Learning method, state estimation method, and state estimation device for state estimation model of secondary battery

1. A learning method of a state estimation model of a secondary battery by a first processor, the secondary battery being included in a vehicle powered by the secondary battery, the learning method using machine learning, the state estimation model estimating a charge rate and/or a deterioration degree of the operating secondary battery, the secondary battery being connected to a load or a charger, the state estimation model being stored in a memory, the learning method comprising:

a step of measuring state variables at predetermined time intervals, the state variables including terminal currents and terminal voltages of the operating secondary battery;

a step of calculating state estimation input data preprocessing the state variables;

a step of training the state estimation model to learn a relationship of the state estimation input data with the charge rate and/or the deterioration degree of the operating secondary battery, by machine learning,

wherein the step of calculating:

uses time-series data of the terminal currents and time-series data of the terminal voltages to calculate current differences and voltage differences, each current difference being a difference in the terminal currents, each voltage difference being a difference in the terminal voltages;

uses time-series data of the current differences and time-series data of the voltage differences to calculate difference gradients, each difference gradient being a change rate of the voltage differences with respect to the current differences in a period to a present from a past that goes back a first predetermined time from the present; and

generates the state estimation input data including:

time-series data of the difference gradients;

time-series data of open circuit voltages of the operating secondary battery;

time-series data of open circuit voltage change amounts, each open circuit voltage change amount being obtained by subtracting the open circuit voltage in a past that goes back a second predetermined time from a present, from the present open circuit voltage;

time-series data of difference gradient change amounts, each difference gradient change amount being obtained by subtracting the difference gradient in the past that goes back the second predetermined time from the present, from the present difference gradient; and

time-series data of integrated current values, each integrated current values being a sum of the terminal current values continuously acquired in a period to the present from the past that goes back the second predetermined time from the present; and

a step of displaying a message suggesting at least one of charging and replacing the secondary battery in the vehicle based on the generated state estimation input data.

2. The learning method of the state estimation model of the secondary battery according to claim 1 , wherein

the state estimation input data further includes time-series data of the terminal currents.

3. The learning method of the state estimation model of the secondary battery according to claim 1 , wherein

the step of calculating:

uses time-series data of the terminal currents and time-series data of the terminal voltages, and timeseries data of the difference gradients, as voltage estimation input data, to estimate an open circuit voltage of the operating secondary battery; and

uses the estimated open circuit voltage to calculate the state estimation input data.

4. The learning method of the state estimation model of the secondary battery according to claim 1 , wherein

each difference gradient is calculated using the least squares method.

5. The learning method of the state estimation model of the secondary battery according to claim 1 , wherein

each current difference and each voltage difference are respectively a fourth-order difference of time-series data of the terminal currents and a fourth-order difference of time-series data of the terminal voltages.

6. The learning method of the state estimation model of the secondary battery according to claim 1 , wherein

the state estimation model is configured of an RNN (Recurrent Neural Network).

7. The learning method of the state estimation model of the secondary battery according to claim 6 , wherein

an intermediate layer of the RNN configuring the state estimation model is configured of an LSTM (Long Short Term Memory) or a GRU (Gated Recurrent Unit).

8. The learning method of the state estimation model of the secondary battery according to claim 1 , wherein

the state estimation model is configured of a one-dimensional CNN (Convolutional Neural Network).

9. The learning method of the state estimation model of the secondary battery according to claim 1 , wherein

the state estimation model is generated by learning using time-series data of state variables including terminal currents and terminal voltages for each of a plurality of secondary batteries with different electrical characteristics, the secondary batteries each being connected to a load or a charger.

10. A state estimation method of a secondary battery by a second processor, comprising:

a step of measuring state variables including terminal currents and terminal voltages of an operating secondary battery to which a load or a charger is connected, at predetermined time intervals;

a step of calculating state estimation input data by preprocessing the state variables; and

a step of estimating a present charge rate and/or a deterioration degree of the operating secondary battery, from the state estimation input data, using a state estimation model trained by a learning method of the secondary battery state estimation model according to claim 1 ,

wherein the step of calculating:

uses time-series data of the terminal currents and time-series data of the terminal voltages to calculate current differences and voltage differences, each current difference being a difference in the terminal currents, each voltage difference being a difference in the terminal voltages;

uses time-series data of the current differences and time-series data of the voltage differences to calculate difference gradients, each difference gradient being a change rate of the voltage differences with respect to the current differences in a period to a present from a past that goes back a first predetermined time from the present; and

generates the state estimation input data including:

time-series data of the difference gradients;

time-series data of open circuit voltages of the operating secondary battery;

time-series data of open circuit voltage change amounts, each open circuit voltage change amount being obtained by subtracting the open circuit voltage in a past that goes back a second predetermined time from the present, from the present open circuit voltage;

time-series data of difference gradient change amounts, each difference gradient change amount being obtained by subtracting the difference gradient in the past that goes back the second predetermined time from the present, from the present difference gradient; and

time-series data of integrated current values, each integrated current values being a sum of the terminal current values continuously acquired in a period to the present from the past that goes back the second predetermined time from the present.

11. A state estimation device of a secondary battery,

the device comprising a second processor,

wherein the second processor is configured to:

measure state variables at predetermined time intervals, the state variables including terminal currents and terminal voltages of the operating secondary battery;

perform preprocessing of the measured state variables, to calculate state estimation input data; and

estimate a present charge rate and/or a deterioration degree of the operating secondary battery, from the state estimation input data, using a state estimation model trained by a learning method of the state estimation model of the secondary battery according to claim 1 ,

wherein in the preprocessing, the second processor:

uses time-series data of the measured terminal currents and time-series data of the measured terminal voltages, to calculate current differences and voltage differences, each current difference being a difference in the terminal currents, each voltage difference being a difference in the terminal voltages;

uses time-series data of the current differences and time-series data of the voltage differences to calculate difference gradients, each difference gradient being a change rate of the voltage differences with respect to the current differences in a period to a present from a past that goes back a first predetermined time from the present; and

generates the state estimation input data including:

time-series data of the difference gradients;

time-series data of open circuit voltages of the operating secondary battery;

time-series data of open circuit voltage change amounts, each open circuit voltage change amount being obtained by subtracting the open circuit voltage in a past that goes back a second predetermined time from a present, from the present open circuit voltage;

time-series data of difference gradient change amounts, each difference gradient change amount being obtained by subtracting the difference gradient in the past that goes back the second predetermined time from the present, from the present difference gradient; and

time-series data of integrated current values, each integrated current values being a sum of the terminal current values continuously acquired in a period to the present from the past that goes back the second predetermined time from the present.