Ammonia as a green fuel and hydrogen source for vehicular applications

Ammonia, mostly known as a nitrogen feedstock for agriculture or as a refrigerant, has been occasionally used in the past as fuel for internal combustion engines and fuel cells. Because it is a carbon-free substance and has high octane number there is currently a renewed interest for using ammonia as a fuel. The potential benefits and technical advantages of using ammonia as a sustainable fuel for power generation on vehicles are analyzed here based on some performance indicators including the system effectiveness, the driving range, fuel tank compactness and the cost of driving. The cooling effect of ammonia is another advantage and is included in the efficiency calculations. Cooling with ammonia represents up to 20% from the engine's power, being thus a valuable side benefit of ammonia's presence on-board allowing for downsizing of the engine cooling system and obtaining some air conditioning. If the cooling effect is taken into consideration, the system's effectiveness can be improved by ~ 11%. It is shown that a medium size hydrogen car converted to NH3 becomes more effective per driving range cost at CN$3.2/100 km and fuel tank compactness with 18 L/100 km with a cost of ammonia assumed to be CN$0.3/kg.     

Regulated and unregulated emissions of a light duty vehicle operated on diesel/palm-based methyl ester blends over NEDC and a non-legislated driving cycle

In this study, regulated, unregulated exhaust emissions and fuel consumption with diesel fuel and palm-based biodiesel blends at proportions of 5%, 20% and 40% (v/v) have been investigated. A Euro 3 compliant light duty vehicle was tested on a chassis dynamometer over the new European driving cycle (NEDC) and the non-legislated Athens driving cycle (ADC). The experimental results showed that the addition of biodiesel increased NO_x emissions. This increase was more significant with the use of B20 over both cycles (13.7% and 23.2% over the NEDC and ADC, respectively). Biodiesel addition resulted to increases in CO emissions with the highest increase being 11.78% for B20 over NEDC and 11.62% for B40 over ADC. HC emissions increased with biodiesel over the NEDC, while over the ADC the addition of biodiesel led to reductions with the highest being with the use of B40 (about 26.47%). The same observation holds for PM emissions. Over the ADC the most beneficial reduction was in the order of 50% for the B40. CO₂ emissions and fuel consumption followed similar patterns. B20 led to increases up to 6.16% and 2.94% in fuel consumption over NEDC and ADC, respectively. Some PAH compounds demonstrated an increase with biodiesel, while nitro-PAHs decreased with most of them being almost undetectable. Most carbonyl emissions decreased with biodiesel.     

电动汽车充电设施分层递进式定址定容最优规划

电动汽车充电设施作为发展电动汽车产业的重要配套基础设施,是电动汽车产业推广与普及的前提。文章采用分层递进式方法对充电设施进行定址定容规划建模求解。首先,根据交通、地理、需求等因素,采用层次分析法来确定充电设施的候选站址;然后,建立以总体建设成本最小为目标,以充电需求和线路传输容量为约束的优化模型,采用遗传算法,对候选站址进行优化,得到充电设施的建设地址和容量。最后通过一个充电设施规划算例,建立最优化模型并予以求解,验证本文所给的充电设施规划方法的合理性和可行性。     

车联网环境下基于节点认知交互的路由算法

针对车联网（IoV）环境下消息传输效率低下、网络资源开销较大等诸多问题，提出一种适用于城市交通场景下基于车辆节点认知交互的路由算法。首先，依据信任理论提出节点认知交互度的概念，并在此基础上对车联网中的车辆节点进行分类，赋予它们不同的认知交互度初值；同时还引入车辆节点交互时间、交互频率、车辆节点物理间隔距离、间隔跳数以及消息生存时间等影响因子，进而构建了车辆节点认知交互评估模型。基于该模型计算并更新节点的认知交互度，并通过比较对应车辆节点间的认知交互度值来选取认知交互度相对较高的邻居节点作为中继节点进行消息转发。仿真实验结果表明，与Epidemic和Prophet路由算法相比，所提路由算法有效提高了消息投递率并降低了消息投递时延，同时显著降低了网络资源的开销，有助于提升车联网环境的消息传输质量。     

