多轴专用车底盘轴荷估算方法

在理论计算和实践经验的基础上,论述和介绍了多轴(三轴及三轴以上)专用汽车底盘方案设计阶段轴荷估算的方法.     

桑塔纳汽车后桥强度有限元分析及改进方案

根据桑塔纳2000汽车后桥的实际结构，利用通用有限元软件ANSYS，对后桥进行了弹塑性静态结构有限元分析。主要研究了结构焊接处强度、横梁破坏处在半个周期载荷波内应力状态变化；给出了在最大剪应变幅平面上，影响疲劳强度及寿命的基本参量的计算结果；提出了提高后桥强度的改进方案。     

三轴汽车前后轮角输入时的响应特性

本文详细推导了三轴汽车线性二自由度模型的运动微分方程，分析了汽车对前后轮角输入时的移居记响应特性。从汽车动力学的角度讨论了前后轮转应具备的比例关系。该方法同样适用于其它多轴汽车的建模分析。     

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。     

应用线弹性疲劳损伤理论的沥青路面轴载换算

应用疲劳损伤力学基本原理,探讨了适用于沥青路面轴载换算的新方法,并对该方法拟合的公式与规范按弯沉等效推荐的当量轴载换算公式进行了比较。计算结果表明在进行沥青路面轴载换算时,采用不同的等效换算原理,将导致非标准轴载与标准轴载当量换算系数上的不同,前者较后者大。这种差异一方面直接影响沥青路面的结构厚度设计,对设计年限内累计当量标准轴载作用次数的低估,必然导致沥青路面早期破损现象的发生;另一方面揭示弯沉等效表征的是路面结构的瞬间刚度性质,代替不了路面结构的长期疲劳强度特性。     

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.     

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.