电动汽车电磁辐射骚扰测量技术研究与实践

论述电动汽车的电磁场辐射骚扰的测量技术,重点论述频率范围为9kHz~30MHz的辐射骚扰测量方法中的难点问题,并对2种实验样车的电场和磁场骚扰分别进行了测量验证。     

汽车风机用电动机抗扰度及其测试--在馈电线上因传导和耦合引起的瞬态骚扰

介绍汽车风机用电动机在馈电线上因传导和耦合引起的瞬态电磁骚扰抗扰度测试和所需仪器。     

采用小波分析进行电喷汽油机失火诊断的研究

采取小波变换的方法对电喷汽油机的瞬时转速波动信号进行分解去噪处理。采用小波变换的Mallat快速算法,并从分解后的信号中提取特征值,用于诊断汽油机的失火故障。通过台架验证试验表明:文中采取的方法可以有效去除高速时的干扰信号,能满足失火诊断的要求。     

Effect of speed limits in degradable transport networks

This paper studies how link-specific speed limits influence the performance of degradable transport networks, in which the capacity of each link is a degradable random variable. The distribution and cumulative distribution of link travel time have been presented with the effect of speed limits taken into account. The mean and variance of link and route travel time are formulated. Three link states have been classified, and their physical meanings have been discussed. The relationship between critical capacity, travel time and speed limit has been elaborated. We have proposed a Speed Limit- and Reliability-based User Equilibrium (SLRUE), adopting travel time budget as the principle of travelers’ route choice. A heuristic method employing the method of successive averages is developed to solve the SLRUE in degradable networks. Through numerical studies, we find that for some networks both the mean and standard deviation of the total travel time could be reduced simultaneously by imposing some speed limits. The speed limit design problem has been studied, and it is found that imposing speed limits cannot always reduce the total travel time budget of a network.     

Estimating cycleway capacity and bicycle equivalent unit for electric bicycles

With the rapid increase of electric bicycles (E-bikes) in China, the heterogeneous bicycle traffic flow comprising regular bicycles and E-bikes using shared cycleway creates issues in terms of efficiency as well as safety. Capacity and bicycle equivalent units (BEUs) for E-bikes are two most important parameters for the planning, design, operation, and management of bicycle facilities. In this paper, eight traffic flow fundamental diagrams are developed for one-way cycleway capacity estimation, and a novel BEU estimation model is also proposed. Eleven datasets from different shared cycleway sections with different cycleway widths were collected in Hangzhou, China for estimation and evaluation purposes. The results indicate that, with around 70% share of E-bikes, the mean estimated capacity is 2348 bicycle/h/m. The effects on the capacity of the proportions of E-bikes, gender of cyclists, age of cyclists, and cyclists carrying things were also analyzed. The results implied that the estimated capacity is independent of a cyclist’s gender and age, but increases with the proportion of E-bikes. According to this study, the mean BEU for the E-bike is 0.66, and the converted capacities of pure regular bicycles and pure E-bikes are 1800 and 2727 bicycle/h/m, respectively. These findings can be used to propose practical countermeasures to improve the capacity of heterogeneous bicycle traffic flow on shared cycleway.     

A dynamic Bayesian network model for real-time crash prediction using traffic speed conditions data

Traffic crashes occurring on freeways/expressways are considered to relate closely to previous traffic conditions, which are time-varying. Meanwhile, most studies use volume/occupancy/speed parameters to predict the likelihood of crashes, which are invalid for roads where the traffic conditions are estimated using speed data extracted from sampled floating cars or smart phones. Therefore, a dynamic Bayesian network (DBN) model of time sequence traffic data has been proposed to investigate the relationship between crash occurrence and dynamic speed condition data. Moreover, the traffic conditions near the crash site were identified as several state combinations according to the level of congestion and included in the DBN model. Based on 551 crashes and corresponding speed information collected on expressways in Shanghai, China, DBN models were built with time series speed condition data and different state combinations. A comparative analysis of the DBN model using flow detector data and a static Bayesian network model was also conducted. The results show that, with only speed condition data and nine traffic state combinations, the DBN model can achieve a crash prediction accuracy of 76.4% with a false alarm rate of 23.7%. In addition, the results of transferability testing imply that the DBN models are applicable to other similar expressways with 67.0% crash prediction accuracy.     

