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

据独立预测，到2020年，英国将有数十万辆插电式电动汽车和混合动力汽车上路。虽然这项技术的采用目前是由环境问题驱动的，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，为了使这项技术达到所需的渗透，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，纯电动汽车进入大众市场的主要障碍是对续航里程限制和汽车成本的担忧。“里程焦虑”是由车辆对剩余可用里程的不准确反馈引起的。成本的上升主要是由于电池容量和寿命的限制，这两者都受到充电周期的次数和剧烈程度的影响。众所周知，电动汽车的行驶里程以及最终的充电需求受到许多因素的显著影响，例如汽车行驶轨迹上的速度分布和地理位置、道路状况或天气。例如，在交通堵塞的情况下反复加速上山，比在平地上匀速巡航更需要负荷。然而，这些见解很少能改善个人终端用户的体验：在考虑日常部署中的车辆续航里程或电池寿命时，既没有驾驶员特定的信息，如驾驶行为或通常行驶的路线，也没有路线特定的信息，如交通量、速度限制或停车标志和交通信号灯的位置。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行驶里程以及优化电池寿命，解决了这些缺点。在机器人导航和感知环境中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，核心机器人技术涉及强大的规划和决策，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，通过考虑车辆行驶的方式、地点和时间，该项目将改进对车辆行驶里程的预测，并制定更合理的充电制度。

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.