Abstract: In one aspect, computer-implemented method may include receiving, from a cloud-based computing system, one or more machine learning model parameters that are configured to enable predicting a remaining useful life of each cell of a battery pack of a vehicle. The method may include loading, into memory of a processing device at the vehicle, the one or more machine learning model parameters, receiving data comprising one or more measurements and one or more user battery usage profiles, and based on the data, executing a trained machine learning model with the one or more parameters to input the data and to output the remaining useful life of each cell of the battery pack.

Abstract: In one aspect, a computer-implemented method for a cloud-based computing system may include receiving data pertaining to a battery pack of a vehicle, wherein the data is measured by one or more sensors associated with the vehicle. The method may include determining, based on the data, whether a sum of a physics-based model estimated terminal voltage minus an actual voltage of the battery pack satisfies a threshold. Responsive to determining the threshold is satisfied, the method may include determining a trigger event has occurred and calibrate one or more parameters of the physics-based model, a machine learning model, or both, wherein the physics-based model outputs one or more properties pertaining to the battery pack of the vehicle, and the machine learning model uses the one or more properties to predict a remaining useful life of each cell of the battery pack.

Abstract: In one aspect, a method for a cloud-based computing system may include training, using test data, machine learning models to predict a remaining useful life of each cell of a battery pack of a vehicle. The method may include using a rule-based evaluator to determine first scores for the machine learning models, using a machine learning based metric evaluator to determine second scores for the machine learning models, using a model selection inference engine to select, based on the first and second scores for the machine learning models, a machine learning model to use to predict the remaining useful life of each cell of the battery pack of the vehicle, and transmitting, to a processing device of the vehicle, the selected machine learning model and parameters to predict the remaining useful life of each cell of the battery pack of the vehicle.

Abstract: In one aspect, a method comprises receiving first data pertaining to a battery pack of a vehicle, wherein the first data is received from sensors associated with the vehicle, and the first data pertains to a battery pack current, a cell voltage, a cell current, a cell temperature, or some combination thereof; receiving second data pertaining to simulation of the battery pack; receiving third data from a manufacturer; receiving historical data on a fleet of vehicles that use the battery pack; predicting a remaining useful life of the battery pack of the vehicle by using a hybrid model comprising a physics-based model that receives the based on the first, second, third, and historical data, and generates properties pertaining to the battery pack; a machine learning model that uses the properties to predict the remaining useful life of each cell of the battery pack; and transmitting the remaining useful life.