“双碳”目标下我国船舶减排技术创新知识图谱分析

为在“双碳”目标下全面系统提升我国船舶减排技术创新能力，基于知识图谱并综合运用 CiteSpace与SATI等计量可视化工具，以船舶减排技术创新中外文核心期刊文献为主要数据源， 对我国船舶减排技术创新研究进行系统分析。结果表明：我国船舶减排技术创新研究大致经历4 个阶段，分别是起步阶段(2001—2007年)、成长阶段(2008—2014年)、回稳阶段(2015—2018年)及繁荣阶段(2019—2021年)；研究热点主要集中在船舶节能减排技术及措施，绿色船舶及绿色航运， 船舶能源效率及能效管理这3方面；未来研究朝着碳中和、替代燃料、岸电、低硫燃油及绿色船舶设计等方向发展。通过系统梳理和深入分析现有核心文献，揭示我国船舶减排技术创新领域当前研究现状及未来方向，为应对“双碳”目标下我国船舶减排技术创新带来的广泛而深刻“经济社会系统性变革”提供参考借鉴。     

喷油策略对柴油机燃烧噪声及排放影响的模拟研究

进行了通过优化高压共轨系统燃油喷射策略来改善缸内燃烧排放性能的研究,基于AVL Fire软件,针对1015柴油机开展了不同燃油喷射策略下的喷雾燃烧和排放的数值模拟,建立了数学模型并验证了模型的可靠性,分析了一次预喷的预喷时刻、预喷量、主喷提前角对燃烧噪声及排放的影响,揭示了各参数对燃烧噪声和NOx及炭烟生成的影响机理,为进一步优化预喷方案提供了理论依据。     

船用中速主柴油机降低NOx排放试验分析

分析两种改进方法对减少某中速主柴油机的NOx排放的效果,根据IMO推荐的试验程序进行NOx排放测试,测得排放的数据,使用根据IMO的计算方法开发的计算程序进行NOx排放值的计算,并对改进前后的排放结果进行对比,结果证明,只改变喷油提前角虽然能降低排放但牺牲了较大的经济性。     

11m长公路客车国Ⅳ排放排气管设计

根据公司新开发的一款11m长公路客车的底盘布置和不同的尾气后处理系统,结合将要实施的国Ⅳ排放标准,分别设计出相应的排气管。     

基于工况分布的重型环卫货车NOx排放模型

为提高城市重型环卫货车的NOx排放测算精度，本文提出一个基于工况分布的重型环卫货车NOx排放模型.首先，根据基于实测逐秒速度数据分析的环卫重型货车工况特征和 NO_x排放特性对不同负载货车的 VSP区间进行划分；其次，结合货车瞬时速度建立不同负载的环卫重型货车运行模式区间划分方法，并对不同负载货车NO_x排放因子进行测算.结果显示，空载货车在速度区间[0, 20) km/h 上，NOx排放因子大于满载，其他速度区间上相反.与基于 MOVES模型测算结果对比，在不同速度区间上，基于 MOVES的测算结果均比本文提出模型的测算结果偏低，如在低速区间[0，20) km/h，中速区间[20，50) km/h，高速区间[50，+∞) km/h：空载行驶时，分别低24.67%、6.82%和23.81%；满载行驶时，分别低12.38%、18.81%和26.43%.     

Dynamic calculation of ship exhaust emissions based on real-time AIS data

The activity-based methodology is becoming an increasing way to calculate exhaust emissions from ships in a port. Existing studies make great effort to build and analyze ship emission inventory in a variety of ports by applying this method to historical ship trajectory data. This kind of static emission inventory however, cannot meet the needs of real-time ship emission monitoring. This article proposes a method of dynamic calculation of ship exhaust emissions based on real-time ship trajectory data. Firstly, real-time ship AIS messages are partitioned into continuous data blocks and go through a series of pre-processing operations, including trajectory extraction, association and interpolation. Ship activity parameters are then determined by database querying and regression analysis based on ship attributes. Subsequently, an improved activity-based methodology is employed to estimate exhaust emissions from ships in a distributed way. Based on the grid model, regional ship exhaust emissions can be statistically and dynamically calculated by the spatial allocation of all ship emissions. In a case study, a real-time monitoring platform for ship exhaust emissions in Shenzhen port is developed to demonstrate the effectiveness of the proposed method.     

