Real-Time Battery Health Monitoring with Built-in Ultrasonic Techniques for Electric Aerial Vehicles
Abstract
Due to recent rapid advancements in high-energy storage units such as lithium-ion batteries, electric aerial vehicles are receiving more and more attention in aviation industry. However, one of the most fundamental challenges in battery-powered vehicle applications is the lack of reliable prediction methods for battery state of health (SoH) and remaining service life (RSL). This may lead to a major roadblock for the realization of electric aerial vehicles if battery health cannot be accurately predicted in during operation. Therefore, the objective of this investigation is to develop a robust and accurate diagnostic method for real-time monitoring of the health and onsets of failure of advanced high-density lithium-ion batteries by using built-in, low-profile ultrasonic sensors. The uniqueness and innovation of the proposed technique is to use ultrasonic stress wave data generated from on-board sensors, combined with machine learning techniques, to interrogate SoH and predict RSL of the batteries in real-time during operation. The outcome of the project will demonstrate a field-deployable ultrasonic hardware-software suite that will overcome challenges faced by state-of-the-art voltage-based battery management systems. It will have strong impacts and improvements on the current battery management systems, particularly in electric drone applications. By allowing a more accurate and fast estimation of the health-state of the batteries, operational and inspection cost can be significantly reduced. The overall safety will also be enhanced by reducing potential battery failures that can be predicted and prevented in real-time. The work will be conducted in collaboration with the Power and Control Division at the Air Force Research Laboratory, Wright-Patterson Air Force Base, Ohio. The AFRL will provide facilities for validation and testing of proposed technique and integrated system. It is anticipated that upon completion of the project, the proposed technique could lead to a paradigm-shifting change in industry-standard battery management systems, particularly for aviation industry.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 07, 2023
- Source ID
- FA95502210003
Entities
People
- Fu-Kuo Chang
Organizations
- Air Force Office of Scientific Research
- Stanford University
- United States Air Force