插电式混合动力汽车的智能能源管理:ITS基础设施在汽车电气化中的作用

V. Marano, G. Rizzoni, P. Tulpule, Q. Gong, H. Khayyam
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引用次数: 25

摘要

在过去的十年里,为了减少交通运输造成的碳排放,人们开发了新的推进技术,重点是汽车电气化(包括混合动力汽车、插电式混合动力汽车和电池电动汽车)。这些推进技术,加上电信和计算能力的进步,有可能使乘用车和商用车更加节能和环保。特别是,能量管理算法是插电式汽车不可分割的一部分,对于实现性能效益非常重要。能源管理算法的最佳性能在很大程度上取决于对车辆能源需求的预测能力。有关环境(温度、湿度、风、道路坡度等)和交通(交通密度、交通灯等)的信息对于车辆的最佳效率运行非常重要。本文概述了一些可以帮助实现这一最佳效率目标的当前技术。除了远程信息处理和地理信息系统提供的信息外,了解电网上预计的车辆充电需求对于为未来的插电式混合动力汽车和电动汽车建立智能能源管理控制器是必要的。数以百万计的汽车从电网中充电的影响可能是巨大的,其形式是增加发电厂、输电和配电线路的负荷、排放和经济(针对美国的情况给出并讨论了相关信息)。因此,应该通过基于通信的分布式控制来智能地控制/调度充电,从而考虑这种影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Energy Management for Plug-in Hybrid Electric Vehicles: The Role of ITS Infrastructure in Vehicle Electrification
The desire to reduce carbon emissions due to transportation sources has led over the past decade to the development of new propulsion technologies, focused on vehicle electrification (including hybrid, plug-in hybrid and battery electric vehicles). These propulsion technologies, along with advances in telecommunication and computing power, have the potential of making passenger and commercial vehicles more energy efficient and environment friendly. In particular, energy management algorithms are an integral part of plug-in vehicles and are very important for achieving the performance benefits. The optimal performance of energy management algorithms depends strongly on the ability to forecast energy demand from the vehicle. Information available about environment (temperature, humidity, wind, road grade, etc.) and traffic (traffic density, traffic lights, etc.), is very important in operating a vehicle at optimal efficiency. This article outlines some current technologies that can help achieving this optimum efficiency goal. In addition to information available from telematic and geographical information systems, knowledge of projected vehicle charging demand on the power grid is necessary to build an intelligent energy management controller for future plug-in hybrid and electric vehicles. The impact of charging millions of vehicles from the power grid could be significant, in the form of increased loading of power plants, transmission and distribution lines, emissions and economics (information are given and discussed for the US case). Therefore, this effect should be considered in an intelligent way by controlling/scheduling the charging through a communication based distributed control.
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