Prediction of fatigue life and estimation of its reliability on the parts of an air suspension system

Air suspension systems have been implemented in various commercial vehicles, such as buses and special purpose trucks, because of the comfortable ride and easy height control. An evaluation of the durability of vehicle parts has been required for service life and safety starting in the early stages of design. The cyclic load applied to the vehicle can cause fatigue failure of parts, such as the suspension frame. This paper presents a method to predict the fatigue life of the suspension frame at the design stage of the air suspension system used in a heavy-duty vehicle. To estimate the fatigue life using the SN method, the Dynamic Stress Time History (DSTH) is necessary for the part of interest. DSTH can be obtained from the results of the flexible body dynamic analysis using the Belgian road simulation and the Modal Stress Recovery (MSR) method. Furthermore, the reliability of the predicted fatigue life can be evaluated by considering the variations in material properties. The probability and distribution of the expected life cycle can be obtained using experimental design with a minimum number of simulations. The advantage of using statistical methods to evaluate the life cycle is the ability to predict replacement time and the probability of failure of mass-produced parts. This paper proposes a rapid and simple method that can be effectively applied to the design of vehicle parts.     

Transient characteristics of emissions during engine start/stop operation employing a conventional gasoline engine for HEV application

In this paper, we investigate the transient characteristics of combustion and emissions during engine start/stop operations in hybrid electric vehicle (HEV) applications. Hydrocarbon (HC) emissions during the initial 2^nd∼9^th cycles are found to be significantly greater when the engine is quickly started under the original engine calibration mode. Lower intake manifold absolute pressure (MAP) was also found to cause larger residual gas dilution and poor combustion, resulting in a higher HC concentration when the cranking speed was increased. The post-catalyst HC concentration was found in the way of initially decrease and then to increase again as the cranking speed was increased. A lowest concentration value was achieved at a cranking speed of 1000 r/min. Engine shut-down by fuel cut-off was shown to produce lower emissions than shut-down by ignition cut-off as one can avoid misfire of the last fuel injection cycle. The fuel deposited during the stop process seems to impact engine restart enrichment mostly during the initial 0.7 s for this engine, whose performance is dominated by the MAP transition characteristic and the time coefficient for fuel vaporization in this time period     

A self-powering system based on tire deformation during driving

Tire intelligence is vital in the improvement of the safety of vehicles because the tire supports the car body and is the contact point between the vehicle and the road. To create an intelligent tire, sensors must be installed to measure the behavior of the tire. However, it is difficult to apply a wired sensor system on the wheel of the tire. Hence, it is necessary to implement a self-powering, wireless system (a type of energy harvesting system) that can be mounted inside the tire. The purpose of this study is to convert the strain energy caused by deformation of the tire while driving into useful electrical energy to supply the sensor system. A flexible piezofiber is utilized for the energy conversion. The variation in strain, due to changes in speed, load, and the internal pressure of the tire, was measured along two axial directions to evaluate the amount of available strain energy. The amount of strain changed from 0.15% to 0.8%. To predict the amount of available energy from a tire, we perform an analysis of the relationship between the strain and the voltage. In addition, experiments for impedance matching between piezofiber and related circuits were conducted to optimize the external loads for transferring energy efficiently. Based on the procedure mentioned above, at least 0.58 mJ of electrical energy can be generated by using the laterally oriented strain (1500 to 2500 micro strain). The result of this study is expected to enhance the potential realization of self-generating wireless sensor systems for so-called ??intelligent?? tires.     

Optimal synthesis of function generation in steering linkages

This paper presents an approach to optimize planar mechanisms for function generation synthesis. It is applied to the design of different steering linkages used in road vehicles where the Ackermann condition is formulated as the objective function. Four different mechanisms are kinematically defined and synthesized in terms of the proposed method. Limitations of the size of the elements are also considered by means of inequality constraints. Solutions are presented as the difference between desired behaviour, established by Ackermann, and actual generated performance. The results show that the accuracy achieved by the proposed procedure is satisfactory for working conditions in this type of linkages.     

High-strain-rate fracture of adhesively bonded composite joints in DCB and TDCB specimens

Double-cantilever beam (DCB) and tapered double-cantilever beam (TDCB) specimens are the test configurations most commonly used to measure the fracture toughness of composites and adhesive joints. Strain rates of 1 to 18.47 m/s were applied to the test specimens via high-speed hydraulic test equipment. Because the fracture occurs through the adhesively bonded joints and the cracks grow rapidly, the crack length and beam displacement were recorded by a high-speed camera. An energy range from 0 to 10 J was often observed in the high-strain-rate fracture experiments for nonlinear plastic behavior of the dynamically loaded adhesively bonded DCB and TDCB specimens. The range of energy release rates (fracture energy) for TDCB specimen was 2 to 3 times higher than that of a DCB specimen for all high strain rates. The fracture energy of automotive adhesive joints can be estimated using the experimental results in this study for the fracture toughness (G_IC) under high rates of loading. The crack grows as the applied fracture energy exceeds the value of the critical energy release rate (G_IC) at the crack tip. The energy release rate was calculated using the fracture mechanics formula. The key fracture mechanics parameter, the fracture energy GIC, was ascertained as a function of the test rate and can be used to assess and model the overall joint performance.     

