JP2006220616A - Internal state detection system for charge accumulating device for vehicle - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an internal state detector for a charge accumulating device for a vehicle of a neural network type, capable of measuring precisely electric energy as to a battery deterioration condition. <P>SOLUTION: An SOC and an SOH are computed using an open circuit voltage and an internal resistance in prescribed capacity of discharge from determination of full charge, in addition to a voltage history and a current history, in order to find the battery deterioration condition by neural network computation, and a degree of battery deterioration is determined using the SOC and the SOH. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an improvement in a technique for detecting an internal state (particularly a deteriorated state) of a power storage device for a vehicle using a neural network.

  For example, in a secondary battery such as a lead-acid battery, the SOC and the remaining capacity (SOH) and the internal state quantity (voltage, open circuit voltage, internal resistance, etc.) of the battery vary depending on the degree of deterioration. In other words, there is a problem that the detection accuracy of SOH and SOH deteriorates and variations in SOC and SOH for each battery, and it has been difficult to individually detect SOC and SOH of secondary batteries that are mass-produced. For this reason, from the viewpoint of safety, there is also a problem that the usable charge / discharge range of the secondary battery must be set narrow including these variations.

In order to improve this problem, a method of detecting a lifetime using a neural network that can flexibly cope with variations in characteristics of an object to be measured (hereinafter referred to as a neural network battery state detection technique) has been proposed (Patent Document 1, 2).
Japanese Patent Laid-Open No. 2003-24971 JP-A-9-243716

  However, the determination of the life, that is, the battery deterioration using the neural network type battery state detection technology according to Patent Documents 1 and 2 described above, is a neural network calculation that can flexibly change the battery function calculation function due to the progress of deterioration of each battery. Despite its use, it was still not practical enough. This is because a new power storage device and a deteriorated power storage device have various correlations between current history and voltage history as input parameters input to the neural network and deterioration related parameters as output parameters. This is because these variations cannot be sufficiently absorbed even by using a net operation.

  In order to remedy this problem, the present applicant has determined that the function value of an approximate expression between current and voltage obtained by the method of least squares, particularly the current value or slope (internal resistance) of the intercept (open circuit voltage) of the above approximate expression. It has been discovered and filed that it is possible to improve the detection accuracy of the battery deterioration state by performing a neural network operation using the present value of the current value as an input parameter in addition to the voltage history and the current history.

  However, even in the neural network operation in which the approximate expression-related values are added to the voltage history and current history by the applicant, the battery deterioration state is not sufficiently detected.

  The present invention has been made in view of the above problems, and provides a deterioration state detection method for a power storage device for a vehicle that realizes further improvement of a neural network calculation method that can flexibly cope with the progress of battery deterioration over time. That is the purpose.

  The neural network calculation method for a power storage device for a vehicle according to the present invention includes a voltage / current history detecting means for detecting and outputting a voltage history and a current history for a predetermined time immediately before a chargeable / dischargeable battery, and the voltage history and current In a neural network calculation method for a power storage device for a vehicle, which includes a calculation unit for performing a neural network calculation on an amount of electricity related to a current deterioration state of the battery using a history as an input parameter, the calculation unit includes a predetermined value from a fully charged state of the battery. A predetermined capacity discharge deterioration state amount that is a predetermined amount of electricity related to the deterioration state of the battery during discharge amount discharge is calculated, and in addition to the voltage history and current history, the deterioration state amount during predetermined capacity discharge is used as an input parameter. The present invention is characterized by performing a neural network operation on the current deterioration state of the battery as an output parameter.

  Deterioration state amount during predetermined capacity discharge at the time of discharging from the full charge state of the battery as described above includes open circuit voltage from full charge to predetermined capacity discharge, internal resistance of the battery from full charge to predetermined capacity discharge, etc. Is mentioned.

