混合动力汽车产业发展现状及趋势

混合动力汽车(Hybrid Electric Vehicle, HEV)结合了传统驱动系统和能量存储系统,利用内燃机和电机来驱动车辆,是传统汽车向纯电动汽车过渡的重要桥梁,在如今电动汽车全球大爆发的背景下,混合动力汽车将成为未来节能减排愿景的重要组成部分。文章探讨了近年来混合动力汽车产业的发展状况,介绍了几种新型的混合动力技术,对混合动力汽车的技术优势和不足进行了分析研究,指出了当前混合动力汽车产业面临的一些问题,并对未来混合动力汽车的发展进行了展望。     

基于马尔可夫决策理论的燃料电池混合动力汽车能量管理策略

根据道路试验记录的数据建立驾驶员需求功率的马尔可夫模型,利用马尔可夫决策理论获得混合动力汽车的随机能量管理策略。借助燃料电池混合动力汽车控制系统的仿真平台进行仿真计算。北京公交车中速工况的仿真结果表明,与原先的恒电压控制策略相比,随机能量管理策略可以降低燃料消耗。     

基于能量法的汽车白车身焊接接头尺寸偏差分析

采用能量法分析了在交叉平面连接方式下零件尺寸偏差、夹具尺寸偏差及零件几何性质对装配尺寸偏差的影响,并提出了零件与夹具的偏差敏感度因子的概念。分析结果表明,装配偏差不仅是零件自身偏差和夹具偏差共同作用的结果,还与零件及夹具的敏感度有关;零件及夹具的敏感度因子由零件的材料、宽度及厚度共同决定,与零件的长度无关。     

Environmental benefits analysis of electric vehicles in the Czech Republic

The article evaluates the environmental benefits of electric vehicles using well-to-wheel analysis in the Czech Republic. The power consumption per kilometer is determined from the combined cycle of the New European Driving Cycle. Using information from the integrated registry of polluters and mandatory disclosures of the CEZ company the specific harmful emissions production per 1 kW h of electricity is determined. The combination of electricity consumed and the production of harmful emissions per 1 kW h of electricity determine the indirect harmful emissions of an electric vehicle per kilometer. Computer simulation of the indirect production of harmful emissions is performed on the Mitsubishi MiEV engine, typical for an electric vehicle.     

Quantifying sustainable strategies for the construction of highway pavements in Illinois

The environmental and economic burdens of various pavement construction strategies are evaluated in this study. A partial life-cycle approach was used to determine the environmental and economic benefits of asphalt concrete and Portland concrete mix designs as well as pavement-related pay items. Approximately 920 designs were assessed to determine the upstream energy consumption and global warming potential (GWP) of producing these mixes. In general, it was found that transportation hauling distances as well as asphalt binder type and production imposed the greatest variability on the environmental and economic costs of the mixes. In many cases, these variabilities were seen to reduce some of the benefits from using increased recycled content. A similar analysis was performed for pay items where it was found that the contribution of environmental and economic impacts to a project followed a trend with upper pavement layers having the greatest impact, followed by subsequently lower layers, and finally earth exaction and preparation. A cost effectiveness (CE) analysis was then conducted for 18 sustainable strategies, the majority of which had, on average, cost savings as well as environmental savings for both energy and GWP at the mix design level. Overall, this study systematically used common reference units (i.e., mix designs and pay items) from the industry to assess general trends, inconsistencies, and implications from using sustainable strategies in pavement construction.     

Decomposing passenger transport futures: Comparing results of global integrated assessment models

The transport sector is growing fast in terms of energy use and accompanying greenhouse gas emissions. Integrated assessment models (IAMs) are used widely to analyze energy system transitions over a decadal time frame to help inform and evaluating international climate policy. As part of this, IAMs also explore pathways of decarbonizing the transport sector. This study quantifies the contribution of changes in activity growth, modal structure, energy intensity and fuel mix to the projected passenger transport carbon emission pathways. The Laspeyres index decomposition method is used to compare results across models and scenarios, and against historical transport trends. Broadly-speaking the models show similar trends, projecting continuous transport activity growth, reduced energy intensity and in some cases modal shift to carbon-intensive modes - similar to those observed historically in a business-as-usual scenario. In policy-induced mitigation scenarios further enhancements of energy efficiency and fuel switching is seen, showing a clear break with historical trends. Reduced activity growth and modal shift (towards less carbon-intensive modes) only have a limited contribution to emission reduction. Measures that could induce such changes could possibly complement the aggressive, technology switch required in the current scenarios to reach internationally agreed climate targets.     