Fuel consumption of fuel cell hybrid vehicles considering battery SOC differences

Fuel cell hybrid vehicles (FCHVs) have become one of the most promising candidates for future transportation due to current energy supply problem and environmental problem. Fuel economy is an important factor in FCHVs. In order to properly evaluate the fuel economy of an FCHV, the initial battery state of charge (SOC) and the final battery SOC have to be identical so that the effect of the battery energy usage on the fuel economy is neglected. In the simulation or in the real driving, however, the final battery SOC is usually different from the initial battery SOC, and the final battery SOC often depends on the power management strategy. To consider the difference between the two battery SOC values, the concept of equivalent fuel consumption is presented by two methods. One is based on the relationship between delta SOC and delta fuel consumption, and the other is based on the optimal control theory. Two rule-based power management strategies for an FCHV are presented, and for each strategy, the fuel economy is evaluated based on the two methods. The characteristics of the two methods are discussed and compared, and the superior one is selected based on the comparison.     

电力牵引系统用优化的传动机组

原边能源是电动汽车牵引系统中易造成故障的一个组件,力争对应用了成熟技术的发动机组进行优化.     

Enhanced traffic information dissemination to facilitate toll road utilization: a nested logit model of a stated preference survey in Texas

This paper investigates the effects of the provision of traffic information on toll road usage based on a stated preference survey conducted in central Texas. Although many researchers have studied congestion pricing and traffic information dissemination extensively, most of them focused on the effects that these instruments individually produce on transportation system performance. Few studies have been conducted to elaborate on the impacts of traffic information dissemination on toll road utilization. In this study, 716 individuals completed a survey to measure representative public opinions and preferences for toll road usage in support of various traffic information dissemination classified by different modes, contents, and timeliness categories. A nested logit model was developed and estimated to identify the significant attributes of traffic information dissemination, traveler commuting patterns, routing behavior, and demographic characteristics, and analyze their impacts on toll road utilization. The results revealed that the travelers using dynamic message sign systems as their primary mode of receiving traffic information are more likely to choose toll roads. The potential toll road users also indicated their desire to obtain traffic information via internet. Information regarding accident locations, road hazard warnings, and congested roads is frequently sought by travelers. Furthermore, high-quality congested road information dissemination can significantly enhance travelers’ preferences of toll road usage. Specifically the study found that travelers anticipated an average travel time saving of about 11.3 min from better information; this is about 30 % of travelers’ average one-way commuting time. The mean value of the time savings was found to be about $11.82 per hour, close to ½ of the average Austin wage rate. The model specifications and result analyses provide in-depth insights in interpreting travelers’ behavioral tendencies of toll road utilization in support of traffic information. The results are also helpful to shape and develop future transportation toll system and transportation policy.     

Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems

Map-matching (MM) algorithms integrate positioning data from a Global Positioning System (or a number of other positioning sensors) with a spatial road map with the aim of identifying the road segment on which a user (or a vehicle) is travelling and the location on that segment. Amongst the family of MM algorithms consisting of geometric, topological, probabilistic and advanced, topological MM (tMM) algorithms are relatively simple, easy and quick, enabling them to be implemented in real-time. Therefore, a tMM algorithm is used in many navigation devices manufactured by industry. However, existing tMM algorithms have a number of limitations which affect their performance relative to advanced MM algorithms. This paper demonstrates that it is possible by addressing these issues to significantly improve the performance of a tMM algorithm. This paper describes the development of an enhanced weight-based tMM algorithm in which the weights are determined from real-world field data using an optimisation technique. Two new weights for turn-restriction at junctions and link connectivity are introduced to improve the performance of matching, especially at junctions. A new procedure is developed for the initial map-matching process. Two consistency checks are introduced to minimise mismatches. The enhanced map-matching algorithm was tested using field data from dense urban areas and suburban areas. The algorithm identified 96.8% and 95.93% of the links correctly for positioning data collected in urban areas of central London and Washington, DC, respectively. In case of suburban area, in the west of London, the algorithm succeeded with 96.71% correct link identification with a horizontal accuracy of 9.81 m (2σ). This is superior to most existing topological MM algorithms and has the potential to support the navigation modules of many Intelligent Transport System (ITS) services.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.