NASA Internship: Battery Modeling for Electric Aircraft

Project Overview

In Fall 2023 I had the pleasure of working an internship at NASA Langley Research Center with the Aeronautics Systems Analysis Branch (ASAB). During my internship I worked on modeling constant power requirements for electric aircraft alongside my mentors Nathanial Blaesser, Nicholas Borer, and Tommy Hallock. The battery modeling tool I developed using Python will be incorporated in the Rapid Conceptual Design Environment (RCDE) so that future conceptual aircraft designs can be rapidly iterated. I am also a co-author for a technical conference publication to the AIAA based on our work in modeling constant power requirements for electric aircraft.

Co Authored NASA Research Publication to the AIAA

A_Parametric_Battery_Model_Hallock_Aviation_2024 (1).pdf

Modeling Battery Performance Basics

X57 Data

To start, data from the X57 was used as a baseline for expected thrust, weight, and constant power requirements throughout entire mission profiles. This would eventually include balked landing conditions as well.

Cell Discharge Curves

The battery modeling tool starts by incorporating battery cell data, typically provided by battery manufacturers. To model constant power requirements we are looking to find the useable energy under varying discharge curves. This becomes more difficult as voltage and current levels will change throughout the course of the flight while having to maintain a constant power output.

Interpolated 3D Plot

Interpolating discharge curves to generate a 3D surface plot played a pivotal role in our project. By interpolating discharge curves, we were able to create a 3D representations of battery behavior across various operating conditions. This allowed us to visualize how battery performance changes with varying levels of voltage, current, and state of charge. The data from the 3D surface plot proved useful when examining how constant power loads impact battery efficiency and capacity.

Once we had generated the 3D surface plot, we could overlay mission segment data onto the plot to visualize entire mission profiles for electric aircraft. By superimposing takeoff and landing power demands onto the surface plot, we could analyze how different segments of a flight affect battery performance. This capability was useful in predicting expected cruise lengths based on power demands during different phases of the mission. Ultimately, this approach allowed us to optimize battery management strategies for electric aircraft, ensuring that mission requirements could be met efficiently and reliably.

Depth of Discharge Limits

To avoid charging and degradation constraints, we avoided using the top 10% and bottom 20% of the useable energy provided from the battery. Cell cycling can become a major area of concern for aircraft in which to avoid this issue we avoid depleting the battery fully for standard missions.

Internal Resistance Growth

Resistance growth was handled by assuming a constant resistance value. DOD limitations restricted us from operating in the region where internal resistance growth is exponential in which it was not heavily considered but is still an area of interest for the future.

Project Flow Chart

The project flow chart provides a comprehensive visualization of the functionalities and applications of our battery modeling tool. Beyond its higher fidelity model, the tool also integrates a conventional battery modeling approach commonly utilized in industry settings. This inclusion enables a comparative analysis of the efficacy between more advanced and traditional modeling techniques, particularly their influence on mission profiles for electric aircraft. Overall, this project stands as an exciting endeavor at the forefront of battery modeling research. I am grateful to have been able to work on this project with my ASAB mentors.

Page updated

Google Sites

Report abuse