System and method for using artificial intelligence to detect lithium plating
In one aspect, computer-implemented method may include receiving, from computing devices, fleet data pertaining to battery packs each including first cells. The fleet data includes false positive images of lithium plating affecting at least a first cell, true positive images of the lithium plating affecting at least a second cell, or both. The method may include training, using at least the fleet data, machine learning models to predict occurrences of the lithium plating, receiving, from sensors associated with second cells, measurements pertaining to current, voltage, temperature, or some combination thereof, and inputting the measurements into the machine learning models to predict the occurrences of the lithium plating for the second cells.
1. A computer-implemented method comprising:
receiving, from one or more computing devices, fleet data pertaining to one or more battery packs each comprising one or more first cells, wherein the fleet data comprises one or more false positive images of lithium plating affecting at least a first cell, one or more true positive images of the lithium plating affecting at least a second cell, or both;
training, using at least the fleet data, one or more machine learning models to predict one or more occurrences of the lithium plating;
receiving, from one or more sensors associated with one or more second cells, one or more measurements pertaining to current, voltage, temperature, or some combination thereof; and
inputting the one or more measurements into the one or more machine learning models to predict the one or more occurrences of the lithium plating for the one or more second cells.
2. The computer-implemented method of claim 1 , based on the one or more predicted occurrences of lithium plating, performing one or more actions.
3. The computer-implemented method of claim 2 , wherein the one or more actions comprise transmitting a notification to a computing device for presentation on the computing device.
4. The computer-implemented method of claim 2 , wherein the one or more actions comprise causing a computing device powered by the battery pack to cease operation.
5. The computer-implemented method of claim 1 , wherein the training is performed via supervised learning.
6. The computer-implemented method of claim 1 , wherein the one or more battery packs are used to at least partially power a vehicle, a computing device, or both.
7. The computer-implemented method of claim 2 , wherein the one or more actions comprise automatically removing, via a robot, the one or more second cells predicted to be associated with the one or more occurrences of the lithium plating.
8. A tangible, non-transitory computer-readable medium storing instructions that, when executed, cause a processing device to:
receive, from one or more computing devices, fleet data pertaining to one or more battery packs each comprising one or more first cells, wherein the fleet data comprises one or more false positive images of lithium plating affecting at least a first cell, one or more true positive images of the lithium plating affecting at least a second cell, or both;
train, using at least the fleet data, one or more machine learning models to predict one or more occurrences of the lithium plating;
receive, from one or more sensors associated with one or more second cells, one or more measurements pertaining to current, voltage, temperature, or some combination thereof; and
input the one or more measurements into the one or more machine learning models to predict the one or more occurrences of the lithium plating for the one or more second cells.
9. The computer-readable medium of claim 8 , wherein, based on the one or more predicted occurrences of lithium plating, the processing device performs one or more actions.
10. The computer-implemented method of claim 9 , wherein the one or more actions comprise transmitting a notification to a computing device for presentation on the computing device.
11. The computer-readable medium of claim 9 , wherein the one or more actions comprise causing a computing device powered by the battery pack to cease operation.
12. The computer-readable medium of claim 8 , wherein the training is performed via supervised learning.
13. The computer-readable medium of claim 8 , wherein the one or more battery packs are used to at least partially power a vehicle, a computing device, or both.
14. The computer-implemented method of claim 9 , wherein the one or more actions comprise automatically removing, via a robot, the one or more second cells predicted to be associated with the one or more occurrences of the lithium plating.
15. A system comprising:
a memory device storing instructions;
a processing device communicatively coupled to the processing device, wherein the processing device executes the instructions to:
receive, from one or more computing devices, fleet data pertaining to one or more battery packs each comprising one or more first cells, wherein the fleet data comprises one or more false positive images of lithium plating affecting at least a first cell, one or more true positive images of the lithium plating affecting at least a second cell, or both;
train, using at least the fleet data, one or more machine learning models to predict one or more occurrences of the lithium plating;
receive, from one or more sensors associated with one or more second cells, one or more measurements pertaining to current, voltage, temperature, or some combination thereof; and
input the one or more measurements into the one or more machine learning models to predict the one or more occurrences of the lithium plating for the one or more second cells.
16. The system of claim 15 , wherein, based on the one or more predicted occurrences of lithium plating, the processing device performs one or more actions.
17. The system of claim 16 , wherein the one or more actions comprise transmitting a notification to a computing device for presentation on the computing device.
18. The system of claim 16 , wherein the one or more actions comprise causing a computing device powered by the battery pack to cease operation.
19. The system of claim 15 , wherein the training is performed via supervised learning.
20. The system of claim 15 , wherein the one or more battery packs are used to at least partially power a vehicle, a computing device, or both.