双离合自动变速器电控电动换挡执行机构仿真优化与试验对比

为了提高双离合变速器在车辆上的换挡品质,提升电气化换挡执行机构的工作效率,给出执行机构性能与换挡品质评价准则;通过两种不同双离合变速器电控电动执行机构的工作原理与性能分析,基于MATLAB数值仿真方法优化出最高效率进行试验.基于车辆6个维度的换挡工况对二者所搭载的变速器分别进行试验与CAN线数据采集,对比研究两种机构优缺点,通过雷达图的方式进行优劣说明.结果表明,两种执行机构传动效率可达70％且换挡品质相当.可见Tip in tip out工况对二者要求最为严格.     

市域(郊)铁路车辆选型方案探讨

随着城镇化进程的加快和城市经济辐射能力的增强,以特大城市中心城为核心的都市圈逐渐形成,都市圈内的经济联系更加紧密,都市圈核心区与外围的出行需求迅速增长,市域(郊)铁路的概念被引进,选择符合市域(郊)铁路特点的车辆类型是设计的重点。     

Estimating emissions based on the integration of microscopic traffic simulation and vehicle dynamics model

This study proposes an integrated simulation approach, which consists of a microscopic traffic simulation model, a vehicle dynamics model, and an emission estimation model, in order to estimate emissions based on more reliable vehicle performance measures. The vehicle performance measures such as engine power and engine speed significantly relate to the amount of emissions, and road curvatures and inclinations are the core inputs affecting these vehicle performance measures. Therefore, providing reliable vehicle performance measures reflecting the road geometric attributes is critical for a reliable emission estimation. This study proposes to use the microscopic traffic simulation model for generating vehicle trajectories, which is advantageous in modeling various traffic situations, and the vehicle dynamics model for producing the vehicle performance measures based on the vehicle trajectories. Finally, the outputs from the vehicle dynamics model are fed into the emission estimation model to compute emission measures. This study conducted a case-study using two road sections, one is a hypothesized road section, including various curvatures and inclinations with regular variations, and the other is a Kesselberg road section, which is an actual geometry in Bayern, Germany. The emission measures are estimated in these case-study road sections using both an existing simulation approach and the proposed integrated simulation approach. The difference between these two emission estimation approaches is discussed in terms of the emission measures, including fuel consumption, nitrogen oxides, and particulate matters.     

Analysis of the effect of charging infrastructure design on electric taxi driving profiles: A case study approach on the example of Singapore

Replacing conventional vehicle taxis with electric vehicles would be an efficient measure to reduce greenhouse gas emissions. Due to the limited range and long charging times of current battery electric vehicles, it is of utmost importance to provide sufficient charging facilities. This article analyses the impact of the placement and charging power of charging stations on potential mileage and revenue of electric taxis on the example of Singapore. Therefore, we developed an agent-based electric taxi simulation model to investigate electric taxis’ driving profiles with respect to different vehicle types and charging infrastructure designs. This model is also capable of simulating conventional taxi driving profiles. The validation of these simulation results with real taxi data showed that the model is reproducing taxi driving profiles with high accuracy in great detail. We found out that electric taxis could reach the same mileage and revenue as conventional taxis if charging with a power of 160?kW is possible. Furthermore, we discovered that waiting times for available charging stations have a stronger effect on revenue than the length of detours to reach charging stations. Based on these findings, we concluded that it is more important to reduce waiting times by placing sufficient numbers of charging stations at each location before expanding the charging network by installing small numbers of charging stations at many locations.     

Fuel consumption for various driving styles in conventional and hybrid electric vehicles: Integrating driving cycle predictions with fuel consumption optimization*

Improving fuel economy and lowering emissions are key societal goals. Standard driving cycles, pre-designed by the US Environmental Protection Agency (EPA), have long been used to estimate vehicle fuel economy in laboratory-controlled conditions. They have also been used to test and tune different energy management strategies for hybrid electric vehicles (HEVs). This paper aims to estimate fuel consumption for a conventional vehicle and a HEV using personalized driving cycles extracted from real-world data to study the effects of different driving styles and vehicle types on fuel consumption when compared to the estimates based on standard driving cycles. To do this, we extracted driving cycles for conventional vehicles and HEVs from a large-scale U.S. survey that contains real-world GPS-based driving records. Next, the driving cycles were assigned to one of three categories: volatile, normal, or calm. Then, the driving cycles were used along with a driver-vehicle simulation that captures driver decisions (vehicle speed during a trip), powertrain, and vehicle dynamics to estimate fuel consumption for conventional vehicles and HEVs with power-split powertrain. To further optimize fuel consumption for HEVs, the Equivalent Consumption Minimization Strategy (ECMS) is applied. The results show that depending on the driving style and the driving scenario, conventional vehicle fuel consumption can vary widely compared with standard EPA driving cycles. Specifically, conventional vehicle fuel consumption was 13% lower in calm urban driving, but almost 34% higher for volatile highway driving compared with standard EPA driving cycles. Interestingly, when a driving cycle is predicted based on the application of case-based reasoning and used to tune the power distribution in a hybrid electric vehicle, its fuel consumption can be reduced by up to 12% in urban driving. Implications and limitations of the findings are discussed.     

