SBIR Phase II: Data Analytics and Physics-Based Insights into Vehicle Mobility Patterns

这项小型企业创新研究（SBIR）项目的更广泛影响/商业潜力是为消费者提供有关其车辆使用的新见解，为环境和经济影响提供信息。不幸的是，目前典型的购车者并不了解他们的出行模式，尤其是电动汽车的燃料成本和续航能力将如何影响他们的日常生活。因此，潜在的购车者可能不会欣赏潜在的财务和实际节省。SBIR第二阶段项目建议支持数据收集、处理和分析方法的开发，以测量个人（或车队）的驾驶倾向，以预测电动汽车使用的价值和可行性。这可能会节省数十亿加仑的石油使用，以及数千亿美元的相关成本，并显著减少排放。该项目将推进数据科学、机器学习和凸优化技术来评估车辆性能。该项目分为三个阶段：(1)开发基于数学和物理的算法，用于预测任何电动汽车在任何行程中的能量或充电需求和续航能力，包括计算结果的不确定性界限；(2)整合行程能量计算，开发预测和优化电动汽车充电部署的算法；(3)开发大规模使用的算法。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 provide new insight to consumers on their vehicle use, to inform environmental and economic impact. Unfortunately, typical car buyers currently do not understand their mobility patterns, and in particular how electric vehicle (EV) fuel costs and range viability will impact their day-to-day lives. Thus, prospective car buyers may not appreciate the potential financial and practical savings. This SBIR Phase II project proposes to support the development of data collection, processing, and analytical methods to measure an individual's (or fleet) driving tendencies to predict the value and viability of EV use. This can potentially save billions of gallons of avoided petroleum use as well as hundreds of billions in associated costs, and dramatically reduced emissions. The proposed project advances data science, machine learning, and convex optimization techniques to estimate vehicle performance. This project has three stages: (1) develop mathematical and physics-based algorithms for predicting the energy or charge requirements and range viability for any electric vehicle on any trip, including uncertainty bounds on the calculated results; (2) integrate the trip energy calculations to develop algorithms that predict and optimize EV charging deployments; (3) develop algorithms for use at scale.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.