IP Library › Granted Patent US 12,372,583
Granted Patent B2
US 12,372,583 · App. 17/497,537 · Granted Jul 29, 2025

Neural network State-of-Charge estimation

Inventors: Ephram Chemali (Ontario, CA); Matthias Preindl (New York, NY)
Assignees: The Trustees of Columbia University in the City of New York; McMaster University
G01R31/382G06N3/04H02J7/0047H02J7/0048H02J7/007192G06N3/08H01M10/4257H02J7/005
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Quick Facts
Patent No.
US 12,372,583
App. No.
17/497,537
Granted
Jul 29, 2025
Kind
B2
Abstract

An approach to control or monitoring of battery operation makes use of a recurrent neural network (RNN), which receives one or more battery attributes for a Lithium ion (Li-ion) battery, and determines, based on the received one or more battery attributes, a state-of-charge (SOC) estimate for the Li-ion battery.

Claims (49)

1. A method for monitoring battery operation, the method comprising:

receiving, by a trained model, a time series of values of battery attributes for a battery that are determined based on measurements performed by one or more sensors monitoring the battery,

wherein the battery attributes include:

an ambient temperature, and

one or both of a battery voltage and a battery current, and

wherein a portion of the time series of values of battery attributes represents battery attributes of the battery during a charging cycle and another portion of the time series of values of battery attributes of the battery represents battery attributes of the battery during a discharging cycle; and

generating, by the trained model, based on the received time series, a state estimate for the battery,

wherein the trained model is a single, processor-based trained model that is trained to generate state estimates for the battery during charging cycles of the battery and during discharging cycles of the battery.

2. The method of claim 1 ,

controlling, by a controller, operation of at least one of the battery or a system powered by the battery based on the state estimate for the battery.

3. The method of claim 1 , wherein the trained model is configured to provide the state estimates, including the state estimate, with a mean absolute error of less than 2.088%.

4. The method of claim 1 , wherein the trained model is configured to provide the state estimates with the mean absolute error of less than 2.088% over at least a 10 degree Celsius range of the ambient temperatures of the time series of values.

5. The method of claim 1 , wherein the trained model includes a recurrent structure such that the state estimate is also based on a previous state estimate provided by the trained model.

6. The method of claim 5 , wherein training datasets used to train the trained model include vectors of values for the battery attributes, and different vectors of the vectors of values for the battery attributes include different ambient temperature values.

7. The method of claim 1 , wherein the trained model encodes characteristic behavior of the battery at multiple different ambient temperatures.

8. The method of claim 1 , wherein the trained model is one selected from the group of a recurrent neural network (RNN) and a recurrent neural network with a long short-term memory cell (LSTM-RNN).

9. The method of claim 1 , further comprising:

generating, by an environmental sensor of the one or more sensors, the ambient temperature for the trained model; and

generating, by a sensor of the one or more sensors, the one or both of the battery voltage and the battery current for the trained model.

10. The method of claim 1 , further comprising:

outputting, via a gauge, the state estimate.

11. The method of claim 1 , wherein the state estimate indicates an estimate of one or more of a state-of-charge (SOC), a battery capacity, and a battery resistance.

12. The method of claim 1 , wherein the state estimate for the battery includes a time series of values of state estimates corresponding to the time series of values of the battery attributes.

13. The method of claim 1 , wherein the trained model is a single neural network.

14. The method of claim 1 ,

wherein the generating, by the trained model, of the state estimate comprises: generating, by the trained model when the battery is discharging, based on the received time series, the state estimate for the battery when the battery is discharging, and wherein

the method further comprises:

receiving, by the trained model, a further time series of values of the battery attributes for the battery; and

generating, by the trained model when the battery is charging, based on the received further time series of values, a further state estimate for the battery when the battery is charging.

15. A system for monitoring battery operation comprising:

at least one sensor configured to measure battery attributes of a battery; and

a trained model implemented by an electronic processor, the trained model configured to:

receive a time series of values of the battery attributes for the battery,

wherein the battery attributes include:

an ambient temperature, and

one or both of a battery voltage and a battery current, and

wherein a portion of the time series of values of battery attributes represents battery attributes of the battery during a charging cycle and another portion of the time series of values of battery attributes of the battery represents battery attributes of the battery during a discharging cycle; and

generate, based on the received time series, a state estimate for the battery,

wherein the trained model is a single, processor-based trained model that is trained to generate state estimates for the battery during charging cycles of the battery and during discharging cycles of the battery.

16. The system of claim 15 , further comprising:

a controller configured to control operation of at least one of the battery or a system powered by the battery based on the state estimate for the battery.

17. The system of claim 15 , wherein the trained model is configured to provide the state estimates, including the state estimate, with a mean absolute error of less than 2.088% over at least a 10 degree Celsius range of the ambient temperatures of the time series of values.

18. The system of claim 15 , wherein the trained model includes a recurrent structure such that the state estimate is also based on a previous state estimate provided by the trained model.

19. The system of claim 18 , wherein training datasets used to train the trained model include vectors of values for the battery attributes, and different vectors of the vectors of values for the battery attributes include different ambient temperature values.

20. The system of claim 15 , wherein the trained model encodes characteristic behavior of the battery at multiple different ambient temperatures.

21. The system of claim 15 , wherein the trained model is one selected from the group of a recurrent neural network (RNN) and a recurrent neural network with a long short-term memory cell (LSTM-RNN).

22. The system of claim 15 , wherein the at least one sensor includes:

an environmental sensor configured to generate the ambient temperature for the trained model; and

a sensor configured to generate the one or both of the battery voltage and the battery current for the trained model.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 12, 2021
From: CHEMALI, EPHRAM
To: MCMASTER UNIVERSITY
Reel/Frame 057760/0193 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 12, 2021
From: PREINDL, MATTHIAS
To: THE TRUSTEES OF COLUMBIA UNIVERSITY IN THE CITY OF NEW YORK
Reel/Frame 057760/0391 →
Continuity (3)
Continuation 16194596 · Nov 19, 2018
Provisional Application 62588510 · Nov 20, 2017
Related Publication 20220271549A1 · Aug 25, 2022
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