A comparative study on fault detection methods of rail vehicle suspension systems based on acceleration measurements

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. On-line health condition monitoring for the suspension system of rail vehicles offers a number of benefits such as preventing further deterioration of vehicle performance, enhancing vehicle safety, increasing operational reliability and availability, and reducing maintenance costs. It is desirable to timely detect the fault and monitor the performance degradation of vehicle suspension systems. In this paper, a comparative study on fault detection methods of urban rail vehicle suspension systems is considered. A novel sensor configuration is proposed where the underlying vehicle system is equipped with only acceleration sensors in the four corners of the carbody, the leading and trailing bogie, respectively. A mathematical model is developed for the considered vehicle suspension system. Both model-based and data-driven approaches are studied for the suspension fault detection problem. The robust observer, the Kalman filter combined with the generalised likelihood ratio test method, the dynamical principle components analysis and the canonical variate analysis approaches are applied to the fault detection problem. The simulation is carried out by means of the professional multi-body simulation tool, SIMPACK. In addition, the advantages and disadvantages of these methods are compared. The simulation results show that the data-driven methods outperform the model-based methods.     

On fault isolation for rail vehicle suspension systems

Reliability of the railway vehicle suspension system is of critical importance to the safety of the vehicle. It is very desirable to monitor the health condition and the performance degradation of the suspension system online, which offers the important information of the suspension system and is critically important for the condition-based maintenance rather than scheduled maintenance in the future. Advanced fault diagnosis method is one of the most effective means for the health monitoring of the suspension system. In this paper, taking the lateral suspension system as an examcple, the fault isolation issue for different component faults occurring in the suspension system is concerned. The sensor configuration for obtaining the vehicle state information and the mathematical model for the lateral suspension system are presented. Four fault features in the time domain and three fault features in the frequency domain are used for each sensor signal. Three different methods, Dempster–Shafer (D–S) evidence theory, Fisher discrimination analysis (FDA) and support vector machine (SVM) techniques are applied to the fault isolation problem. Simulation study is carried out by means of the professional multi-body simulation tool, SIMPACK. The simulation results show that these methods can isolate the considered component faults effectively with a high accuracy. The D–S evidence-based fault isolation approach outperforms the other two methods.     

并联混合动力汽车扭转减振器性能仿真分析

为改善并联混合动力汽车传动系统的扭振特性,开展了扭转减振器的结构及仿真分析研究。对并联混合动力汽车传动系现有扭转减振器进行分析,提出了一种具有新型结构的弧形弹簧式从动盘扭转减振器;针对某款车型建立 8 自由度集中质量模型,采用 AMESim 仿真软件搭建仿真模型;通过对离合器从动盘扭转减振器、双质量飞轮和弧形弹簧式从动盘扭转减振器 3 种不同结构减振器的扭振特性进行仿真对比,分析了它们在典型工况下的扭振特性,并对扭转刚度和迟滞力矩进行了灵敏度分析。结果表明,弧形弹簧式从动盘扭转减振器能保证较短的发动机启动时间,且拥有较好的减振特性;在混合驱动行驶工况下扭转减振器的减振效果与扭转刚度及迟滞力矩的大小呈负相关。     

基于电压频域特征和异常系数的动力电池故障诊断方法

动力电池系统是电动汽车(EV)的关键部件和主要故障源,因而提高动力电池故障诊断的效率和准确率显得尤为重要。基于此提出一种基于快速傅里叶变换(FFT)和异常系数评估(ACE)的动力电池电压不一致性故障诊断方法。针对6辆发生故障或热失控事故的电动汽车和1辆电压一致性良好的电动汽车,基于其在新能源汽车国家监管平台的全生命周期运行数据,经过电压数据的数据清洗、数据变换等大数据预处理后,利用FFT技术时频变换,提取频域中的幅值作为故障诊断的特征参数;然后,引进基于Z分数理论的异常系数对故障程度进行定量评估,以实现故障单体的检测和定位;此外,针对存在多个故障单体的情况,基于单体异常率的计算,实现单体故障程度的判定和排序;在此基础上,详细分析电压数据长度及采样间隔、FFT采样点数对模型的影响;最后,与基于熵和Z分数的电压故障诊断方法进行比较。研究结果表明:在上述研究条件下,该诊断方法对于电压一致性良好的车辆未产生误报警,且可以有效地检测出事故车辆动力电池系统存在的电压不一致性故障;相比之下,模型平均计算准确率提高了3.25%,模型平均耗时仅为熵值模型的0.55%;验证了该方法故障单体定位更精准、数据适用性更好及计算速度更快的优点。该研究成果能有效实现动力电池电压不一致性故障诊断,具有较高的工程应用价值。     

