Real-Time Prognostics of Lithium-ion Batteries in Electric Unmanned Aerial Vehicles

锂离子电池具有能量和功率密度高、电压容量大、重量轻等优点，正成为电动无人机的主要动力来源。电池性能通常会由于电化学反应而降低，也称为电池老化。电池老化可能会导致容量衰减，甚至灾难性的故障。因此，电池健康状况的实时管理对电动无人机的安全可靠性至关重要。现有电池健康管理技术的局限性包括：(1)很少有电池健康管理技术能够实现放电容量和放电结束的实时预测，因为现有技术需要充电周期中的状态监测数据，而这些数据在飞行过程中并不总是可用的；(2)当前的电池健康管理技术仅在简单、固定的飞行计划和恒定有效载荷下有效。有效的实时电池健康管理技术不仅将对航空航天行业产生重大影响，还将对电池应用广泛的医疗、汽车、国防和物流行业产生重大影响。这项研究的目的是开发一种新的电池健康管理技术，能够实时预测电动无人机中锂离子电池的放电容量和放电结束时间。具体地说，将开发一种结合两种深度学习算法的新型计算框架。一种深度学习算法通过提取放电周期之间的空间相关性来预测放电容量；另一种通过捕获放电周期内的时间相关性来预测放电结束。此外，将开发一种基于运输的最佳领域适应技术，通过在飞行计划和有效载荷之间传递知识，预测不同飞行计划和有效载荷下的卸货能力和卸货结束时间。所提出的实时电池健康管理技术将使用从电动无人机收集的实验数据进行验证。这项拟议的研究将通过回答以下研究问题来推进电池健康管理领域：(1)只能在飞行期间使用在放电周期中收集的状态监测数据来实时预测锂离子电池的放电容量和放电结束时间？(2)在一个飞行计划和有效载荷下获得的锂离子电池放电容量和放电结束时间的知识是否可以用于预测另一飞行计划和有效载荷下的放电容量和放电结束时间？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，认为值得支持。

Due to high energy and power density, high voltage capacity, and light weight, Lithium-ion batteries are becoming the primary source of power propulsion for electric unmanned aerial vehicles. Battery performance usually degrade due to electrochemical reactions, also known as battery aging. Battery aging could result in capacity fade and even catastrophic failure. Therefore, real-time battery health management is crucial to the safety and reliability of electric unmanned aerial vehicles. The limitations of existing battery health management techniques include (1) few battery health management techniques can achieve real-time prediction of discharge capacity and end-of-discharge because existing techniques require condition monitoring data in charge cycles, which are not always available during flight; (2) current battery health management techniques are effective only under simple, fixed flight plans and constant payloads. Effective real-time battery health management techniques will make a significant impact on not only the aerospace but also healthcare, automotive, defense, and logistics industries where batteries have a wide range of applications. The objective of the proposed research is to develop a novel battery health management technique that enables real-time prediction of discharge capacity and end-of-discharge of Lithium-ion batteries in electric unmanned aerial vehicles. Specifically, a novel computational framework that combines two deep learning algorithms will be developed. One deep learning algorithm predicts discharge capacity by extracting spatial correlations between discharge cycles; the other predicts end-of-discharge by capturing temporal dependencies within a discharge cycle. In addition, an optimal transport-based domain adaptation technique will be developed to predict discharge capacity and end-of-discharge under varying flight plans and payloads through the transfer of knowledge across flight plans and payloads. The proposed real-time battery health management technique will be validated using experimental data collected from electric unmanned aerial vehicles. The proposed research will advance the field of battery health management by answering the following research questions: (1) Can discharge capacity and end-of-discharge of Lithium-ion batteries be predicted in real-time using condition monitoring data collected in discharge cycles only during flight? (2) Can knowledge gained on discharge capacity and end-of-discharge of Lithium-ion batteries under one flight plan and payload be used to predict discharge capacity and end-of-discharge under another flight plan and payload?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.