Tyre pressure monitoring using a dynamical model-based estimator

In the last few years, various control systems have been investigated in the automotive field with the aim of increasing the level of safety and stability, avoid roll-over, and customise handling characteristics. One critical issue connected with their integration is the lack of state and parameter information. As an example, vehicle handling depends to a large extent on tyre inflation pressure. When inflation pressure drops, handling and comfort performance generally deteriorate. In addition, it results in an increase in fuel consumption and in a decrease in lifetime. Therefore, it is important to keep tyres within the normal inflation pressure range. This paper introduces a model-based approach to estimate online tyre inflation pressure. First, basic vertical dynamic modelling of the vehicle is discussed. Then, a parameter estimation framework for dynamic analysis is presented. Several important vehicle parameters including tyre inflation pressure can be estimated using the estimated states. This method aims to work during normal driving using information from standard sensors only. On the one hand, the driver is informed about the inflation pressure and he is warned for sudden changes. On the other hand, accurate estimation of the vehicle states is available as possible input to onboard control systems.     

Literature review and fundamental approaches for vehicle and tire state estimation*

ABSTRACT

Most modern day automotive chassis control systems employ a feedback control structure. Therefore, real-time estimates of the vehicle dynamic states and tire-road contact parameters are invaluable for enhancing the performance of vehicle control systems, such as anti-lock brake system (ABS) and electronic stability program (ESP). Today's production vehicles are equipped with onboard sensors (e.g. a 3-axis accelerometer, 3-axis gyroscope, steering wheel angle sensor, and wheel speed sensors), which when used in conjunction with certain model-based or kinematics-based observers can be used to identify relevant tire and vehicle states for optimal control of comfort, stability and handling. Vehicle state estimation is becoming ever more relevant with the increased sophistication of chassis control systems. This paper presents a comprehensive overview of the state-of-the-art in the field of vehicle and tire state estimation. It is expected to serve as a resource for researchers interested in developing vehicle state estimation algorithms for usage in advanced vehicle control and safety systems.     

Estimation of the tyre–road maximum friction coefficient and slip slope based on a novel tyre model

In this article, a new approach to estimate the vehicle tyre forces, tyre–road maximum friction coefficient, and slip slope is presented. Contrary to the majority of the previous work on this subject, a new tyre model for the estimation of the tyre–road interface characterisation is proposed. First, the tyre model is built and compared with those of Pacejka, Dugoff, and one other tyre model. Then, based on a vehicle model that uses four degrees of freedom, an extended Kalman filter (EKF) method is designed to estimate the vehicle motion and tyre forces. The shortcomings of force estimation are discussed in this article. Based on the proposed tyre model and the improved force measurements, another EKF is implemented to estimate the tyre model parameters, including the maximum friction coefficient, slip slope, etc. The tyre forces are accurately obtained simultaneously. Finally, very promising results have been achieved for pure acceleration/braking for varying road conditions, both in pure steering and combined manoeuvre simulations.     

基于交互多模型的车辆质量与道路坡度估计（双语出版）

车辆结构参数和道路环境信息的实时准确获取是提高智能汽车运动控制性能的重要因素之一，而车辆质量与道路坡度信息是多种汽车控制系统的必要信息，因此质量与坡度在线估计的研究一直受到关注。针对车辆质量与道路坡度的联合估计问题，提出了一种基于交互多模型的质量与坡度融合估计方法。首先，设定了适宜进行质量精确估计的工况条件，据此提出了基于模糊规则的质量估计置信度因子计算算法，进而设计了基于置信度因子的递推最小二乘车辆质量估计算法，以实现质量的在线估计。然后，以车辆纵向动力学模型为基础，建立了运动学和动力学2种坡度估计模型，并设计了基于运动学模型的线性卡尔曼滤波坡度观测器，基于电子稳定性程序ESP的纵向加速度信息实现坡度估计，设计了基于动力学模型的无迹卡尔曼滤波坡度观测器，基于ESP和发动机管理系统EMS的力信息实现坡度估计。运动学模型未考虑车辆姿态信息，坡度估算结果与实际值有偏差；动力学模型对模型精度要求高，算法稳定性差，为充分发挥2种方法优势实现坡度的精确估计，采用交互多模型算法实现了2种坡度估计方法的加权融合。最后，对所设计的算法进行了实车试验验证。结果表明：所设计的质量与坡度估算算法具有较好的实时性和准确性，适合智能汽车运动控制的应用需求。     

