JP2015121520A - Storage battery state monitoring device and storage battery device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To update storage battery degradation predicated values and increase the likelihood of lifetime prediction.SOLUTION: A storage battery state monitoring device 3 includes: a database 32 storing therein degradation rate predicted values corresponding to a plurality of use conditions for a storage batteries; a degradation rate update unit 34 updating the degradation rate predicted values using the use conditions for the storage battery or those for another storage battery aggregated for every predetermined period and degradation rate data under the use conditions; and a degradation prediction unit 35 predicting storage battery degradation using the degradation rate predicted values.

Description

  Embodiments described herein relate generally to a storage battery state monitoring device and a storage battery device.

  Storage battery devices are widely used as backup power sources or distributed power sources in factories, commercial facilities, or general houses. In recent years, in power generation using natural energy such as sunlight and wind power, it is often used to suppress fluctuations in power.

  The storage battery device has a configuration in which a plurality of storage battery cells are connected in series. The storage battery cell is repeatedly charged and discharged, so that the internal resistance increases and the battery capacity decreases, so that the deterioration progresses, and eventually replacement is necessary. Storage battery status monitoring devices are required not only to measure battery temperature, current and voltage of storage battery cells, but to perform real-time status monitoring such as abnormality detection, as well as predict future degradation and predict life. ing.

  In order to predict deterioration progress and life prediction, it is necessary to accurately grasp the deterioration tendency of the storage battery, but the storage battery has different deterioration rates depending on the usage environment such as the ambient temperature and the use conditions such as the charge / discharge rate or the charge / discharge depth. It is. Therefore, the storage battery charge / discharge test is performed under different use conditions, the internal resistance and the battery capacity are measured, and the deterioration rate for each use condition is calculated. A table of deterioration rate prediction values for each use condition is created from the data obtained in this charge / discharge test. And with reference to this table, the deterioration rate prediction value corresponding to the use conditions obtained by measuring the storage battery is acquired, and the progress of deterioration and life prediction are performed using this deterioration rate prediction value.

Japanese Patent No. 44452146

  However, since the data obtained by the charge / discharge test is limited and does not reflect the use conditions of the actually installed storage battery, it is difficult to accurately predict the progress of deterioration and the lifetime.

  As means for reflecting the actual use conditions, for example, the battery temperature of the storage battery cell is measured, and based on the Arrhenius rule that the life becomes 1/2 when the temperature increases by 10 ° C., the unit period of the storage battery life at the measured temperature There is a method for determining the hit rate. The product of the deterioration rate and the detection timing interval of the temperature detection unit is sequentially subtracted from the predicted life period at a preset reference temperature (for example, 25 ° C.) to obtain the remaining life of the storage battery.

  However, this method calculates the battery deterioration rate based on a predetermined storage battery cell lifetime-temperature characteristic. As described above, there are various factors other than temperature, such as standby SOC (State of Charge, charging rate) and charge / discharge depth (DOD: Depth of Discharge) as usage conditions that affect deterioration. Therefore, accurate deterioration progress prediction and life prediction cannot be performed even if only one change in use condition is taken into consideration.

  In order to solve the above problems, the embodiment of the present invention performs accurate prediction of the deterioration progress and life of the storage battery by increasing the accuracy of the deterioration rate prediction value corresponding to a plurality of use conditions of the storage battery, It aims at providing the storage battery state monitoring apparatus and storage battery apparatus which can contribute to the stable operation of a storage battery apparatus.

  The storage battery state monitoring device of the embodiment includes a database that stores deterioration rate prediction values corresponding to a plurality of use conditions of a storage battery, use conditions of the storage battery or another storage battery that are aggregated every predetermined period, and deterioration in the use conditions. A deterioration rate update unit that updates the deterioration rate prediction value using rate data, and a deterioration prediction unit that performs deterioration prediction of the storage battery using the deterioration rate prediction value.

  The storage battery device according to the embodiment includes a total data creation unit that creates total data regarding the use conditions and the deterioration rate of the storage battery for each predetermined period based on data including charge / discharge current, voltage, and temperature measured from the storage battery, A deterioration rate update unit that updates the deterioration rate prediction value by using a database that stores deterioration rate prediction values corresponding to a plurality of use conditions, and aggregated data relating to use conditions and deterioration rates of storage batteries that are aggregated every predetermined period. And a deterioration prediction unit that performs deterioration prediction of the storage battery using the deterioration rate prediction value.

