Using GPS with a model-based estimator to estimate critical vehicle states

This paper demonstrates a method to estimate the vehicle states sideslip, yaw rate, and heading using GPS and yaw rate gyroscope measurements in a model-based estimator. The model-based estimator using GPS measurements provides accurate and observable estimates of sideslip, yaw rate, and heading even if the vehicle model is in neutral steer or if the gyro fails. This method also reduces estimation errors introduced by gyroscope errors such as the gyro bias and gyro scale factor. The GPS and Inertial Navigation System measurements are combined using a Kalman filter to generate estimates of the vehicle states. The residuals of the Kalman filter provide insight to determine if the estimator model is correct and therefore providing accurate state estimates. Additionally, a method to predict the estimation error due to errors in the estimator model is presented. The algorithms are tested in simulation with a correct and incorrect model as well as with sensor errors. Finally, the estimation scheme is tested with experimental data using a 2000 Chevrolet Blazer to further validate the algorithms.     

Estimation of longitudinal speed robust to road conditions for ground vehicles

This article seeks to develop a longitudinal vehicle velocity estimator robust to road conditions by employing a tyre model at each corner. Combining the lumped LuGre tyre model and the vehicle kinematics, the tyres internal deflection state is used to gain an accurate estimation. Conventional kinematic-based velocity estimators use acceleration measurements, without correction with the tyre forces. However, this results in inaccurate velocity estimation because of sensor uncertainties which should be handled with another measurement such as tyre forces that depend on unknown road friction. The new Kalman-based observer in this paper addresses this issue by considering tyre nonlinearities with a minimum number of required tyre parameters and the road condition as uncertainty. Longitudinal forces obtained by the unscented Kalman filter on the wheel dynamics is employed as an observation for the Kalman-based velocity estimator at each corner. The stability of the proposed time-varying estimator is investigated and its performance is examined experimentally in several tests and on different road surface frictions. Road experiments and simulation results show the accuracy and robustness of the proposed approach in estimating longitudinal speed for ground vehicles.     

Effective assessment of tyre–road friction coefficient using a hybrid estimator

Vehicle stability and active safety control depend heavily on tyre forces available on each wheel of a vehicle. Since tyre forces are strongly affected by the tyre–road friction coefficient, it is crucial to optimise the use of the adhesion limits of the tyres. This study presents a hybrid method to identify the road friction limitation; it contributes significantly to active vehicle safety. A hybrid estimator is developed based on the three degrees-of-freedom vehicle model, which considers longitudinal, lateral and yaw motions. The proposed hybrid estimator includes two sub-estimators: one is the vehicle state information estimator using the unscented Kalman filter and another is the integrated road friction estimator. By connecting two sub-estimators simultaneously, the proposed algorithm can effectively estimate the road friction coefficient. The performance of the proposed estimation algorithm is validated in CarSim/Matlab co-simulation environment under three different road conditions (high-μ, low-μ and mixed-μ). Simulation results show that the proposed estimator can assess vehicle states and road friction coefficient with good accuracy.     

基于扩展卡尔曼滤波的汽车质心侧偏角估计

基于二自由度汽车动力学模型和轮胎模型,运用扩展卡尔曼滤波方法建立了汽车质心侧偏角估计器.利用汽车动力学仿真平台,通过仿真对比了线性轮胎模型和非线性轮胎模型的质心侧偏角估计结果.仿真结果表明,轮胎模型对于质心侧偏角估计精度至关重要,而采用非线性轮胎模型能显著提高质心侧偏角估计精度,估计结果能满足ESC控制的要求.     