  That is, in the present invention, by performing an neural network operation on an input data set in which the amount of electricity related to the deterioration state of the battery when a predetermined discharge amount is discharged from a full charge in addition to the voltage history and current history of the conventional battery is added. Estimate the deterioration state of the battery. According to the test, it has been found that this can greatly improve the calculation accuracy compared with the case where the neural network calculation is simply performed using the voltage history and current history of the battery.

  In a preferred aspect, the calculation means calculates an open circuit voltage current value obtained based on an approximate expression obtained from the voltage history and current history by a least square method, and the calculation means includes the voltage history, current history, The present invention is characterized in that the current deterioration state of the battery is subjected to a neural network calculation using the current value of the open circuit voltage and the deterioration state amount during the predetermined capacity discharge as input parameters. In this way, deterioration with time of the battery can be calculated with higher accuracy.

  In a preferred aspect, the deterioration state quantity at the time of predetermined capacity discharge includes the open circuit voltage of the battery at the time of discharge of a predetermined discharge amount from the full charge state of the battery, and the capacity at the time of discharge of the predetermined capacity from the full charge state of the battery. It consists of the internal resistance of the battery. In this way, battery degradation over time (cycle degradation) is much more accurate than conventional neural network operations that do not use the open circuit voltage and internal resistance of the battery when discharging a predetermined capacity from a fully charged state as input data. ) Can be determined. This is considered to be because the open circuit voltage and the internal resistance at the time of discharging a predetermined discharge amount from this fully charged state have a correlation with the change in SOC due to battery deterioration.

  In a preferred embodiment, the current deterioration state of the battery as the output parameter is a signal including both SOC (charging rate) and SOH (remaining capacity), or a function including them as variables. In this way, it was found that the deterioration state of the battery was detected satisfactorily. In this way, it was found that the degree of battery deterioration can be detected with higher accuracy than in the past. This is because the open circuit voltage and the internal resistance at the time of discharging a predetermined discharge amount from this fully charged state have a correlation with the battery deterioration.

  In a preferred embodiment, the function is expressed as: Degradation = Remaining capacity (SOH) / (Initial full charge capacity × Charge rate (SOC)).

  In a preferred embodiment, the SOH is calculated by performing a neural network operation on the SOC, an open circuit voltage at a predetermined capacity discharge, and an internal resistance at a predetermined capacity discharge. In this way, the amount of neural network calculation can be reduced.

  A neural network calculation method for a power storage device for a vehicle according to the present invention will be specifically described with reference to the accompanying drawings.

(Circuit configuration)
A neural network calculation method for the power storage device for a vehicle according to the first embodiment will be described below. First, the circuit configuration of the apparatus will be described with reference to the block diagram shown in FIG.

  101 is an in-vehicle power storage device (hereinafter also referred to as a battery), 102 is an in-vehicle generator that charges the in-vehicle power storage device, 103 is an electric device that forms an in-vehicle electric load fed from the in-vehicle power storage device 101, and 104 is an in-vehicle power storage device 101. A current sensor for detecting the charging / discharging current of the battery, 105 is a storage battery state detection device which is an electronic circuit device for detecting the state of the in-vehicle power storage device 101, and 106 stores the voltage and current of the input battery as a voltage history and a current history. And a buffer unit 107 that calculates and outputs the current value of the open circuit voltage and / or the current value of the internal resistance, and 107 outputs various input signals input from the buffer unit 106 and a correction signal generator 109 described later as a neural network. A neural network unit that calculates and outputs a predetermined storage state quantity (SOC in this embodiment), 108 is a neural network A generator control device 109 that controls the amount of power generated by the in-vehicle generator 102 based on a signal read from 107 or the like, 109 is a correction signal generation unit that calculates calibration data, which will be described later, and outputs it as input data to the neural network unit 107. is there.

  That is, in this embodiment, the storage battery state detection device 105 calculates the open circuit voltage and internal resistance at the time of predetermined capacity discharge, in addition to the buffer unit 106 and the neural network unit 107, and inputs the calibration input to the neural network unit 107. It has a feature that it has a correction signal generation unit 109 that outputs it as data. In this embodiment, the buffer unit 106, the neural network unit 107, and the correction signal generation unit 109 are realized by software calculation by a microcomputer device, but may be configured by a dedicated hardware circuit.

