IP Library Granted Patent US 12,467,976
Granted Patent B2
US 12,467,976 · App. 18/584,144 · Granted Nov 11, 2025

System and method for determining a battery condition

Inventors: Evan Murphy (San Francisco, CA); Patrick Herring (San Francisco, CA); Daniel Vickery (San Francisco, CA); Elizabeth Goldberg (San Francisco, CA); Jacqueline Maslyn (San Francisco, CA); Matthew Bohan (San Francisco, CA); Shyam Srinivasan (San Francisco, CA); Dustin Summy (San Francisco, CA); Zachary Gima (San Francisco, CA); Brian Goodall (San Francisco, CA); Mark Tobenkin (San Francisco, CA)
Assignee: Zitara Technologies, Inc.
G01R31/367G01R31/385G01R31/396
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Quick Facts
Patent No.
US 12,467,976
App. No.
18/584,144
Granted
Nov 11, 2025
Kind
B2
Abstract

A system and method for determining whether a battery is operating in an anomalous condition can include or be configured to measure sensor data, estimate a local property of the battery using on the sensor data, and classify whether the battery is operating in the anomalous condition based on the local property.

Claims (31)

1 . A method for determining whether a battery is experiencing a thermal disconnection comprising:

at each of a plurality of times, measuring a temperature proximal the battery;

for each temperature, estimating a local temperature of a battery cell of the battery by:

detecting a plurality of temperature jumps in the plurality of measured temperatures, wherein each temperature jump of the series of temperature jumps exceeds a threshold temperature jump amplitude; and

calculating a thermal resistance at each of the temperature jumps; and

performing a statistical test on the plurality of local temperatures to determine whether the battery is experiencing the thermal disconnection comprising performing a statistical test on the plurality of thermal resistances to determine whether the battery is experiencing the thermal disconnection.

2 . The method of claim 1 , wherein a threshold temperature jump amplitude is 0.1° C./s.

3 . The method of claim 1 , wherein the statistical test comprises at least one of a student t-test, a z-test, or an F-test.

4 . The method of claim 1 , wherein the plurality of the temperatures comprises at least 3 temperature values.

5 . The method of claim 1 , wherein estimating the local temperatures comprises using a state estimator using a thermal model of the battery to estimate the local temperature of the battery cell from the measured temperature.

6 . The method of claim 5 , further comprising: selecting the thermal model from a thermal model library; where each thermal model of the thermal model library is operable to estimate the local temperature of the battery cell; wherein each thermal model of the thermal model library is associated with the battery experiencing either a nominal condition or a thermal disconnection; wherein a statistical probability is assigned to each thermal model of the thermal model library based on the local temperature of the battery cell; wherein when the statistical probability of a thermal model associated with the battery experiencing the thermal disconnection has the greatest statistical probability, the battery is classified as experiencing the thermal disconnection.

7 . The method of claim 6 , further comprising, when the battery is classified as experiencing the thermal disconnection, determining a corrective action.

8 . A method for determining whether a battery is experiencing a thermal anomaly comprising:

at each of a plurality of times, measuring a temperature proximal the battery;

for each temperature, estimating a local temperature of a battery cell of the battery, wherein the plurality of temperatures do not include temperature readings that exceed a temperature jump larger than a threshold temperature jump, wherein the threshold temperature jump is between 0.1° C./s and 10° C./s;

performing a statistical test on the plurality of local temperatures to determine whether the battery is experiencing the thermal anomaly.

9 . The method of claim 8 , wherein the thermal anomaly comprises a thermal runaway or an anomalous battery temperature.

10 . The method of claim 8 , wherein the statistical test is performed on a plurality of local temperatures determined during steady state operation of the battery.

11 . The method of claim 8 , wherein the statistical test comprises at least one of a student t-test, a z-test, or an F-test.

12 . The method of claim 8 , wherein the plurality of the temperatures comprises at least 3 temperature values.

13 . The method of claim 8 , wherein estimating the local temperatures comprises using a state estimator using a thermal model of the battery to estimate the local temperature of the battery cell from the measured temperature.

14 . The method of claim 13 , further comprising: selecting the thermal model from a thermal model library; where each thermal model of the thermal model library is operable to estimate the local temperature of the battery cell; wherein each thermal model of the thermal model library is associated with the battery experiencing either a nominal condition or a thermal anomaly; wherein a statistical probability is assigned to each thermal model of the thermal model library based on the local temperature of the battery cell; wherein when the statistical probability of a thermal model associated with the battery experiencing the thermal anomaly has the greatest statistical probability, the battery is classified as experiencing the thermal anomaly.

15 . The method of claim 14 , further comprising, when the battery is classified as experiencing the thermal anomaly, determining a corrective action.

16 . A system for detecting anomalous thermal behavior in a battery comprising:

a thermometer configured to measure a temperature dataset associated with the battery;

a state estimator operable to determine a local battery cell temperature of the battery using the temperature dataset;

a classifier configured to classify whether the battery is experiencing anomalous thermal behavior based on the determined local battery cell temperature, wherein the classifier is operable in:

a thermal disconnection mode wherein the classifier is operable to classify the anomalous thermal behaviour as a thermal disconnection using a thermal resistance dataset derived from jump changes in the local battery cell temperature exceeding a threshold jump change, wherein the jump changes are at least about 0.1° C./s to classify the thermal disconnection; and

an anomalous temperature mode wherein the classifier is operable to classify the anomalous thermal behavior based on steady state readings of the local battery cell temperature.

17 . The system of claim 16 , wherein the local temperature of the battery cell is modelled as a function of the temperature dataset, a historic temperature dataset, an ambient temperature, and a heat flux.

18 . The system of claim 16 , wherein the classifier is configured to classify whether the battery is operating in the anomalous condition during operation of the battery.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 23, 2026
From: ZITARA TECHNOLOGIES, INC.
To: FORTESCUE ZERO LIMITED
Reel/Frame 074158/0058 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 28, 2024
From: MURPHY, EVAN; HERRING, PATRICK; VICKERY, DANIEL; GOLDBERG, ELIZABETH; MASLYN, JACQUELINE; BOHAN, MATTHEW; SRINIVASAN, SHYAM; SUMMY, DUSTIN; GIMA, ZACH; GOODALL, BRIAN; TOBENKIN, MARK
To: ZITARA TECHNOLOGIES, INC.
Reel/Frame 066594/0007 →
Continuity (4)
Continuation 18078814 · Dec 9, 2022
Provisional Application 63388141 · Jul 11, 2022
Provisional Application 63287819 · Dec 9, 2021
Related Publication 20240192276A1 · Jun 13, 2024
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