A diagnosis-based approach for tire-road forces and maximum friction estimation

A new approach to estimate vehicle tire forces and road maximum adherence is presented. Contrarily to most of the previous works on this subject, it is not an asymptotic observer-based estimation, but a combination of elementary diagnosis tools and new algebraic techniques for filtering and estimating derivatives of noisy signals. In a first step, instantaneous friction and lateral forces will be computed within this framework. Then, extended braking stiffness concept is exploited to detect which braking efforts allow to distinguish a road type from another. A weighted Dugoff model is used during these ‘distinguishable’ intervals to estimate the maximum friction coefficient. Very promising results have been obtained in noisy simulations and real experimentations for most of the driving situations.     

Estimation of automotive tire force characteristics using wheel velocity

It is important to estimate friction force characteristics between tire and road in order to improve the control performance of a vehicle in critical motions. In this paper, an estimation method which estimates parameters concerned with a friction force margin is proposed by applying the on-line least-squares method to wheel rotational velocities. The effect of the estimation is evaluated by applying the method to the braking control.     

Corner-based estimation of tire forces and vehicle velocities robust to road conditions

Recent developments in vehicle stability control and active safety systems have led to an interest in reliable vehicle state estimation on various road conditions. This paper presents a novel method for tire force and velocity estimation at each corner to monitor tire capacities individually. This is entailed for more demanding advanced vehicle stability systems and especially in full autonomous driving in harsh maneuvers. By integrating the lumped LuGre tire model and the vehicle kinematics, it is shown that the proposed corner-based estimator does not require knowledge of the road friction and is robust to model uncertainties. The stability of the time-varying longitudinal and lateral velocity estimators is explored. The proposed method is experimentally validated in several maneuvers on different road surface frictions. The experimental results confirm the accuracy and robustness of the state estimators.     

基于道路摩擦系数估计的智能小车紧急避障策略研究

雾霾环境下驾驶员的视野受到限制,无法准确估计周围的环境信息,对行车安全具有重大影响.自主紧急制动(AEB)系统是一种重要的车辆主动安全功能,用来避免碰撞或减轻碰撞程度.通常,AEB系统利用一个碰撞时间TTC衡量与障碍物发生碰撞的危险程度.通常设计用于制动的TTC门槛值时假设道路摩擦系数为常数,然而,道路情况复杂多变,道路摩擦系数也是变化的,驾驶员在雾霾环境下更难准确估计道路摩擦系数.因此,开发了一个考虑不同摩擦系数对TTC门槛值影响的AEB控制策略.首先用一个复合滑移率轮胎模型来估计峰值道路摩擦系数,再用该系数计算TTC的门槛值,进而利用该Trc门槛值衡量与障碍物发生碰撞的危险程度.因为可以实时识别道路摩擦系数,提出的AEB策略可以自适应雾霾环境下不同的道路表面.仿真结果表明了该方法的有效性.     

基于并行学习鲁棒自适应的行驶车辆特性参数估计方法研究

针对车辆行驶过程中的特性参数估计问题,基于并行学习思想提出一种鲁棒自适应参数估计方法.通过低通滤波技术,设计一组系统状态和响应函数的一阶滤波变量.结合并行学习,构建特性参数估计的回归向量,并基于参数估计误差向量,设计鲁棒自适应参数更新律.以某型车辆为例,对该方法的有效性进行仿真验证.仿真结果表明,在无/有扰动情形下,该...     

Vehicle state estimation for anti-lock control with nonlinear observer

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results.     

Nonlinear vehicle side-slip estimation with friction adaptation

A nonlinear observer for estimation of the longitudinal velocity, lateral velocity, and yaw rate of a vehicle, designed for the purpose of vehicle side-slip estimation, is modified and extended in order to work for different road surface conditions. The observer relies on a road-tire friction model and is therefore sensitive to changes in the adhesion characteristics of the road surface. The friction model is parametrized with a single friction parameter, and an update law is designed. The adaptive observer is proven to be uniformly globally asymptotically stable and uniformly locally exponentially stable under a persistency-of-excitation condition and a set of technical assumptions, using results related to Matrosov's theorem. The observer is tested on recorded data from two test vehicles and shows good results on a range of road surfaces.     

Switching mode generation and optimal estimation with application to skid-steering

Skid-steered vehicles, by design, must skid in order to maneuver. The skidding causes the vehicle to behave discontinuously during a maneuver as well as introduces complications to the observation of the vehicle’s state, both of which affect a controller’s performance. This paper addresses estimation of contact state by applying switched system optimization to estimate skidding properties of the skid-steered vehicle.In order to treat the skid-steered vehicle as a switched system, the vehicle’s ground interaction is modeled using Coulomb friction, thereby partitioning the system dynamics into four distinct modes, one for each combination of the forward and back wheel pairs sticking or skidding. Thus, as the vehicle maneuvers, the system propagates over some mode sequence, transitioning between modes over some set of switching times. This paper presents second-order optimization algorithms for estimating these switching times. We emphasize the importance of the second-order algorithm because it exhibits quadratic convergence and because even for relatively simple examples, first-order methods fail to converge on time scales compatible with real-time operation. Furthermore, the paper presents a technique for estimating the mode sequence by optimizing a relaxation of the switched system.     

