SBIR Phase II: Improving fleet operational metrics through service optimization with automated learning of vehicle energy performance models for zero-emission public transport

这个小型企业创新研究第二阶段(SBIR)项目将研究和验证物联网(IOT)平台，以帮助商业车队过渡到零排放汽车(ZEV)。ZEV过渡是运输部门脱碳的主要解决方案，运输部门是美国最大的温室气体排放国。该项目以过境机构为重点，最终目标是降低ZEV机队的运营成本和资本成本。再加上联邦、州和地方政府目前对过境机构购买ZEV的资金支持，该项目可能会加速美国过境车队的脱碳。美国公交车队50%转向ZEV将减少近2亿公吨二氧化碳(CO2)当量，提供更清洁的空气质量并减少城市噪音污染，特别是在交通需求更依赖交通服务的低收入社区。运输管理系统的潜在市场将从2020年的88亿美元增长到2028年的274.8亿美元。在交通领域展示成功将使这种方法能够复制到其他车队领域，如校车车队、最后一英里和中英里送货车队以及长途卡车运输车队。该项目的智力价值在于设计和实现了一个人工智能软件平台，可以自动学习公交ZEV的预测车辆模型，并为公交机构提供推荐服务。第二阶段项目有三个综合目标。第一个目标是开发可伸缩和高精度的能量预测算法。公交ZEV车队具有随机载荷变化、对操作员驾驶方式高度敏感以及电池大小、重量和行驶里程的高度变化，即使是类似的车辆也是如此。这些挑战将通过开发基于第一阶段开发的算法的自动学习技术来解决，该算法使用ZEV停靠站和行程的情景数据。第二个目标是通过ZEV车队提供定期巴士服务的飞行员来验证预测的准确性。最终目标是开发实时、可扩展的车队优化算法，以优化ZEV车队的日常分配和充电管理。机会约束优化将与预测控制理论相结合，以解决由此产生的优化算法的可扩展性和实时性能。如果成功，这些建议将展示对充电使用的高度准确预测，ZEV机队利用率的大幅增加，以及过境ZEV机队运营成本的降低。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。

This Small Business Innovation Research Phase (SBIR) II project will research and validate an internet-of-things (IOT) platform to help commercial fleets transition to zero-emission vehicles (ZEVs). The ZEV transition is the primary solution to the decarbonization of the transportation sector, which is the largest emitter of greenhouse gases in the US. The project focuses on transit agencies, with the ultimate goal of lowering both operating costs and capital costs of their ZEV fleets. Coupled with the current funding support by federal, state and local governments to transit agencies to purchase ZEVs, this project could accelerate the decarbonization of the US transit fleet. A 50% transition of the US transit fleet to ZEVs will reduce nearly 200 million metric tons of carbom dioxide (CO2) equivalent, providing cleaner air quality and reducing urban noise pollution, particularly in low-income communities that rely more heavily on transit services for their transportation needs. The addressable market of Transportation Management Systems will grow from $8.8 billion in 2020 to $27.48 billion in 2028. Demonstrating success in the transit segment will enable the replication of this approach to other fleet segments like school bus fleets, last-mile and mid-mile delivery fleets, and long-haul trucking fleets. The intellectual merit of this project is the design and implementation of an artificial intelligence software platform to automatically learn predictive vehicle models of transit ZEVs and provide recommendation services to transit agencies. The Phase II project has three integrated goals. The first goal is the development of energy prediction algorithms which are scalable and highly accurate. Transit ZEV fleets have stochastic load changes, high sensitivity to operator driving style and high variation of battery size, weight and driving range, even for similar vehicles. These challenges will be addressed by developing automated learning techniques built on algorithms developed in Phase I, which use contextualized data from ZEV stops and trips. The second goal is to validate the prediction accuracy via pilots with ZEV fleets providing scheduled bus services. The final goal is development of real-time, scalable, fleet optimization algorithms which optimize daily assignment and charge management of ZEV fleets. Chance-constrained optimization will be merged with predictive control theory to address scalability and real-time performance of the resulting optimization algorithms. These recommendations will, if successful, demonstrate highly accurate predictions of charge usage, a substantial increase in ZEV fleet utilization, and a reduction of transit ZEV fleet operating costs.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.