(Buffer 106)
The buffer unit 106 is a pre-signal processing circuit of the neural network unit 107, and simultaneously samples the voltage of the in-vehicle power storage device 101 and the current from the current sensor 104 at regular intervals to obtain a battery voltage history and a current history. The voltage and current at each time point are stored in parallel and output to the neural network unit 107 in parallel. In order to reduce the numerical limit of the input cell of the neural network unit 107 and the calculation burden, the voltage / current sampling data constituting the battery voltage history and current history is composed of data up to a predetermined time point retroactive from the present time. .

  In addition to the voltage history and current history of the battery, the buffer unit 106 calculates and creates an approximate expression indicating the relationship between voltage and current from the voltage history and current history by the least square method. The intercept (open circuit voltage) is calculated every time voltage and current data is input to obtain the current value of the open circuit voltage of the battery, and this open circuit voltage current value is used as data for associating the voltage history with the current history. Output to the net unit 107. Note that the details of the creation of the approximate expression and the calculation of the current value of the open circuit voltage of the battery are well known, and further detailed description is omitted.

(Correction signal generator 109)
The correction signal generation unit 109 calculates the open circuit voltage and internal resistance at the time of predetermined capacity discharge from full charge, and outputs the open circuit voltage and internal resistance at the time of predetermined capacity discharge to the neural network unit 107 as calibration data in the neural network calculation. To do. The correction signal generator 109 is illustrated in the flowchart of FIG.

The correction signal generator 109 is started by starting running (step 601), and detects the battery current and terminal voltage (step 602). Perform full charge determination described later for the detected current / terminal voltage (step 603).
Thereafter, the integration of the charge / discharge current is started (step 604), it is determined whether the integrated current value (Ah) has reached the predetermined discharge amount (step 605), and when this is reached, the open circuit voltage at this time is calculated (step 605). 606), it is rewritten as an open circuit voltage at the time of predetermined capacity discharge (step 607), then, the internal resistance of the battery at this time is calculated (step 608), and it is rewritten as the internal resistance at the time of predetermined capacity discharge (step 609) )

  The full charge determination described in step S603 will be described in more detail with reference to FIG. The full charge determination is stored in advance as a predetermined area in the two-dimensional space of the battery voltage and current. When the input current / voltage characteristics enter the predetermined area (see FIG. 3), it is determined that the battery is fully charged.

  The calculation for obtaining the open circuit voltage and the internal resistance at the time of discharging the predetermined capacity from the full charge described in Steps 606 and 608 will be described in more detail with reference to FIG.

  An approximate expression indicating the relationship between voltage and current is obtained by a least square method from a predetermined number of voltage / current pairs input during a predetermined period immediately before the discharge of a predetermined capacity from full charge, and the open circuit voltage ( Battery voltage when the current is considered to be 0, which is also referred to as open-circuit voltage), and the internal resistance is obtained as the slope of this approximate expression, and these are used as the open circuit voltage and internal resistance during the above-mentioned predetermined capacity discharge. . In order to improve the accuracy of the above linear approximation, it is preferable to obtain the polarization state of the battery from the past current information and express it as a polarization index, and to select data in which this polarization index is within a predetermined range. Since the creation of a linear approximation formula using this kind of least square method and the extraction of the open circuit voltage and internal resistance using this linear approximation formula are known matters, further explanation is omitted.

(Neural network unit 107)
As shown in FIG. 5, the neural network unit 107 is displayed by two circuit blocks: a neural network unit 1071 for SOC (charge rate) calculation and a neural network unit 1072 for SOH (remaining capacity) calculation shown in FIG. Is done. These circuit blocks are actually constituted by different software processes that are sequentially executed at a predetermined calculation interval. That is, since the neural network unit 107 is actually configured by software calculation of a microcomputer circuit, the circuit configuration shown in FIG. 5 is only functional.