It is a block diagram which shows the structure of the storage battery apparatus which concerns on 1st Embodiment. It is a block diagram which shows the structure of the storage battery control apparatus which concerns on 1st Embodiment. It is a graph which shows the internal resistance value computed from the ratio of the wavelet coefficient of an electric current and a voltage. It is a block diagram which shows the structure of the storage battery state monitoring apparatus which concerns on 1st Embodiment. It is a figure which shows an example of the total data table stored in the database of the storage battery state monitoring apparatus which concerns on 1st Embodiment. It is a figure which shows an example of the deterioration rate estimated value table stored in the database of the storage battery state monitoring apparatus which concerns on 1st Embodiment. It is a flowchart which shows operation | movement of the storage battery state monitoring apparatus which concerns on 1st Embodiment. It is a block diagram which shows the structure of the storage battery control apparatus which concerns on 2nd Embodiment. It is a figure which shows an example of the total data table stored in the database of the storage battery state monitoring apparatus which concerns on 2nd Embodiment. It is a figure which shows an example of the deterioration rate estimated value table stored in the database of the storage battery state monitoring apparatus which concerns on 2nd Embodiment. It is a block diagram which shows the structure of the storage battery control apparatus which concerns on 3rd Embodiment. It is a figure which shows an example of the battery capacity maintenance rate table stored in the memory | storage part of the storage battery control apparatus which concerns on 3rd Embodiment. It is a graph which shows the correlation of internal resistance and battery capacity. It is a figure which shows an example of the total data table stored in the database of the storage battery state monitoring apparatus which concerns on 3rd Embodiment.

[First Embodiment]
Hereinafter, embodiments will be described in detail with reference to the drawings.

[Storage battery device]
As shown in FIG. 1, the storage battery device includes a storage battery unit 1, a storage battery control device 2, and a storage battery state monitoring device 3.

  The storage battery unit 1 includes a plurality of storage battery cells 10, and includes an assembled battery 11 that charges and discharges an external circuit, and measurement units 12, 13, and 14 that measure various data from the plurality of storage battery cells 10. ing.

  The storage battery control device 2 is connected to the storage battery unit 1 and monitors and controls each storage battery cell 10. Furthermore, as the data creation unit of the storage battery device, data measured by the measurement units 12 to 14 of the storage battery unit 1 is collected for monitoring the state of the storage battery, and necessary data is also created therefrom.

  The storage battery state monitoring device 3 is connected to the storage battery control device 2 via the network N, and monitors the state of the storage battery device based on data transmitted from the storage battery control device 2. As state monitoring, first, data about each storage battery cell 10 is constantly monitored to detect an abnormality. In the present embodiment, in addition to this, deterioration prediction of the entire assembled battery 11 is also performed.

Hereinafter, each configuration will be described in detail.
[Storage battery unit]
As shown in FIG. 1, in the storage battery unit 1, a plurality of storage battery cells 10 are connected in series or in series and in parallel to form an assembled battery 11. The assembled battery 11 is connected to an external system (not shown), is charged by supplying power from the external system, and supplies power to the external system by discharging the charged power.

  A voltage measuring unit 12 is connected between the positive terminal and the negative terminal of each storage battery cell 10. The voltage measuring unit 12 measures the voltage between the terminals. A current measuring unit 13 is inserted in the current path of the assembled battery 11. The current measuring unit 13 measures the charge / discharge current to the assembled battery 11. A battery temperature measurement unit 14 is disposed in the vicinity of the assembled battery 11. The battery temperature measurement unit 14 measures the battery temperature of the assembled battery 11.

[Storage battery control device]
FIG. 2 is a block diagram showing a detailed configuration of the storage battery control device 2. As shown in FIG. 2, the storage battery control device 2 includes a measurement value collection unit 21, an internal resistance value calculation unit 23, an SOC calculation unit 22, a charge / discharge depth calculation unit 27, and a total data creation unit 24. Moreover, the memory | storage part 25 which memorize | stores the data collected, calculated and totaled by each part, and the transmission part 26 which transmits the data memorize | stored in the memory | storage part 25 to the storage battery state monitoring apparatus 3 are provided. Further, although not shown, a control unit for controlling each unit is also provided.

  The measurement value collection unit 21 is connected to the voltage measurement unit 12, the current measurement unit 13, and the battery temperature measurement unit 14 of the storage battery unit 1. The measured values of the inter-terminal voltage, the charge / discharge current, and the battery temperature measured by each measurement unit are collected and stored in the storage unit 25.

  The internal resistance value calculation unit 23 calculates the internal resistance value of the assembled battery 11 from the measurement values of the voltage measurement unit 12 and the current measurement unit 13. As a calculation method, wavelet transform is used to obtain current and voltage wavelet coefficients for each frequency, and the internal resistance value of the assembled battery 11 is calculated from these ratios. In this method, it is not necessary to connect a special analysis device as in the AC impedance method, and the internal resistance value can be calculated even during operation of the storage battery device.

A specific calculation method is as follows. First, the wavelet transform WΨf of the waveform f (t) can be obtained by the following equation (1).

Ψa, b (t) is called an analyzing wavelet, and is defined as shown in Expression (2), where a parameter for dilation (enlargement / reduction) is a real number a, and a parameter for shift on the t-axis is a real number b.

Various Ψ (t) have been proposed and can be selected as appropriate. The following formula (3) shows the definition of the Gabor wavelet as an example.

Assuming that the measured current waveform is i (t) and the voltage waveform is v (t), the respective wavelet transformations are as shown in the following equations (4) and (5), and the transformation results are called wavelet coefficients. .

Then, as shown in FIG. 3, the internal resistance value can be calculated from the ratio of the current and voltage wavelet coefficients of the same dilation a, shift b, by the following equation (6).

