A high fidelity system for estimating the remaining useful life (RUL) for Li-ion batteries for aerospace applications is presented. The system employs particle filtering coupled with outlier detection to predict RUL. Calculations of RUL are based on autonomous measurements of the battery state-ofhealth by onboard electronics. Predictions for RUL are fed into a maintenance advisor which allows operators to more effectively plan battery removal. The RUL algorithm has been exercised under stressful conditions to assert robustness.

In this paper we will discuss some practical aspects of health management for a rechargeable Li-ion battery system for aerospace applications. Industry working groups have developed guidance for the flight certification of this type of battery system, and we will show how this guidance is used in the design. We will also discuss safety features embedded in the battery system related to industry guidance; including cell energy balancing, internal temperature monitoring and emergency fuses. The keys to battery prognostics and health management (PHM) are analytic State of Charge (SoC) and State of Health (SoH) algorithms implemented in these battery systems. We show how these are developed and how we have tested them before deployment. These battery systems also collect data that is made available to the aircraft processing systems, e.g., Aircraft Health Management System, On-board Maintenance System, etc.. This allows for near real-time confirmation of proper operation of these battery systems as well as adherence to MSG-3 maintenance standards. We close with a brief discussion of the practical limitations in our implementation and a discussion of our ongoing and future development in this area.

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