Back to results
US 12,224,616 B1Granted
Systems and methods of dynamic adaptive fast charging in batteries to reduce lithium plating
AssigneeEatron Technologies Limited
InventorsMuharrem Ugur Yavas, Can Kurtulus
Life of this patent
Estimating the term…
Abstract
In one aspect, while a battery pack is charging, a computer-implemented method may include predicting an anode potential of the battery pack, and determining whether the anode potential satisfies a threshold condition. Responsive to determining that the anode potential satisfies the threshold condition, the method may include modifying a charging policy of a battery pack to adjust an anode potential offset, and controlling, based on the charging policy, charging of the battery pack to adjust the anode potential offset.
Claims30
A computer-implemented method comprising:
- determining, using one or more trained machine learning models, a first likelihood of occurrence of lithium plating of a battery pack;
- comparing the first likelihood of occurrence of lithium plating with tear-down data comprising severity levels of actual occurrences of lithium plating of a fleet of battery packs;
- based on the comparison, determining whether there is a threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data;
- responsive to determining that there is the threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data, retraining the one or more trained machine learning models with the tear-down data comprising the severity levels of actual occurrences of lithium plating of the fleet of battery packs;
- determining, using the one or more retrained machine learning models, a second likelihood of occurrence of lithium plating of the battery pack;
- based on the second likelihood of occurrence, predicting an anode potential of the battery pack;
- determining whether the anode potential satisfies a threshold condition;
- responsive to determining that the anode potential satisfies the threshold condition, modifying a charging policy of the battery pack to adjust an anode potential offset;
- and
- controlling, based on the charging policy, charging of the battery pack to adjust the anode potential offset.
7 more elements
One or more tangible, non-transitory computer-readable media storing instructions that, when executed, cause one or more processing devices to:
- determine, using one or more trained machine learning models, a first likelihood of occurrence of lithium plating of a battery pack;
- compare the first likelihood of occurrence of lithium plating with tear-down data comprising severity levels of actual occurrences of lithium plating of a fleet of battery packs;
- based on the comparison, determine whether there is a threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data;
- responsive to determining that there is the threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data, retrain the one or more trained machine learning models with the tear-down data comprising the severity levels of actual occurrences of lithium plating of the fleet of battery packs;
- determine, using the one or more retrained machine learning models, a second likelihood of occurrence of lithium plating of the battery pack;
- based on the second likelihood of occurrence, predict an anode potential of the battery pack;
- determine whether the anode potential satisfies a threshold condition;
- responsive to determining that the anode potential satisfies the threshold condition, modify a charging policy of the battery pack to adjust an anode potential offset;
- and
- control, based on the charging policy, charging of the battery pack to adjust the anode potential offset.
7 more elements
A system comprising:
- one or more memory devices storing instructions;
- and
- one or more processing devices communicatively coupled to the one or more memory devices, the one or more processing devices execute the instructions to:
- determine, using one or more trained machine learning models, a first likelihood of occurrence of lithium plating of a battery pack;
- compare the first likelihood of occurrence of lithium plating with tear-down data comprising severity levels of actual occurrences of lithium plating of a fleet of battery packs;
- based on the comparison, determine whether there is a threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data;
- responsive to determining that there is the threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data, retrain the one or more trained machine learning models with the tear-down data comprising the severity levels of actual occurrences of lithium plating of the fleet of battery packs;
- determine, using the one or more retrained machine learning models, a second likelihood of occurrence of lithium plating of the battery pack;
- based on the second likelihood of occurrence, predict an anode potential of the battery pack;
- determine whether the anode potential satisfies a threshold condition;
- responsive to determining that the anode potential satisfies the threshold condition, modify a charging policy of the battery pack to adjust an anode potential offset;
- and
- control, based on the charging policy, charging of the battery pack to adjust the anode potential offset.
10 more elements
An apparatus comprising:
- one or more memory devices storing instructions;
- and
- one or more processing devices communicatively coupled to the one or more memory devices, the one or more processing devices execute the instructions to:
- determine, using one or more trained machine learning models, a first likelihood of occurrence of lithium plating of a battery pack;
- compare the first likelihood of occurrence of lithium plating with tear-down data comprising severity levels of actual occurrences of lithium plating of a fleet of battery packs;
- based on the comparison, determine whether there is a threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data;
- responsive to determining that there is the threshold difference between the first likelihood of occurrence of lithium plating and the tear-down data, retrain the one or more trained machine learning models with the tear-down data comprising the severity levels of actual occurrences of lithium plating of the fleet of battery packs;
- determine, using the one or more retrained machine learning models, a second likelihood of occurrence of lithium plating of the battery pack;
- based on the second likelihood of occurrence, predict an anode potential of the battery pack;
- determine whether the anode potential satisfies a threshold condition;
- responsive to determining that the anode potential satisfies the threshold condition, modify a charging policy of the battery pack to adjust an anode potential offset;
- and
- control, based on the charging policy, charging of the battery pack to adjust the anode potential offset.
10 more elements
Dependencies are inferred from the claim text.