(Neural network unit 1071 for calculating SOC (charge rate))
A functional configuration of the neural network unit 1071 for calculating the SOC (charging rate) will be described with reference to FIG. The neural network unit 1072 for calculating the SOH (remaining capacity) shown in FIG. 7 is the same as the neural network unit 1071 for calculating the SOC (charge rate) shown in FIG. Therefore, further explanation is omitted.

  The neural network unit 1071 for SOC (charge rate) calculation shown in FIG. 6 is a three-layer feedforward type that is learned by the error back propagation method, but is not limited to this type. The input layer 201 includes a predetermined number of input cells. Each input cell has voltage history data Vi and current history data Ii from the buffer unit 106 as well as the current value (current value) of the open circuit voltage and a predetermined capacity discharge as calibration data input from the correction signal generation unit 109. The open circuit voltage Vo is output to all the computation cells of the intermediate layer 202. In this embodiment, the voltage history data Vi and the current history data Ii are each composed of five points of data sampled at a constant interval, but are not limited thereto.

  Each computation cell in the intermediate layer 202 performs a neural network computation described later on each input data input from each input cell in the input layer 201, and outputs the computation result to an output cell in the output layer 203. The output cell of the output layer 203 outputs a charging rate (SOC).

If the input data of the jth cell of the input layer 201 of the neural network unit 107 is INj, and the coupling coefficient of the jth cell of the input layer 201 and the kth cell of the intermediate layer 202 is Wjk, the input data to the kth cell of the intermediate layer The input signal is
INPUTk (t) ＝ Σ (Wjk * INj) (j = 1 to 2m + 3)
It becomes. The output signal from the kth cell of the intermediate layer is
OUTk (t) = f (x) = f (INPUTk (t) + b)
It is represented by b is a constant. f (INPUTk (t) + b) is a non-linear function called a sigmoid function with INPUTk (t) + b as an input variable.
f (INPUTk (t) + b) = 1 / (1 + exp (− (INPUTk (t) + b)))
Is a function defined by If the coupling coefficient between the kth cell of the intermediate layer 202 and the cell of the output layer 203 is Wk, the input signal to the output layer is
INPUTo (t) = Σ Wk * OUTk (t)
k = 1 to Q
It is represented by Q is the number of cells in the intermediate layer 202. The output signal at time t is
OUT (t) = L * INPUTo (t)
It becomes. L is a linear constant.

  In this specification, the learning process means that each cell has a minimum error between a final output OUT (t) at time t and a teacher signal (that is, a true value tar (t)) measured in advance. Is to optimize the coupling coefficient between. The output OUT (t) is an output parameter to be output by the output layer 203, and here is the SOC at time t.

  Next, a method for updating each coupling coefficient will be described.

The update of the coupling coefficient Wk between the kth cell in the intermediate layer and the cell in the output layer is
Wk = Wk + △ Wk
Done in Here, ΔWk is defined as follows.

△ Wk = −η * ∂Ek / ∂Wk η; Constant
= η * [OUT (t) − tar (t)] * [∂OUT (t) / ∂Wk]
= η * [OUT (t) − tar (t)] * L * [∂INPUTo (t) / ∂Wk]
= η * L * [OUT (t) − tar (t)] * OUTk (t)
It is represented by Ek is an amount that represents the error between teacher data and network output, and is defined by the following equation
Ek = [OUT (t)-tar (t)] x [OUT (t)-tar (t)] / 2
Next, a rule for updating the coupling coefficient Wjk between the kth cell in the intermediate layer 202 and the jth cell in the input layer 201 will be described. The update of the coupling coefficient Wjk is realized by the following equation.

Wjk = Wjk + △ Wjk
Here, ΔWjk is defined as follows.

