable search domains stage by stage using the identified results of the previous stage. Besides the basic operators in simple genetic algorithms, some advanced genetic operators and techniques are adopted here. Therefore, it makes the proposed identification procedure much more efficient than other traditional optimization methods. The identification procedure is then verified with a few test cases. The estimated vehicle parameters can also be used to get the time varying contact forces between the vehicles and bridge surface.

Flat-earth guidance law using inflight vehicle parameter identification

Abstract This paper describes the formulation and simulation testing of a Flat Earth Guidance Law using a quadratic injection criterion and inflight vehicle parameter identification and updating. It avoids the usual instability close to injection without necessitating a separate injection guidance routine and eliminates the distortion of the attitude law at injection due to vehicle parameter deviations from nominal. It is suited for injection into circular and elliptical orbits. Apart from effectiveness and parameter sensitivity of the guidance law the discussion of results also covers the practical aspects of implementation in and onboard computer with limited resolution accuracy.

Investigation of a Flat Earth Guidance Law Using a Quadratic Injection Criterion and Inflight Vehicle Parameter Identification

This paper describes the formulation and simulation testing of a Flat Earth Guidance Law using a quadratic injection criterion and inflight vehicle parameter identification and updating.It avoids the usual instability close to injection without necessitating a separate injection guidance routine and eliminates the distortion of the attitude law at injection due to vehicle parameter deviations from nominal. It is suited for injection into circular and elliptical orbits.Apart from effectiveness and parameter sensitivity of the guidance law the discussion of results also covers the practical aspects of implementation in an onboard computer with limited resolution accuracy.