Abstract: A processor-implemented method includes performing first parameter optimization of a battery model through a first predetermined optimization technique; switching, based on a count accumulated while performing the first parameter optimization indicating that a switching criterion has been met, from the first optimization technique to a second predetermined optimization technique; performing second parameter optimization of the battery model through the second predetermined optimization technique; and determining a final parameter combination as an optimized parameter of the battery model, in response to an occurrence of an optimization end event during the performance of the second parameter optimization.

Abstract: A method of estimating a state of health of a battery is performed by at least one computing device, and includes preparing a pre-trained artificial neural network, generating input data by measuring at least one parameter of the battery, inputting the input data into the pre-trained artificial neural network to obtain a health state estimation value of the battery and an attention map, calculating an attention score based on the attention map, and determining whether to trust the health state estimation value based on the attention score.

Abstract: A battery charging method is provided. The battery charging method may include obtaining a reference charging current and a reference lithium dendrite growth rate at the reference charging current, detecting a battery voltage, a battery current, and a battery temperature of a battery in use, based on the battery current, the battery voltage, and the battery temperature, estimating an internal electrochemical parameter of the battery, based on the internal electrochemical parameter, calculating an overpotential steady-state distribution of an active material-electrolyte interface according to a charging current of the battery, based on the overpotential steady-state distribution, calculating a lithium dendrite growth rate according to the charging current of the battery, and based on the reference lithium dendrite growth rate and the lithium dendrite growth rate according to the charging current, determining a charging current value of the battery.

Abstract: A method of estimating a state of health of a battery, the method being performed by a computing apparatus, the method including: preparing a trained artificial neural network; generating input data by measuring at least one parameter of a battery; acquiring a plurality of output values each corresponding to a plurality of classes by inputting the input data into the trained artificial neural network; and generating a state of health estimation value of the battery using a plurality of preset health state sections each corresponding to the plurality of classes and the plurality of output values each corresponding to the plurality of classes.

Abstract: A system and method of predicting variations in a capacity of a battery, the system including an ADFM management device including an experimental data collector to collect at least one first piece of data about the capacity of the battery, an ADF optimizer to optimize a first calculation equation, and a virtual data generator to generate at least one second piece of data; and a server including a training unit to train an artificial intelligence model for outputting the relative capacity variation value by using the at least one first piece of data and the at least one second piece of data as training data, and a prediction unit to obtain a relative capacity variation prediction value, which is output from the artificial intelligence model when the number of charge and discharge cycles and the charge and discharge conditions of the battery are input to the artificial intelligence model.

Abstract: A method of estimating a state of health of a battery, the method being performed by a computing apparatus, the method including: preparing a trained artificial neural network; generating input data by measuring at least one parameter of a battery; acquiring a plurality of output values each corresponding to a plurality of classes by inputting the input data into the trained artificial neural network; and generating a state of health estimation value of the battery using a plurality of preset health state sections each corresponding to the plurality of classes and the plurality of output values each corresponding to the plurality of classes.

Abstract: A system and method of predicting variations in a capacity of a battery, the system including an ADFM management device including an experimental data collector to collect at least one first piece of data about the capacity of the battery, an ADF optimizer to optimize a first calculation equation, and a virtual data generator to generate at least one second piece of data; and a server including a training unit to train an artificial intelligence model for outputting the relative capacity variation value by using the at least one first piece of data and the at least one second piece of data as training data, and a prediction unit to obtain a relative capacity variation prediction value, which is output from the artificial intelligence model when the number of charge and discharge cycles and the charge and discharge conditions of the battery are input to the artificial intelligence model.