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/商业潜力是使车辆能源建模的科学技术民主化，使全国所有购车者都能直观地获得这些技术。不幸的是，典型的购车者目前无法获得信息，以确定他们可能考虑根据自己的驾驶条件购买的不同车辆的燃料消耗、成本和续航能力。此外，购车者通常不知道他们在当前车辆中的移动模式，这使得比较汽车变得更加困难。因此，购车者理解选择节油汽车的经济价值的能力有限，这种汽车可能会在前期花费更多，但从长远来看会为他们节省大量资金。通过为消费者提供更多关于燃油消耗和成本的信息，这个项目可以加速节能汽车的普及。该团队预计，通过加速节油汽车的普及，该项目可以节省300 - 500亿加仑的石油消耗，节省高达4500 - 6800亿美元的燃料成本。该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可用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遇到的用例。为了克服购车者在汽车比较过程中只有稀疏和不同的数据来源所带来的挑战，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汽车能量模型的技术。此外，该项目将开发行程剖面生成的概率技术，利用起点-目的地-出发时间数据或沿着行程的速度位置间歇测量来创建给定行程的速度/地形剖面。这些生成行程轮廓的概率技术可以与车辆能量模型相结合，从而使购车者能够根据自己的驾驶条件对他们考虑购买的任何汽车进行比较。开发的技术将通过智能手机应用程序和基于网络的工具提供给购车者使用，购车者在购车过程中可以轻松直观地使用。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。

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.