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

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.