Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting.     

Development and validation of a direct mode choice model

A direct discrete mode choice model is introduced using relative attributes of competing modes as well as socioeconomic characteristics of travelers. The model is calibrated and validated for two available historic databases in the Dallas–Fort Worth region. The validation is conducted against the outputs of a current nested logit model used by the regional planning organization as well as the observed values based on transit ridership surveys for a newly inaugurated commuter rail service. The calibrated model is applied after the introduction of this new transit mode. The results show that the estimated mode shares by the proposed model have a statistically better consistency with the observed values than the estimates of the conventional nested logit model. Unlike the logit model, the structure of the direct model based on relative attributes also has the advantage of not needing recalibration each time a new travel mode is introduced. The model is found to be easier to calibrate and produces more accurate results than the nested logit model, commonly used by many metropolitan planning organizations.     

基于非线性扰动观测的商用车ESC自适应滑模控制研究

针对商用车ESC控制中,实际车辆存在各种扰动,难以建立精确的车辆模型,传统滑模控制存在较大抖振等问题,本文中提出基于非线性扰动观测(NDOB)的自适应滑模控制(ADSMC)算法。首先,利用非线性扰动观测器对车辆建模的扰动项进行估计;然后,采用径向基神经网络对滑模控制器的关键参数进行自适应调节,以简化参数调节过程、减小滑模抖振、提高控制精度;最后,在TruckSim中建立车辆模型,在MATLAB中建立控制策略模型,在电控气压硬件在环试验台上,对控制算法进行试验验证。试验结果表明,NDOB-ADSMC算法的ESC控制效果良好,能够满足车辆ESC控制需求。     

洋山深水港区地基加固试验的几点浅见

对于疏松粉细砂地基土的加固处理,目前尚无成熟的经验和现成的规范。通过洋山深水港区一期工程1#试验区的加固试验证明,采取合理的振冲辅以振动碾压的施工工艺,有效地提高了地基承载力,达到集装箱港区道路、堆场的使用要求,且施工方便、快速。     

优化参数VMD和MED在列车齿轮箱滚动轴承故障诊断中的应用

针对强噪声情况下列车齿轮箱滚动轴承早期故障特征提取困难的问题,提出基于最小熵解卷积(minimum entropy deconvolution,MED)与参数优化变分模态分解(variational mode decomposition,VMD)相结合的故障诊断方法。首先利用MED对轴承振动信号进行降噪;其次,采用离散差分进化算法(discrete differential evolution algorithm,DDE)对VMD的参数进行优化搜索,并利用优化参数的变分模态分解算法对降噪后的故障信号进行处理,得到一系列本征模态函数;最后,选择最佳的本征模态函数进行包络分析,从而提取出故障特征。试验结果表明,该方法能有效提取列车齿轮箱滚动轴承故障特征,可用于轴承故障诊断。     

高速列车滑模自抗扰黏着控制方法

为解决高速列车运行过程中因轨面情况改变,导致列车没有达到最大黏着利用而出现空转或滑行等问题,设计了一种基于最大黏着系数的滑模自抗扰(SM-ADRC)黏着控制器;考虑轮轨间黏着特性的复杂、时变与非线性等特点,基于黏着机理分析,建立了轮轨间牵引系统的力学模型;采用极大似然估计(MLE)方法对不同轨面的相关参数进行辨识,计算了当前轨面的最大黏着系数,保证列车始终能达到最大黏着利用;通过引入滑模算法改进了自抗扰控制(ADRC)中非线性误差反馈控制律部分,设计了一种SM-ADRC黏着控制算法,利用Levant跟踪微分器减小初始跟踪误差,利用扩张状态观测器(ESO)估计和补偿系统总的外部扰动,由滑模控制提高系统的鲁棒性;采用MATLAB软件对CRH380A型高速列车进行仿真,在轨面情况改变时,由SM-ADRC黏着控制器控制列车跟踪设定速度,并将其与比例积分微分(PID)控制器、滑模控制器、ADRC的仿真结果进行对比。仿真结果表明：干燥轨面的最大黏着系数是0.160,16 s时辨识出真值;潮湿轨面的最大黏着系数是0.106,18 s时辨识出真值;ADRC的速度跟踪误差范围为±1 km·h^-1,轨面变化后,速度跟踪误差波动幅度较大;SM-ADRC黏着控制器的速度跟踪误差范围为±0.4 km·h^-1,轨面变化后,速度跟踪误差波动幅度较小,更加平滑稳定,速度控制跟踪精度更高,且优于PID和滑模控制方法。可见,所提出的SM-ADRC黏着控制器能够实现列车的快速黏着控制,并达到最大的黏着利用。     