Real-time optimization of ship energy efficiency based on the prediction technology of working condition

Ship energy efficiency management and control is an effective strategy to improve the marine economy and reduce CO₂ emission. The determination of the best navigation speed under different working conditions is the basis and premise for real-time improvement of ship energy efficiency. In this paper, the working condition in short distance ahead of the ship related to navigation environment factors was predicted by the method of wavelet neural network, and then the best engine speed for the optimal energy efficiency under different working conditions could be determined through the established ship energy efficiency real-time optimization model. Further, by presetting the ship engine at this optimal speed, the ship energy efficiency could be guaranteed at the optimal state when the ship arrived at the navigation environment ahead of the ship, thus achieving real-time optimization of ship energy efficiency under different navigation environment factors. Experimental studies showed that the proposed optimization model was effective in energy saving and emission reduction, which could provide theoretical guidance for optimal sailing of the ship in service. Compared to traditional setting speed navigation methods, our proposed method has more practical significance to the improvement of ship energy efficiency.     

Impact of license plate restriction policy on emission reduction in Hangzhou using a bottom-up approach

Many emission models have been developed for estimating the impact of transport policies on vehicle emissions. Macroscopic models, such as MOBILE and COPERT, are used for area analysis, while microscopic models, such as CMEM, are applied for corridor analysis. It is well known that driving dynamics are critical for estimating vehicle emissions. MOVES can be used for both macroscopic and microscopic emission analysis, and its advantage lies in the consideration of driving dynamics. Using a bottom-up approach, we study the impact of license plate restriction policy on vehicle emission reduction by localizing the emission rates in MOVES according to the vehicle emission standards in China. We implement the approach to evaluate the impact on the total vehicle emissions in Hangzhou, China before and after the implementation of license plate restriction policy. In the restricted region, the reductions of total Vehicle Kilometer Traveled (VKT) and total emissions are 9.6% and 6.9%, respectively. The result shows that the license plate restriction policy is effective in achieving the targeted emission reduction.     

Integrated analysis of GHGs and public health damage mitigation for developing urban road transportation strategies

This study attempts to present an urban road transportation strategy focusing on the mitigation of both GHGs emission and public health damage, taking Xiamen City as a case study. We developed a Public Health and GHGs Emission model to estimate the impacts of direct energy-consumption-related GHGs emissions and public health damage in Xiamen’s road transportation strategies from 2008 to 2025, considering the environmental benefits and economic costs. Two scenarios were designed to describe future transportation strategies for Xiamen City, and mitigation potentials for both GHGs emission and public health costs were estimated from 2008 to 2025 under a series of options. The results show that enacting controls on private vehicles would be most effective to GHGs mitigation, while enacting controls on government and rental vehicles would contribute the most to NO₂ and PM2.5 reductions. Compared with the Business as Usual scenario, the Integrated scenario would achieve about a 68% energy consumption reduction and save 0.23 billion yuan (95% CI: 0.16, 0.32) in health costs in 2025. It is clear that integrated and advisable strategies need to mitigate the adverse impacts of urban road vehicles on GHGs emissions and public health and economic costs, particularly in regions of rapid urbanization.     

Spatiotemporal intersection control in a connected and automated vehicle environment

Current research on traffic control has focused on the optimization of either traffic signals or vehicle trajectories. With the rapid development of connected and automated vehicle (CAV) technologies, vehicles equipped with dedicated short-range communications (DSRC) can communicate not only with other CAVs but also with infrastructure. Joint control of vehicle trajectories and traffic signals becomes feasible and may achieve greater benefits regarding system efficiency and environmental sustainability. Traffic control framework is expected to be extended from one dimension (either spatial or temporal) to two dimensions (spatiotemporal). This paper investigates a joint control framework for isolated intersections. The control framework is modeled as a two-stage optimization problem with signal optimization at the first stage and vehicle trajectory control at the second stage. The signal optimization is modeled as a dynamic programming (DP) problem with the objective to minimize vehicle delay. Optimal control theory is applied to the vehicle trajectory control problem with the objective to minimize fuel consumption and emissions. A simplified objective function is adopted to get analytical solutions to the optimal control problem so that the two-stage model is solved efficiently. Simulation results show that the proposed joint control framework is able to reduce both vehicle delay and emissions under a variety of demand levels compared to fixed-time and adaptive signal control when vehicle trajectories are not optimized. The reduced vehicle delay and CO₂ emissions can be as much as 24.0% and 13.8%, respectively for a simple two-phase intersection. Sensitivity analysis suggests that maximum acceleration and deceleration rates have a significant impact on the performance regarding both vehicle delay and emission reduction. Further extension to a full eight-phase intersection shows a similar pattern of delay and emission reduction by the joint control framework.