Optimum routing of battery electric vehicles: Insights using empirical data and microsimulation

This study investigates the energy consumption impact of route selection on battery electric vehicles (BEVs) using empirical second-by-second Global Positioning System (GPS) commute data and traffic micro-simulation data. Drivers typically choose routes that reduce travel time and therefore travel cost. However, BEVs’ limited driving range makes energy efficient route selection of particular concern to BEV drivers. In addition, BEVs’ regenerative braking systems allow for the recovery of energy while braking, which is affected by route choices. State-of-the-art BEV energy consumption models consider a simplified constant regenerative braking energy efficiency or average speed dependent regenerative braking factors. To overcome these limitations, this study adopted a microscopic BEV energy consumption model, which captures the effect of transient behavior on BEV energy consumption and recovery while braking in a congested network. The study found that BEVs and conventional internal combustion engine vehicles (ICEVs) had different fuel/energy-optimized traffic assignments, suggesting that different routings be recommended for electric vehicles. For the specific case study, simulation results indicate that a faster route could actually increase BEV energy consumption, and that significant energy savings were observed when BEVs utilized a longer travel time route because energy is regenerated. Finally, the study found that regenerated energy was greatly affected by facility types and congestion levels and also BEVs’ energy efficiency could be significantly influenced by regenerated energy.     

Impacts of replacement of engine powered vehicles by electric vehicles on energy consumption and CO2 emissions

This paper evaluates the impacts on energy consumption and carbon dioxide (CO₂) emissions from the introduction of electric vehicles into a smart grid, as a case study. The AVL Cruise software was used to simulate two vehicles, one electric and the other engine-powered, both operating under the New European Driving Cycle (NEDC), in order to calculate carbon dioxide (CO₂) emissions, fuel consumption and energy efficiency. Available carbon dioxide data from electric power generation in Brazil were used for comparison with the simulated results. In addition, scenarios of gradual introduction of electric vehicles in a taxi fleet operating with a smart grid system in Sete Lagoas city, MG, Brazil, were made to evaluate their impacts. The results demonstrate that CO₂ emissions from the electric vehicle fleet can be from 10 to 26 times lower than that of the engine-powered vehicle fleet. In addition, the scenarios indicate that even with high factors of CO₂ emissions from energy generation, significant reductions of annual emissions are obtained with the introduction of electric vehicles in the fleet.     

Energy factors for flexible fuel engines and vehicles operating with gasoline-ethanol blends

This work investigates the energy factors for fuel conversion from the analysis of brake specific fuel consumption (BSFC) maps of a sample of 15 engines, representative of 75% of current models available in the Brazilian market. The method also employs the engine driving patterns of power output versus crankshaft speed obtained from bench dynamometer tests. The energy factors obtained from the engine analysis was validated against experiments carried out with two production vehicles in laboratory tests following the 1975 US Federal Test Procedure (FTP-75) procedure and road tests following 16 different urban and highway routes. The fuels used in the tests were hydrous ethanol (E100, 6 v/v % water) and a blend of 22 v/v % anhydrous ethanol and 78 v/v % gasoline (E22). The energy factors found from the 3D engine BSFC map analysis were higher than those obtained from the Willans line, currently adopted as a standard, by 52% for E22 and 57% for E100. The results from the 3D engine BFSC maps and the first vehicle following the FTP-75 cycle and 15 road routes were similar, also close to the results from the second vehicle, qualifying them to be representative of modern flexible fuel spark ignition engines and vehicles.     

Lightweighting shipping containers: Life cycle impacts on multimodal freight transportation

Freight transportation by truck, train, and ship accounts for 5% of the United States’ annual energy consumption (U.S. Energy Information Administration, 2017a). Much of this freight is transported in shipping containers. Lightweighting containers is an unexplored strategy to decrease energy and GHG emissions. We evaluate life cycle fuel savings and environmental performance of lightweighting scenarios applied to a forty-foot (12.2 meters) container transported by ship, train, and truck. Use phase burdens for both conventional and lightweighted containers (steel reduction, substitution with aluminum, or substitution with high tensile steel) were compared to life cycle burdens. The study scope ranged from the transportation of one container 100 km to the lifetime movement of the global container fleet on ships. Case studies demonstrated the impact of lightweighting on typical multimodal freight deliveries to the United States. GREET 1 and 2 (Argonne National Laboratory, 2016a,b) were used to estimate the total fuel cycle burdens associated with use phase fuel consumption. Fuel consumption was determined using modal Fuel Reduction Values (FRV), which relate mass reduction to fuel reduction. A lifetime reduction of 21% in the fuel required to transport a container, and 1.4% in the total fuel required to move the vehicles, cargo, and containers can be achieved. It was determined that a 10% reduction in mass of the system will result in a fuel reduction ranging from 2% to 8.4%, depending on the mode. Globally, container lightweighting can reduce energy demand by 3.6 EJ and GHG emissions by 300 million tonnes CO₂e over a 15-year lifetime.     