基于非线性超声的CL60车轮和U75V钢轨磨损检测方法

针对早期轮轨滚动磨损变化过程难以通过无损手段进行表征的问题,提出了非线性超声技术对不同磨损程度的CL60车轮与U75V钢轨试样进行检测评估;建立了基于轮轨试样表面磨损特征的Murnaghan模型,并利用非线性超声有限元仿真,通过塑性变形层厚度变化情况模拟不同程度的摩擦损伤,分析了其相对非线性系数变化规律及其产生原因。试验结果表明：轮轨的早期磨损会导致材料表面产生塑性变形层,随着塑性变形的加剧,材料损伤将以微裂纹为主,车轮角加速度越大,轮轨间相对滑动作用时间越短,塑性变形层越薄,且CL60车轮较U75V钢轨磨损程度更为严重;CL60车轮试样在车轮角加速度分别为10、250、1 500 r·min^-2时,对应的相对非线性系数分别为12.19、8.43、5.68,U75V钢轨试样在车轮角加速度分别为10、250、1 500 r·min^-2时,对应的相对非线性系数分别为7.57、6.09、5.04,与CL60车轮试样相比,U75V钢轨试样的相对非线性系数变化缓慢。可知,相对非线性系数与塑性变形层厚度呈正相关,微裂纹产生的非线性效应比塑性变形层更强,相对非线性系数增幅更大,因此,可通过材料的相对非线性系数变化判断材料的磨损阶段。     

基于线性矩阵不等式的船舶动力定位滑模控制

为了解决具有非线性和环境干扰的船舶动力定位系统的控制问题, 提出了一种基于线性矩阵不等式的滑模控制算法; 将跟踪误差设计为滑模函数, 设计线性矩阵不等式, 求解状态反馈增益; 基于二次型Lyapunov函数证明了闭环系统的稳定性; 设计切换函数, 使系统对不确定性和外加干扰具有较强的鲁棒性, 避免出现抖振现象; 对基于线性矩阵不等式的滑模控制器进行仿真, 计算出动力定位船舶在无扰动的匀速运动和有外界环境扰动的变速运动2种不同情况下的前进速度、横荡速度、艏向角速度、前进加速度、横荡加速度、艏向角加速度、前进控制力、横荡控制力和艏向控制力矩等; 分析了状态反馈增益线性矩阵、边界层、切换项增益等参数对控制性能的影响。研究结果表明: 采用基本滑模控制使前进速度达到期望值所需的上升时间为29s, 而新算法为15s, 节约了48.28%;采用基本滑模控制使横荡速度达到期望值所需的上升时间为24s, 而新算法为14s, 节约了41.67%;采用基本滑模控制使艏向角速度达到期望值所需的上升时间为13s, 而新算法为10s, 节约了23.08%。可见, 设计的控制器对有非线性和环境干扰的船舶动力定位系统都具有较强的鲁棒性, 具有控制输入连续、控制抖振小、不存在过高增益等特点。     

非稳态载荷对二维轮轨纯滚动接触应力和变形的影响

为了揭示轮轨波状表面与非稳态载荷的内在联系, 利用有限元法, 建立了二维弹塑性轮轨纯滚动接触计算模型, 分析法向接触载荷波动值对钢轨残余应力、应变和变形的影响。模型中材料本构采用考虑棘轮效应的Jiang-Sehitoglu模型, 非稳态仅考虑法向接触载荷的简谐变化, 用弹塑性无限半空间表面上重复移动赫兹法向压力分布模拟反复纯滚动接触过程。发现非稳态法向接触载荷作用下产生同样波长的波状接触表面; 随滚动次数的增加, 残余应力增大, 但很快趋于稳定, 而残余应变也增大, 但增大速率衰减; 载荷波动值越大, 波谷和波峰处的纵向残余应力越大, 波谷处的轴向残余应力、残余剪应变和表面纵向位移越大, 而波峰处的轴向残余应力、残余剪应变和表面纵向位移越小, 波深越大。     

A new model for temporal–spatial stochastic analysis of vehicle–track coupled systems

The vehicle–track coupled system has a random nature in the time–space domain. This paper proposes a computational model to analyse the temporal–spatial stochastic vibrations of vehicle–track systems, where the vehicle–track system is divided into a vehicle subsystem, track subsystem, and interfacial subsystem between the wheel and rail. In this model, the time-varying randomicity of dynamical parameters of the vehicle system, correlation, and randomness of the track structural parameters in the time–space joint dimensions, and randomness of the track random irregularities are considered. A probability dimension-reduction method was used to randomly combine different random variables. Furthermore, the probability density evolution method was applied to solve the delivery problem of probabilities between excitation inputs and response outputs. The temporal–spatial stochastic vibrations of the vehicle–track system with different coefficients of variation were studied, in which we assumed that the dynamic parameters obeyed the normal distribution, and the stochastic simulation method of the track random irregularities is probed into. The calculated results from this model are consistent with the actual measured results and physical conceptions. Thus, the temporal–spatial stochastic evolutionary mechanism can be explored, and the limits of dynamic indices can be formulated by using this developed model.