Development of algorithms for commercial vehicle mass and road grade estimation

Estimation algorithms for road slope angle and vehicle mass are presented for commercial vehicles. It is well known that vehicle weight and road grade significantly affect the longitudinal motion of a commercial vehicle. However, it is very difficult to measure the weight and road slope angle in real time because of lack of sensor technology. In addition, the total weight of a commercial vehicles varies depending on the freight. In this study, the road grade and vehicle mass estimation algorithms are proposed using the RLS (Recursive Least Square) method and only the in-vehicle sensors. The proposed algorithms are verified in experiments using a commercial vehicle under various conditions.     

A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty

The ability of ground vehicles to quickly and accurately analyse their dynamic response to a given input is critical to their safety and efficient autonomous operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this uncertainty must be considered in the analysis of vehicle motion dynamics. Here, polynomial chaos approaches that explicitly consider parametric uncertainty during modelling of vehicle dynamics are presented. They are shown to be computationally more efficient than the standard Monte Carlo scheme, and experimental results compared with the simulation results performed on ANVEL (a vehicle simulator) indicate that the method can be utilised for efficient and accurate prediction of vehicle motion in realistic scenarios.     

Effect of number of wheels on high speed UGV traversability: Online terrain assessment approach

This paper addresses the problem of determining the maximum allowable speed (V _max ) of Unmanned Ground Vehicles (UGVs) traversing off-road heterogeneous rough/complex terrain. The maximum speed is calculated based on online terrain assessment and the vehicle’s characteristics; this speed achieves the high speed navigation without exceeding a predefined allowable range of transmitted force and moments (i.e., moments in the roll and pitch directions) to the vehicle’s frame. The proposed system enables UGV’s to change their speed autonomously and transit between terrains with different characteristics (e.g., pavement, rocky) safely. This paper proposes a computationally inexpensive approach to process acquired data and assess the terrain roughness. The proposed Roughness Index (RI) is used to represent the terrain roughness on a scale from 0.0 (highly rough to be traversed by a particular vehicle) to 1.0 (perfectly flat/smooth surface). A general vehicle model (workable for any vehicle size and wheel configuration) is presented in this paper. A closed form expression of the maximum allowable vehicle speed is developed. Simulation results obtained on real vehicles (e.g., military tank LEOPARD I) show the effect of increasing the number of wheels to improve the capability of a ground vehicle to traverse rough terrains at high speeds. In addition, the proposed terrain assessment system is a key module in UGVs navigation systems enabling them to be truly autonomous by providing the navigation system with the necessary information for path and speed planning.     

汽车运动状态在线测量及预报技术

为了有效地对汽车自身可能的运动状态进行预报,并对潜在的行车危险进行预警,开展了汽车运动状态在线测量及预报技术研究。设计了微惯性测量单元,实现汽车运动状态参数在线测量,介绍了汽车姿态解算及其速度积分算法;设计了Kalman滤波器,通过信号融合处理获取汽车运动状态参数的最优估计值;阐述了自回归建模预报方法,并开发了汽车运动状态在线测量及预报软件;最后搭建了车载试验平台并进行了实车道路试验。结果表明:汽车运动状态在线测量及预报技术具有很好的预报效果,为未来开发性能更可靠、效果更佳的汽车主动安全预警系统提供了一定的理论依据和技术途径。     

Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

A highly accurate and reliable vehicle position estimation system is an important component of an autonomous driving system. In generally, a global positioning system (GPS) receiver is employed for the vehicle position estimation of autonomous vehicles. However, a stand-alone GPS does not always provide accurate and reliable information of the vehicle position due to frequent GPS blockages and multipath errors. In order to overcome these problems, a sensor fusion scheme that combines the data from the GPS receiver and several on-board sensors has been studied. In previous researches, a single model filter-based sensor fusion algorithm was used to integrate information from the GPS and on-board sensors. However, an estimate obtained from a single model is difficult to cover the various driving environments, including urban areas, off-road areas, and highways. Thus, a multiple models filter (MMF) has been introduced to address this limitation by adapting multiple models to a wide range of driving conditions. An adaptation of the multiple model is achieved through the use of the model probability. The MMF combines several vehicle models using the model probabilities, which indicate the suitability of the current driving condition. In this paper, we propose a vehicle position estimation algorithm for an autonomous vehicle that is based on a neural network (NN)-based MMF. The model probabilities are determined through the NN. The proposed position estimation system was evaluated through simulations and experiments. The experimental results show that the proposed position estimation algorithm is suitable for application in an autonomous driving system over a wide range of driving conditions.     

Effect of vehicle model on the estimation of lateral vehicle dynamics

A methodology is presented for estimating vehicle handling dynamics, which are important to control system design and safety measures. The methodology, which is based on an extended Kalman filter (EKF), makes it possible to estimate lateral vehicle states and tire forces on the basis of the results obtained from sinusoidal steering stroke tests that are widely used in the evaluation of vehicle and tire handling performances. This paper investigates the effect of vehicle-road system models on the estimation of lateral vehicle dynamics in the EKF. Various vehicle-road system models are considered in this study: vehicle models (2-DOF, 3-DOF, 4-DOF), tire models (linear, non-linear) and relaxation lengths. Handling tests are performed with a vehicle equipped with sensors that are widely used by vehicle and tire manufacturers for handling maneuvers. The test data are then used in the estimation of the EKF and identification of lateral tire model coefficients. The accuracy of the identified values is validated by comparing the RMS error between experimentally measured states and regenerated states simulated using the identified coefficients. The results show that the relaxation length of the tire model has a notable impact on the estimation of lateral vehicle dynamics.     

Fault Diagnostics for GPS-based Lateral Vehicle Control

Summary This paper develops a fault diagnostic system to monitor the health of the lateral motion sensors on an instrumented highway vehicle. The fault diagnostic system utilizes observer design with the observer gains chosen so as to ensure that each sensor failure causes estimation errors to grow in an unique direction. The performance of the fault diagnostic system is verified through extensive experimental results obtained from an instrumented truck called the “Safetruck”. The fault diagnostic system is able to monitor the health of a GPS system, a gyroscope and an accelerometer on the Safetruck. It can correctly detect a failure in any one of the three sensors and accurately identify the source of the failure. A GPS-based geographic database containing information on road coordinates, curvature and bank angles plays a key role in ensuring accurate experimental performance of the observers.     

Lateral dynamics of single unit skid-steered tracked vehicle

In the design and development of high-speed tracked vehicles, it is necessary to have an understanding of the interrelationship between the terrain factors and the vehicle characteristics during steering. The handling behavior of skid-steered tracked vehicles is more complex than that of wheeled vehicles because of non-linear characteristics arising from the sliding interface between the track and the ground. In the present work, a five degree-of-freedom (DOF) steering model of a tracked vehicle is developed, and the handling behavior during non-stationary motion is studied when operating at high and low speeds. It is demonstrated that the inclusion of roll and pitch DOF changes the steering response when compared to the response from three DOF models proposed earlier by several researchers. This is due to the strong coupling between the pitch and yaw motions. The effect of the initial forward velocities on the trajectory of the vehicle during non-stationary motion is also studied. It is observed from the results that the stability is influenced by the type of steering input, steering ratio and vehicle forward speed.     

Fault Diagnostics for GPS-based Lateral Vehicle Control

Summary This paper develops a fault diagnostic system to monitor the health of the lateral motion sensors on an instrumented highway vehicle. The fault diagnostic system utilizes observer design with the observer gains chosen so as to ensure that each sensor failure causes estimation errors to grow in an unique direction. The performance of the fault diagnostic system is verified through extensive experimental results obtained from an instrumented truck called the “Safetruck”. The fault diagnostic system is able to monitor the health of a GPS system, a gyroscope and an accelerometer on the Safetruck. It can correctly detect a failure in any one of the three sensors and accurately identify the source of the failure. A GPS-based geographic database containing information on road coordinates, curvature and bank angles plays a key role in ensuring accurate experimental performance of the observers.     

