Patent ID: 11879945
Assignee: MONA INC.
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A battery diagnosis method performed by a computing device that drives a battery prediction model trained to predict battery state information, the battery diagnosis method comprising:
receiving time series data including at least one of voltages, currents, and temperatures of a battery sequentially measured for a certain time period;
receiving non-time series data including battery impedance measured at a certain time point; and
predicting the battery state information by inputting the time series data and the non-time series data to the battery prediction model,
wherein, training data for the battery prediction model comprises time series training data of at least one of a voltage, a current, and a temperature measured for a certain time period for a plurality of battery samples, non-time series training data includes impedances of the plurality of battery samples, and validation data includes battery state information, and
the battery prediction model is a supervised learning model made by comparing the validation data with prediction data based on the time series training data and the non-time series training data,
wherein the battery prediction model comprises a recursive neural network and a fully connected layer,
wherein the predicting comprises:
generating a first feature value by inputting the time series data to the recursive neural network;
generating a second feature value by concatenating the first feature value with the non-time series data; and
inputting the second feature value to the fully connected layer.