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。     

几种柴油发动机的电控特点介绍

为满足汽车更高的尾气排放标准,许多客车厂已开始批量使用电控柴油发动机。这些发动机电路控制差异较大。本文重点介绍几种机器控制原理的差异。     

新的轮式驱动电动车电子差速控制算法的研究

提出了一种用于轮式驱动电动车的电子差速控制算法,将转弯时转矩分配计算和基于车轮滑移率的开关控制相结合,对车辆左右驱动轮输入不同的转矩,同时根据轮胎偏转角的变化率确定目标滑移率。仿真研究证明,与采用机械差速器相比,新的电子差速控制系统鲁棒性好,车辆的驾驶更安全平稳,并能获得更优异的转向性能和更快的响应特性。     

电动汽车电源变换器的柔性安全设计

根据电动汽车电源变换器功率开关管的温度-电流特性,提出了基于温度变化的动态电流限制方法,用来避免功率开关管过热及由过热保护而引起的切断电源变换器输出功率的现象,保证电动汽车的安全运行。在此基础上开发了单片机测控单元,实现功率开关管电流、温度的监测以及电源变换器输出电流的实时调节,保证电源变换器的持续、安全工作。通过车载CAN网络,实现电源变换器与整车监测系统的通信。     

电动助力转向系对汽车角输入响应影响的仿真

详细地分析和推导出了具有不同控制方式的EPS系统的传递函数,在Matlab环境中进行了仿真计算并对结果进行了分析,定性地说明了EPS系统的控制方式和结构参数对汽车转向盘角阶跃输入下的稳态、瞬态和频率响应特性的影响。     

动力分散型城郊快速电动车组转向架方案设计

介绍一种动力分散型城郊快速电动车组转向架的主要技术参数和基本结构方案。     

解放牌CA1111P1K2L7型柴油载货汽车起动系统电路故障诊断与排除

解放牌CA1111P1K2L7型柴油载货汽车起动系统电路中增设了DK238型电源总开关和断油电磁铁，该装置具有电源电路和起动电路的保护作用，介绍了该起动系统电路的结构及工作原理，以及该电路的常见故障与诊断。     

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.     

Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach

Vehicle electrification is a promising approach towards attaining green transportation. However, the absence of charging stations limits the penetration of electric vehicles. Current approaches for optimizing the locations of charging stations suffer from challenges associated with spatial–temporal dynamic travel demands and the lengthy period required for the charging process. The present article uses the electric taxi (ET) as an example to develop a spatial–temporal demand coverage approach for optimizing the placement of ET charging stations in the space–time context. To this end, public taxi demands with spatial and temporal attributes are extracted from massive taxi GPS data. The cyclical interactions between taxi demands, ETs, and charging stations are modeled with a spatial–temporal path tool. A location model is developed to maximize the level of ET service on the road network and the level of charging service at the stations under spatial and temporal constraints such as the ET range, the charging time, and the capacity of charging stations. The reduced carbon emission generated by used ETs with located charging stations is also evaluated. An experiment conducted in Shenzhen, China demonstrates that the proposed approach not only exhibits good performance in determining ET charging station locations by considering temporal attributes, but also achieves a high quality trade-off between the levels of ET service and charging service. The proposed approach and obtained results help the decision-making of urban ET charging station siting.     

The impact of car specifications,prices and incentives for battery electric vehicles in Norway: Choices of heterogeneous consumers

Electric vehicles (EVs), specifically Battery EVs (BEVs), can offer significant energy and emission savings over internal combustion engine vehicles. Norway has a long history of research and government incentives for BEVs. The BEV market in Norway allows us to fully examine consumers’ BEV choices influenced by car specifications, prices and government incentives (public bus lanes access, toll waiver and charging stations). The Random-Coefficient Discrete Choice Model (referred to as the BLP model) is applied to understand the choices of heterogeneous personal consumers and business buyers. Our study is instantiated on the entire EV sales data in Norway from 2011 to 2013, as well as a set of demographics at the municipality level. The results suggest significant positive effects of BEV technology improvement, space, toll waiver and charging station density on EV demand for both personal consumers and business buyers. However, the effects on business buyers may be generally less pronounced than on personal consumers. Interestingly, bus lanes access demonstrates a negative impact for personal consumers, possibly due to consumers’ concern regarding bus lane congestion. In addition, preferences on the BEV price can vary statistically among consumers with different income levels. Compared to the BEV technology development, demographical features and municipal incentives may have generally less impacts on market shares within the BEV market.