基于蚁狮算法的UKF车辆状态参数估计器

为了准确获取分布式驱动电动汽车状态参数信息，满足车辆稳定性控制系统的需求，提出一种基于蚁狮算法的无迹卡尔曼滤波状态参数估计器。针对无迹卡尔曼滤波（UKF）过程中噪声协方差矩阵的不确定性，采用蚁狮优化算法（ALO）对其进行寻优，并引入奇异值分解（SVD）的方法来维持噪声协方差矩阵的正定性，此外，基于指数加权最小二乘法对车辆侧偏刚度进行辨识并将其作为状态参数估计器输入。基于MATLAB/Simulink和CarSim联合仿真平台，建立分布式驱动电动汽车参数估计模型，分别进行双移线工况和正弦迟滞工况仿真，并基于A&D5435快速原型开发平台进行双移线工况实车试验。仿真与试验结果表明：相比于SVDUKF算法估计结果，双移线仿真工况下，基于ALO-SVDUKF算法估计得到的质心侧偏角和横摆角速度的均方根误差分别降低了55.7%、30.7%，正弦迟滞仿真工况下，均方根误差分别降低了58.1%、85.1%，且在车辆处于极限失稳状态时仍能维持较好的估计效果；双移线试验工况下，横摆角速度的估计值与实际测量值之间的均方根误差仅为0.938 4（°）·s^-1；提出的基于ALO-SVDUKF算法的分布式驱动电动汽车状态参数估计器能够有效提高质心侧偏角和横摆角速度的估计精度，可为车辆稳定性控制提供精确的状态信息。     

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.     

基于探测车数据的路段行程时间估计

利用探测车数据进行路段行程时间估计面临着两类误差：采样误差和非采样误差,从而导致估计结果精度不高和可靠性差。在回顾已有估计方法的基础上,有针对性地引入了自适应式卡尔曼滤波,建立了相应的状态方程和观测方程,利用相似时间特征的历史数据标定了状态转移系数,并对滤波进行了求解。以实际数据对估计方法进行了验证,平均相对误差为13.13%。研究表明,自适应式卡尔曼滤波能够应用到基于探测车数据的路段行程时间估计中来,并具有估计精度高、收敛速度快、参数少、对初值不敏感等优点。     

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。     

路面附着系数的自适应衰减卡尔曼滤波估计

为了获得实时、准确的路面附着系数，进一步提高观测路面附着系数算法的精度和收敛速度，结合非线性车辆动力学模型和轮胎力修正模型，搭建分布式驱动电动汽车联合仿真平台，提出一种基于自适应衰减无迹卡尔曼滤波的路面附着系数观测算法。该算法设计与各轮对应的路面附着系数观测器，应用协方差匹配判据对观测器发散趋势进行判别，设计自适应加权系数修正预测协方差，以增强新近观测数据的利用率；同时采用次优Sage-Husa噪声估计器对未知的系统过程噪声进行估计，抑制观测器的记忆存储长度，调整过程噪声和测量噪声的均值与协方差，提高观测器的跟踪能力。利用分布式驱动电动汽车分别进行高、低附着路面和对开路面直线制动试验，并将自适应衰减无迹卡尔曼滤波路面附着系数观测器的观测结果与无迹卡尔曼滤波观测值、参考路面附着系数进行比较和分析。结果表明：高附着路面条件下，所设计的算法估计误差可控制在0.64%以内；低附着路面条件下，所设计的算法估计误差可控制在1.03%以内；对开路面条件下估计误差可控制在1.26%以内；自适应衰减无迹卡尔曼滤波算法相比无迹卡尔曼滤波算法响应速率更快，具有更高的估计精度和较强的自适应能力，估计结果整体上维持稳定，能够适应各种不同路面的估计。     

Unscented Kalman filter for vehicle state estimation

Vehicle dynamics control (VDC) systems require information about system variables, which cannot be directly measured, e.g. the wheel slip or the vehicle side-slip angle. This paper presents a new concept for the vehicle state estimation under the assumption that the vehicle is equipped with the standard VDC sensors. It is proposed to utilise an unscented Kalman filter for estimation purposes, since it is based on a numerically efficient nonlinear stochastic estimation technique. A planar two-track model is combined with the empiric Magic Formula in order to describe the vehicle and tyre behaviour. Moreover, an advanced vertical tyre load calculation method is developed that additionally considers the vertical tyre stiffness and increases the estimation accuracy. Experimental tests show good accuracy and robustness of the designed vehicle state estimation concept.     