Nonlinear adaptive sliding mode control for vehicle handling improvement via steer-by-wire

This paper proposes a nonlinear adaptive sliding mode control that aims to improve vehicle handling through a Steer-By-Wire system. The designed sliding mode control, which is insensitive to system uncertainties, offers an adaptive sliding gain to eliminate the precise determination of the bound of uncertainties. The sliding gain value is calculated using a simple adaptation algorithm that does not require extensive computational load. Achieving the improved handling characteristics requires both accurate state estimation and well-controlled steering inputs from the Steer-By-Wire system. A second order sliding mode observer provides accurate estimation of lateral and longitudinal velocities while the driver steering angle and yaw rate are available from the automotive sensors. A complete stability analysis based on Lyapunov theory has been presented to guarantee closed loop stability. The simulation results confirmed that the proposed adaptive robust controller not only improves vehicle handling performance but also reduces the chattering problem in the presence of uncertainties in tire cornering stiffness.     

New approaches to enhance active steering system functionalities: preliminary results

An important development of the steering systems in general is active steering systems like active front steering and steer-by-wire systems. In this paper the current functional possibilities in application of active steering systems are explored. A new approach and additional functionalities are presented that can be implemented to the active steering systems without additional hardware such as new sensors and electronic control units. Commercial active steering systems are controlling the steering angle depending on the driving situation only. This paper introduce methods for enhancing active steering system functionalities depending not only on the driving situation but also vehicle parameters like vehicle mass, tyre and road condition. In this regard, adaptation of the steering ratio as a function of above mentioned vehicle parameters is presented with examples. With some selected vehicle parameter changes, the reduction of the undesired influences on vehicle dynamics of these parameter changes has been demonstrated theoretically with simulations and with real-time driving measurements.     

Comparative study of vehicle tyre–road friction coefficient estimation with a novel cost-effective method

This paper qualitatively and quantitatively reviews and compares three typical tyre–road friction coefficient estimation methods, which are the slip slope method, individual tyre force estimation method and extended Kalman filter method, and then presents a new cost-effective tyre–road friction coefficient estimation method. Based on the qualitative analysis and the numerical comparisons, it is found that all of the three typical methods can successfully estimate the tyre force and friction coefficient in most of the test conditions, but the estimation performance is compromised for some of the methods during different simulation scenarios. In addition, all of these three methods need global positioning system (GPS) to measure the absolute velocity of a vehicle. To overcome the above-mentioned problem, a novel cost-effective estimation method is proposed in this paper. This method requires only the inputs of wheel angular velocity, traction/brake torque and longitudinal acceleration, which are all easy to be measured using available sensors installed in passenger vehicles. By using this method, the vehicle absolute velocity and slip ratio can be estimated by an improved nonlinear observer without using GPS, and the friction force and tyre–road friction coefficient can be obtained from the estimated vehicle velocity and slip ratio. Simulations are used to validate the effectiveness of the proposed estimation method.     

Customer loads of two-wheeled vehicles

Customer usage profiles are the most unknown influences in vehicle design targets and they play an important role in durability analysis. This publication presents a customer load acquisition system for two-wheeled vehicles that utilises the vehicle's onboard signals. A road slope estimator was developed to reveal the unknown slope resistance force with the help of a linear Kalman filter. Furthermore, an automated mass estimator was developed to consider the correct vehicle loading. The mass estimation is performed by an extended Kalman filter. Finally, a model-based wheel force calculation was derived, which is based on the superposition of forces calculated from measured onboard signals. The calculated wheel forces were validated by measurements with wheel–load transducers through the comparison of rainflow matrices. The calculated wheel forces correspond with the measured wheel forces in terms of both quality and quantity. The proposed methods can be used to gather field data for improved vehicle design loads.     

