Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach

This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.     

C波段低相噪频率源的设计

介绍基于DRO（Dielectric Resonator Oscillator）技术的低相噪微波频率源,由于DRO的高Q值其相比于锁相环（PLL）技术具有更低的相位噪声,用低温度系数的介质谐振器,有效地改善了振荡器的频率温度稳定性.同时减小介质谐振器与微带线之间的耦合度,能进一步改善相位噪声.论文设计的输出频率为4GHz的DRO在频偏10KHz处相位噪声-122dBc/Hz,输出功率8.5dBm,二次谐波抑制-42dBc,证明了其在低相位噪声频率源应用上的优势.     

A generalized sensor location model for the estimation of network origin–destination matrices

This study examined the network sensor location problem by using heterogeneous sensor information to estimate link-based network origin–destination (O–D) demands. The proposed generalized sensor location model enables different sensors’ traffic monitoring capabilities to be used efficiently and the optimal number and deployment locations of both passive- and active-type sensors to be determined simultaneously without path enumeration. The proposed sensor location model was applied to solve the network O–D demand estimation problem. One unique aspect of the proposed model and solution algorithms is that they provide satisfactory network O–D demand estimates without requiring unreasonable assumptions of known prior information on O–D demands, turning proportions, or route choice probabilities. Therefore, the proposed model and solution algorithms can be practically used in numerous offline transportation planning and online traffic operation applications.     

Minimum acceptable time for turning off idling engines: Evidence from Taiwan

This paper examines the influence of different factors on motorist willingness to comply with idling stop regulations, as determined using stated preference analysis. Motorists were surveyed at urban roadsides in Taiwan, and the results obtained were analyzed using a partially adaptive model. The analysis showed that the standing time and turnoff idling engine while parking are both significant variables, and arise from risk aversion behavior. Environmental perceptions and convenience of use are the most influential factors, according to elasticity analysis. The study also verifies that a partially adaptive model is an appropriate model to consider censored data in a Triple-Bounded Dichotomous Choice analysis. These results will be useful as a reference for improving implementation of idling reduction regulations.     

A planar quasi-static constraint mode tyre model

The fast-paced, iterative, vehicle design environment demands efficiency when simulating suspension loads. Towards that end, a computationally efficient, linear, planar, quasi-static tyre model is developed in this work that accurately predicts a tyre's lower frequency, reasonably large amplitude, nonlinear stiffness relationship. The axisymmetric, circumferentially isotropic, stiffness equation is discretised into segments, then parameterised by a single stiffness parameter and two shape parameters. The tyre's deformed shape is independent of the overall tyre stiffness and the forces acting on the tyre. Constraint modes capture enveloping and bridging properties and a recursive method yields the set of active constraints at the tyre–road interface. The nonlinear stiffness of a tyre is captured by enforcing unidirectional geometric boundary conditions. The model parameters are identified semi-empirically; simulated cleat test loads match experiments within 7% including nonlinear stiffness when simulating a flat plate test and a discontinuous stiffness when simulating a cleat test.     

基于直驱阀的快速响应线控制动系统液压力精确控制

为突破响应时间与控制精度的性能瓶颈，提出一种基于直驱阀的快速响应线控制动系统，通过基于Halbach永磁阵列的电磁直线执行器直接驱动阀芯，实现制动轮缸液压力的迅速调节。建立线控制动系统电磁、机械和液压子系统模型，设计基于逻辑门限的线控制动系统液压力-直驱阀位置切换控制架构。压力环采用滑模变结构控制，使轮缸液压力迅速逼近目标液压力值；设计结合摩擦补偿自适应控制律、稳定反馈和鲁棒控制的自适应鲁棒控制方法的直驱阀位置环，使直驱阀芯能够迅速通过阀死区；基于李雅普诺夫函数方法证明算法的稳定性。以线控制动系统的响应时间、控制精度等性能参数作为目标函数，通过相关矩阵分析控制参数对性能的影响规律，并通过多目标粒子群算法优化控制器参数。研究结果表明：提出的切换控制方法与PID控制和滑模控制相比，目标压力10 MPa的阶跃响应时间为0.05 s，稳态误差不超过2％；ARTEMIS欧洲循环工况下的均方根误差为0.33 MPa；设计的直驱阀结构与控制方法有利于提高制动系统的响应速度和控制精度。     

An optimal pole-matching observer design for estimating tyre–road friction force

In this paper, considering the dynamical model of tyre–road contacts, we design a nonlinear observer for the on-line estimation of tyre–road friction force using the average lumped LuGre model without any simplification. The design is the extension of a previously offered observer to allow a muchmore realistic estimation by considering the effect of the rolling resistance and a term related to the relative velocity in the observer. Our aim is not to introduce a new friction model, but to present a more accurate nonlinear observer for the assumed model. We derive linear matrix equality conditions to obtain an observer gain with minimum pole mismatch for the desired observer error dynamic system. We prove the convergence of the observer for the non-simplified model. Finally, we compare the performance of the proposed observer with that of the previously mentioned nonlinear observer, which shows significant improvement in the accuracy of estimation.     

A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

ABSTRACT

Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.     

A reduced-order nonlinear sliding mode observer for vehicle slip angle and tyre forces

In this paper, a reduced-order sliding mode observer (RO-SMO) is developed for vehicle state estimation. Several improvements are achieved in this paper. First, the reference model accuracy is improved by considering vehicle load transfers and using a precise nonlinear tyre model ‘UniTire’. Second, without the reference model accuracy degraded, the computing burden of the state observer is decreased by a reduced-order approach. Third, nonlinear system damping is integrated into the SMO to speed convergence and reduce chattering. The proposed RO-SMO is evaluated through simulation and experiments based on an in-wheel motor electric vehicle. The results show that the proposed observer accurately predicts the vehicle states.