Battery information processing system, method of estimating capacity of secondary battery, and battery assembly and method of manufacturing battery assembly

A secondary battery full charge capacity estimation method of estimating a full charge capacity of a target secondary battery comprising:

• obtaining a result of measurement of an AC impedance of the target secondary battery;
• and
• estimating a full charge capacity of the target secondary battery from a Nyquist plot showing the result of measurement of the AC impedance of the target secondary battery by using a trained neural network model, the trained neural network model being a neural network model which has been trained based on Nyquist plots of a plurality of secondary batteries of which full charge capacity is within a reference range;
• determining to which of a first group and a second group the target secondary battery belongs based on discriminant analysis, the discriminant analysis adopting a feature value extracted from the Nyquist plot of the target secondary battery as an explanatory variable, the first group being defined as a group of secondary batteries of which full charge capacity is within the reference range, the second group being defined as a group of secondary batteries of which full charge capacity is out of the reference range;
• and
• estimating a full charge capacity of the target secondary battery by using the trained neural network model when it is determined that the target secondary battery belongs to the first group.

A method of manufacturing a battery assembly comprising:

• obtaining a result of measurement of an AC impedance of a target secondary battery;
• estimating a full charge capacity of the target secondary battery from a Nyquist plot showing the result of measurement of the AC impedance of the target secondary battery by using a trained neural network model, the trained neural network model being a neural network model which has been trained based on Nyquist plots of a plurality of secondary batteries of which full charge capacity is within a reference range;
• determining to which of a first group and a second group the target secondary battery belongs based on discriminant analysis, the discriminant analysis adopting a feature value extracted from the Nyquist plot of the target secondary battery as an explanatory variable, the first group being defined as a group of secondary batteries of which full charge capacity is within the reference range, the second group being defined as a group of secondary batteries of which full charge capacity is out of the reference range;
• estimating a full charge capacity of the target secondary battery by using the trained neural network model when it is determined that the target secondary battery belongs to the first group;
• and
• manufacturing a battery assembly from a plurality of the target secondary batteries of which full charge capacity has been estimated in the estimating a full charge capacity.