  At this time, the dilation a corresponds to the frequency, and if the dilation a is determined, the internal resistance is considered to be constant regardless of the shift b. Therefore, when the shift b is changed with respect to a specific dilation a and the relationship between (WΨi) (a, b) and (WΨv) (a, b) is linearly approximated using the least squares method, the slope thereof is obtained. From this, the internal resistance value R (a) for each frequency is calculated.

  Here, a determination coefficient R2 representing the accuracy of linear approximation is calculated. When the dispersion and covariance of (WΨi) (a, b) and (WΨv) (a, b) are νwi, νwv, and νwiv, respectively, νwi, νwv, and νwiv are obtained by equations (7) to (9), respectively. It is done.

Thereby, the determination coefficient R2 when the relationship between (WΨi) (a, b) and (WΨv) (a, b) is linearly approximated is obtained by the following expression.

  As described above, the internal resistance value calculation unit 23 performs discrete wavelet transform on the current value and voltage value of each storage battery cell 10 and calculates the internal resistance value of the assembled battery 11 from the ratio of the wavelet coefficients. When the relationship between the current and voltage wavelet coefficients is linearly approximated, the internal resistance value of the assembled battery 11 is calculated except for the case where the value of the correlation coefficient R2 is lower than that of a preset reference. Yes. The calculated internal resistance value is stored in the storage unit 25.

  The SOC calculation unit 22 calculates the SOC of the battery pack 11 based on the measurement value. The calculated SOC is stored in the storage unit 25.

  A known method can be used to calculate the SOC. For example, the SOC can be calculated by obtaining a time integral value of the charge / discharge current for the assembled battery 11 measured by the current measurement value and dividing the time integral value by the full charge capacity (Ah) of the assembled battery 11. .

  Alternatively, since there is a correlation between SOC and OCV (Open Circuit Voltage), a correspondence table table of SOC and OCV is stored in the storage unit 25, and the open circuit voltage measured by the voltage measurement unit 12 is stored. By referring to this table, the SOC can be obtained.

  The charge / discharge depth calculation unit 27 calculates the charge / discharge depth of the entire assembled battery 11 based on the measurement value. The charge / discharge depth is a numerical value representing the charge / discharge state of the battery, and generally represents the ratio of the charge / discharge power amount in one cycle to the rated capacity of the assembled battery 11 as a percentage. For the rated capacity of the storage battery cell 10, refer to that stored in the storage unit 25 in advance. The charge / discharge power amount is obtained by acquiring the current value and the assembled battery voltage from the measured value and integrating the current value, the assembled battery voltage, and the charge / discharge time. The charge / discharge depth calculation unit 27 stores the charge / discharge depth of the assembled battery 11 in the storage unit 25.

  The total data creation unit 24 calculates each measurement value stored in the storage unit 25 and collected by the measurement value collection unit 21, the internal resistance value calculation unit 23, the SOC calculation unit 22, and the charge / discharge depth calculation unit 27. Aggregated values are aggregated to create aggregated data. Although the total data creation unit 24 can create arbitrary data as necessary, in this embodiment, in order to increase the accuracy of the predicted deterioration rate for each use condition necessary for the life prediction of the assembled battery 11. Create data for.

  Although the timing for performing data aggregation can be set arbitrarily, in this embodiment, for example, data aggregation is performed at a timing corresponding to one charge / discharge cycle. Charging / discharging corresponding to one cycle indicates that charging and discharging corresponding to the rated power amount of the battery are performed once.

  The total data creation unit 24 collects internal resistance value data in one cycle from the storage unit 25 and obtains an average value thereof.

  Furthermore, the battery temperature, SOC, and charge / discharge depth data for a predetermined period are collected from the storage unit 25. For battery temperature and SOC, an average value for one cycle is calculated.

  The control unit of the storage battery control device 2 includes cell balance control of the assembled battery 11 of the storage battery unit 1, control of each measurement unit, and the measurement value collection unit 21, internal resistance value calculation unit 23, and SOC calculation unit 22 of the storage battery control device 2. The operation timing of the charge / discharge depth calculation unit 27, the total data creation unit 24, and the transmission unit 26 is controlled. That is, the control unit includes a ROM, a CPU, and a driver that store a control program, and outputs an operation signal at each timing to each unit via the interface according to the control program. Or you may make it implement | achieve all the functions of each part other than the measured value collection part 21 by the process of a computer.

  The transmission unit 26 includes a communication interface for transmitting data, and is connected to the network N via a router. The transmission unit 26 transmits the total data created by the total data creation unit 24 to the storage battery state monitoring device 3 via the network N.

[Storage battery status monitoring device]
FIG. 4 is a block diagram showing a configuration of the storage battery state monitoring device 3. The storage battery state monitoring device 3 includes a receiving unit 31 that receives aggregate data transmitted from the storage battery control device 2 and a database 32 that stores and stores the received data. The storage battery state monitoring device 3 also includes a deterioration rate calculation unit 33 and a deterioration rate update unit 34 in order to update the deterioration rate prediction value of the assembled battery 11 using the aggregate data transmitted from the storage battery control device 2. I have. The storage battery state monitoring device 3 further includes a deterioration prediction unit 35 that performs life prediction of the assembled battery 11 using the deterioration rate prediction values accumulated and updated in the database 32. In addition, although not shown in figure, the storage battery state monitoring apparatus 3 is provided with the control part which controls each part. In addition, a display device 50 is connected to the storage battery state monitoring device 3 so that the operator can visually check the monitoring result.