基于Sigma点Kalman滤波的车载导航系统状态估计

针对扩展卡尔曼滤波(EKF)在车载组合导航系统状态估计问题中的缺陷,本文介绍了一种新的方法——Sigma点卡尔曼滤波(SPKF)用于车载组合导航系统的非线性状态估计。其思想是基于非线性函数的加权统计线形化,SPKF滤波算法能够给出随机变量非线性变换以后更精确的均值和协方差的估计,从而带来更高的精度。最后通过GPS/DR组合导航模型时间序列的状态估计仿真实例说明:同EKF相比,SPKF的滤波精度和稳定性都显著提高了,还可避免计算烦琐的Jacobi矩阵,是一种良好的非线形滤波方法。     

非结构道路环境下的智能汽车质心侧偏角估计

车辆质心侧偏角是描述车辆侧向运动状态的重要参量之一,其估计的精度直接影响车辆的安全控制,传统的质心侧偏角估计方法不能满足非结构道路环境下的智能汽车质心侧偏角估计的要求。通过建立3自由度智能汽车动力学模型,采用CarSim和MATLAB构建智能汽车整车参数化模型;基于扩展kalman滤波(EKF)算法,设计非结构道路环境下的状态观测器对智能汽车质心侧偏角进行估计。在高、低附着系数路面双移线工况和蛇形工况下,对状态观测器的估计效果进行联合仿真验证。仿真结果表明:该方法能较精确地估计出非结构道路环境下智能汽车的质心侧偏角。     

Feature correspondence and motion recovery in vehicle planar navigation

This paper describes a strategy to feature point correspondence and motion recovery in vehicle navigation. A transformation of the image plane is proposed that keeps the motion of the vehicle on a plane parallel to the transformed image plane. This permits to define linear tracking filters to estimate the real-world positions of the features, and allows us to select the matches that accomplish the rigidity of the scene by a Hough transform. Candidate correspondences are selected by similarity, taking into account the smoothness of motion. Further processing brings out the final matching. The methods have been tested in a real application.     

Estimation of Road Friction Coefficient and Vehicle States by 3‐DOF Dynamic Model and HSRI Model Based on Information Fusion

Vehicle states and the road friction coefficient in active safety control systems have become increasingly prominent. However, a low‐cost, high‐precision system in real‐time has yet to be achieved. The use of complex models has led to poor real‐time estimation, while variations in the road friction coefficient have often been neglected. This paper adopts information fusion technology by using DEKF theory for rapid simulation and estimation of these parameters. Using a vehicle dynamic model based on three degrees of freedom (3‐DOF) and the Highway Safety Research Institute tire model, DEKF recursive estimation models are established and verified. In the DEKF, two recursive state and parameter estimation models exist in parallel. The models are dependent upon each other and have real‐time interaction correction in order to forecast information, which quickly yields true value estimation in simulation. Experimental brake test results show that the DEKF estimator not only accurately estimates the vehicle state parameters, but also estimates the road friction coefficient in real‐time. This can reduce the cost of the vehicle sensor, and can estimate the status parameter, which is difficult to measure. The validity and feasibility of this algorithm have been verified by an HIL driving simulator, offering the possibility of future application in real cars.     

Estimation of the tire slip angle under various road conditions without tire–road information for vehicle stability control

This paper presents a tire slip angle estimator based on an Interacting Multiple Model (IMM) algorithm for vehicle stability control. The proposed algorithm is capable of estimating the tire slip angles under various road conditions without the prior knowledge of tire and road condition by only using on-board vehicle sensors. Instead of employing tire and road information, the proposed algorithm utilizes multiple numbers of model candidates to represent all aspects of vehicle motions under various road conditions. Each model candidate is a combination of lateral vehicle dynamics and transient/steady state tire dynamics. The proposed algorithm evaluates the fidelity of each model candidate to the current vehicle dynamics with probability. Moreover, in the proposed algorithm, multiple numbers of Kalman filters are embedded with these model candidates as process models. The final estimate of the proposed algorithm in each time step is a linear sum of the posteriori states from multiple embedded filters with the calculated probability as coefficients. The proposed estimation algorithm has been evaluated via vehicle tests. The tests have been conducted on dry asphalt and wet asphalt using a luxury passenger car equipped with a high-performance GPS for reference and data logging computer. The results have shown that the proposed estimator can successfully estimate tire slip angles with satisfactory accuracy under various road conditions     

汽车行驶状态参数估计研究进展与展望

准确而实时地获得汽车的行驶状态参数信息是实现汽车主动安全控制的关键问题,也是车载故障诊断的重要技术之一.随着估计理论的发展,利用车辆上已装备的传感器获得汽车行驶状态信息,进行汽车行驶状态参数估计是近年来的研究热点.本文首先给出汽车系统中需要进行估计的状态参数的分类及现有估计方案;然后对现有的各种汽车行驶状态参数估计方法加以综述,并分析了各种方法在汽车行驶状态参数估计方面的优缺点;最后对汽车行驶状态参数估计的进一步研究提出几点展望.     