Development and validation of energy demand uncertainty model for electric city buses

The prediction of electric city bus energy demand is crucial in order to estimate operating costs and to size components such as the battery and charging systems. Unfortunately, there are unpredictable dynamic factors that can cause variation in the energy demand, particularly concerning driver choices and traffic levels. The impact of these factors on energy demand has been difficult to study since fast computing sufficiently accurate dynamic simulation models have been missing, properly quantified in terms of relevant inputs which contribute to energy demand. The objective is to develop and validate a novel electric city bus model for computing the energy demand, to study the nature and impact of various input factors. The developed equation-based model predicted real-world electric city bus energy consumption within 0.1% error. The most crucial unmeasurable input factors were the driven bus route, the number of stops, the elevation profile, the traffic level and the driving style. This understanding can be used to specify routes and stops for a given electric bus battery capacity. Worst-case scenarios are also necessary for electric bus sizing analysis. The best- and worst-case levels of the crucial factors were identified and with them synthetic best- and worst-case speed profiles were generated to demonstrate their effect to the energy demand. While the measured nominal consumption was 0.70 kWh/km, the computed range of variation was between 0.19 kWh/km and 1.34 kWh/km. For design sizing purposes, an electric city bus can have a broad range of possible energy consumption rates due to mission condition variations.     

A two-sided market platform analysis for the electric vehicle adoption: Firm strategies and policy design

This study explores how to facilitate the electric vehicle (EV) diffusion from a two-sided market platform competition. We develop a stylized model depicting the platform competition between electric and gasoline vehicles by combining indirect network effects of consumer and energy supplier sides as well as vehicle manufacturers’ profits. The findings of this study provide several meaningful strategic and policy implications for EV manufacturers and policymakers who wish to enhance EV diffusion. First, EV sales are significantly influenced by indirect network effects from the energy supplier side to the consumer side, and vice versa. This implies that EV manufacturers who wish to boost EV diffusion should implement a strategy providing energy suppliers with incentives to willingly join the EV platform. Second, the dynamic nature of the effects of energy costs on platform competition might render counter-intuitive evidence that the drop in oil prices does not always negatively influence EV sales. This requires EV manufacturers to prepare a contingent strategy adjusting to such unexpected conditions. Third, governments should consider the energy supplier side as well as the consumer side in designing EV diffusion policies. When governments have a very challenging EV diffusion target, a balanced policy, which treats both gasoline and electric vehicle technologies fairly, may be more effective than a consumer subsidy policy.     

Assessing energy consumption impacts of traffic shifts based on real-world driving data

Information and communication technologies used for on-board vehicle monitoring have been adopted as an additional tool to characterize mobility flows. Furthermore, traffic volumes are traditionally measured to understand cities traffic dynamics. This paper presents an innovative methodology that uses an extensive and complementary real-world dataset to make a scenario-based analysis allowing assessing energy consumption impacts of shifting traffic from peak to off-peak hours. In the specific case of the city of Lisbon, a sample of 40 drivers was monitored for a period of six months. The obtained data allowed testing the impacts of increasing the percentage of traffic shifting from peak to off-peak hours in energy consumption. Both average speed and energy consumption variations were quantified for each of the tested percentages, allowing concluding that for traffic shifts of up to 30% a positive impact in consumption can be observed. In terms of potential gains associated to shifting traffic from peak hours, reductions in energy consumption from 0.1% to 0.4% can be obtained for traffic volumes shifts from 5 to 30%. Overall, the maximum reduction in energy consumption is achieved for a 20% traffic shift. Average speed variation follows the same trend as energy consumption, but in the opposite direction, i.e. instead of decreasing, average speed increases. For the best case scenario, considering only the sections of roads with traffic sensors, a 1.4% reduction in trip time may be achieved, as well as savings of up to 6 l of fuel and 14.5 kg of avoided CO₂ emissions per day.     

Dynamic optimization of ship energy efficiency considering time-varying environmental factors

Nowadays, optimization of ship energy efficiency attracts increasing attention in order to meet the requirement for energy conservation and emission reduction. Ship operation energy efficiency is significantly influenced by environmental factors such as wind speed and direction, water speed and depth. Owing to inherent time-variety and uncertainty associated with these various factors, it is very difficult to determine optimal sailing speeds accurately for different legs of the whole route using traditional static optimization methods, especially when the weather conditions change frequently over the length of a ship route. Therefore, in this paper, a novel dynamic optimization method adopting the model predictive control (MPC) strategy is proposed to optimize ship energy efficiency accounting for these time-varying environmental factors. Firstly, the dynamic optimization model of ship energy efficiency considering time-varying environmental factors and the nonlinear system model of ship energy efficiency are established. On this basis, the control algorithm and controller for the dynamic optimization of ship energy efficiency (DOSEE) are designed. Finally, a case study is carried out to demonstrate the validity of this optimization method. The results indicate that the optimal sailing speeds at different time steps could be determined through the dynamic optimization method. This method can improve ship energy efficiency and reduce CO₂ emissions effectively.