SBIR Phase I: Automated Learning of Vehicle Energy Performance Models

这个小型企业创新研究（SBIR）第一阶段项目将研究一个物联网（IoT）平台，以自动学习汽车能源性能模型（vepm）。vepm用于以每辆车、每名驾驶员、每条路线为基础，预测电动汽车的行驶里程和电池健康状态，其准确性是目前的8-10倍。据估计，未来十年将有超过1500亿美元投资于电动汽车生态系统。阻碍电动汽车快速普及的一个重要障碍是里程焦虑，即用户对可实现的距离和充电地点/时间的担忧。通过基于出行计划、驾驶行为和车型，为电动汽车驾驶员提供关于其实际行驶里程和推荐充电策略的情境智能，可以缓解里程焦虑。消费者越来越多地采用电动汽车，可以减少运输系统的化石燃料消耗和排放。作为这项工作的一部分，云应用程序编程接口（API）将提供基于学习到的vepm的预测；这也将在单个车辆和车队层面实现节能应用，如生态路线、生态巡航、生态动力系统控制和充电站规划等。节能应用可以提高电动汽车的整体能源效率。该项目的智力优势在于推进物联网架构，以自动从实时车辆传感器遥测和其他数据（如地图和路线地形）中学习vepm。该计划分为三个综合目标：(1)利用物理原理构建物联网框架，以捕获车辆运动和动力系统效率，以及数据驱动方法，以捕获人为因素，以及地图和测量中的不确定性；(2)努力解决数据有限和专家监督减少的地理区域学习的可扩展性和泛化问题；(3)在一组代表性条件下收集的真实驾驶数据上对平台进行实验验证。统计学习理论将与预测控制理论合并，在学习过程中混合使用基于物理和数据驱动的模型。通过利用数据实时更新模型和在同一制造商的车辆之间共享模型，将获得可扩展性和准确性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Small Business Innovation Research (SBIR) Phase I project will research an internet-of-things (IoT) platform to automatically learn vehicle energy performance models (VEPMs). VEPMs are used to predict driving range and battery state of health in electric vehicles (EVs) on a per-vehicle, per-driver, per-route basis, and 8-10 times more accurately than today. It is estimated that over $150 billion will be invested in the electric vehicle ecosystem over the next decade. A significant obstacle hindering rapid EV adoption is range anxiety representing user concerns over the achievable distance and where/ when to charge the EV. Range anxiety can be alleviated by providing EV drivers with contextual intelligence on their realistic driving range and recommended charging strategy, based on travel plans, driving behavior and vehicle model. Increasing EV adoption by consumers reduces transportation system fossil fuel consumption and emissions. As part of this effort, a cloud application programming interface (API) will deliver predictions based on the learned VEPMs; this will also enable energy-aware applications such as eco-routing, eco-cruising, eco-powertrain control, and planning of charging stops, among others, both at the individual vehicle and at the fleet level. Energy-aware applications can increase the overall energy efficiency of electrified fleets.The intellectual merit of this project is to advance an IoT architecture to automatically learn VEPMs from real-time vehicle sensor telemetry and other data, such as maps and route topography. The plan is divided into three integrated goals: (1) the building of an IoT framework leveraging physics principles to capture the vehicle motion and powertrain efficiencies, as well as data-driven approaches to capture human factors, and uncertainty in maps and measurements, (2) efforts to address scalability and generalization of the learning in geographical areas with limited data and reduced expert supervision, and (3) the experimental validation of the platform on real-world driving data collected in a set of representative conditions. Statistical learning theory will be merged with predictive control theory using a mix of physics-based and data-driven models in the learning process. Scalability and accuracy will be attained by updating models in real-time using data and sharing models among vehicles of the same manufacturer.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.