Method and apparatus for detecting charged state of secondary battery based on neural network calculation

1. A neural network type of apparatus for detecting an internal state of a secondary battery implemented in a battery system, the apparatus comprising:

detecting means for detecting, in real time, electric signals indicating an operating state of the battery during a predetermined period of time, the electric signals being voltage and current of the battery;

producing means for producing, using the electric signals, an input parameter required for estimating the internal state of the battery, the input parameter reflecting calibration of a present charged state of the battery and consisting of a first input parameter indicating the operating state of the battery and a second input parameter indicating a degraded state of the battery,

wherein the producing means includes:

means for calculating the first input parameter on the basis of data of the voltage and current of the battery, and

means for calculating the second input parameter in response to a state of predetermined charge of the battery; and

calculating means for calculating an output parameter indicating the charged state of the battery by processing both the first and second input parameters based on a neural network calculation technique,

wherein

the second input parameter calculating means includes

means for calculating data of history of both the voltage and the current into an approximate expression on a least-squares methods,

means for calculating a present value of an open-circuit voltage of the battery on the approximate expression, the present value being included in the first input parameters, and

the output parameter calculating means is configured to calculate the output parameter by using both the first parameter consisting of the data of the voltage history, the data of the current history, and the present value of the open-circuit voltage and the second input parameter;

the second input parameter indicating the degraded state of the battery is both an open-circuit voltage and an internal resistance of the battery detected in response to a state of predetermined charge of the battery,

the output parameter indicating the present charged state is one of and SOC (state of charge) of the battery, an SOH (state of health) of the battery, and a function whose variables including information indicative of the SOC and SOH, and

the function is a degree of degradation of the battery which is defined by an expression of:

the degree of desegregation=SOH/(initial full charge capacity×SOC).

2. A neural network type of apparatus for detecting an internal state of a secondary battery implemented in a battery system, the apparatus comprising:

detecting means for detecting, in real time, electric signals indicating an operating state of the battery during a predetermined period of time, the electric signals being voltage and current of the battery;

producing means for producing, using the electric signals, an input parameter required for estimating the internal state of the battery, the input parameter reflecting calibration of a present charged state of the battery and consisting of a first input parameter indicating the operating state of the battery and a second input parameter indicating a degraded state of the battery,

wherein the producing means includes:

means for calculating, as the first input parameter, voltage history data and current history data based on data of the received voltage and current of the battery, and

means for calculating, as the second input parameter, an open-circuit voltage of the battery and an internal resistance of the battery using both the voltage history data and the current history data in response to a state of predetermined charge of the battery, by using a least-squares method so as to create an approximate linear expression from both the voltage history data and the current history data,

first calculating means for calculating an output parameter indicating, as the charged state, a full charge capacity of the battery by processing both the first and second input parameters based on a neural network calculation technique, the full charge capacity being expected at present; and

second calculating means for calculating, as the degraded state of the battery, a degree of degradation of the battery based on an expression of:

DD=Q present /Q initial

wherein DD denotes the degree of degradation of the battery, Q present denotes a present value of the full charge capacity estimated by the output parameter calculating means, and Q initial denotes an initial value of the full charge capacity given when the battery is manufactured.

3. A neural network type of apparatus for detecting an internal state of a secondary battery implemented in a battery system, the apparatus comprising:

detecting means for detecting electric signals indicating an operating state of the battery;

producing means for producing, using the electric signals, an input parameter required for estimating the internal state of the battery the input parameter reflecting calibration of a present charged state of the battery; and

estimating means for estimating an output parameter indicating the charged state of the battery by applying the input parameter to neural network calculation,

wherein the producing means is configured to produce the input parameter which is calibrated depending on a charge and discharge characteristic of the battery which is attributable to at least one of the degraded state of the battery and the difference in types of the battery, and

the input parameter includes either a voltage Vof the battery or a ratio V/Vf,

wherein Vf is a voltage of the battery detected when the battery is in a fully charged state, either an open-circuit voltage Vo of the battery or a ratio of Vo/Vof wherein Vof is an open-circuit voltage detected when the battery is in a fully charged state, either an internal resistance R of the battery or a ratio of R/Rf wherein Rf is an internal resistance detected when the battery is in a fully charged state, a predetermined function f(Vo, R) using, as input variables, the open-circuit voltage Vo and the internal resistance R and expressing a correlation to an amount of current of the battery which is dischargeable at present, and a current I of the battery.

4. The apparatus of claim 3 , wherein

the function f(Vo, R) is a function of which input variable is based at least on a ratio of Vo/R.

5. The apparatus of claim 4 , wherein

the function f(Vo, R) is a function of which input variable is based on a ratio of Vo·Vo/R.

6. The apparatus of claim 4 , wherein

the function f(Vo, R) is a function of which input variable is based on a ratio of (Vm−Vo)/R wherein Vm is a predetermined voltage of the battery.

7. The apparatus of claim 4 , wherein

the predetermined voltage Vm is a discharge stop voltage of the battery.

8. The apparatus of claim 3 , wherein

the function f(Vo, R) is a function defined by f(Vop, Rp)/f(Vof, Rf) wherein f(Vop, Rp) denotes a present value of the function f(Vo, R) and f(Vof, Rf) denotes a value of the function f(Vop, Rp) obtained when the battery is in a fully charged state.

9. The apparatus of claim 8 , wherein

the function f(Vo, R) is a function defined by (Vo·Vo/R)/(Vof·Vof/Rf) wherein Vof and Rf denote an open-circuit voltage and an internal resistance of the battery detected when the battery is in a fully charged state, respectively.