SBIR Phase I: Electric Vehicle Useful Life Prediction

SBIR 第一阶段：电动汽车使用寿命预测

• 批准号: 2052407
• 负责人: Scott Case
• 金额: $ 25.2万
• 依托单位: RECURRENT MOTORS INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2052407&HistoricalAwards=false
• 关键词: SBIR; Phase; Electric; Vehicle; Useful

The broader impact of this SBIR Phase I project is enabling middle and low income car buyers to fully participate in the electrification of transportation, ensuring that all Americans can benefit from lower fuel bills, maintenance costs, and healthier community environments. It is not well understood how electric vehicle (EV) battery packs in cars on the road today degrade over time and how they should be valued. This uncertainty in future battery performance directly impacts confidence in buying a used EV. The project proposes the development of a battery report that is able to remotely and rapidly diagnose and predict the state of health of EV battery packs through machine learning models validated by physical inspection and evaluation of a sample of EVs. The project’s scale, scope, and commercial result would accelerate the adoption and accessibility of EVs. This Small Business Innovation Research Phase I project addresses the need for scalable, accessible and non-invasive state of health prediction on a wide variety of plug-in electric vehicles (PEVs) by using a set of machine learning models. This will be accomplished by collecting daily observation data from an unprecedented number of PEVs on the road from a diverse set of makes, models, years, and climates. This dataset will be used to train machine learning algorithms on a key set of remotely accessible features to predict range and battery health metrics. The machine learning approach builds on a combination of tree-based ensemble models and linear models informed by physics-based experiments, and will eventually expand to other more advanced algorithms as appropriate. The goals of this Phase I project are to (1) recruit 25,000+ more PEV drivers over 6 months, (2) demonstrate a set of tools that are accurate enough to meet consumer demand for near-term degradation models for the used PEVs on the road, (3) validate machine learning determined state of health estimates with hands-on experimentation with a subset of vehicles, and (4) use these hands-on experiments to better understand the relationships between remotely accessible proxy data and state of health.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.

SBIR第一期项目的更广泛影响是使中低收入购车者能够充分参与交通电气化，确保所有美国人都能从更低的燃油费、维护成本和更健康的社区环境中受益。目前人们还不太清楚电动汽车(EV)电池组是如何随着时间的推移而退化的，以及它们应该如何估值。未来电池性能的这种不确定性直接影响人们购买二手电动汽车的信心。该项目提出开发一种电池报告，该报告能够通过对电动汽车样本的物理检测和评估验证的机器学习模型，远程快速诊断和预测电动汽车电池组的健康状态。该项目的规模、范围和商业结果将加速电动汽车的采用和可获得性。这个小型企业创新研究第一阶段项目通过使用一套机器学习模型，解决了对各种插电式电动汽车(PEV)进行可扩展、可访问和非侵入性健康状态预测的需求。这将通过收集来自不同品牌、车型、年份和气候的史无前例数量的电动汽车的每日观察数据来实现。该数据集将用于在一组关键的远程可访问功能上训练机器学习算法，以预测射程和电池健康指标。机器学习方法建立在基于物理实验的树集合模型和线性模型的组合上，并最终将适当地扩展到其他更先进的算法。这个第一阶段项目的目标是(1)在6个月内招募25,000多名电动汽车司机，(2)展示一套足够准确的工具，以满足消费者对道路上使用的电动汽车短期退化模型的需求，(3)通过与车辆子集的动手实验来验证机器学习确定的健康状态估计，以及(4)使用这些动手实验来更好地了解远程访问代理数据和健康状态之间的关系。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。