Estimation of road frictional force and wheel slip for effective antilock braking system (ABS) control

The introduction of electric braking via brake‐by‐wire systems in electric vehicles) has reduced the high transportation delays usually involved in conventional friction braking systems. This has facilitated the design of more efficient and advanced control schemes for antilock braking systems (ABSs). However, accurate estimation of the tire‐road friction coefficient, which cannot be measured directly, is required. This paper presents a review of existing estimation methods, focusing on sliding‐mode techniques, followed by the development of a novel friction estimation technique, which is used to design an efficient ABS control system. This is a novel slip‐based estimation method, which accommodates the coupling between the vehicle dynamics, wheel dynamics, and suspension dynamics in a cascaded structure. A higher‐order sliding‐mode observer–based scheme is designed, considering the nonlinear relationship between friction and slip. A first‐order sliding‐mode observer is also designed based on a purely linear relationship. A key feature of the proposed estimation schemes is the inclusion of road slope and the effective radius of the tire as an estimated state. These parameters impact significantly on the accuracy of slip and friction estimation. The performance of the proposed estimation schemes are validated and benchmarked against a Kalman filter (KF) by a series of simulation tests. It is demonstrated that the sliding‐mode observer paradigm is an important tool in developing the next generation ABS systems for electric vehicles.     

电动汽车动力学控制研究进展

近年来随着全球资源、环境问题日益严峻,节能、环保的电动汽车得到快速发展。电动汽车采用电机驱动系统,具有转矩快速响应、易于精确测量、可实现动力分散控制、可实现制动能量回收等优点。充分挖掘并利用这些优点可显著提升车辆动力学控制性能。文中从电动汽车动力学控制运行参数的识别、动力学控制结构与方法两个角度综述了十多年来的研究成果,重点介绍了轮胎-路面接触条件识别方法、驱动防滑控制方法等。对车辆横向动力学控制,包括电子差速控制、直接横摆控制、底盘集成控制等研究现状也做了总结。最后对未来电动车辆动力学控制的发展方向作了几点展望。     

An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles

As the demand for electric vehicle (EV)'s remaining operation range and power supply life, Lithium-ion (Li-ion) battery state of charge (SOC) and state of health (SOH) estimation are important in battery management system (BMS). In this paper, a proposed adaptive observer based on sliding mode method is used to estimate SOC and SOH of the Li-ion battery. An equivalent circuit model with two resistor and capacitor (RC) networks is established, and the model equations in specific structure with uncertainties are given and analyzed. The proposed adaptive sliding mode observer is applied to estimate SOC and SOH based on the established battery model with uncertainties, and it can avoid the chattering effects and improve the estimation performance. The experiment and simulation estimation results show that the proposed adaptive sliding mode observer has good performance and robustness on battery SOC and SOH estimation.     

装甲车辆作战仿真平台设计

该文介绍了装甲车辆作战仿真平台设计方案，在该平台上可以进行装甲车辆作战仿真，作战效能评估，该平台为装甲车辆改型以及研制方案论证提供决策依据，也可以用于研究作战理论。     

车用锂离子电池SOC估算算法的研究

针对纯电动汽车的锂离子电池容量损失而导致估算电池电荷状态(SOC)精度降低的问题,本文分析了影响电池容量损失的因素,提出容量修正算法。通过改进电池模型,把电池容量作为状态变量,将电池容量修正算法运用于Kalman滤波算法估计SOC,解决了锂离子电池容量损耗使得误差累积的问题。实验证明,本文提出的基于容量修正的Kalman最优滤波算法提高了SOC估算的精度,并且对初始误差有很强的修正作用,可以保证纯电动汽车锂离子电池的稳定工作。     

基于递推最小二乘估计的动力学模型参数识别

动力学建模过程中,模型参数的不确定性和参数的未知变化使得建模精度大为降低。通过对非线性最小二乘估计理论的研究,提出动力学模型参数的最小二乘估计,并设计出相应的估计器,基于现场数据给予模型较为准确的定论。应用于曲柄滑块机构中滑块与滑动面间的摩擦系数的估计,经过较少的迭代次数获得满意的估计效果。为动力学模型参数识别,建立更精确模型提供一个有效的途径。