  The receiving unit 31 includes a communication interface for receiving data, and is connected to the network N via a router, a modem, or the like. The control unit includes a ROM, a CPU, and a driver for storing a control program, and outputs an operation signal to each unit at each timing through an interface according to the control program. Or you may make it implement | achieve all the functions of each part by the process of a computer.

  First, the database 32 stores a total data table as shown in FIG. The total data table is a table in which the total data transmitted from the storage battery control device 2 is collected for each total time. In this embodiment, since data is totaled for every cycle of charge / discharge of the assembled battery 11, the total time indicates the timing of one cycle.

  The total data table includes the internal resistance, battery temperature, SOC, and charge / discharge depth of the assembled battery 11. As will be described later, the internal resistance is used to calculate the deterioration rate of the assembled battery 11. The battery temperature, the SOC, and the charge / discharge depth are the usage conditions of the assembled battery 11. The use conditions mean various factors that affect the deterioration of the assembled battery 11 in the environment where the storage battery device operates.

  Since the present embodiment aims at deterioration prediction reflecting various factors, it is preferable that the use conditions are a plurality, that is, a combination of at least two or more. In FIG. 5, three examples of the battery temperature, the SOC, and the charge / discharge depth are shown as usage conditions, but only two of them may be used, and other usage conditions may be added. Other use conditions include, for example, a charge / discharge rate.

  Next, the database 32 stores a deterioration rate prediction value table as shown in FIG. In the deterioration rate prediction value table, deterioration rate prediction values in one cycle of the assembled battery 11 are registered for different use conditions. As will be described later, this deterioration rate prediction value table is used in the deterioration prediction unit 35 for deterioration prediction and life prediction of the assembled battery 11.

  The deterioration rate obtained in the deterioration characteristic test is stored as an initial value of the deterioration rate prediction value. The deterioration characteristic test is a test in which various use conditions are set for each type of storage battery such as a lithium ion secondary battery and a lead storage battery, and the deterioration rate under the use conditions is calculated by measurement.

  However, it is difficult to make the environment where the deterioration characteristic test is performed exactly the same as the environment where the assembled battery 11 actually operates. The deterioration of the assembled battery 11 is influenced not only by clear use conditions but also by various fine factors that do not appear on the surface layer. Therefore, even if the use conditions are the same, the predicted deterioration rate obtained in the deterioration characteristic test may not necessarily match the actual deterioration rate.

  In addition, it is difficult to test for all use conditions. Therefore, the data obtained in the deterioration characteristic test for some use conditions are linearly interpolated, and the deterioration rate of the use conditions not obtained in the test is estimated to be an initial value. However, since there is a limit to such estimation, as shown in FIG. 6, there is a portion where the deterioration rate prediction value is not specified depending on the use conditions.

  Therefore, the deterioration rate calculation unit 33 and the deterioration rate update unit 34 update the deterioration rate prediction value using the aggregate data table in which the measurement data of the assembled battery 11 that is actually operating is accumulated. That is, the accuracy of deterioration prediction and life prediction is improved by reflecting the actual operation status of the assembled battery 11 in the existing data called the deterioration characteristic test. In addition, a deterioration rate prediction value is newly created for a portion for which a deterioration rate prediction value has not been created. Processing in the deterioration rate calculation unit 33 and the deterioration rate update unit 34 will be described with reference to the flowchart of FIG.

The deterioration rate calculation unit 33 extracts data at the total time with the same use condition from the total data table (step S01). Specifically, the use conditions are the same, that is, the battery temperature, the SOC, and the charge / discharge depth are the same value data. Here, these usage conditions are collectively referred to as usage conditions Q. Next, the deterioration rates x ₁ , x ₂ ,..., X _n at the respective aggregation times are calculated using the internal resistance values included in the extracted n aggregation data (step S02).

For example, the deterioration rate is calculated by calculating a difference ΔR between the internal resistance Rt at a certain counting time t and the internal resistance Rt−1 at the counting time t−1 before the certain counting time t. This internal resistance difference ΔR can be regarded as a deterioration amount indicating how much deterioration has progressed in one cycle from the previous counting time t−1 to the current counting time t. The difference ΔR is divided by the internal resistance value at the total time t−1 to calculate the deterioration rate at the total time t. In this process, n deterioration rates x ₁ , x ₂ ,..., X _n are calculated in order to obtain the deterioration rates at the total times of use conditions Q.

  Subsequently, the deterioration rate updating unit 34 refers to the deterioration rate predicted value table and determines whether or not the deterioration rate predicted value has already been registered in the use condition Q (step S03). If the deterioration rate prediction value is registered (step S03: Yes), the deterioration rate prediction value P under the use condition Q is acquired (step S04).

