基于客户关联的纯电动轻卡驱动电机可靠性台架试验研究

为研究轻卡BEV驱动电机可靠性台架试验标准与实际客户的关联,文章基于整车CAN网络,采集实际客户日常行驶过程中的电机扭矩、转速等数据,通过Ncode软件计算出电机的扭矩谱。根据Miner准则,计算出电机20万公里的疲劳伪损伤。对比电机可靠性台架试验的电机伪损伤,确定电机台架试验标准是否满足客户需求。     

工程机械底盘液压驱动装置性能分析(1)

液压驱动装置的结构形式与性能是工程机械底盘液压驱动与控制的两大重要组成部分之一。分析讨论了各类结构形式的液压驱动装置及其与行走机构组成不同形式的液压车辆底盘时的性能与特点。对车辆工作所需要的特殊功能，如液压同步、限速、制动以及补油回路等也进行了讨论，有助于工程车辆的理论研究与产品设计。     

发动机冷却风扇温控液力驱动系统

本文介绍了发动机冷却风扇温控系统，这种新型的控制系统具有自动测量，微机控制，液压驱动，无级变速等特点，可以使发动机在最佳温度下工作，文中详细介绍了系统构成，测控元件选择及软件设计。     

接地面部分溶化条件下冰面轮胎摩擦力的研究

综合运用流体动力润滑理论的能量守恒定律，提出了接地面部分溶化条件下冰面上汽车轮胎的摩擦力或牵引力的理论计算模型。证明了正常工作条件下，接地面上冰产生溶化的可能。对不同制动和驱动条件下轮胎摩擦力进行了计算，取得了同试验数据吻合很好的预测结果。并同已有预测模型进行了比较，比较结果表明：本文依据严格的润滑理论所推导出的冰面轮胎牵引力模型比其它已有理论模型更接近试验结果。     

Transport energy consumption in mountainous roads. A comparative case study for internal combustion engines and electric vehicles in Andorra

This paper analyses transport energy consumption of conventional and electric vehicles in mountainous roads. A standard round trip in Andorra has been modelled in order to characterise vehicle dynamics in hilly regions. Two conventional diesel vehicles and their electric-equivalent models have been simulated and their performances have been compared. Six scenarios have been simulated to study the effects of factors such as orography, traffic congestion and driving style. The European fuel consumption and emissions test and Artemis urban driving cycles, representative of European driving cycles, have also been included in the comparative analysis. The results show that road grade has a major impact on fuel economy, although it affects consumption in different levels depending on the technology analysed. Electric vehicles are less affected by this factor as opposed to conventional vehicles, increasing the potential energy savings in a hypothetical electrification of the car fleet. However, electric vehicle range in mountainous terrains is lower compared to that estimated by manufacturers, a fact that could adversely affect a massive adoption of electric cars in the short term.     

Congestion and pollution consequences of driving-to-school trips: A case study in Beijing

Parents compete for high-quality education for their children by enrolling them in good schools. However, in a Chinese mega-city like Beijing, three factors jointly lead to the spatial separation between schools and homes: the centralized public goods provision mechanism, the historical dependency in school location, and the constrained supply of housing in downtown. Without an adequate number of school buses, this spatial separation of schools and homes triggers the numerous long-distance driving-to-school trips by private vehicle during workday morning rush hours in Beijing. We use the start and end dates of “school holiday” as exogenous repeated shocks to the aggregate traffic congestion, and employ the two-stage least squares (2SLS) regression approach to examine the congestion and pollution consequences of such driving-to-school trips in Beijing. We find that, all else being equal, workdays during school holidays have a traffic congestion index 20% lower than that of non-school-holiday workdays. Such a sharp reduction in congestion leads to a significant decrease in PM₁₀ concentration. Policymakers should lower such “extra” congestion and environmental costs via optimizing the spatial balance between school supply and demand.     

Driving cycle prediction model based on bus route features

Bus fuel economy is deeply influenced by the driving cycles, which vary for different route conditions. Buses optimized for a standard driving cycle are not necessarily suitable for actual driving conditions, and, therefore, it is critical to predict the driving cycles based on the route conditions. To conveniently predict representative driving cycles of special bus routes, this paper proposed a prediction model based on bus route features, which supports bus optimization. The relations between 27 inter-station characteristics and bus fuel economy were analyzed. According to the analysis, five inter-station route characteristics were abstracted to represent the bus route features, and four inter-station driving characteristics were abstracted to represent the driving cycle features between bus stations. Inter-station driving characteristic equations were established based on the multiple linear regression, reflecting the linear relationships between the five inter-station route characteristics and the four inter-station driving characteristics. Using kinematic segment classification, a basic driving cycle database was established, including 4704 different transmission matrices. Based on the inter-station driving characteristic equations and the basic driving cycle database, the driving cycle prediction model was developed, generating drive cycles by the iterative Markov chain for the assigned bus lines. The model was finally validated by more than 2 years of acquired data. The experimental results show that the predicted driving cycle is consistent with the historical average velocity profile, and the prediction similarity is 78.69%. The proposed model can be an effective way for the driving cycle prediction of bus routes.     

改进的锤击法测量原点阻抗

机械阻抗是机械系统特性的集中参数表示, 是解决机械系统耦合问题的一个重要工具。现有的机械阻抗方法有激振器激振传感器,冲击锤敲击实验结构等方法,但都有各自的局限性。本文提出了改进的锤击法测量原点阻抗。通过实验对比,对其可行性进行了研究,证明了此种方法可以快速并较为准确的获得结构的原点阻抗,具有工程实用性。     

Customizing driving cycles to support vehicle purchase and use decisions: Fuel economy estimation for alternative fuel vehicle users

Wider deployment of alternative fuel vehicles (AFVs) can help with increasing energy security and transitioning to clean vehicles. Ideally, adopters of AFVs are able to maintain the same level of mobility as users of conventional vehicles while reducing energy use and emissions. Greater knowledge of AFV benefits can support consumers’ vehicle purchase and use choices. The Environmental Protection Agency’s fuel economy ratings are a key source of potential benefits of using AFVs. However, the ratings are based on pre-designed and fixed driving cycles applied in laboratory conditions, neglecting the attributes of drivers and vehicle types. While the EPA ratings using pre-designed and fixed driving cycles may be unbiased they are not necessarily precise, owning to large variations in real-life driving. Thus, to better predict fuel economy for individual consumers targeting specific types of vehicles, it is important to find driving cycles that can better represent consumers’ real-world driving practices instead of using pre-designed standard driving cycles. This paper presents a methodology for customizing driving cycles to provide convincing fuel economy predictions that are based on drivers’ characteristics and contemporary real-world driving, along with validation efforts. The methodology takes into account current micro-driving practices in terms of maintaining speed, acceleration, braking, idling, etc., on trips. Specifically, using a large-scale driving data collected by in-vehicle Global Positioning System as part of a travel survey, a micro-trips (building block) library for California drivers is created using 54 million seconds of vehicle trajectories on more than 60,000 trips, made by 3000 drivers. To generate customized driving cycles, a new tool, known as Case Based System for Driving Cycle Design, is developed. These customized cycles can predict fuel economy more precisely for conventional vehicles vis-à-vis AFVs. This is based on a consumer’s similarity in terms of their own and geographical characteristics, with a sample of micro-trips from the case library. The AFV driving cycles, created from real-world driving data, show significant differences from conventional driving cycles currently in use. This further highlights the need to enhance current fuel economy estimations by using customized driving cycles, helping consumers make more informed vehicle purchase and use decisions.