Impacts of plug-in electric vehicles in the portuguese electrical grid

Powertrain electrification is currently the best alternative to ensure sustainable energy efficient personal mobility, increasing the integration of intermittent Renewable Energy Sources (RES), improving air quality in urban centres, and reducing greenhouse gas emissions from the transport sector and their dependence on fossil energy sources. With the increasing number of Electric Vehicles (EVs) available from automotive manufacturers, one key question that arises is the capability of the electrical grid to feed the increasing energy demand of the EV fleet without major investments. This paper shows that a progressive penetration of EVs, even at a rapid rate, is perfectly possible for vehicles that offer autonomy, energy consumption and charging characteristics that are currently available in the market. This analysis is based on data acquired during a year, using a Plug-in Hybrid Electric Vehicle (PEV) as the only vehicle for a typical, Southern European Portuguese family. The energy consumption of a gasoline and electric vehicle is presented, as well as its impact on the household load pattern. An analysis of the impact on the grid is also presented, considering several penetration rates (100 thousand, 500 thousand and 1 million vehicles). As well as the avoided use of fossil fuel per vehicle and consequent reduction in overall emissions when compared with a conventional vehicle.     

Lifecycle modeling and assessment of unmanned aerial vehicles (Drones) CO2e emissions

There are no studies that model the potential effectiveness of Unmanned Aerial Vehicles (UAVs) or drones to reduce CO₂e lifecycle (including both utilization and vehicle phase) emissions when compared to conventional diesel vans, electric trucks, electric vans, and tricycles. This study presents a novel analysis of lifecycle UAV and ground commercial vehicles CO₂e emissions. Different route and customer configurations are modeled analytically. Utilizing real-word data, tradeoffs and comparative advantages of UAVs are discussed. Breakeven points for operational emissions are obtained and the results clearly indicate that UAVs are more CO₂e efficient, for small payloads, than conventional diesel vans in a per-distance basis. Drastically different results are obtained when customers can be grouped in a delivery route. UAV deliveries are not more CO₂e efficient than tricycle or electric van delivery services if a few customers can be grouped in a route. Vehicle phase CO₂e emissions for UAVs are significant and must be taken into account. Ground vehicles are more efficient when comparing vehicles production and disposal emissions per delivery.     

Help or hindrance? The travel,energy and carbon impacts of highly automated vehicles

Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5–10 years, and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.     

Time-based life-cycle assessment for environmental policymaking: Greenhouse gas reduction goals and public transit

As decision-makers increasingly embrace life-cycle assessment (LCA) and target transportation services for regional environmental goals, it becomes imperative that outcomes from changes to transportation infrastructure systems are accurately estimated. Greenhouse gas (GHG) reduction policies have created interest in better understanding how public transit systems reduce emissions. Yet the use of average emission factors (e.g., grams CO₂e per distance traveled) persists as the state-of-the-art masking the variations in emissions across time, and confounding the ability to accurately estimate the environmental effects from changes to transit infrastructure and travel behavior. An LCA is developed of the Expo light rail line and a competing car trip (in Los Angeles, California) that includes vehicle, infrastructure, and energy production processes, in addition to propulsion. When results are normalized per passenger kilometer traveled (PKT), life-cycle processes increase energy use and GHG emissions up to 83%, and up to 690% for smog and respiratory impact potentials. However, the use of a time-independent PKT normalization obfuscates a decision-maker’s ability to understand whether the deployment of a transit system reduces emissions below a future year policy target (e.g., 80% of 1990 emissions by 2050). The year-by-year marginal effects of the decision to deploy the Expo line are developed including reductions in automobile travel. The time-based marginal results provide clearer explanations for how environmental effects in a region change and the critical life-cycle processes that should be targeted to achieve policy targets. It shows when environmental impacts payback and how much reduction is achieved by a policy-specified future year.     

Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions

This paper examines the life-cycle inventory impacts on energy use and greenhouse gas (GHG) emissions as a result of candidate travelers adopting carsharing in US settings. Here, households residing in relatively dense urban neighborhoods with good access to transit and traveling relatively few miles in private vehicles (roughly 10% of the U.S. population) are considered candidates for carsharing. This analysis recognizes cradle-to-grave impacts of carsharing on vehicle ownership levels, travel distances, fleet fuel economy (partly due to faster turnover), parking demand (and associated infrastructure), and alternative modes. Results suggest that current carsharing members reduce their average individual transportation energy use and GHG emissions by approximately 51% upon joining a carsharing organization. Collectively, these individual-level effects translate to roughly 5% savings in all household transport-related energy use and GHG emissions in the U.S. These energy and emissions savings can be primarily attributed to mode shifts and avoided travel, followed by savings in parking infrastructure demands and fuel consumption. When indirect rebound effects are accounted for (assuming travel-cost savings is then spent on other goods and services), net savings are expected to be 3% across all U.S. households.     