Next, the degradation rate update unit 34 reflects the _n degradation rates x ₁ , x ₂ ,..., X _n calculated by the degradation rate calculation unit 33 in the degradation rate prediction value P using Bayes' theorem. (Step S05). Specifically, the deterioration rate predicted value P and the prior distribution [pi (mu), the degradation rate calculator 33 of n deterioration rate x _1, x ₂ obtained, ..., the data D x _n likelihoods Assuming that f (D | μ), the following posterior distribution π (μ | D) is obtained.
Posterior distribution π (μ | D) ∝f (D | μ) × π (μ)
This posterior distribution π (μ | D) becomes a new deterioration rate prediction value P reflecting data obtained from the assembled battery 11 in operation.

Here, if the prior distribution π (μ) follows a normal distribution having an average value μ ₀ and a variance σ ₀ ² , the prior distribution π (μ) of the population average μ is expressed by the following probability density function. Here, it is assumed that the deterioration rate prediction value, which is an initial value, follows a normal distribution with variance σ ₀ ² = 1.

On the other hand, the likelihood f (D | μ) composed of _n deterioration rates x ₁ , x ₂ ,..., X _n is expressed by the following expression. Here, n deterioration rates x ₁ , x ₂ ,..., X _n follow a normal distribution with variance σ ₀ ² = 1.

As described above, the posterior distribution π (μ | D) can be obtained by the following equation.

When the prior distribution π (μ) is a normal distribution N (μ ₀ , σ ₀ ² ) with an average value μ ₀ and variance σ ₀ ² , the posterior distribution posterior distribution π (μ | D) is also a normal distribution, and the average value μ _The following relationship holds between ₁ and variance σ ₁ ² .
σ ₀ ² > σ ₁ ²
That is, by reflecting the deterioration rates x ₁ , x ₂ ,..., X _n obtained from the aggregate data in the prior distribution π (μ), the variation of the population average value μ is reduced, and the deterioration rate predicted value The accuracy of P can be increased.

  The deterioration rate update unit 34 updates the deterioration rate predicted value P by registering the calculated posterior distribution π (μ | D) in the deterioration rate predicted value table as a new deterioration rate predicted value under the use condition Q ( Step S06).

A specific example will be described. In the deterioration rate calculation unit 33, three pieces of data are extracted from the total data table under the same use conditions of an average temperature of 25 ° C., an average SOC of 50%, and a charge / discharge depth of 80% (step S01). , 0.014, 0.016 (% / cycle) are calculated (step S02). Next, the deterioration rate update unit 34 refers to the deterioration rate prediction value table, and acquires a deterioration rate prediction value 0.01 (% / cycle) of an average temperature of 25 ° C., an average SOC of 50%, and a charge / discharge depth of 80% (step / step). S03-S04). When the deterioration rate predicted value 0.01 is set to a prior distribution π (μ) and the respective deterioration rates 0.008, 0.014, and 0.016 are substituted into the above equation 13, the posterior distribution π (μ | D) is obtained. Average value μ ₁ = 0.012 (% / cycle) and variance σ ₁ ² = 0.25 are obtained (steps S05 to S06).

This average value μ ₁ = 0.012 (% / cycle) is updated as a new predicted deterioration rate under the use conditions of an average temperature of 25 ° C., an average SOC of 50%, and a charge / discharge depth of 80%.

  In step S03, when the deterioration rate update unit 34 refers to the deterioration rate predicted value table and there is no registered deterioration rate predicted value in the use condition Q (step S03: No), the use condition Q approximates the use condition Q. The “deterioration rate prediction value P” is obtained (step S07).

  The approximate use condition Q ′ can be appropriately selected from the cause and effect relationship between the use condition and the deterioration. For example, from the temperature, the SOC, and the charge / discharge depth, only one may not match and the remaining two use conditions may match. If all the use conditions do not match, the use condition having the smallest difference from the use condition Q may be selected.

  As a specific example, in the deterioration rate prediction value table of FIG. 6, no deterioration rate prediction value is registered at an average temperature of 30 ° C., an average SOC of 50%, and a charge / discharge depth of 100%. In this case, the deterioration rate prediction value 0.02 (% / cycle) at an average temperature of 25 ° C., an average SOC of 50%, and a charge / discharge depth of 100% is acquired as the approximate use condition Q ′.

Next, as in step S05, the deterioration rate update unit 34 adds the n deterioration rates x ₁ , x ₂ ,... Calculated by the deterioration rate calculation unit 33 to the deterioration rate predicted value P ′ of the approximate use condition Q ′. Reflect x _n (step S08). That is, the deterioration rate prediction value P ′ of the approximate use condition Q ′ is a prior distribution π (μ), and the n deterioration rates x ₁ , x ₂ ,..., X _n are likelihoods f (D | μ). The posterior distribution π (μ | D) is calculated using Bayes' theorem. This posterior distribution π (μ | D) is registered in the deterioration rate prediction value table as a new deterioration rate prediction value P under the use condition Q (step S09).

  In the above-described example, data having the same use condition is extracted from the total data accumulated in the database 32, and the deterioration rate is calculated and the deterioration rate prediction value is updated collectively. The calculation of the deterioration rate and the update of the deterioration rate prediction value may be performed every period in which a certain amount of total data is accumulated, for example, every month or every six months. Alternatively, the degradation rate prediction value may be updated each time when the total data under the same use condition is constantly extracted and a certain number of data is collected.

