Abstract: A method is provided. The method includes determining a first difference in a current flowing via the battery of the vehicle during a driving event. The method also includes determining a second difference in voltage of the current flowing via the battery of the vehicle during the driving event. The method further includes determining an impedance indicator based the first difference in the current and the second difference in the voltage of the current.

Abstract: A method is provided. The method includes determining, by a battery management system of the vehicle, a difference in a voltage of a current provided to a battery of a vehicle. The method also includes determining, by the battery management system, an impedance indicator based on the current provided to the battery and the difference in the voltage of the current provided to the battery. The method further includes determining, by the battery management system, a health of the battery based on the impedance indicator.

Abstract: A method is provided. The method includes determining an open circuit voltage of a battery of a vehicle. The method also includes determining a voltage of a current provided to the battery of the vehicle. The method further includes determining a impedance indicator based on the voltage, the open circuit voltage, and the current provided to the battery of the vehicle. The method further includes determining a health of the battery based on the impedance indicator.

Abstract: Approaches, techniques, and mechanisms are disclosed for assessing battery states of batteries. According to one embodiment, raw sensor data are collected from a battery module. The battery module includes multiple battery cells. Input battery features are extracted from the raw sensor data collected from the battery module. The input battery features are used to update node states of a GNN. The GNN include multiple GNN nodes each of which representing a respective battery cell in the multiple battery cells. Estimation of one or more battery state of health (SoH) indicators is generated based at least in part on individual output states of individual GNN nodes in the multiple GNN nodes. The individual output states of individual GNN nodes in the multiple GNN nodes are determined based at least in part on the updated node states of the GNN.

Abstract: In one embodiment, a method is provided. The method includes obtaining a set of graphs for a set of material structures. Each graph of the set of graphs is associated with a material structure of the set of material structures. The method also includes determining first sets of stress values and first sets of strain values for the set of graphs based on a neural network. The method further includes obtaining second sets of stress value and second sets of strain values for a subset of the set of material structures. The method further includes updating the neural network based on the first sets of stress values, the first sets of strain values, the second sets of stress values, and the second sets of strain values.

Abstract: Approaches, techniques, and mechanisms are disclosed for assessing battery states of batteries. According to one embodiment, raw sensor data are collected from a battery module. The battery module includes multiple battery cells. Input battery features are extracted from the raw sensor data collected from the battery module. The input battery features are used to update node states of a GNN. The GNN include multiple GNN nodes each of which representing a respective battery cell in the multiple battery cells. Estimation of one or more battery state of health (SoH) indicators is generated based at least in part on individual output states of individual GNN nodes in the multiple GNN nodes. The individual output states of individual GNN nodes in the multiple GNN nodes are determined based at least in part on the updated node states of the GNN.

Abstract: In one embodiment, a method is provided. The method includes obtaining a sequence of images of a three-dimensional volume of a material. The method also includes determining a set of features based on the sequence of images and a first neural network. The set of features indicate microstructure features of the material. The method further includes determining a set of material properties of the three-dimensional volume of the material based on the set of features and a first transformer network.

Abstract: A method, apparatus, system for batter material screening is disclosed. First, microstructure generation parameters for a plurality of microstructures are received, where the microstructure generation parameters include microstructure characteristics. Microstructure statistics are generated using a first artificial intelligence (“AI”) model, where the received microstructure generation parameters are inputs for the first AI model. Microstructure properties are predicted using a second AI model for the microstructures based on the generated microstructure statistics, the received microstructure generation parameters, and battery cell characteristics. It is determined whether at least one of the microstructures is within a predefined energy profile range based on the predicted microstructure properties.

Abstract: A method, apparatus, system for batter material screening is disclosed. First, microstructure generation parameters for a plurality of microstructures are received, where the microstructure generation parameters include microstructure characteristics. Microstructure statistics are generated using a first artificial intelligence (“AI”) model, where the received microstructure generation parameters are inputs for the first AI model. Microstructure properties are predicted using a second AI model for the microstructures based on the generated microstructure statistics, the received microstructure generation parameters, and battery cell characteristics. It is determined whether at least one of the microstructures is within a predefined energy profile range based on the predicted microstructure properties.