System and Method Implementing a Battery Avionics System for Electric-Powered Aircraft

Patent metadata

Field	Value
Publication number	US-2024217391-A1
Application number	US-202218563433-A
Country	US
Kind code	A1
Filing date	May 31, 2022
Priority date	Jun 4, 2021
Publication date	Jul 4, 2024
Grant date	—

Abstract

Disclosed herein is a system and method implementing a battery avionics system for integrating battery monitoring, control, and management functions with an avionics system of an aircraft. The system uses a model implementing a battery pack digital twin, which is a continuous simulation of the operation of the battery pack within the aircraft, receives data regarding the battery pack generated by the digital twin model and provides optimized parameters to the battery avionics system. The system enables high precision, cell-level resolution control of the battery pack. The system estimates the state of charge, state of health, state of safety, and state of function of the cells and the battery pack as a whole and uses this information to manage the battery pack, given a particular flight profile of the aircraft.

First claim

What is claimed:

1 . A system comprising:
a battery avionics system for managing a battery pack powering an electric aircraft; and one or more models, each model comprising a digital twin of the battery pack; wherein observable data from the battery pack collected by the battery avionics system is used by the digital twins to optimize parameters for the battery pack; and wherein the optimized parameters are communicated to the battery avionics system.

2 . The system of claim 1 wherein the optimized parameters are communicated to the battery avionics system via one or more digital specification sheets.

3 . The system of claim 1 wherein the one or more digital twins use a physics-based model combined with a data-driven model.

4 . The system of claim 3 wherein the physics-based model is the Doyle-Fuller-Newman model.

5 . The system of claim 1 , the battery avionics system comprising a battery management system for managing the battery pack.

6 . The system of claim 5 , the battery avionics system optimizing management of the battery based on the one or more parameters provided by the digital twins.

7 . The system of claim 1 wherein the observable data collected from the battery pack includes, voltage, current and temperature.

8 . The system of claim 6 wherein the battery pack comprises a plurality of cells.

9 . The system of claim 8 wherein the observable data is collected from each cell or from a portion of the plurality of cells in the battery pack.

10 . The system of claim 1 wherein the battery avionics system and the digital twins are agnostic to cell chemistry and electrochemical properties of the battery pack.

11 . The system of claim 1 wherein the battery management system is integrated or in communication with other avionics sub-systems of the aircraft.

12 . The system of claim 11 wherein the battery avionics system and the digital twins are provided with a common battery specification sheet describing electrochemical and thermal performance metrics and material composition of the battery pack.

13 . The system of claim 12 wherein the battery specification sheet is used by the digital twins as a differentiable modelling block enabled by a machine learning model.

14 . The system of claim 1 wherein the battery avionics system provides real-time monitoring and control of the battery pack.

15 . The system of claim 14 wherein the battery avionics system further provides integrated trajectory and recharge planning for the battery pack.

16 . A method comprising:
receiving data characterizing a battery pack in an aircraft from a battery avionics system in the aircraft. inputting the data to a model modelling a digital twin of the battery pack; receiving optimized parameters for the battery pack from the model; and communicating the optimized parameters to the battery avionics system.

17 . The method of claim 16 wherein the model is a combination of a physics-based model combined with a data-driven model and further wherein the data received from the battery avionics system comprises externally observable data from the battery pack.

18 . A method comprising:
reading observable data characterizing a battery pack powering an aircraft by a battery avionics system; communicating the data off-aircraft to a model modelling a digital twin of the battery pack; receiving optimized parameters for the battery pack from the model; and using the optimized parameters to manage the battery pack.

19 . The method of claim 18 further comprising:
further optimizing parameters received from the model using a neural ODE.

20 . The method of claim 19 wherein the neural ODE provides predictions of battery degradation based on the optimized parameters.

Assignees

• Univ Carnegie Mellon

Inventors

• Viswanathan Venkatasubramanian
• Bills Alexander
• Sripad Shashank

Classifications

• H02J7/855

  with circuits adapted for supplying loads from the battery · CPC title

• H02J7/80

  including monitoring or indicating arrangements · CPC title

• B64D27/34

  All-electric aircraft · CPC title

• B64D31/16

  for electric power plants · CPC title

• B64D27/357

  using batteries · CPC title