SYSTEM AND METHOD FOR DETECTING ELECTROLYTE AND COOLANT LEAKAGE FROM LITHIUM-ION BATTERY SYSTEMS
A computer implemented method includes monitoring a gas analyte level associated with a battery system using a first gas sensor and monitoring at least one variable of the battery system. The method includes determining whether there exists a correlation between the monitored gas analyte level and the monitored at least one variable of the battery system. The method includes determining whether there is an electrolyte leak from the battery system based on the determination of the correlation.
1 . A computer implemented method comprising:
monitoring a gas analyte level associated with a battery system using a first gas sensor;
monitoring at least one variable of the battery system;
determining whether there exists a correlation between the monitored gas analyte level and the monitored at least one variable of the battery system; and
determining whether there is an electrolyte leak from the battery system based on the determination of the correlation.
2 . The computer implemented method of claim 1 , wherein the correlation comprises an increasing trend of the monitored gas analyte level as the monitored at least one variable of the battery system increases.
3 . The computer implemented method of claim 1 , wherein the battery system is a lithium-ion battery system.
4 . The computer implemented method of claim 1 , wherein the monitored gas analyte comprises electrolyte gases and non-off-gas-event (non-OGE) interfering gases.
5 . The computer implemented method of claim 4 , wherein the non-OGE interfering gases comprise hydrogen and/or a coolant.
6 . The computer implemented method of claim 1 , wherein the monitored at least one variable of the battery system comprise a temperature of the battery system.
7 . The computer implemented method of claim 1 , wherein the monitored at least one variable of the battery system comprise an electrical current of the battery system
8 . The computer implemented method of claim 1 , wherein the monitored at least one variable of the battery system comprise a relative humidity surrounding the battery system.
9 . The computer implemented method of claim 1 , wherein the monitored at least one variable of the battery system comprise an airflow surrounding the battery system.
10 . The computer implemented method of claim 1 , comprising:
modulating a sensor-operational-variable profile to each of second gas sensors configured to monitor the gas analyte associated with the battery system;
monitoring the gas analyte using the second gas sensors throughout the modulated sensor-operational-variable profiles;
developing a data matrix comprising sensor signals generated by the second gas sensors as a function of the modulated sensor-operational-variable profiles;
differentiating gas species of the gas analyte based on a comparison of various features in the data matrix; and
determining a condition of the battery system based on the differentiation of gas species.
11 . The computer implemented method of claim 10 , comprising pre-training the second gas sensors based on a machine learning (ML) algorithm before an initial field deployment of the second gas sensors.
12 . The computer implemented method of claim 10 , wherein the condition of the battery system comprises one or more of electrolyte leakage, coolant leakage, cell venting, thermal runaway, water-ingress, and off-gas.
13 . The computer implemented method of claim 10 , comprising identifying a poisoned sensor of the second gas sensors.
14 . The computer implemented method of claim 10 , the sensor-operational-variable comprises temperature, power, voltage, polarity, and/or electrical current.
15 . A monitoring system, comprising:
at least one gas sensor configured to monitor for a gas analyte associated with a battery system;
at least one sensor configured to monitor one or more variables of the battery system; and
a controller, comprising:
a memory to store machine readable instructions; and
a processor to access the memory and execute the machine-readable instructions, the machine-readable instructions causing the processor to:
monitor the gas analyte using the at least one gas sensor;
monitor the one or more variables of the battery system using the at least one sensor;
determine a correlation between monitored gas analyte level and the one or more variables; and
determine whether there is an electrolyte leak from the battery system based on the correlation.
16 . The monitoring system of claim 15 , wherein the battery system is a lithium-ion battery system.
17 . The monitoring system of claim 15 , wherein the at least one sensor comprises one or more of a temperature sensor, a relative humidity sensor, an electrical current sensor, and an airflow sensor.
18 . The monitoring system of claim 15 , wherein the machine-readable instructions cause the processor to:
modulate a sensor-operational-variable profile to the at least one gas sensor;
monitor gas analyte level using the at least one gas sensor throughout the modulated sensor-operational-variable profile;
develop a data matrix comprising sensor signals generated by the at least one gas sensor as a function of the modulated sensor-operational-variable profile;
differentiate gas species of the gas analyte based on a comparison of various features in the data matrix; and
determine a condition of the battery system based on the differentiation of gas species.
19 . The monitoring system of claim 18 , wherein the machine-readable instructions cause the processor to determine whether there is a coolant leak in the battery system based on the differentiation of gas species.
20 . The monitoring system of claim 18 , wherein the machine-readable instructions cause the processor to determine whether there is a water ingress in the battery system based on the differentiation of gas species.
21 . The monitoring system of claim 18 , wherein the machine-readable instructions cause the processor to determine whether the at least one gas sensor comprises a poisoned sensor.
22 . The monitoring system of claim 18 , wherein the sensor-operational-variable comprises temperature, power, voltage, polarity, and/or electrical current.
23 . A computer implemented method comprising:
modulating a sensor-operational-variable profile to each of gas sensors configured to monitor a gas analyte associated with a battery system;
monitoring the gas analyte using the gas sensors throughout the modulated sensor-operational-variable profiles;
developing a data matrix comprising sensor signals generated by the gas sensors as a function of the modulated sensor-operational-variable profiles;
differentiating gas species of the gas analyte based on a comparison of various features in the data matrix; and
determining a condition of the battery system based on the differentiation of gas species.
24 . The computer implemented method of claim 23 , comprising pre-training the second gas sensors based on a machine learning (ML) algorithm before an initial field deployment of the gas sensors.
25 . The computer implemented method of claim 23 , wherein the condition of the battery system comprises one or more of electrolyte leakage, coolant leakage, cell venting, thermal runaway, water-ingress, and off-gas.
26 . The computer implemented method of claim 23 , comprising:
differentiating gas sensor responses based on a comparison of various features in the data matrix; and
identifying a poisoned sensor of the second gas sensors based on the differentiation of gas sensor responses.
27 . The computer implemented method of claim 23 , wherein the sensor-operational-variable comprises temperature, power, voltage, polarity, and/or electrical current.
28 . A monitoring system, comprising:
at least one gas sensor configured to monitor for a gas analyte associated with a battery system; and
a controller, comprising:
a memory to store machine readable instructions; and
a processor to access the memory and execute the machine-readable instructions, the machine-readable instructions causing the processor to:
modulate a sensor-operational-variable profile to each of the at least one gas sensor;
monitor gas analyte level using the at least one gas sensor throughout the modulated sensor-operational-variable profile;
develop a data matrix comprising sensor signals generated by the at least one gas sensor as a function of the modulated sensor-operational-variable profile;
differentiate gas species of the gas analyte based on a comparison of various features in the data matrix; and
determine a condition of the battery system based on the differentiation of gas species.
29 . The monitoring system of claim 28 , wherein the machine-readable instructions cause the processor to determine whether there is a coolant leak in the battery system based on the differentiation of gas species.
30 . The monitoring system of claim 28 , wherein the machine-readable instructions cause the processor to determine whether there is a water ingress in the battery system based on the differentiation of gas species.
31 . The monitoring system of claim 28 , wherein the machine-readable instructions cause the processor to differentiate gas sensor responses based on a comparison of various features in the data matrix; and identify a poisoned sensor of the at least one gas sensors based on the differentiation of gas sensor responses.
32 . The monitoring system of claim 28 , wherein the sensor-operational-variable comprises temperature, power, voltage, polarity, and/or electrical current.