船用柴油机混合能源系统配置优化

考虑新能源的间歇性以及船舶电力系统负载的突变性和波动性,本文首先根据船舶航行中经纬度和海上环境的变化对新能源的参数进行修正,然后构造同时计及成本与使用寿命的船用柴油机混合能源系统优化配置目标函数,再利用人工蜂群算法进行优化,从而获得最佳的系统配置。船舶航行实测试验验证该方法的可行性,同时表明了它比陆地上传统方法具有更好的优化配置性能。     

高速列车及其速度目标值的探讨

根据铁路运输高速化的发展趋势,初步分析了旅行速度、票价、运输管理、高速列车等因素和速度目标值的关系。提出高速列车是影响速度目标值的关键问题。从高速列车的牵引、能耗、制动能力和噪音方面对轮轨方式和磁悬浮方式的高速列车按不同速度目标值进行分析比较。以正在设计中的京沪高速铁路为例,在京沪高速列车运行仿真研究的基础上,按直达方式和沿线停站方式,对不同速度目标值的地面干线运输系统其旅行时间和能耗的经济问题进行简要的计算对比。提出选择我国高速铁路速度目标值的建议。     

空调节能的研究及其实现

随着人们生活水平的不断提高,空调的使用越来越普及。空调是用电最大的家用电器之一,如何进一步提高空调的制冷效率以达到空调节能是人们所关注的焦点。文章经过理论研究提出了将室内机蒸发器上的低温凝结水引流到室外机的冷凝器上,降低了冷凝温度,将会大大提高空调的制冷系数,即达到空调节能增效之目的。并在理论研究的基础上提出了通过降低冷凝温度来达到提高制冷效率的实现方案。     

Development of low-CO2-emission vehicles and utilization of local renewable energy for the vitalization of rural areas in Japan

Most of Japan's energy supply depends on imports from foreign countries, making the independence ratio of energy in Japan very low. The Fukushima nuclear power plant accident triggered by the Great East Japan Earthquake and Tsunami led to a mass shutdown of all the nuclear plants in Japan, a stoppage that is still in effect. In this paper, we review the energy supply situation and some social problems faced by rural areas in Japan. Given that lifestyles in rural Japan are reliant on automobiles, there is significant demand for the establishment of a sustainable mobility society. Furthermore, Japan is now entering an aging society ahead of other countries. In order to enhance the vitalization of rural areas and accelerate the establishment of sustainable society, our project developed low-CO₂-emission vehicles (i.e., a single-driver EV [micro-EV] and a low-speed E-bus) for elderly people and tourists through the cooperation of regional industries, a local university, and a city office. This paper also reports some trial test results on renewable energy utilization as the driving energy supply for these low-emission vehicles.     

基于SEA的海洋平台典型舱室噪声预报及设备优化布置研究

基于统计能量法,利用VAONE软件对海洋平台典型舱室结构在分体空调室外机空气噪声作用下的舱室噪声进行预报。在保证结构尺寸与载荷大小的前提下,改变分体空调室外机出风口朝向、对比分析舱室噪声,得出分体空调室外机出风口朝向会对舱室噪声产生较大影响。该研究对船舶与海洋工程舱室噪声预报及设备布置方面具有一定的参考价值。     

Energy-efficient operation of rail vehicles

This paper describes an analytical process that computes the optimal operating successions of a rail vehicle to minimize energy consumption. Rising energy prices and environmental concerns have made energy conservation a high priority for transportation operations. The cost of energy consumption makes up a large portion of the Operation and Maintenance (O&M) costs of transit especially rail transit systems. Energy conservation or reduction in energy cost may be one of the effective ways to reduce transit operating cost, therefore improve the efficiency of transit operations.From a theoretical point of view, the problem of energy efficient train control can be formulated as one of the functions of Optimal Control Theory. However, the classic numerical optimization methods such as discrete method of optimum programming are too slow to be used in an on-board computer even with the much improved computation power, today. The contribution of this particular research is the analytical solution that gives the sequence of optimal controls and equations to find the control change points. As a result, a calculation algorithm and a computer program for energy efficient train control has been developed. This program is also capable of developing energy efficient operating schedules by optimizing distributions of running time for an entire route or any part of rail systems.We see the major application of the proposed algorithms in fully or partially automated Train Control Systems. The modern train control systems, often referred as “positive” train control (PTC), have collected a large amount of information to ensure safety of train operations. The same data can be utilized to compute the optimum controls on-board to minimize energy consumption based on the algorithms proposed in this paper. Most of the input data, such as track plan, track profile, traction and braking characteristics, speed limits and required trip time are located in an on-board database and/or they can be transmitted via radio link to be processed by the proposed algorithm and program.