  Alternatively, the deterioration rate may be calculated for each aggregation time, and the deterioration rate predicted value under the same use condition may be updated.

  Moreover, in the above-mentioned example, the measurement data of the assembled battery 11 incorporated in the same storage battery device was received, and the deterioration rate update unit 34 updated the deterioration rate predicted value using the data. Instead, measurement data of the assembled battery 11 incorporated in another storage battery device may be used. In this case, the storage battery state monitoring device 3 is configured to be able to communicate with the storage battery control device 2 configuring the other storage battery device via the network and receive measurement data.

  It is desirable that this other storage battery device is similar in installation environment and purpose of use. If the installation environment and purpose of use are similar, the deterioration tendency is expected to be similar. Even when the measurement data of the battery pack that performs deterioration prediction is not enough to accumulate the total data, by updating the deterioration rate prediction value using the measurement data of another battery pack that operates in a similar environment, The accuracy of the predicted deterioration rate can be increased.

  The deterioration prediction unit 35 performs deterioration prediction and life prediction of the assembled battery 11 using the deterioration rate prediction value table updated in the above-described manner. Various known methods can be used as the prediction method. Here, an example of the life prediction method will be described.

The life of the storage battery can be defined as, for example, the time point when the battery capacity has decreased to 70% of the initial battery capacity. Further, the rated power amount W ₀ of the storage battery is set to 100 (kWh). Further, the current battery capacity C ₁ (relative ratio with respect to the initial battery capacity) is 90 (%), and the charge / discharge power amount W ₁ per year is 40,000 (kWh). The battery capacity and annual charge / discharge energy data may be stored in the database 32 in advance, or may be acquired from the storage battery control device 2.

The deterioration predicting unit 35 acquires the latest use condition of the assembled battery 11 in operation from the total data table. Furthermore, 0.01 (% / cycle) is acquired from the deterioration rate prediction value table as the deterioration rate prediction value P under the same use conditions as the latest use conditions. Then, 10 years is calculated as the remaining life period L ₁ of the assembled battery 11 by the following formula.

The calculated remaining life period L ₁ is displayed on a display device 50 such as a display. The operator can refer to this remaining life period L ₁ and can use it for the operation plan of the storage battery device.

  In the above example, the period until the battery capacity of the assembled battery 11 reaches 70% defined as the lifetime is calculated and the lifetime is predicted. Instead, for example, it is also possible to calculate the period until 85% and 80%, respectively, and predict the progress of deterioration of the storage battery. By predicting the progress of deterioration, it can be used to create a detailed operation schedule of the storage battery device.

[Function and effect]
(1) The storage battery state monitoring device 3 includes a database 32 that stores predicted deterioration rates corresponding to a plurality of use conditions of the storage battery, a use condition of the storage battery or another storage battery that is tabulated for each predetermined period, and the use condition. A degradation rate update unit 34 that updates the degradation rate prediction value using the degradation rate data, and a degradation prediction unit 35 that performs degradation prediction of the storage battery using the degradation rate prediction value.

  Predicted deterioration rate values corresponding to a plurality of use conditions are prepared, and further, the predicted deterioration rate values are updated using the use conditions and deterioration rate data obtained from the actually operating storage battery. Accordingly, the accuracy of the predicted deterioration rate can be increased, and accurate deterioration progress and life prediction can be performed in consideration of various environments and purposes of the storage battery, thereby contributing to stable operation of the storage battery device.

(2) The plurality of use conditions of the storage battery include at least two of the battery temperature, the SOC, and the charge / discharge depth of the storage battery, and the use condition and the deterioration rate data in the use condition are the charge / discharge cycle of the storage battery or another storage battery. It is counted every time.

  Usage conditions such as battery temperature, SOC, and charge / discharge depth of the storage battery greatly affect the cycle life of the storage battery. As the number of charge / discharge cycles increases, the storage battery increases in internal resistance and progresses in deterioration. Therefore, by combining two or more of these use conditions and further collecting data for each charge / discharge cycle, it is possible to calculate a deterioration rate that accurately reflects the use conditions.

(3) The storage battery state monitoring device 3 determines the deterioration rate of the storage battery in one period t, for example, the internal resistance Rt in one cycle with charge / discharge of the storage battery and the internal resistance Rt− in the period t−1 before one period. 1 further includes a deterioration rate calculation unit 33 that calculates the difference ΔR from 1.

  As described above, the internal resistance value can be calculated by using the wavelet transform even when the storage battery device is in operation. Since the internal resistance difference ΔR can be used as the deterioration rate, the deterioration rate can be easily calculated.

(4) The deterioration rate calculation unit 33 calculates deterioration rates x ₁ , x ₂ ,..., X _n for a plurality of periods having the same use condition Q, and the deterioration rate update unit 34 calculates deterioration rate x ₁ of a plurality of periods, x _2, ···, with x _n, and updates the deterioration rate prediction value P corresponding to the use condition Q.

Deterioration rate x ₁ of a plurality of periods, x _2, ···, calculated collectively x _n, the plurality of deterioration rate x _1, x _2, ···, and updates the deterioration rate prediction value P at x _n As a result, it is possible to absorb the variation in the deterioration rate for each cycle and to further increase the accuracy of the predicted deterioration rate.

