System and method for using artificial intelligence to detect lithium plating

1. A computer-implemented method comprising:

while a battery pack is charging, receiving, from one or more sensors, one or more measurements associated with the battery pack, wherein the battery pack comprises one or more cells;

separating the one or more measurements into separate profiles for the one or more cells, wherein the separate profiles include data pertaining to current, voltage, temperature, or some combination thereof;

identifying, using the separate profiles, one or more features;

generating a training dataset by reducing the one or more features based on a mean-comparison technique, a minority scaling technique, or both;

generating a trained machine learning model using the training dataset comprising the one or more reduced features as labeled input and one or more true lithium plating occurrence statuses as labeled output; and

predicting, using the trained machine learning model, an occurrence of lithium plating by inputting subsequently received data into the trained machine learning model.

2. The computer-implemented method of claim 1 , based on the predicted occurrence 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 trained machine learning model is trained via supervised learning.

6. The computer-implemented method of claim 1 , wherein the battery pack is 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, one or more 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:

while a battery pack is charging, receive, from one or more sensors, one or more measurements associated with the battery pack, wherein the battery pack comprises one or more cells;

separate the one or more measurements into separate profiles for the one or more cells, wherein the separate profiles include data pertaining to current, voltage, temperature, or some combination thereof;

identify, using the separate profiles, one or more features;

generate a training dataset by reducing the one or more features based on a mean-comparison technique, a minority scaling technique, or both;

generate a trained machine learning model using the training dataset comprising the one or more reduced features as labeled input and one or more true lithium plating occurrence statuses as labeled output; and

predict, using the trained machine learning model, an occurrence of lithium plating by inputting subsequently received data into the trained machine learning model.

9. The computer-readable medium of claim 8 , wherein, based on the predicted occurrence of lithium plating, the processing device performs one or more actions.

10. The computer-readable medium 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 1 , wherein the trained machine learning model is trained via supervised learning.

13. The computer-readable medium of claim 1 , wherein the battery pack is used to at least partially power a vehicle, a computing device, or both.

14. The computer-readable medium of claim 9 , wherein the one or more actions comprise automatically removing, via a robot, one or more cells predicted to be associated with the one or more occurrences of the lithium plating.

15. A system comprising:

A memory device storing instructions; and

A processing device communicatively coupled to the memory device, wherein the processing device executes the instructions to:

while a battery pack is charging, receive, from one or more sensors, one or more measurements associated with the battery pack, wherein the battery pack comprises one or more cells;

separate the one or more measurements into separate profiles for the one or more cells, wherein the separate profiles include data pertaining to current, voltage, temperature, or some combination thereof;

identify, using the separate profiles, one or more features;

generate a training dataset by reducing the one or more features based on a mean-comparison technique, a minority scaling technique, or both;

generate a trained machine learning model using the training dataset comprising the one or more reduced features as labeled input and one or more true lithium plating occurrence statuses as labeled output; and

predict, using the trained machine learning model, an occurrence of lithium plating by inputting subsequently received data into the trained machine learning model.

16. The system of claim 15 , wherein, based on the predicted occurrence 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 trained machine learning model is trained via supervised learning.

20. The system of claim 15 , wherein the battery pack is used to at least partially power a vehicle, a computing device, or both.