Eco-efficiency management program (EEMP)—a model for road fleet operation

Fuel consumption has always been a matter of economic concern in road fleet management, giving rise to many initiatives aimed at fostering more efficient energy use. The increasingly awareness of environmental problems now requires these programs to include environmental aspects. A structured Eco-efficiency Management Program (EEMP) is proposed for road fleet operation, taking into account the traditional approach that strives to minimise fuel consumption as well as wider economic and environmental aspects. The EEMP has its potential evaluated in a case study undertaken for INFRAERO, Brazilian’s airport authority, on the operation of its road fleet supporting aircraft ground operations at Rio de Janeiro International Airport. The paper looks at EEMP’s implementation by identifying the program’s phases, its participants and their competencies, eco-efficiency indicators, and performance targets.     

生命能量系统动力学方程平衡点值的一种定量分析法

用解析几何作为一种近似的方法讨论了生命能量系统动力学模型中两个典型方程解函数平衡点的求解问题，研究结果表明，每一方程的奇点轨迹是一对双曲线，而方程组的平衡点则是两组双曲线的交点，结果认为：方程组的平衡点可以通过求解两组双曲线的渐近线的交点得到近似值，这是生命能量系统的动力学特性定性分析工作的一个重要部分。     

轻型汽车CO2排放测量不确定度评定

CO₂排放是汽车污染物排放检测的一项重要技术指标,它表征的是汽车排出污染物的多少和车辆能耗高低的参考标准之一,用以控制汽车使用中产生的污染物排放量,避免对环境产生损害。按照GB 18352.6-2016《轻型汽车污染物排放限值及测量方法(中国第六阶段)》的规定使用全球统一的轻型车测试循环工况进行常温下冷启动后污染物排放试验,并对测量结果进行不确定度评定。文章阐述了轻型汽油车CO₂排放测试所用的测试原理、法规工况曲线、测试设备、数学模型等,通过分析影响试验结果的各因素来源,综合计算得出轻型车CO₂排放测试结果的扩展不确定度和相对扩展不确定度。     

Sensitivity analysis for energy demand estimation of electric vehicles

We present a sensitivity analysis for a mechanical model, which is used to estimate the energy demand of battery electric vehicles. This model is frequently used in literature, but its parameters are often chosen incautiously, which can lead to inaccurate energy demand estimates. We provide a novel prioritization of parameters and quantify their impact on the accuracy of the energy demand estimation, to enable better decision making during the model parameter selection phase. We furthermore determine a subset of parameters, which has to be defined, in order to achieve a desired estimation accuracy. The analysis is based on recorded GPS tracks of a battery electric vehicle under various driving conditions, but results are equally applicable for other BEVs. Results show that the uncertainty of vehicle efficiency and rolling friction coefficient have the highest impact on accuracy. The uncertainty of power demand for heating and cooling the vehicle also strongly affects the estimation accuracy, but only at low speeds. We also analyze the energy shares related to each model component including acceleration, air drag, rolling and grade resistance and auxiliary energy demand. Our work shows that, while some components make up a large share of the overall energy demand, the uncertainty of parameters related to these components does not affect the accuracy of energy demand estimation significantly. This work thus provides guidance for implementing and calibrating an energy demand estimation based on a longitudinal dynamics model.     

Electric vehicles’ energy consumption estimation with real driving condition data

The use of electric vehicles (EVs) is viewed as an attractive option to reduce CO₂ emissions and fuel consumption resulted from transport sector, but the popularization of EVs has been hindered by the cruising range limitation and the charging process inconvenience. Energy consumption characteristics analysis is the important foundation to study charging infrastructures locating, eco-driving behavior and energy saving route planning, which are helpful to extend EVs’ cruising range. From a physical and statistical view, this paper aims to develop a systematic energy consumption estimation approach suitable for EV actual driving cycles. First, by employing the real second-by-second driving condition data collected on typical urban travel routes, the energy consumption characteristics analysis is carried out specific to the microscopic driving parameters (instantaneous speed and acceleration) and battery state of charge (SOC). Then, based on comprehensive consideration of the mechanical dynamics characteristics and electric machine system of the EVs, a set of energy consumption rate estimation models are established under different operation modes from a statistical perspective. Finally, the performance of proposed model is fully evaluated by comparing with a conventional energy consumption estimation method. The results show that the proposed modeling approach represents a significant accuracy improvement in the estimation of real-world energy consumption. Specifically, the model precision increases by 25.25% in decelerating mode compared to the conventional model, while slight improvement in accelerating and cruising mode with desirable goodness of fit.