Energy Requirements of a Passive and an Electromechanical Active Suspension System

Passive suspensions are designed to dissipate the energy otherwise transferred to a vehicle's body through interactions with a roadway or terrain. A bond graph representation of an independent suspension design was developed to study the energy flow through a vehicle. The bond graph model was tuned and validated through experimental tests and was found to produce suitable results. Examining the bond graph reveals that the dissipated energy associated with vertical and transverse coordinates generally originates from the longitudinal motion of the vehicle and is transferred through the tire-ground contact patch. Additionally, since the longitudinal energy originates from the vehicle's engine, the energy dissipated via the suspension shock absorber as well as other components (e.g., mechanical joints, etc.) essentially dissipate some engine energy. The plots presented in the paper support this theory by showing that upon traveling a rough terrain, the vehicle's longitudinal velocity drops more when vertical vibrations increase. Results show that a vehicle equipped with a passive suspension experiences a larger velocity drop compared to one with an active suspension traversing the same rough terrain. The paper compares the results of simulation of an analytical bond graph model of an active suspension system with experimental results and finds good agreement between the two. Other simulations show that relative to passive suspensions, not only do active suspensions yield substantial improvement in ride quality, they can also result in substantial energy savings. This paper concludes that if electromechanical actuators are supplemented by passive springs to support the vehicle static weight, the amount of energy required for operation of actuators is significantly less than the amount dissipated by conventional shock absorbers.     

Energy Requirements of a Passive and an Electromechanical Active Suspension System

Passive suspensions are designed to dissipate the energy otherwise transferred to a vehicle's body through interactions with a roadway or terrain. A bond graph representation of an independent suspension design was developed to study the energy flow through a vehicle. The bond graph model was tuned and validated through experimental tests and was found to produce suitable results. Examining the bond graph reveals that the dissipated energy associated with vertical and transverse coordinates generally originates from the longitudinal motion of the vehicle and is transferred through the tire-ground contact patch. Additionally, since the longitudinal energy originates from the vehicle's engine, the energy dissipated via the suspension shock absorber as well as other components (e.g., mechanical joints, etc.) essentially dissipate some engine energy. The plots presented in the paper support this theory by showing that upon traveling a rough terrain, the vehicle's longitudinal velocity drops more when vertical vibrations increase. Results show that a vehicle equipped with a passive suspension experiences a larger velocity drop compared to one with an active suspension traversing the same rough terrain. The paper compares the results of simulation of an analytical bond graph model of an active suspension system with experimental results and finds good agreement between the two. Other simulations show that relative to passive suspensions, not only do active suspensions yield substantial improvement in ride quality, they can also result in substantial energy savings. This paper concludes that if electromechanical actuators are supplemented by passive springs to support the vehicle static weight, the amount of energy required for operation of actuators is significantly less than the amount dissipated by conventional shock absorbers.     

Using the lead vehicle as preview sensor in convoy vehicle active suspension control

Both ride quality and roadholding of actively suspended vehicles can be improved by sensing the road ahead of the vehicle and using this information in a preview controller. Previous applications have used look-ahead sensors mounted on the front bumper to measure terrain beneath. Such sensors are vulnerable, potentially confused by water, snow, or other soft obstacles and offer a fixed preview time. For convoy vehicle applications, this paper proposes using the overall response of the preceding vehicle(s) to generate preview controller information for follower vehicles. A robust observer is used to estimate the states of a quarter-car vehicle model, from which road profile is estimated and passed on to the follower vehicle(s) to generate a preview function. The preview-active suspension, implemented in discrete time using a shift register approach to improve simulation time, reduces sprung mass acceleration and dynamic tyre deflection peaks by more than 50% and 40%, respectively. Terrain can change from one vehicle to the next if a loose obstacle is dislodged, or if the vehicle paths are sufficiently different so that one vehicle misses a discrete road event. The resulting spurious preview information can give suspension performance worse than that of a passive or conventional active system. In this paper, each vehicle can effectively estimate the road profile based on its own state trajectory. By comparing its own road estimate with the preview information, preview errors can be detected and suspension control quickly switched from preview to conventional active control to preserve performance improvements compared to passive suspensions.