Robot Navigation, Perception and Planning for Intelligent Energy Management in Electric Vehicles

到2020年，独立预测预测英国道路上将有数十万辆插电式电动和混合动力汽车。虽然目前采用这项技术是出于环境考虑，但电动和混合动力汽车技术对可持续经济增长的巨大潜力正变得越来越明显。然而，为了使这项技术实现必要的渗透，成为传统汽车的可行的大众市场替代品，它需要被视为满足消费者的需求。最近的研究表明，这种大众市场的渗透主要是阻碍了全电动汽车的范围限制以及车辆成本的恐惧。“里程焦虑”是由来自车辆的关于剩余可用里程的不准确反馈引起的。电池容量和寿命的限制是导致成本上升的主要原因，而这两者都受到充电周期的数量和速度的影响。众所周知，电动汽车的续航里程以及最终的充电需求受到许多因素的影响，例如车辆行驶轨迹上的速度分布和沿着地理位置、道路状况或天气状况。例如，在交通堵塞时反复加速上山，比在平地上以恒定速度巡航更负荷密集。然而，这些见解很少改善个人最终用户的体验：在考虑日常部署中的车辆范围或电池寿命时，目前既没有利用驾驶员特定的信息，如驾驶行为或常用路线，也没有利用路线特定的信息，如交通量，速度限制或停车标志和交通灯的位置。该项目通过利用最先进的机器人技术和机器学习技术来预测车辆范围以及优化电池寿命来解决这些缺点。在机器人导航和感知的背景下建立的方法非常适合于提供关于驾驶员行为和路线基础设施的不断发展的原位信息。与这种特定于驾驶员的汽车使用情况相一致，涉及稳健规划和决策的核心机器人技术可以解决决定何时以及多长时间为车辆充电的任务，从而保持电池寿命并最大限度地降低充电成本。因此，通过考虑车辆如何、在何处以及何时行驶，该项目将导致对车辆范围的改进预测以及更密切的充电制度。

By 2020 independent forecasts predict hundreds of thousands of plug-in electric and hybrid vehicles on UK roads. While the adoption of this technology is currently driven by environmental concerns, the significant potential of electric and hybrid vehicle technology for sustainable economic growth is becoming increasingly apparent. However, in order for this technology to achieve the penetration required to become a viable mass-market alternative to conventional cars it needs to be perceived as meeting consumers' needs. Recent studies have shown that this mass-market penetration is primarily impeded for all-electric vehicles by fears over range limitations as well as vehicle cost. 'Range Anxiety' is fueled by inaccurate feedback from the vehicle regarding the remaining range available. Costs are driven up primarily by limitations on battery capacity and life, both of which are affected by the number and ferocity of charging cycles.It is an established fact that the range of an electric vehicle, and therefore the eventual need for charging, is significantly influenced by a number of factors such as the velocity profile and geography along the vehicle's trajectory, the condition of the road or the weather. Repeatedly accelerating up a hill in a traffic jam, for example, is more load-intensive than cruising at constant speed on level ground. However, few of these insights improve the experience of the individual end-user: neither driver-specific information such as driving behaviour or commonly driven routes nor route-specific information such as traffic volume, speed limits or the location of stop-signs and traffic lights are currently exploited when considering vehicle range or battery longevity in every-day deployment. This project addresses these shortcomings by leveraging state-of-the-art Robotics and Machine Learning techniques for the prediction of vehicle range as well as the optimisation of battery longevity. Methods established in the context of robot navigation and perception are ideally suited to provide evolving, in-situ information on driver behaviour and route infrastructure. In concert with such a driver-specific usage profile of a car, core robotics technologies concerning robust planning and decision making can address the task of deciding when and how long for to charge a vehicle such that battery life is preserved and charging costs are minimised. Therefore, by considering how, where and when a vehicle is traveling this project will lead to improved forecasts of vehicle range as well as to more germane charging regimes.