A Fuzzy Logic Direct Yaw-Moment Control System for All-Wheel-Drive Electric Vehicles

Summary In-wheel-motors are revolutionary new electric drive systems that can be housed in vehicle wheel assemblies. Such E-wheels permit packaging flexibility by eliminating the central drive motor and the associated transmission and driveline components, including the transmission, the differential, the universal joints and the drive shaft. Apart from many advantages of such a system, unequalled independent wheel control allows vehicle dynamic improvement to assist the driver in enhancing cornering and straight-line stability on slippery roads and in adverse ground conditions. In this paper a Fuzzy logic driver-assist stability system for all-wheel-drive electric vehicles based on a yaw reference DYC is introduced. The system assists the driver with path correction, thus enhancing cornering and straight-line stability and providing enhanced safety. A feed-forward neural network is employed to generate the required yaw rate reference. The neural net maps the vehicle speed and the steering angle to give the yaw rate reference. The vehicle true speed is estimated using a multi-sensor data fusion method. Data from wheel sensors and an embedded accelerometer are fed into an estimator, where a Fuzzy logic system decides which input is more reliable. The efficiency of the proposed system is approved by conducting a computer simulation. The proposed control system is an effective and easy to implement method to enhance the stability of all-wheel-drive electric vehicles.     

基于系统效率最优的CVT混合动力轿车转矩优化分配方法

无级变速器CVT消除了挡位概念,其速比在一定范围内连续可调。配备CVT的混合动力汽车能够实现动力源转矩和传动系统的优化匹配。针对此问题,提出了基于系统效率最优的CVT中度混合动力轿车动力源转矩优化分配方法:。该方法:综合考虑了各个关键部件的效率,以混合动力系统的总体效率为优化目标,以车速、车辆加速度、电池SOC为状态变量,优化分配了驱动工况下各动力源输出转矩,为整车能量管理策略的制定奠定了基础。     

纯电动商用车绝缘故障分析

电动汽车通过高压电能进行整车驱动及附件控制,为保证整车安全性,高压系统均设置有绝缘检测及断电保护功能。文章针对纯电动车辆最受关注的绝缘故障进行原理及故障分析,在设计、装配、使用等环节进行详细原因及排查方案介绍,用于指导市场故障车辆的排查及后期设计方案的优化。     

A Fuzzy Logic Direct Yaw-Moment Control System for All-Wheel-Drive Electric Vehicles

Summary In-wheel-motors are revolutionary new electric drive systems that can be housed in vehicle wheel assemblies. Such E-wheels permit packaging flexibility by eliminating the central drive motor and the associated transmission and driveline components, including the transmission, the differential, the universal joints and the drive shaft. Apart from many advantages of such a system, unequalled independent wheel control allows vehicle dynamic improvement to assist the driver in enhancing cornering and straight-line stability on slippery roads and in adverse ground conditions. In this paper a Fuzzy logic driver-assist stability system for all-wheel-drive electric vehicles based on a yaw reference DYC is introduced. The system assists the driver with path correction, thus enhancing cornering and straight-line stability and providing enhanced safety. A feed-forward neural network is employed to generate the required yaw rate reference. The neural net maps the vehicle speed and the steering angle to give the yaw rate reference. The vehicle true speed is estimated using a multi-sensor data fusion method. Data from wheel sensors and an embedded accelerometer are fed into an estimator, where a Fuzzy logic system decides which input is more reliable. The efficiency of the proposed system is approved by conducting a computer simulation. The proposed control system is an effective and easy to implement method to enhance the stability of all-wheel-drive electric vehicles.     

纯电动汽车MCU母线电流采样电路及故障机制

设计一种纯电动汽车电机控制器直流母线电流采样电路,在此基础上提出一种电流采样故障的故障处理方法,该方法根据驱动系统当前状态实现了对电机控制器输入端直流母线电流的有效估算。当发生电机控制器直流母线电流采样回路故障后,利用估算值继续保证整车控制逻辑的正常执行,在保证安全行车的前提下,尽可能对驾驶员的驾驶感受进行保护。最后通过实车对该采样电路及故障机制进行验证。     

基于局部均值分解与局部离群因子动力电池故障诊断

动力电池故障诊断是保证电动汽车正常运行的关键。提出一种基于局部均值分解和局部离群因子的动力电池故障诊断方法,用于电池组故障识别与定位。通过局部均值分解对电压信号预处理,并根据相关系数高低重构电压信号。进一步提取重构信号的峭度因子作为故障特征输入到局部离群因子算法中,根据局部离群因子算法自适应阈值输出故障电池。采用实车数据验证了所提方法能有效、准确地检测出故障,具有较好的可靠性与鲁棒性。     