△ Wjk = -η * ∂Ek / ∂Wjk
= -Η * [∂Ek / ∂INPUTk (t)] * [∂INPUTk (t) / ∂Wjk]
= -Η * [∂Ek / ∂OUTk (t)] * [∂OUTk (t) / ∂INPUTk (t)] * INj
= -Η * [∂Ek / ∂OUT (t)] * [∂OUT (t) / ∂INPUTo] *
[∂INPUTo / OUTk (t)] * f '(INPUTk (t) + b) * INj
= −η * (OUT (t) −tar (t)) * L * Wk * f ′ (INPUTk (t) + b) * INj
= −η * L * Wk * INj * (OUTsoc (t) −tar (t)) * f ′ (INPUTk (t) + b)
Here, f ′ (INPUTk (t) + b) is a differential value of the transfer function f.

  The output OUT (t), that is, the SOC at time t is calculated again with the new coupling coefficients Wk and Wjk updated in this manner, and the coupling coefficient is continuously updated until the error function Ek becomes a predetermined minute value or less. The process of updating the coupling coefficient so that the error function Ek becomes a predetermined value or less is the learning process.

  A flowchart of the learning process will be described with reference to FIG. However, the state of charge of the power storage device to be output by the neural network unit 107 is SOC (charge rate).

  First, an appropriate initial value of each coupling coefficient of the neural network unit 107 is set (step 302). This may be determined appropriately using, for example, a random number. Next, an input signal for learning is individually input to each cell of the input layer 201 of the neural network unit 107 (step 303), and this input signal is subjected to a neural network calculation using the initial value of the coupling coefficient described above. The SOC as an output parameter is calculated (step 304).

  Next, the error function Ek is calculated by the above method (step 305), and it is determined whether or not this error function is smaller than a predetermined minute value th (step 306). If the error function Ek is larger than a predetermined minute value th, the update amount ΔW of each coupling coefficient defined in the learning process is calculated (step 307), and each coupling coefficient is updated (step 308).

  Next, the learning input signal is input again to each cell of the input layer 201 to calculate the SOC as an output parameter. Next, the miscalculation function Ek is evaluated, and if it is below the minute value th, it is determined that the learning has been completed (step 309), and this learning process is terminated. If the error function Ek is not below the minute value, the coupling coefficient is updated again, SOC calculation is performed, the error function Ek is evaluated, and this process is repeated until the error function Ek falls below the minute value.

  Therefore, if the neural network unit 107 learns in advance the learning process described above for the representative charge / discharge patterns for several battery types before the product is shipped, the SOC of the on-vehicle storage battery running on the market can be calculated sequentially. It becomes possible.

  When the full charge is not determined or when the open circuit voltage at the predetermined capacity discharge is not detected from the full charge, the previously obtained value is held as the open circuit voltage at the predetermined capacity discharge. In this way, by updating the value of the full-charge open circuit voltage, it is possible to accurately detect the SOC according to the deterioration during use of the battery.

(Neural network unit 1072 for SOH (remaining capacity) calculation)
The functional configuration of the neural network unit 1072 for calculating the SOH (remaining capacity) is simply the addition of an internal resistance at the time of discharging a predetermined capacity in the calculation of the neural network unit 1071 for calculating the SOC (charge rate). Omitted. Since the calculation of the internal resistance during the predetermined capacity discharge has already been described, further explanation is omitted.

(Test results)
A charge / discharge pattern (10.15 mode) of another new deteriorated battery is applied to the neural network unit 1071 for SOC (charge rate) calculation in which charge / discharge patterns (10.15 mode) of several batteries including the deteriorated battery are learned. FIG. 7 shows the result of calculating the charging rate (SOC) by inputting and performing neural network calculation. However, an input signal to the neural network unit 1071 includes a voltage history, a current history, and a current value of an open circuit voltage (a current value of an intercept obtained from a least square approximation), and a predetermined capacity (from a full charge as a calibration signal). Here, the open circuit voltage Vo was set to 0.5 Ah. It was found that a very good neural network calculation with a detection error of 1.9% becomes possible.