一种基于AD HOC网络的车辆定位系统的组网框架设计

本文提出了一种基于AD HOC网络的车辆定位系统的组网框架，着重分析了该网络的路由协议选择及优化措施，它能通过车载设备自动组成一个网络来传送系统的位置信息，控制指令和语音信息等信息，可以取得灵活的组网模式，降低系统的运行费用。     

基于区块链技术的车联网高效匿名认证方案

针对车联网（IOV）中心化认证效率低和隐私保护差的问题,提出一种基于区块链技术的高效匿名认证方案。该方案基于IOV开放、自组织、快速移动的特点,利用区块链技术防篡改和分布式的特性来完成车辆临时身份的生成和区块链存储。车辆相互通信时,通过触发智能合约实现高效匿名的双向身份认证。实验结果表明,在认证效率上,与传统公钥基础设施（PKI）认证、假名授权身份认证相比,随着验证量的增加,所提方案的匿名身份认证的时延增长较慢,效率较高;在安全性能上,所提方案中存入区块链的临时身份具有不可篡改、不可否认、可追溯等特点。所提方案中,恶意车辆身份可回溯并进行权限控制,并且,公钥密码体制和数字签名技术保证了通信数据的保密性和完整性。     

Privacy Protection Algorithm for the Internet of Vehicles Based on Local Differential Privacy and Game Model

In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protection of the IoV has become the focus of the society. This paper analyzes the advantages and disadvantages of the existing location privacy protection system structure and algorithms, proposes a privacy protection system structure based on untrusted data collection server, and designs a vehicle location acquisition algorithm based on a local differential privacy and game model. The algorithm first meshes the road network space. Then, the dynamic game model is introduced into the game user location privacy protection model and the attacker location semantic inference model, thereby minimizing the possibility of exposing the regional semantic privacy of the k-location set while maximizing the availability of the service. On this basis, a statistical method is designed, which satisfies the local differential privacy of k-location sets and obtains unbiased estimation of traffic density in different regions. Finally, this paper verifies the algorithm based on the data set of mobile vehicles in Shanghai. The experimental results show that the algorithm can guarantee the user’s location privacy and location semantic privacy while satisfying the service quality requirements, and provide better privacy protection and service for the users of the IoV.     

车联网中基于多参数决策的中继选择方案

5G时代,车联网是最具发展潜力的应用,也是未来智能交通管理系统的重要组成部分。但是,车联网本身也存在局限性。车联网中的车辆节点移动迅速,网络拓扑变化快,道路环境复杂,这些问题都将导致通信链路不稳定,甚至产生中断。因此,选择可靠、高效的中继节点来保障车联网的有效通信连接极为重要。提出基于多参数决策的中继选择方案,综合考虑候选中继节点的带宽、时延、节点切换预测值以及用户节点的相应需求,利用简单的线性加权函数评估候选中继的性能,最终得到最优中继。仿真结果表明,该方案在系统吞吐量和中继切换次数方面比传统方案更具优越性。     

基于改进SOS算法的UCAV鲁棒机动决策研究

针对无人作战飞机自主空战机动决策问题,提出了一种鲁棒机动决策方法。设计了反映空战态势的鲁棒隶属函数,并基于此设计鲁棒多目标决策函数;针对动作库在机动决策中的不完备性与传统优化方法求解时效性缺陷,运用基于自适应和精英反向学习策略改进的共生生物算法,对控制量进行优化进而完成机动决策;仿真结果表明,鲁棒机动决策结果更具优势且改进算法求解具有实时性,满足机动决策需求。     

A mobile edge computing-based applications execution framework for Internet of Vehicles

Mobile edge computing (MEC) is a promising technology for the Internet of Vehicles, especially in terms of application offloading and resource allocation. Most existing offloading schemes are sub-optimal, since these offloading strategies consider an application as a whole. In comparison, in this paper we propose an application-centric framework and build a finer-grained offloading scheme based on application partitioning. In our framework, each application is modelled as a directed acyclic graph, where each node represents a subtask and each edge represents the data flow dependency between a pair of subtasks. Both vehicles and MEC server within the communication range can be used as candidate offloading nodes. Then, the offloading involves assigning these computing nodes to subtasks. In addition, the proposed offloading scheme deal with the delay constraint of each subtask. The experimental evaluation show that, compared to existing non-partitioning offloading schemes, this proposed one effectively improves the performance of the application in terms of execution time and throughput.