混合动力--未来城市公交的明星动力

本文阐述了目前国内外混合动力总成常用的几种方案,介绍了一种适合康明斯发动机的混合动力方案,展望了该动力总成的开发前景。     

车辆排放污染的综合控制

随着我国汽车保有量急剧的增加,车辆排放污染已成为亟待解决的重要课题,控制排放污染是一个大型系统工程,文中从车辆、道路交通环境、交通管理3个方面论述了对排放污染的综合控制。     

复杂路况下汽车主动避撞报警技术研究

汽车主动避撞报警技术对提高行车安全有重要意义,但如何减少复杂路况下行车时的误警率是当前需解决的关键问题。通过测量车载信息如汽车转向角度、车速、制动信号、转向灯信号等获知汽车的行驶状态,利用雷达技术感知路况和危险,基于合理的报警算法和控制策略,研制出复杂路况下的汽车主动避撞报警系统。通过实际道路报警试验,结果表明所采取的减少误警率的方法是有效的,所开发的报警系统更趋实用化。     

Experimental evaluation of Heavy Duty Vehicle speed patterns in urban and port areas and estimation of their fuel consumption and exhaust emissions

Exhaust emissions and fuel consumption of Heavy Duty Vehicles (HDVs) in urban and port areas were evaluated through a dedicated investigation. The HDV fleet composition and traffic driving from highways to the maritime port of Genoa and crossing the city were analysed. Typical urban trips linking highway exits to port gates and HDV mission profiles within the port area were defined. A validation was performed through on-board instrumentation to record HDV instantaneous speeds in urban and port zones. A statistical procedure enabled the building-up of representative speed patterns. High contrasts and specific driving conditions were observed in the port area. Representative speed profiles were then used to simulate fuel consumption and emissions for HDVs, using the Passenger car and Heavy duty Emission Model (PHEM). Complementary estimations were derived from Copert and HBEFA methodologies, allowing the comparison of different calculation approaches and scales. Finally, PHEM was implemented to assess the performances of EGR or SCR systems for NO_X reduction in urban driving and at very low speeds.The method and results of the investigation are presented. Fuel consumption and pollutant emission estimation through different methodologies are discussed, as well as the necessity of characterizing very local driving conditions for appropriate assessment.     

高速公路计重收费面临的问题与应对措施

阐述了高速公路计重收费面临的问题,提出应对措施。     

Evolution of the household vehicle fleet: Anticipating fleet composition, PHEV adoption and GHG emissions in Austin, Texas

In today’s world of volatile fuel prices and climate concerns, there is little study on the relationship between vehicle ownership patterns and attitudes toward vehicle cost (including fuel prices and feebates) and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s personal-fleet evolution.Opinion survey results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are price, type/class, and fuel economy. Most (56%) respondents also indicated that they would consider purchasing a Plug-in Hybrid Electric Vehicle (PHEV) if it were to cost $6000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings.Twenty five-year simulations of Austin’s household vehicle fleet suggest that, under all scenarios modeled, Austin’s vehicle usage levels (measured in total vehicle miles traveled or VMT) are predicted to increase overall, along with average vehicle ownership levels (both per household and per capita). Under a feebate, HEVs, PHEVs and Smart Cars are estimated to represent 25% of the fleet’s VMT by simulation year 25; this scenario is predicted to raise total regional VMT slightly (just 2.32%, by simulation year 25), relative to the trend scenario, while reducing CO₂ emissions only slightly (by 5.62%, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 24% and CO₂ emissions by 30% (relative to trend).Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross-over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet). Feebate-policy receipts are forecasted to exceed rebates in each simulation year.In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have added effects on energy dependence and greenhouse gas emissions.     

汽车车架动特性分析及应用

应用大型结构分析软件ＳＡＰ５Ｐ，对某车架者了有限元动态分析和实验模态分析，通过分析所提供的信息，对某客货车在使用中发生的故障进行了诊断，提出了相应的改进措施，从而使该车具有更好的动态性能。     

基于支持向量机的水下机器人操纵运动参数辨识

根据平面运动机构试验所得到的水动力系数确定水下运载器的运动方程,应用最小二乘支持向量机对水下运载器的试验数据进行分析,辨识得到了水动力系数,并将辨识得到的参数同平面运动机构试验所确定的参数相比,验证了最小二乘支持向量机进行水下运载器操纵运动参数辨识的有效性。根据所辨识的水动力系数进行了模型验证,证明所辨识得到的水动力参数是可靠的。该方法对水下运载器的操纵和控制的研究有重要意义。     

应用Cruise(v3.0)的纯电动汽车仿真方法

本文阐述了利用奥地利AVL公司开发的Cruise(V3.0)软件进行纯电动汽车整车建模、制定仿真计算任务、运行计算并分析计算结果等的具体方法。着重介绍了Cruise、Matlab\Simulink联合仿真技术,从而将整车控制策略、电源管理策略、电机控制策略和制动回收策略应用到仿真技术中的过程和方法。     

Geometric design safety estimation based on tire–road side friction

A closed-loop drive–vehicle–road–environment system (DVRES) model was established using Adams/Car and Matlab/Simulink. Dynamic responses of lateral tire forces based on tire–road side friction and road geometric characteristics are used to investigate vehicle side slip for geometric design safety estimation. The root mean square, the maximum values of lateral tire forces, comfort limit on curves and vehicle trajectories are used to quantify the safety margin of side friction. The simulation results show that the safety margins of lateral tire forces for radius, operating speed and superelevation rate were 18.2%, 19.3% and 17.6%, respectively, to guarantee good vehicle lateral reliability and ride comfort, while lower speeds are optimal in wet and slippery roads. Finally, a case study was conducted to illustrate the analysis of road design safety, and on-site experiment testing further validated the accuracy and reliability of the closed-loop DVRES model.