Asymptotic sideslip angle and yaw rate decoupling control in four-wheel steering vehicles

This paper shows that, for a four-wheel steering vehicle, a proportional-integral (PI) active front steering control and a PI active rear steering control from the yaw rate error together with an additive feedforward reference signal for the vehicle sideslip angle can asymptotically decouple the lateral velocity and the yaw rate dynamics; that is the control can set arbitrary steady state values for lateral speed and yaw rate at any longitudinal speed. Moreover, the PI controls can suppress oscillatory behaviours by assigning real stable eigenvalues to a widely used linearised model of the vehicle steering dynamics for any value of longitudinal speed in understeering vehicles. In particular, the four PI control parameters are explicitly expressed in terms of the three real eigenvalues to be assigned. No lateral acceleration and no lateral speed measurements are required. The controlled system maintains the well-known advantages of both front and rear active steering controls: higher controllability, enlarged bandwidth for the yaw rate dynamics, suppressed resonances, new stable cornering manoeuvres and improved manoeuvrability. In particular, zero lateral speed may be asymptotically achieved while controlling the yaw rate: in this case comfort is improved since the phase lag between lateral acceleration and yaw rate is reduced. Also zero yaw rate can be asymptotically achieved: in this case additional stable manoeuvres are obtained in obstacle avoidance. Several simulations, including step references and moose tests, are carried out on a standard small SUV CarSim model to explore the robustness with respect to unmodelled effects such as combined lateral and longitudinal tyre forces, pitch, roll and driver dynamics. The simulations confirm the decoupling between the lateral velocity and the yaw rate and show the advantages obtained by the proposed control: reduced lateral speed or reduced yaw rate, suppressed oscillations and new stable manoeuvres.     

Identifying multiclass vehicles using global positioning system data

It has been previously evidenced that global positioning system (GPS) data can be used to distinguish passenger cars from delivery trucks. In this paper, a machine learning approach is proposed to use GPS data to identify multiclass vehicles, including passenger cars, single unit trucks, and multi-trailer trucks. The method is acceleration and deceleration-based since it considers the variations of acceleration and deceleration as the most effective features to classify vehicles. The overall classification result for the three vehicle classes is about 75%. The major challenge is to distinguish single unit trucks from multitrailer trucks due to their somewhat similar mobility patterns. The paper also explores the impacts of GPS sampling frequency on vehicle classification. It is found that the proposed multiclass vehicle classification can be reasonably conducted if the data are collected frequently enough (i.e., every five seconds or more frequently) to capture the major acceleration and deceleration processes. The proposed method can be considered as a low-cost and non-intrusive approach to collect vehicle class information and to potentially supplement the existing classification schemes in urban areas.     

电动汽车整车运行性能检测试验技术

电动汽车的生产和制造需要符合相关的标准,并且还要对整车的性能进行测试,只有这样才能更好的符合汽车上路的条件。本文主要是将国标要求作为基础来对电动汽车的最高车速、滑行能力、加速性能、制动性能和爬坡能力和能量消耗率和续航里程进行测试,通过实施一定的检测试验方法和配套的相关试验设备来做好检测,并且要对测试的结果进行分析,对电动汽车的运行性能进行进一步的优化,从而推进我国电动汽车行业的更好发展和进步。     

线控转向系统故障检测及容错控制协同设计

为解决线控转向系统故障可能导致车辆失控的问题,提出一种故障检测及容错控制协同设计方法。首先,建立了包含线控转向系统加性故障的车辆动力学模型;其次,联合车辆动力学模型及故障检测/容错控制器,建立跟踪误差闭环控制系统;然后,求解满足闭环系统H∞性能的线性矩阵不等式,得到故障检测/容错控制器参数;最后,基于dSPACE Full-Size HIL进行硬件在环仿真测试。结果表明,该方法可快速检测出转向系统故障,同时实现了车辆的容错控制。     

