SBIR Phase I: Democratizing Access to Data Analytics & Physics-Based Insights for Car Buyers

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力是使汽车能源建模中的科学技术民主化，使全国所有汽车购买者都能直观地获得这些技术。不幸的是，典型的汽车购买者目前无法获得信息来确定他们的燃料消耗、成本和他们可能考虑在自己的驾驶条件下购买的不同车辆的续航能力。此外，汽车购买者通常不具有对他们当前车辆的移动性模式的测量，使得更难以比较汽车。因此，汽车购买者有有限的能力来理解经济价值，在选择一个省油的车辆，可能会花费更多的前期，但节省他们大量的钱，从长远来看。通过提供更多的机会，了解汽车购买者在他们考虑的任何车辆上将经历的燃料消耗和成本信息，该项目可以加速采用节能车辆。通过加速采用节能汽车，该团队预计该项目可以避免高达300 - 500亿加仑的石油使用，并避免高达4500 - 6800亿美元的燃料成本。SBIR第一阶段项目提出应用数据科学，机器学习，和凸优化技术，以开发和应用车辆能量模型的情况下，只有稀疏和不同的数据源是可用的。这些情况代表了绝大多数汽车购买者通常遇到的用例。为了克服在汽车购买者的汽车比较过程中只有稀疏和不同的数据源所带来的挑战，该项目将开发使用时间分辨、行程分辨和油箱分辨的燃料消耗数据来制定和校准车辆能源模型的技术。此外，该项目将开发用于生成行程剖面的概率技术，以便使用起点-目的地-出发时间数据或沿行程沿着的速度-位置的间歇测量来生成给定行程的速度/地形剖面。这些用于行程配置文件生成的概率技术可以与车辆能量模型相结合，以允许汽车购买者根据自己的驾驶条件比较他们正在考虑购买的任何汽车。通过智能手机应用程序和基于网络的工具，购车者可以在购车过程中使用开发的技术。该奖项反映了NSF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。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project is to democratize access to scientific techniques in vehicle energy modeling, making them intuitively available to all car buyers across the country. Unfortunately, the typical car buyer does not currently have access to information to determine their fuel consumption, costs, and range viability for different vehicles they may be considering for purchase on their own driving conditions. Further, car buyers typically do not have measurements of their mobility patterns in their current vehicle, making it further difficult to compare cars. Thus, car buyers have limited ability to understand the economic value in choosing a fuel-efficient vehicle which may cost more upfront but save them significant money in the long run. By providing greater access to information on the fuel consumption and costs that car buyers will experience in any vehicle they are considering, this project can accelerate the uptake of fuel-efficient vehicles. By accelerating the uptake of fuel-efficient cars, the team projects this project can enable up to 30-50 billion gallons of avoided petroleum use, and up to $450-680 billion of avoided fueling costs.This SBIR Phase 1 project proposes to apply data science, machine learning, and convex optimization techniques to develop and apply vehicle energy models in circumstances where only sparse and disparate sources of data are available. These circumstances represent use cases that are typically encountered by the vast majority of car buyers. To overcome the challenges posed by only sparse and disparate sources of data being available during the car comparison process for car buyers, this project will develop techniques for formulation and calibration of vehicle energy models using time-resolved, trip-resolved, and tank-resolved fuel consumption data. Further, this project will develop probabilistic techniques for trip profile generation to create speed/terrain profiles for given trips using origin-destination-departure time data or intermittent measurements of speed-position along a trip. These probabilistic techniques for trip profile generation can be combined with vehicle energy models to allow car buyers to compare any car they are considering for purchase, on their own driving conditions. The techniques developed will be made available for use by car buyers through implementation in a smartphone app and web-based tools that are easy and intuitive for car buyers to use during their car shopping process.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.