Data-driven State-of-Charge estimator for electric vehicles battery using robust extended Kalman filter

An accurate battery State-of-Charge (SoC) estimation method is one of the most significant and difficult techniques to promote the commercialization of electric vehicles. This paper tries to make two contributions to the existing literatures through a robust extended Kalman filter (REKF) algorithm. (1) An improved lumped parameter battery model has been proposed based on the Thevenin battery model and the global optimization-oriented genetic algorithm is used to get the optimal polarization time constant of the battery model. (2) A REKF algorithm is employed to build an accurate data-driven based robust SoC estimator for a LiFePO4 lithium-ion battery. The result with the Federal Urban Driving Schedules (FUDS) test shows that the improved lumped parameter battery model can simulate the dynamic performance of the battery accurately. More importantly, the REKF based SoC estimation approach makes the SoC estimation with high accuracy and reliability, it can efficiently eliminate the problem of accumulated calculation error and erroneous initial estimator state of the SoC.     

车辆操纵稳定性状态估计算法比较研究

建立了基于运动学的车辆3自由度状态估计模型,分别将扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）和粒子滤波（PF）应用到车辆状态估计中,通过仿真试验比较了3种算法的估计效果。结果表明,车辆工作在线性稳定区域时,EKF算法效果最优,而车辆工作在强非线性区域并处于失稳状态时,PF算法效果最优。     

Fuzzy Approach to the Real Time Longitudinal Velocity Estimation of a FWD Car in Critical Situations

This paper presents a new approach to the fuzzy estimation of the variables of complex, fast, closed-loop systems. It is used to develop an original real-time longitudinal velocity estimator for FWD cars. Its application covers highly critical driving situations and avoids the use of an expensive optical cross-correlation sensor. The aim is to provide vehicle monitoring processes with a reliable value of the longitudinal velocity. Fuzzy aggregate indicators are used to identify and detect the different ways a vehicle behaves. Then, a fuzzy expert system with rules based on these indicators decides which values should be used among those which allow the estimation of the longitudinal velocity.     

基于随机加权自适应容积卡尔曼的电池SOC估计

准确估计锂离子电池荷电状态（SOC）对于突破电动汽车发展瓶颈，推动电动汽车商业化至关重要。针对动力电池模型参数辨识问题，提出基于遗忘因子的递推最小二乘法（FRLS）的模型参数在线识别方法。实时测量动力电池电流和电压数据，在线辨识模型参数并实时更新，实时反映电池内部参数的变化过程，对电池动态特性进行实时模拟。针对容积卡尔曼（CKF）滤波过程中对噪声敏感的问题，提出一种基于随机加权思想的自适应容积卡尔曼滤波（ARWCKF）方法。相比于常规CKF容积点权值始终不变，通过引入随机加权因子，自适应调整容积点权值并对系统噪声、状态向量及观测向量进行预测，抑制系统噪声对状态估计的干扰，避免因容积点权重值固定所带来的误差。针对CKF算法在容积点计算过程中由于状态方差矩阵失去正定性导致的平方根分解无法使用的问题，提出基于奇异值分解的容积点计算方法，克服由于先验协方差矩阵负定性变化而导致的滤波精度下降等问题，并进行多种工况、温度下不同SOC初值的对比验证。结果表明：所提出的基于遗忘因子的递推最小二乘法的在线参数辨识及ARWCKF滤波方法具备良好的估计精度及收敛能力，最大电压估计误差不超过40 mV，SOC估计误差不超过1%。     

Estimation of lateral offset and drift angle for application in secondary collision avoidance system

Recent reports show that the secondary collision on the road gives much higher fatality rate than the other traffic accidents. Many studies have been carried out to prevent the secondary accidents and as a result automotive companies began to introduce brake-based secondary collision avoidance systems. To prevent the secondary accidents it is important to monitor and control the lateral deviation of the vehicle after the primary collision. An estimator for the vehicle’s lateral offset and drift angle based on the in-vehicle sensors and the camera was developed in this paper. By employing sensor fusion scheme and applying extended Kalman filter, the estimator has been designed so that it works even when the camera loses the image of the lanes due to sudden change of the vehicle’s heading angle. For validation of the estimator, simulation has been carried out on various collision scenarios. The simulation results indicated that the estimator of this paper could calculate the vehicle’s lateral deviation with robustness that may be required for application in the secondary collision avoidance systems.     

