Abstract: The present disclosure relates to a method for optimizing the formation protocol of a battery. The method can include the steps of: (a) providing a battery cell structure comprising an anode, an electrolyte, and a cathode including cations that move from the cathode to the anode during charging; (b) performing a first charge of the battery cell structure using a predetermined formation protocol to create a formed battery cell; and (c) determining a cell internal resistance of the formed battery cell. Therefore, one can compare the cell internal resistances of two battery cells formed by using identical battery cell structures and different formation protocols, and select a formation protocol if the first cell internal resistance of a first formed battery is greater than or less than the second cell internal resistance of a second formed battery.

Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.

Abstract: A method of probing a multidimensional parameter space of battery cell test protocols is provided that includes defining a parameter space for a plurality of battery cells under test, discretizing the parameter space, collecting a preliminary set of cells being cycled to failure for sampling policies from across the parameter space and include multiple repetitions of the policy, specifying resource hyperparameters, parameter space hyperparameters, and algorithm hyperparameters, selecting a random subset of charging policies, testing the random subset of charging policies until a number of cycles required for early prediction of battery lifetime is achieved, inputting cycle data for early prediction into an early prediction algorithm to obtain early predictions, inputting the early predictions into an optimal experimental design (OED) algorithm to obtain recommendations for running at least one next test, running the recommended tests by repeating from the random subset testing step above, and validating final

Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.

Abstract: A method of probing a multidimensional parameter space of battery cell test protocols is provided that includes defining a parameter space for a plurality of battery cells under test, discretizing the parameter space, collecting a preliminary set of cells being cycled to failure for sampling policies from across the parameter space and include multiple repetitions of the policy, specifying resource hyperparameters, parameter space hyperparameters, and algorithm hyperparameters, selecting a random subset of charging policies, testing the random subset of charging policies until a number of cycles required for early prediction of battery lifetime is achieved, inputting cycle data for early prediction into an early prediction algorithm to obtain early predictions, inputting the early predictions into an optimal experimental design (OED) algorithm to obtain recommendations for running at least one next test, running the recommended tests by repeating from the random subset testing step above, and validating final