基于CAN的混合动力汽车控制系统体系结构研究

在分析某型混合动力汽车的结构与工作原理的基础上，基于CAN总线技术，研究了控制系统的网络拓扑结构、节点功能及信息传递方式，以及硬件设计中的节点通信接口结构和控制系统的软件结构。其研究方法和结论为混合动力汽车以及其它类型车辆控制系统的研究和开发提供了理论依据和技术基础。     

我国清洁汽车的发展及对策

简述了我国清洁汽车的发展概况和存在的主要问题,重点提出我国清洁汽车“应加快燃油汽车先进排放控制技术的研发与应用;代用燃料汽车的发展重点应继续放在单一燃料燃气汽车上;混合动力汽车应加快发展,但自主开发应谨慎”等对策及建议。     

EQ6110混合动力电动汽车再生制动控制策略研究

分析了电机再生制动对车辆制动性能的影响以及典型城市公交客车运行工况特点，提出了适于EQ6110HEV的再生制动控制策略——低制动强度时优先采用再生制动，高强度时按比例复合再生制动与摩擦制动。仿真计算表明：在各种循环工况下，EQ6110HEV采用这种再生制动控制策略均有较好的节能效果，可降低能耗10％～25％。     

基于ADVISOR2002混合动力汽车控制策略模块开发

介绍了混合动力汽车仿真软件ADVISOR中的并联控制策略，结合课题的实际情况，对ADVISOR2002的并联控制策略进行了二次开发，并针对某一车型进行了仿真对比，结果表明所提出的控制策略不仅能够鲁棒地在ADVISOR2002界面中运行，而且合理、可靠，满足实际需要。     

基于MTG的永磁电机启动性能研究

高速永磁电机是MTG发电的关键技术之一，通常采用变频调速的启动方法。高速和高频的特性，决定了高速电机的设计与普通电机有较大的不同。通过建立数学模型，采用仿真的方法可以较准确获得永磁电机的启动性能。     

低噪声电站噪声测量问题

分析了现实采用的几种移动电站噪声测量方法，根据实际发展需要。针对噪声测量中存在的一些问题，提出了低噪声电站噪声测量方法的一些看法。     

高速公路机电系统升级的趋势、现状和要点

中国高速公路的不断建设,对高速公路机电系统提出了更高的要求.介绍了机电系统现状,分析了其将来的发展趋势,并指出了升级的要点.     

SS4B电力机车转向架可视化标准作业系统研究

SS_4B型电力机车转向架结构复杂,检修工序繁琐,对作业人员的检修技能培训要求高,针对目前培训方式存在的问题,提出建立转向架可视化标准作业系统,综合利用多媒体技术、数字化建模技术以及虚拟现实技术,构建集三维动画和人机交互功能于一体,信息集中化、培训作业标准化、培训时空自由化、周期短且考核针对性强的可视化培训系统,实现对SS_4B型电力机车转向架系统的内部结构、检修流程及其动态拆装过程的逼真模拟,提升实操培训效率和沉浸式体验,提高检修人员技能水平。     

Fast-charging station choice behavior among battery electric vehicle users

This study explores how battery electric vehicle users choose where to fast-charge their vehicles from a set of charging stations, as well as the distance by which they are generally willing to detour for fast-charging. The focus is on fast-charging events during trips that include just one fast-charge between origin and destination in Kanagawa Prefecture, Japan. Mixed logit models with and without a threshold effect for detour distance are applied to panel data extracted from a two-year field trial on battery electric vehicle usage in Japan. Findings from the mixed logit model with threshold show that private users are generally willing to detour up to about 1750 m on working days and 750 m on non-working days, while the distance is 500 m for commercial users on both working and non-working days. Users in general prefer to charge at stations requiring a shorter detour and use chargers located at gas stations, and are significantly affected by the remaining charge. Commercial users prefer to charge at stations encountered earlier along their paths, while only private users traveling on working days show such preference and they turn to prefer the stations encountered later when choosing a station in peak hours. Only private users traveling on working days show a strong preference for free charging. Commercial users tend to pay for charging at a station within 500 m detour distance. The fast charging station choice behavior is heterogeneous among users. These findings provide a basis for early planning of a public fast charging infrastructure.     

Socially optimal electric driving range of plug-in hybrid electric vehicles

This study determines the optimal electric driving range of plug-in hybrid electric vehicles (PHEVs) that minimizes the daily cost borne by the society when using this technology. An optimization framework is developed and applied to datasets representing the US market. Results indicate that the optimal range is 16 miles with an average social cost of $3.19 per day when exclusively charging at home, compared to $3.27 per day of driving a conventional vehicle. The optimal range is found to be sensitive to the cost of battery packs and the price of gasoline. When workplace charging is available, the optimal electric driving range surprisingly increases from 16 to 22 miles, as larger batteries would allow drivers to better take advantage of the charging opportunities to achieve longer electrified travel distances, yielding social cost savings. If workplace charging is available, the optimal density is to deploy a workplace charger for every 3.66 vehicles. Moreover, the diversification of the battery size, i.e., introducing a pair and triple of electric driving ranges to the market, could further decrease the average societal cost per PHEV by 7.45% and 11.5% respectively.