  Moreover, the charge / discharge pattern (10.15 mode) of another new deteriorated battery same as the above is added to the neural network unit 1072 for SOH (remaining capacity) calculation in which the charge / discharge pattern (10.15 mode) using the same battery group is learned. FIG. 10 shows the result of calculating the remaining capacity (SOH) by performing the neural network operation by inputting. However, the input signals to the neural network unit 1072 are the voltage history, current history, current value of open circuit voltage (current value of intercept obtained from the least square approximation equation), and internal resistance current value (current value of slope obtained from the least square approximation equation). Value), and the open circuit voltage at the time of predetermined capacity discharge (at the time of 0.5 Ah discharge) and the internal resistance at the time of predetermined capacity discharge (at the time of 5 Ah discharge) were used as the deterioration state quantity at the time of predetermined capacity discharge. As a result, the residual capacity (SOH) detection error was as extremely small as 1.1 Ah.

  Next, the SOC and SOH obtained as described above are input to the map of FIG. 11 stored in advance, and the battery deterioration degree is applied to somewhere in 12 areas of the map shown in FIG. Thereby, the battery deterioration degree can be classified and detected in 12 stages.

(Modification)
In addition, when SOCINI is the initial full charge capacity of the test product battery that has been examined and stored in advance, the battery deterioration degree was calculated as SOH current value / (SOC current value × SOCINI). Thereby, the battery deterioration degree can be calculated with high accuracy for each SOC value or for each SOH.

  Another embodiment will be described with reference to FIG. In this embodiment, the circuit configuration of the neural network unit 107, in other words, a neural network calculation process is changed. In this embodiment, the SOC calculated in the same manner as in the first embodiment, the open circuit voltage at the time of predetermined capacity discharge, and the internal resistance at the time of predetermined capacity discharge are input parameters, and the neural network unit 1072 for SOH (residual capacity) calculation. Is input as input data, and the remaining capacity (SOH) is learned and calculated. Even in this case, the SOH can be calculated with the detection accuracy almost the same as that of the first embodiment.

  In the neural network calculation of the first embodiment shown in FIG. 5, the input signal to the neural network unit 107 is only the voltage history and current history, and the open circuit voltage at the predetermined capacity discharge as the predetermined capacity discharge deterioration state quantity is used as calibration data. When the internal resistance at the time of the predetermined capacity discharge is not used, and the input signal to the neural network unit 107 is only the voltage history and the current history, and the open circuit voltage at the time of the predetermined capacity discharge as the deterioration state quantity at the time of the predetermined capacity discharge as the calibration data And the SOC detection error of the new deteriorated battery in the case of using the internal resistance at the time of predetermined capacity discharge was obtained. The results are shown in FIGS. FIG. 13 shows the case where calibration data was not used, and the SOC calculation error was as large as 9.1%. FIG. 14 shows the case where calibration data is used, and the SOC calculation error is greatly reduced to 6.8%.

  In the neural network calculation of the first embodiment shown in FIG. 5, the input signal to the neural network unit 107 is only the voltage history, and the open circuit voltage at the predetermined capacity discharge as the deterioration state quantity at the predetermined capacity discharge is not used as the calibration data. SOC detection error of a new deteriorated battery in the case where the input signal to the neural network unit 107 is only the voltage history and the open circuit voltage at the predetermined capacity discharge as the deterioration state quantity at the predetermined capacity is used as the calibration data. Asked. The results are shown in FIGS. FIG. 15 shows a case where calibration data is not used, and FIG. 16 shows a case where calibration data is used. Also in this case, it was found that the SOC calculation error can be greatly reduced by using the calibration data as in the third embodiment.

