Abstract: In one aspect, a computer-implemented method may include receiving charge cycle data pertaining to a battery pack. The method may include determining, based on the charge cycle data, whether a noise level of a battery management system exceeds a first threshold. In response to determining the noise level exceeds the first threshold, the method may include determining an initial state of charge of the battery pack using coulomb counting by reversing the charge cycle data. In response to determining the noise level does not exceed the first threshold, the method may include determining whether a rest time before charge cycle exceeds a second threshold. In response to determining the rest time before charge cycle does not exceed the second threshold, the method may include determining the initial state of charge of the battery pack using coulomb counting by reversing the charge cycle data.

Abstract: In one aspect, computer-implemented method may include receiving, from computing devices, fleet data pertaining to battery packs each including first cells. The fleet data includes false positive images of lithium plating affecting at least a first cell, true positive images of the lithium plating affecting at least a second cell, or both. The method may include training, using at least the fleet data, machine learning models to predict occurrences of the lithium plating, receiving, from sensors associated with second cells, measurements pertaining to current, voltage, temperature, or some combination thereof, and inputting the measurements into the machine learning models to predict the occurrences of the lithium plating for the second cells.

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 threshold is satisfied, wherein the threshold relates to a physics-based model estimated terminal voltage. 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.

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; 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 first 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 for presentation.