Battery life prediction using a global-local decomposition transformer
Systems and methods described herein relate to implementing battery life prediction strategies. In one embodiment, a method includes receiving a first battery dataset containing a first set of battery entries, a first set of historical usage entries, a first set of local parts entries, and a first global parts entry; generating a second battery dataset containing a second set of battery entries and a second set of historical usage entries; generating a second set of local parts entries for the second battery dataset based on comparing the first set of historical usage entries with the second set of historical usage entries; copying the first global parts entry to a second global parts entry of the second battery dataset; and optimizing via one or more global local decomposition transformers the second set of local parts entries and the second global parts entry based on the second set of historical usage entries.
1. A system, comprising:
a processor; and
a memory communicably coupled to the processor and storing machine-readable instructions that, when executed by the processor, cause the processor to:
receive a first battery dataset containing a first set of battery entries, a first set of historical usage entries, a first set of local parts entries, and a first global parts entry;
generate a second battery dataset containing a second set of battery entries and a second set of historical usage entries;
generate a second set of local parts entries for the second battery dataset based on comparing the first set of historical usage entries with the second set of historical usage entries;
copy the first global parts entry to a second global parts entry of the second battery dataset; and
optimize via one or more global local decomposition transformers the second set of local parts entries and the second global parts entry based on the second set of historical usage entries.
2. The system of claim 1 , wherein the machine-readable instructions to generate the second battery dataset further includes utilizing battery sensors on a vehicle to provide the second set of historical usage entries.
3. The system of claim 1 , wherein the first set of historical usage entries contains for each historical usage entry at least one record of a charging capacity relative to a number of charge/discharge cycles where the charging capacity is below a threshold.
4. The system of claim 1 , wherein the machine-readable instructions that, when executed by the processor, further includes causing the processor to:
satisfy a vehicle type condition specified by a vehicle based on the first battery dataset.
5. The system of claim 1 , wherein the machine-readable instructions that, when executed by the processor, further includes causing the processor to:
satisfy a location condition specified by a vehicle based on the first battery dataset.
6. The system of claim 1 , wherein the machine-readable instructions that, when executed by the processor, further includes causing the processor to:
satisfy an environment condition specified by a vehicle based on the first battery dataset.
7. The system of claim 2 , wherein the machine-readable instructions that, when executed by the processor, further includes causing the processor to:
determine a battery life prediction based on the second battery dataset; and
display the battery life prediction.
8. A non-transitory computer-readable medium including instructions that when executed by one or more processors cause the one or more processors to:
receive a first battery dataset containing a first set of battery entries, a first set of historical usage entries, a first set of local parts entries, and a first global parts entry;
generate a second battery dataset containing a second set of battery entries and a second set of historical usage entries;
generate a second set of local parts entries for the second battery dataset based on comparing the first set of historical usage entries with the second set of historical usage entries;
copy the first global parts entry to a second global parts entry of the second battery dataset; and
optimize via one or more global local decomposition transformers the second set of local parts entries and the second global parts entry based on the second set of historical usage entries.
9. The non-transitory computer-readable medium of claim 8 , wherein the instructions to generate the second battery dataset further includes utilizing battery sensors on a vehicle to provide the second set of historical usage entries.
10. The non-transitory computer-readable medium of claim 8 , wherein the first set of historical usage entries contains for each historical usage entry at least one record of a charging capacity relative to a number of charge/discharge cycles where the charging capacity is below a threshold.
11. The non-transitory computer-readable medium of claim 8 , further comprising instructions that when executed by one or more processors cause the one or more processors to:
satisfy a vehicle type condition specified by a vehicle based on the first battery dataset.
12. The non-transitory computer-readable medium of claim 8 , further comprising instructions that when executed by one or more processors cause the one or more processors to:
satisfy a location condition specified by a vehicle based on the first battery dataset.
13. The non-transitory computer-readable medium of claim 9 , further comprising instructions that when executed by one or more processors cause the one or more processors to:
determine a battery life prediction based on the second battery dataset; and
display the battery life prediction.
14. A method, comprising:
receiving a first battery dataset containing a first set of battery entries, a first set of historical usage entries, a first set of local parts entries, and a first global parts entry;
generating a second battery dataset containing a second set of battery entries and a second set of historical usage entries;
generating a second set of local parts entries for the second battery dataset based on comparing the first set of historical usage entries with the second set of historical usage entries;
copying the first global parts entry to a second global parts entry of the second battery dataset; and
optimizing via one or more global local decomposition transformers the second set of local parts entries and the second global parts entry based on the second set of historical usage entries.
15. The method of claim 14 , further comprising:
utilizing battery sensors on a vehicle to provide the second set of historical usage entries.
16. The method of claim 14 , wherein the first set of historical usage entries contains for each historical usage entry at least one record of a charging capacity relative to a number of charge/discharge cycles where the charging capacity is below a threshold.
17. The method of claim 14 , further comprising:
satisfying a vehicle type condition specified by a vehicle based on the first battery dataset.
18. The method of claim 14 , further comprising:
satisfying a location condition specified by a vehicle based on the first battery dataset.
19. The method of claim 14 , further comprising:
satisfying an environment condition specified by a vehicle based on the first battery dataset.
20. The method of claim 15 , further comprising:
determining a battery life prediction based on the second battery dataset; and
displaying the battery life prediction.