1 is a block diagram illustrating a circuit configuration of a device according to Embodiment 1. FIG. 3 is a flowchart illustrating a method of calculating an open circuit voltage and an internal resistance during a predetermined capacity discharge from a full charge during traveling according to the first embodiment. It is a figure which shows the full charge area | region for the full charge determination of Example 1. FIG. It is a figure which shows the example of the approximate expression for obtaining the open circuit voltage and internal resistance at the time of predetermined capacity discharge from the full charge of Example 1. FIG. It is a block diagram which shows the structure of the neural network part which comprises a battery state detection apparatus. It is a block diagram which shows the structure of the neural network part for SOC (charging rate) calculation of FIG. FIG. 6 is a block diagram showing a configuration of a neural network unit for SOH (remaining capacity) calculation of FIG. 5. 6 is a flowchart of a neural network unit for calculating the SOC (charge rate) in FIG. 5. It is a figure which shows a SOC detection result at the time of performing the same neural network calculation as the above using the open circuit voltage at the time of predetermined capacity discharge from full charge. It is a figure which shows the SOH detection result at the time of performing the same neural network calculation as the above using the open circuit voltage and internal resistance at the time of predetermined capacity discharge from full charge. It is a figure which shows the map for detecting a battery deterioration state using SOC and SOH calculated | required in Example 1. FIG. FIG. 10 is a block diagram illustrating a configuration of a neural network unit according to a second embodiment. It is a figure which shows the SOC calculation result of Example 3. It is a figure which shows the SOC calculation result of Example 3. It is a figure which shows the SOC calculation result of Example 4. It is a figure which shows the SOC calculation result of Example 4.

Explanation of symbols

101 on-vehicle power storage device 102 on-vehicle generator 104 current sensor 105 storage battery state detection device (calculation means)
106 Buffer unit 107 Neural network unit (Neural network unit)
108 generator control device 109 correction signal generation unit 201 input layer 202 intermediate layer 203 output layer

Claims (6)

Voltage / current history detection means for detecting and outputting a voltage history and current history for a predetermined time immediately before a chargeable / dischargeable battery, and electricity relating to the current deterioration state of the battery using the voltage history and current history as input parameters In a neural network calculation method of a power storage device for a vehicle comprising a calculation means for calculating a quantity in a neural network,
The computing means is
Calculating a predetermined capacity discharge deterioration state amount, which is a predetermined amount of electricity related to the deterioration state of the battery at the time of discharging a predetermined discharge amount from the fully charged state of the battery;
In addition to the voltage history and current history, a neural network operation is performed for the current degradation state of the battery as an output parameter using the predetermined capacity discharge degradation state quantity as an input parameter, and the neural network of the vehicle power storage device Net operation method. In the neural network calculation method for a power storage device for a vehicle according to claim 1,
The computing means is
Calculate the open circuit voltage current value obtained based on the approximate expression obtained by the least square method from the voltage history and current history,
The power storage device for a vehicle, wherein the calculation means performs a neural network calculation on a current deterioration state of the battery using the voltage history, current history, current value of the open circuit voltage, and the deterioration state amount during the predetermined capacity discharge as input parameters. Neural network operation method. In the neural network calculation method for a power storage device for a vehicle according to claim 2,
The deterioration amount during the predetermined capacity discharge is:
The open circuit voltage of the battery at the time of discharging a predetermined discharge amount from the fully charged state of the battery,
The internal resistance of the battery when discharging a predetermined capacity from the fully charged state of the battery;
A neural network calculation method for a power storage device for vehicles. In the neural network calculation method for a power storage device for a vehicle according to claim 3,
The current degradation state of the battery as the output parameter is
A neural network calculation method for a power storage device for a vehicle, which is a signal including both SOC (charge rate) and SOH (remaining capacity), or a function including them as a variable. In the neural network calculation method for a vehicle power storage device according to claim 4,
The function is
Degree of degradation = remaining capacity (SOH) / (initial full charge capacity × charge rate (SOC))
A neural network calculation method for a power storage device for a vehicle, characterized in that In the neural network calculation method for a power storage device for a vehicle according to claim 4 or 5,
The neural network calculation method for a power storage device for a vehicle, wherein the calculation means calculates the SOH by performing a neural network calculation on the SOC, an open circuit voltage at a predetermined capacity discharge, and an internal resistance at a predetermined capacity discharge.

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