How much are electric vehicles driven?

The prospect for electric vehicles as a climate change solution hinges on their ability to reduce gasoline consumption. But this depends on how many miles electric vehicles are driven and on how many miles would have otherwise been driven in gasoline-powered vehicles. Using newly-available U.S. nationally representative data, this paper finds that electric vehicles are driven considerably fewer miles per year on average than gasoline-powered vehicles. The difference is highly statistically significant and holds for both all-electric and plug-in hybrid vehicles, for both single- and multiple-vehicle households, and both inside and outside California. The paper discusses potential explanations and policy implications. Overall, the evidence suggests that today’s electric vehicles imply smaller environmental benefits than previously believed.     

针对车辆AEB系统失效和误作用场景的研究

本文基于预期功能安全,采用功能分解方法,总结出部分车辆自动紧急制动(AEB)系统失效和误作用场景,并从中选取典型场景作为测试场景,开展不同车型的实车道路试验,总结出AEB系统的缺陷与隐患分类。希望此类研究能为AEB系统失效和误作用测试标准的制定提供参考。     

A decision support model for buying battery electric vehicles considering consumer learning and psychological behavior

Developments in battery electric vehicles (BEVs) have received more and more attentions in the last decades due to alleviating carbon emissions and energy crisis. Consequently, how to rank alternative BEVs to assist consumers make better purchasing decisions is a worthy research study. However, there are still some defects in the existing studies for ranking of BEVs: 1) the evaluation index system of BEVs is not comprehensive; 2) the determination of criteria weights cannot be well applied to the actual purchase scenarios; and 3) the psychological behavior of consumers is ignored. To address those shortcomings, this paper proposes a decision support model to assist with consumers to buy BEVs. First, a systematic evaluation criteria system of BEVs including quantitative and qualitative indicators from parameter configurations and online reviews is constructed. Then, a weight algorithm considering consumer learning is proposed to determine the criteria weights. Furthermore, a decision support process considering consumers' regret avoidance behavior is proposed. Finally, an actual BEV purchase case is given to illustrate the practicability of the decision support model. This can be seen in case studies the proposed support model can be well applied to consumers with different regret avoidance behaviours.     

A mixed behaviour equilibrium model with mode choice and its application to the endogenous demand of automated vehicles

This study develops a mixed behavioural equilibrium model with explicit consideration of mode choice (MBE-MC) in a transportation system where fully automated vehicles (AV) coexist with conventional human-driven vehicles (HV). For the mode choice, travellers select among three options, following a logit modal split: driving their private HV, or taking an AV mobility service provided by either a firm or the government. For the route choice, the HV drivers follow the random utility maximisation principle while central agents route the AV passengers following the Cournot Nash (firm agent) or Social Optimal (government agent) principles. We consider two types of travel costs (i.e. travel time and monetary travel cost) to characterise the new features (e.g. expanded link capacity and reduced value of time) of the mixed AV–HV transportation system. We model the MBE-MC problem in a combined mode–route choice framework and formulate it as a route-based variational inequality (VI) problem. We show the equivalence between the VI formulation and the MBE-MC problem, and the existence of a solution to the MBE-MC problem. Then, we modify a partial linearisation algorithm for solving the proposed model. Numerical results validate the equilibrium conditions and show the efficacy of the new model in capturing the features of the mixed AV–HV transportation system. The impact patterns of different parameters on (1) the network performance in terms of AV share and system cost and (2) on the solution efficiency are analysed.     

增程式电动汽车产业浅析

新能源汽车是未来汽车产业转型发展的重要方向。我国新能源汽车前期发展取得了突出的成绩,产销量位居世界首位。但目前产业发展正面临新形势,未来一段时期内将由“政策驱动”转向“政策+市场双驱动”。到2021年购置补贴完全退出后的后补贴时代,如何保障新能源汽车产业的健康、可持续发展,值得人们共同思考,开发什么类型的产品才能更好地满足消费者的需求,值得行业企业考虑。