Systems and methods of applying artificial intelligence to battery technology
Uses of artificial intelligence in battery technology including a method that includes receiving input data associated with at least one sensor. The method includes executing at least one trained model by a processor. Executing the trained model comprises providing the input data to the at least one trained model to generate a model output. The method further includes determining, based on the model output, whether to initiate or terminate selectively charging a first battery cell of a battery.
1. A method comprising:
receiving input data associated with at least one sensor;
executing at least one trained model by a processor, wherein executing the trained model comprises providing the input data to the at least one trained model to generate a model output; and
determining, based on the model output, whether to initiate or terminate selectively charging a first battery cell of a battery.
2. The method of claim 1 , wherein the at least one trained model corresponds to a neural network.
3. The method of claim 1 , wherein the at least one sensor is coupled to the battery or within the battery.
4. The method of claim 1 , wherein the at least one sensor is an environmental sensor.
5. The method of claim 1 , wherein the input data corresponds to temperature data, weather data, or a combination thereof.
6. The method of claim 1 , wherein the input data corresponds to geographical data.
7. The method of claim 1 , wherein the battery is coupled to an electrical grid.
8. The method of claim 1 , further comprising controlling, based on execution of the at least one trained model, selective charging, selective discharging, selective connection, selective disconnection, or a combination thereof for a plurality of batteries, and wherein an electrical grid comprises the plurality of batteries.
9. The method of claim 1 , wherein the model output corresponds to a prediction associated with the battery.
10. The method of claim 9 , wherein the prediction is associated with a future performance of the battery or the first battery cell.
11. The method of claim 10 , wherein the future performance identifies a predicted failure of the battery or the first battery cell.
12. The method of claim 1 , further comprising determining, based on the model output, to charge or discharge the first battery cell of the battery prior to charging or discharging a second battery cell of the battery.
13. The method of claim 1 , further comprising automatically initiating or terminating charging of the first battery cell but not a second battery cell based on the model output.
14. A system comprising:
a first battery cell of a battery;
a first controller, wherein the first controller is connected to the first battery cell;
at least one sensor; and
a processor configured to receive sensor data from the at least one sensor and, based at least in part on the sensor data:
execute at least one trained model to generate a model output; and
output, based on the model output, a first signal to the first controller to cause the first controller to connect or disconnect a corresponding first battery cell to or from a discharge path.
15. The system of claim 14 , further comprising:
a second battery cell of the battery, wherein the processor is further configured to output, based on the model output, a second signal to cause initiation or termination of charging of the second battery cell.
16. The system of claim 15 , wherein the processor is further configured to output signals to cause the first battery cell and the second battery cell to be charged simultaneously.
17. The system of claim 15 , wherein the model output indicates that the first battery cell has a higher predicted likelihood of failure than the second battery cell or a higher predicted likelihood of overheating than the second battery cell.
18. The system of claim 17 , wherein the processor is further configured to output signals that cause the first battery cell to be electrically isolated from the second battery cell.
19. A computer-readable storage device storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
receiving input data associated with at least one sensor;
executing at least one trained model by a processor, wherein executing the trained model comprises providing the input data to the at least one trained model to generate a model output; and
determining, based on the model output, whether to initiate or terminate selectively charging a first battery cell of a battery.
20. The computer-readable storage device of claim 19 , wherein the operations further comprise determining, based on the model output, to charge or discharge the first battery cell of the battery prior to charging or discharging a second cell of the battery.