(5) The deterioration rate calculation unit 33 calculates deterioration rates x ₁ , x ₂ ,..., X _n for a plurality of periods having the same use condition Q, and the deterioration rate update unit 34 calculates the calculated plurality of deterioration rates x ₁ , x ₂ ,. deterioration rate x _1, x ₂ periods, ..., with x _n, identical use conditions 'corresponds to the deterioration rate prediction value P' approximates use conditions Q of Q is updated and the updated deterioration rate prediction The value is registered as a deterioration rate prediction value P corresponding to the same use condition Q.

  It is difficult to conduct a charge / discharge test in advance for all use conditions of the storage battery. Therefore, for use conditions for which no deterioration rate prediction value is registered, the deterioration rate prediction value for the approximate use condition is updated and newly registered as the deterioration rate prediction value for the use condition, thereby providing a highly accurate deterioration rate prediction value. Can be obtained and is highly convenient.

[Second Embodiment]
Next, a second embodiment will be described. In the second and subsequent embodiments, only differences from the above-described embodiments will be described, and the same portions will be denoted by the same reference numerals and detailed description thereof will be omitted.

  In the first embodiment, the data aggregation and the deterioration rate are calculated for each charge / discharge cycle of the storage battery. The second embodiment aims to predict how much deterioration will progress over a large span of years. The year unit includes half a year, one year, or two years or more, but in this embodiment, the progress of deterioration per year is predicted. Therefore, the charge / discharge electric energy (kWh) per year is added as a use condition of the assembled battery 11 used for deterioration prediction.

  As shown in FIG. 8, the storage battery control device 2 of the second embodiment includes a charge / discharge power amount calculation unit 28. The charge / discharge power amount calculation unit 28 calculates the charge / discharge power amount (Wh) using the current and voltage measurement values collected by the measurement value collection unit 21. The calculated charge / discharge power amount is stored in the storage unit 25.

  The total data creation unit 24 creates total data in which the charge / discharge power amount calculated by the charge / discharge power amount calculation unit is added to the internal resistance value, the battery temperature, the SOC, and the charge / discharge depth. Annual average values are calculated for the internal resistance value, battery temperature, SOC, and charge / discharge depth. An annual integrated value (kWh) is calculated for the charge / discharge electric energy.

  Although the total data table is stored in the database 32 of the storage battery state monitoring device 3, in the second embodiment, as shown in FIG. 9, the total data is collected by year, and the charge / discharge power amount data is also stored. Have been added.

  The database 32 also stores a deterioration rate prediction value table. As shown in FIG. 10, the deterioration rate prediction value per year is shown, and the charge / discharge power amount per year is shown in the usage conditions. (KWh) has been added.

  The deterioration rate calculation unit 33 calculates the deterioration rate in the total year y from the internal resistance value R in the total year y and the internal resistance difference ΔR in the previous total year y-1. In the first embodiment, since data is aggregated for each cycle, data having the same use conditions is extracted in advance. However, in this embodiment, since data is collected in units of one year, the aggregated data for a new year is collected. May be calculated at the stage when is transmitted to the storage battery state monitoring device 3.

  As in the first embodiment, the deterioration rate update unit 34 updates the deterioration rate prediction value stored in the deterioration rate prediction value table using the deterioration rate calculated by the deterioration rate calculation unit 33. However, in the present embodiment, since the deterioration rate is calculated on an annual basis, it may be difficult to collect a plurality of deterioration rates having the same use conditions. Therefore, in the present embodiment, at the stage of calculating the deterioration rate for the year y, which is the deterioration rate calculation unit 33, updating is performed using the deterioration rate. That is, the deterioration rate update unit 34 refers to the deterioration rate prediction value table and determines whether there is a registration of a deterioration rate prediction value for the same use condition Q as the use condition Q for the total year y. When there is registration, the deterioration rate prediction value P in the use condition Q is acquired and updated using the deterioration rate.

  When the deterioration rate prediction value is not registered in the use condition Q, the deterioration rate prediction value P ′ of the approximate use condition Q ′ is acquired and updated as in the first embodiment, and the updated deterioration rate prediction value is used. A new deterioration rate predicted value for condition Q may be newly registered.

Alternatively, when the storage battery state monitoring device 3 can collect data from a plurality of storage battery devices operating in a similar environment and obtain a plurality of deterioration rates having the same use conditions, the first embodiment Similarly, a plurality of deterioration rates may be reflected in the deterioration rate prediction value using Bayes' theorem.
As in the first embodiment, the deterioration prediction unit 35 can predict the life of the assembled battery 11 and the degree of progress of deterioration with reference to the deterioration rate prediction value table. Or you may perform the lifetime prediction of the assembled battery 11 integrated in the other storage battery apparatus which operate | moves in a similar environment.

[Function and effect]
In the second embodiment, the plurality of use conditions of the storage battery include at least one or more of the battery temperature, SOC, and charge / discharge depth of the storage battery, and the amount of charge / discharge power, and further data on the use condition and the deterioration rate in the use condition. Were aggregated by year.

