Granted Patent B2

US 12,443,152 · App. 17/996,277 · Granted Oct 14, 2025

Inventors: Hidenori Shimawaki (Kanagawa, JP); Jiuting Chen (Kanagawa, JP)

Assignee: AESC Japan Ltd.

G05B13/0265G01R31/367G01R31/387H02J7/0048

Abstract

Claims (64)

1. A model generation apparatus comprising:

at least one memory storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

acquiring a plurality of pieces of training data using, as input values, measurement data for training including a current, a voltage, and a temperature of a storage battery and using, as a target value, a remaining capacity value for training being a remaining capacity value of the storage battery; and

generating a model for calculating a remaining capacity of an object storage battery from measurement data for calculation including a current, a voltage, and a temperature of the object storage battery, the object storage battery being an object of processing, by performing machine learning on the plurality of pieces of training data, wherein the operations further comprise:

processing n sets of the training data into an object matrix of 3×n;

performing processing on the object matrix, thereby generating one-dimensional data consisting of L pieces of data;

generating the model using the one-dimensional data as an input value; and

generating the one-dimensional data by performing, at least once, conversion processing of, while expanding one of rows or columns of the object matrix to the number of dimensions larger than the number of dimensions of the one, the one being associated to the current, the voltage, and the temperature, reducing the other of the rows or the columns of the object matrix to the number of dimensions smaller than the number of dimensions of the other by processing a digital filter on the object matrix.

2. A model generation apparatus comprising:

at least one memory storing instructions; and

at least one processor configured to execute the instructions to perform operations comprising:

acquiring a plurality of pieces of training data using, as input values, measurement data for training including a current, a voltage, and a temperature of a storage battery and using, as a target value, a remaining capacity value for training being a remaining capacity value of the storage battery;

processing n sets of the training data into an object matrix of m×n (where m is the number of types of data included in measurement data for training) and performing processing on the object matrix, thereby generating one-dimensional data consisting of z pieces of data; and

generating a model for calculating a remaining capacity of an object storage battery from measurement data for calculation including a current, a voltage, and a temperature of the object storage battery, the object storage battery being an object of processing, by performing machine learning using the one-dimensional data as an input value, wherein the operations comprise:

generating the one-dimensional data by performing, at least once, conversion processing of, while expanding one of rows or columns of the object matrix to the number of dimensions larger than the number of dimensions of the one, the one being associated to the current, the voltage, and the temperature, reducing the other of the rows or the columns of the object matrix to the number of dimensions smaller than the number of dimensions of the other by processing a digital filter on the object matrix.

3. The model generation apparatus according to claim 1 , wherein

the digital filter is a matrix, and

the operations comprise performing following (1) and (2) at least once as the conversion processing:

(1) cutting out a partial matrix consisting of the same number of rows and the same number of columns as the digital filter, from the object matrix; and

(2) setting a value acquired by operating the digital filter on the partial matrix and adding each element of a result of the operation, as an element of the object matrix after processing.

4. The model generation apparatus according to claim 3 , wherein

the operations comprise performing processing of expanding at least one of rows or columns of the object matrix by adding a dummy value to an outer periphery of the object matrix before the (1) in the conversion processing.

5. The model generation apparatus according to claim 1 , wherein

the operations comprise processing n number of the remaining capacity values for training being associated to the n sets of training data, thereby generating the target value consisting of k pieces of data (where k<n).

6. The model generation apparatus according to claim 1 , wherein

the machine learning is performed by using a neural network.

7. The model generation apparatus according to claim 1 , wherein

actual result data including measurement values of a current, a voltage, a temperature, and a remaining capacity value of a storage battery are prepared,

the training data are generated by using a part of the actual result data, and

the operations comprise verifying the model by using at least one piece of the remaining actual result data.

8. The model generation apparatus according to claim 1 , wherein the operations comprise:

acquiring the training data for each type of the storage battery; and

generating the model for each type of the storage battery.

9. A non-transitory computer-readable medium storing a program causing a computer to perform operations comprising:

acquiring a plurality of pieces of training data using, as input values, measurement data for training including a current, a voltage, and a temperature of a storage battery and using, as a target value, a remaining capacity value for training being a remaining capacity value of the storage battery; and

generating a model for calculating a remaining capacity of an object storage battery from measurement data for calculation including a current, a voltage, and a temperature of the object storage battery by performing machine learning on the training data, the object storage battery being an object of processing, wherein the operations further comprise:

processing n sets of the training data into an object matrix of 3×n;

performing processing on the object matrix, thereby generating one-dimensional data consisting of L pieces of data;

generating the model using the one-dimensional data as an input value; and

generating the one-dimensional data by performing, at least once, conversion processing of, while expanding one of rows or columns of the object matrix to the number of dimensions larger than the number of dimensions of the one, the one being associated to the current, the voltage, and the temperature, reducing the other of the rows or the columns of the object matrix to the number of dimensions smaller than the number of dimensions of the other by processing a digital filter on the object matrix.

10. A non-transitory computer-readable medium storing a program causing a computer to perform operations comprising:

acquiring a plurality of pieces of training data using, as input values, measurement data for training including a current, a voltage, and a temperature of a storage battery and using, as a target value, a remaining capacity value for training being a remaining capacity value of the storage battery;

processing n sets of the training data into an object matrix of m×n and performing processing on the object matrix, thereby generating one-dimensional data consisting of z pieces of data; and

generating a model for calculating a remaining capacity of an object storage battery from measurement data for calculation including a current, a voltage, and a temperature of the object storage battery, the object storage battery being an object of processing, by performing machine learning using the one-dimensional data as an input value, wherein the operations comprise:

generating the one-dimensional data by performing, at least once, conversion processing of, while expanding one of rows or columns of the object matrix to the number of dimensions larger than the number of dimensions of the one, the one being associated to the current, the voltage, and the temperature, reducing the other of the rows or the columns of the object matrix to the number of dimensions smaller than the number of dimensions of the other by processing a digital filter on the object matrix.

11. The model generation apparatus according to claim 2 , wherein

the digital filter is a matrix, and

the operations comprise performing following (1) and (2) at least once as the conversion processing:

(1) cutting out a partial matrix consisting of the same number of rows and the same number of columns as the digital filter, from the object matrix; and

(2) setting a value acquired by operating the digital filter on the partial matrix and adding each element of a result of the operation, as an element of the object matrix after processing.

12. The model generation apparatus according to claim 11 , wherein

the operations comprise performing processing of expanding at least one of rows or columns of the object matrix by adding a dummy value to an outer periphery of the object matrix before the (1) in the conversion processing.

13. The model generation apparatus according to claim 2 , wherein

the operations comprise processing n number of the remaining capacity values for training being associated to the n sets of training data, thereby generating the target value consisting of k pieces of data (where k<n).

14. The model generation apparatus according to claim 2 , wherein

the machine learning is performed by using a neural network.

15. The model generation apparatus according to claim 2 , wherein

actual result data including measurement values of a current, a voltage, a temperature, and a remaining capacity value of a storage battery are prepared,

the training data are generated by using a part of the actual result data, and

the operations comprise verifying the model by using at least one piece of the remaining actual result data.

16. The model generation apparatus according to claim 2 , wherein the operations comprise:

acquiring the training data for each type of the storage battery; and

generating the model for each type of the storage battery.

Assignments (3)

Priority Claims (1)

Continuity (1)

References Cited (18)