基于轨迹数据的交叉口相位切换期间危险驾驶行为实证分析

基于交叉口相位切换期间的车辆轨迹数据,分别根据单车和跟车行驶状态,识别和分析了相位切换期间可能发生的危险驾驶行为。通过视频拍摄和图像处理的方式,提取了曹安公路沿线3个交叉口共312条单车状态和四平路-大连路交叉口共449条跟车状态的高精度车辆轨迹数据。针对交叉口相位切换期间的危险驾驶行为特征,利用速度、加减速度、减速度变化率、潜在碰撞时间（TTC）等指标,研究在此期间车辆发生危险驾驶行为的特点和类型。对于单车状态下行驶的车辆,按停止、通过分类,依据减速度、减速度变化率、减速度变化率的峰值差等指标将停止车辆的危险驾驶行为分为紧急减速型、增强减速型和持续急减型,依据过停车线时间、速度、加速度等指标将通过车辆分为闯红灯型、超速过线型、激进加速型和持续高速型。对于在跟车状态下行驶的车辆,按前、后车不同的停止、通过决策组合分类,依据连续5个时间间隔（0.12 s）的TTC分析前、后车的危险驾驶行为及发生追尾事故的危险程度。针对识别出的危险驾驶行为类型,讨论车辆的关键行为参数与危险驾驶行为之间的内在关联。研究结果表明：单车状态下有17%的车辆存在危险驾驶行为,其中53%为紧急减速行为;跟车状态下有19%的跟车行为是危险的,其中停止车辆的比例是通过车辆的2倍以上。研究成果可进一步应用于驾驶行为模型的参数标定、基于车辆轨迹的交叉口安全评价以及预防危险驾驶行为的主动安全控制策略等。     

电动轮驱动的电动汽车动力学仿真

采用自然坐标系下的整车动力学模型,模拟变速或转向过程中可能存在的变化情况,进行了四电动轮独立驱动的电动汽车仿真。仿真试验表明,在变速或转向的过程中,各轮的输出转矩可能会有较大差异。因此在此类电动汽车的设计中应当充分考虑对变速或转向时各轮的转矩加以控制,以提高操控性能。     

Engine clutch torque estimation for parallel-type hybrid electric vehicles

This paper mainly focuses on the accurate estimation of the torque transferred through the engine clutch installed between the engine and the drive motor in parallel-type hybrid electric vehicles. The estimation of the engine clutch torque primarily relies on the forward-direction observer which uses the nominal engine net torque information. To overcome the limitation of using the nominal engine torque information that it may not be accurate during the transient states or due to the influence of external disturbance such as the road condition and wind, the forward-direction observer is supplemented by the use of reverse-direction observer which uses the driveline model and wheel speed measurements. In addition, the drive motor torque information is used to calibrate the nominal engine torque during the idle charging state, so that the driveline characteristic unique to parallel-type hybrid electric vehicle can be utilized to increase the estimation accuracy. Finally, the estimation performance of the designed observer is tested via simulation and experiments based on a real vehicle.     

A modified integral sliding mode control to lateral stabilisation of 4-wheel independent drive electric vehicles

This paper presents a novel sliding mode controller (SMC) and its application in the lateral stability control of a 4-wheel independent drive electric vehicle. The structure of the SMC is modified and online-tuned to ensure vehicle system stability, and to track the desired vehicle motion references when an in-wheel motor fault happens. The proposed controller is faster, more accurate, more robust, and with smaller chattering than common SMCs chatter. The effectiveness of the introduced approach is investigated through conducted simulations in the CARSIM and MATLAB software environments.     

Application of observer-based fault detection in vehicle roll control

This paper describes how observer-based techniques for intelligent fault detection were applied to monitoring an active suspension control system in an experimental articulated heavy vehicle. The aim was to define a practical method for detecting faults, taking into account the nonlinearities of the vehicle. The experimental vehicle was divided conceptually into subsystems, namely the passive dynamics of the trailer, the dynamics of the hydraulic actuators, and the expected response of the closed-loop system. A linear dynamic model was designed for each subsystem. A fault detection observer was then designed for each dynamic model. The observer feedback gains were chosen to optimise estimation by the observer residual of specified errors on the output measurements. The observer residuals were then normalised and combined logically to provide a fault diagnosis. The performance of the fault detection scheme is demonstrated in the case of sensor faults and changes in the operation of the active control system.     

Optimal control of brakes and steering for autonomous collision avoidance using modified Hamiltonian algorithm

ABSTRACT

This paper considers the problem of collision avoidance for road vehicles, operating at the limits of friction. A two-level modelling and control methodology is proposed, with the upper level using a friction-limited particle model for motion planning, and the lower level using a nonlinear 3DOF model for optimal control allocation. Motion planning adopts a two-phase approach: the first phase is to avoid the obstacle, the second is to recover lane keeping with minimal additional lateral deviation. This methodology differs from the more standard approach of path-planning/path-following, as there is no explicit path reference used; the control reference is a target acceleration vector which simultaneously induces changes in direction and speed. The lower level control distributes vehicle targets to the brake and steer actuators via a new and efficient method, the Modified Hamiltonian Algorithm (MHA). MHA balances CG acceleration targets with yaw moment tracking to preserve lateral stability. A nonlinear 7DOF two-track vehicle model confirms the overall validity of this novel methodology for collision avoidance.