GB/T 18655的2010版与2002版的差异分析

GB/T18655的2010新版与其2002旧版相比,在标准名称、频率范围和骚扰限值上都有较大变化。本文详细阐述新旧两版的主要差异,为相关人员提供参考。     

Characterising Green Light Optimal Speed Advisory trajectories for platoon-based optimisation

Conceptually, a Green Light Optimal Speed Advisory (GLOSA) system suggests speeds to vehicles, allowing them to pass through an intersection during the green interval. In previous papers, a single speed is computed for each vehicle in a range between acceptable minimum and maximum values (for example between standstill and the speed limit). This speed is assumed to be constant until the beginning of the green interval, and sent as advice to the vehicle. The goal is to optimise for a particular objective, whether it be minimisation of emissions (for environmental reasons), fuel usage or delay. This paper generalises the advice given to a vehicle, by optimising for delay over the entire trajectory instead of suggesting an individual speed, regardless of initial conditions – time until green, distance to intersection and initial speed. This may require multiple acceleration manoeuvres, so the advice is sent as a suggested acceleration at each time step. Such advice also takes into account a suitable safety constraint, ensuring that vehicles are always able to stop before the intersection during a red interval, thus safeguarding against last-minute signal control schedule changes. While the algorithms developed primarily minimise delay, they also help to reduce fuel usage and emissions by conserving kinetic energy. Since vehicles travel in platoons, the effectiveness of a GLOSA system is heavily reliant on correctly identifying the leading vehicle that is the first to be given trajectory advice for each cycle. Vehicles naturally form a platoon behind this leading vehicle. A time loop technique is proposed which allows accurate identification of the leader even when there are complex interactions between preceding vehicles. The developed algorithms are ideal for connected autonomous vehicle environments, because computer control allows vehicles’ trajectories to be managed with greater accuracy and ease. However, the advice algorithms can also be used in conjunction with manual control provided Vehicle-to-Infrastructure (V2I) communication is available.     

Maritime routing and speed optimization with emission control areas

Strict limits on the maximum sulphur content in fuel used by ships have recently been imposed in some Emission Control Areas (ECAs). In order to comply with these regulations many ship operators will switch to more expensive low-sulphur fuel when sailing inside ECAs. Since they are concerned about minimizing their costs, it is likely that speed and routing decisions will change because of this. In this paper, we develop an optimization model to be applied by ship operators for determining sailing paths and speeds that minimize operating costs for a ship along a given sequence of ports. We perform a computational study on a number of realistic shipping routes in order to evaluate possible impacts on sailing paths and speeds, and hence fuel consumption and costs, from the ECA regulations. Moreover, the aim is to examine the implications for the society with regards to environmental effects. Comparisons of cases show that a likely effect of the regulations is that ship operators will often choose to sail longer distances to avoid sailing time within ECAs. Another effect is that they will sail at lower speeds within and higher speeds outside the ECAs in order to use less of the more expensive fuel. On some shipping routes, this might give a considerable increase in the total amount of fuel consumed and the CO₂ emissions.     

Learning aircraft operational factors to improve aircraft climb prediction: A large scale multi-airport study

Ground-based aircraft trajectory prediction is a major concern in air traffic control and management. A safe and efficient prediction is a prerequisite to the implementation of new automated tools.In current operations, trajectory prediction is computed using a physical model. It models the forces acting on the aircraft to predict the successive points of the future trajectory. Using such a model requires knowledge of the aircraft state (mass) and aircraft intent (thrust law, speed intent). Most of this information is not available to ground-based systems.This paper focuses on the climb phase. We improve the trajectory prediction accuracy by predicting some of the unknown point-mass model parameters. These unknown parameters are the mass and the speed intent. This study relies on ADS-B data coming from The OpenSky Network. It contains the climbing segments of the year 2017 detected by this sensor network. The 11 most frequent aircraft types are studied. The obtained data set contains millions of climbing segments from all over the world. The climbing segments are not filtered according to their altitude. Predictive models returning the missing parameters are learned from this data set, using a Machine Learning method. The trained models are tested on the two last months of the year and compared with a baseline method (BADA used with the mean parameters computed on the first ten months). Compared with this baseline, the Machine Learning approach reduce the RMSE on the altitude by 48% on average on a 10 min horizon prediction. The RMSE on the speed is reduced by 25% on average. The trajectory prediction is also improved for small climbing segments. Using only information available before the considered aircraft take-off, the Machine Learning method can predict the unknown parameters, reducing the RMSE on the altitude by 25% on average.The data set and the Machine Learning code are publicly available.