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

这个小型企业创新研究(SBIR)项目的更广泛的影响/商业潜力是普及汽车能源建模中的科学技术，使其直观地适用于全国各地的所有购车者。不幸的是，典型的购车者目前无法获得信息来确定他们可能考虑在自己的驾驶条件下购买的不同车辆的油耗、成本和续航能力。此外，购车者通常没有对他们当前车辆的机动性模式进行测量，这使得比较汽车变得更加困难。因此，购车者了解选择节能型汽车的经济价值的能力有限，因为这种汽车可能前期成本更高，但从长远来看，可以为他们节省大量资金。通过为购车者提供更多关于他们正在考虑的任何车辆的燃油消耗和成本的信息，该项目可以加快节能型汽车的普及。通过加快节能汽车的普及，该团队预计该项目可以避免高达300-500亿加仑的石油使用，以及高达4,500-6,800亿美元的避免燃料成本。这个SBIR第一阶段项目建议应用数据科学、机器学习和凸优化技术，在只有稀疏和不同数据源的情况下开发和应用车辆能源模型。这些情况代表了绝大多数购车者通常会遇到的用例。为了克服购车者在汽车比较过程中只能获得稀疏和不同来源的数据带来的挑战，该项目将开发使用时间分辨、行程分辨和油箱分辨的油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概况的概率技术，以使用始发地-目的地-出发时间数据或沿行程的速度-位置的间歇性测量来创建给定行程的速度/地形概况。这些用于生成出行概况的概率技术可以与车辆能源模型相结合，以允许购车者在自己的驾驶条件下比较他们正在考虑购买的任何汽车。开发的技术将通过智能手机应用程序和基于网络的工具供购车者使用，购车者在购车过程中可以轻松直观地使用这些工具。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.