Electrochemical battery model and its parameter estimator for use in a battery management system of plug-in hybrid electric vehicles

This paper reports the development of a battery model and its parameter estimator that are readily applicable to automotive battery management systems (BMSs). Due to the parameter estimator, the battery model can maintain reliability over the wider and longer use of the battery. To this end, the electrochemical model is used, which can reflect the aging-induced physicochemical changes in the battery to the aging-relevant parameters within the model. To update the effective kinetic and transport parameters using a computationally light BMS, the parameter estimator is built based on a covariance matrix adaptation evolution strategy (CMA-ES) that can function without the need for complex Jacobian matrix calculations. The existing CMA-ES implementation is modified primarily by region-based memory management such that it satisfies the memory constraints of the BMS. Among the several aging-relevant parameters, only the liquid-phase diffusivity of Li-ion is chosen to be estimated. This also facilitates integrating the parameter estimator into the BMS because a smaller number of parameter estimates yields the fewer number of iterations, thus, the greater computational efficiency of the parameter estimator. Consequently, the BMS-integrated parameter estimator enables the voltage to be predicted and the capacity retention to be estimated within 1 % error throughout the battery life-time.     

Feedback brake distribution control for minimum pitch

The distribution of brake forces between front and rear axles of a vehicle is typically specified such that the same level of brake force coefficient is imposed at both front and rear wheels. This condition is known as ‘ideal’ distribution and it is required to deliver the maximum vehicle deceleration and minimum braking distance. For subcritical braking conditions, the deceleration demand may be delivered by different distributions between front and rear braking forces. In this research we show how to obtain the optimal distribution which minimises the pitch angle of a vehicle and hence enhances driver subjective feel during braking. A vehicle model including suspension geometry features is adopted. The problem of the minimum pitch brake distribution for a varying deceleration level demand is solved by means of a model predictive control (MPC) technique. To address the problem of the undesirable pitch rebound caused by a full-stop of the vehicle, a second controller is designed and implemented independently from the braking distribution in use. An extended Kalman filter is designed for state estimation and implemented in a high fidelity environment together with the MPC strategy. The proposed solution is compared with the reference ‘ideal’ distribution as well as another previous feed-forward solution.     

Fuzzy Approach to the Real Time Longitudinal Velocity Estimation of a FWD Car in Critical Situations

SUMMARY

This paper presents a new approach to the fuzzy estimation of the variables of complex, fast, closed-loop systems. It is used to develop an original real-time longitudinal velocity estimator for FWD cars. Its application covers highly critical driving situations and avoids the use of an expensive optical cross-correlation sensor. The aim is to provide vehicle monitoring processes with a reliable value of the longitudinal velocity. Fuzzy aggregate indicators are used to identify and detect the different ways a vehicle behaves. Then, a fuzzy expert system with rules based on these indicators decides which values should be used among those which allow the estimation of the longitudinal velocity.     

State estimation in roll dynamics for commercial vehicles

Accurate lateral load transfer estimation plays an important role in improving the performance of the active rollover prevention system equipped in commercial vehicles. This estimation depends on the accurate prediction of roll angles for both the sprung and the unsprung subsystems. This paper proposes a novel computational method for roll-angle estimation in commercial vehicles employing sensors which are already used in a vehicle dynamic control system without additional expensive measurement units. The estimation strategy integrates two blocks. The first block contains a sliding-mode observer which is responsible for calculating the lateral tyre forces, while in the second block, the Kalman filter estimates the roll angles of the sprung mass and those of axles in the truck. The validation is conducted through MATLAB/TruckSim co-simulation. Based on the comparison between the estimated results and the simulation results from TruckSim, it can be concluded that the proposed estimation method has a promising tracking performance with low computational cost and high convergence speed. This approach enables a low-cost solution for the rollover prevention in commercial vehicles.     

Cooperative Estimation Using Vehicle Communications

Summary Each vehicle on a section of highway is potentially a driving condition 'sensor.' For example, a vehicle's speed give can give a clue about the traffic conditions in its section of roadway. By 'cooperative estimation,' we mean a system that uses a communication network to combine the experience of many vehicles into parameter estimates that are more useful than the estimates that any individual vehicle could generate by itself. This paper demonstrates the cooperative estimation concept by showing how it can be used to estimate traffic conditions and road friction without using roadside sensors.