  In a situation in which the life of a storage battery is increasing, it is required to perform deterioration prediction from a long-term viewpoint in addition to detailed deterioration prediction in units of cycles. Therefore, by updating the deterioration rate prediction value by collecting data in units of years including the charge / discharge power amount in the use conditions, it is possible to contribute to a long-term operation plan of the storage battery device.

[Third Embodiment]
Next, a third embodiment will be described. In the first and second embodiments, the deterioration rate is calculated from the internal resistance of the assembled battery 11, but in the third embodiment, the battery capacity maintenance rate (hereinafter also simply referred to as “capacity maintenance rate”) is used. The deterioration rate is calculated.

  Therefore, the storage battery control device 2 further includes a capacity maintenance rate estimation unit 29 as shown in FIG. Further, the storage unit 25 holds a capacity maintenance rate table as shown in FIG.

  The battery capacity maintenance rate table stores estimated values of capacity maintenance rates (%) for each internal resistance value. As the number of charge / discharge cycles of the storage battery increases, the internal resistance of each battery cell increases, while the capacity maintenance rate decreases. The correlation among the number of charge / discharge cycles, the internal resistance, and the battery capacity can be expressed by a functional expression. For example, the graph of FIG. 13 shows an example in which these relationships are represented by linear approximation.

  The battery capacity table is obtained by conducting a characteristic evaluation test in advance and collecting the correlation between the internal resistance and the battery capacity as a table. The characteristic evaluation test is performed, for example, by measuring values of internal resistance and battery capacity for each charge / discharge cycle of the storage battery.

  The capacity maintenance rate estimation unit 29 acquires the internal resistance value of the assembled battery 11 calculated by the internal resistance value calculation unit 23, and further refers to the capacity maintenance rate table and acquires the estimated value of the corresponding capacity maintenance rate. .

  In the third embodiment, the total data creation unit 24 calculates the average value of the capacity maintenance ratio for each cycle instead of the internal resistance value.

  The storage battery state monitoring device 3 has the same configuration as that of the first embodiment illustrated in FIG. However, in the total data table stored in the database 32, as shown in FIG. 14, the capacity maintenance rate data is stored instead of the internal resistance value data for each total time.

  As in the first embodiment, the deterioration rate calculation unit 33 extracts n pieces of data at the total time having the same use condition Q from the total data table, and each deterioration rate is calculated from the extracted n pieces of total data. Is calculated. However, in the present embodiment, the deterioration rate is calculated using the capacity maintenance rate instead of the internal resistance value.

The deterioration rate is calculated by calculating a difference ΔC between the capacity maintenance rate Ct at a certain aggregation time t and the capacity maintenance rate Ct−1 at the aggregation time t−1 before the aggregation time t. This difference ΔC in capacity retention rate can be regarded as a deterioration amount indicating how much deterioration has progressed in one cycle from the previous aggregation time t−1 to the current aggregation time t, similarly to the internal resistance. The difference is divided by the capacity maintenance rate Ct−1 at the total time t−1 to calculate the deterioration rate at the total time t. In this process, n deterioration rates x ₁ , x ₂ ,..., X _n are calculated in order to obtain the deterioration rates at the total times of the same usage conditions.

  Since the operations of the deterioration rate update unit 34 and the deterioration rate prediction unit are the same as those in the first embodiment, description thereof is omitted.

  In the above-described embodiment, the calculation is made from the internal resistance value using the correlation between the internal resistance and the capacity. However, a method of directly calculating from the charge / discharge result of the battery panel may be used.

[Function and effect]
In the present embodiment, the deterioration rate is calculated from the difference ΔC between the battery capacity maintenance rate Ct in one period t of the storage battery and the battery capacity maintenance rate Ct-1 in the period t-1 before the one period. Since the battery capacity maintenance rate is correlated with the internal resistance, if the internal resistance can be calculated, the battery capacity maintenance rate can be easily calculated. Since the difference in capacity maintenance rate can also be used as the deterioration rate, the deterioration rate can be easily calculated.

  Note that, similarly to the first embodiment, the capacity maintenance rate data of the assembled battery 11 incorporated in another storage battery device may be used.

[Other Embodiments]
(1) The network N connecting the storage battery control device 2 and the storage battery state monitoring device 3 includes a wide range of transmission lines (transmission lines) capable of exchanging information. As the transmission path, any wired or wireless transmission medium can be applied, and it does not matter what kind of LAN or WAN is used. Any communication protocol that can be used at present or in the future can be applied. As a device for transmitting and receiving information via the network N, any device that can be used at present or in the future can be applied.

(2) In the above-described embodiment, the storage battery control device 2 and the storage battery state monitoring device 3 are separately provided and connected via the network N. However, these devices may be provided integrally. That is, each function of these devices can be realized in the same computer, the control unit and the database 32 can be shared, and the transmission unit 26 and the reception unit 31 can be omitted.

(3) The specific contents and values of the information used in the embodiment are free and are not limited to specific contents and numerical values.

(4) The present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. In addition, various inventions can be formed by appropriately combining a plurality of components disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

DESCRIPTION OF SYMBOLS 1 Storage battery unit 2 Storage battery control apparatus 3 Storage battery state monitoring apparatus 10 Storage battery cell 11 Assembled battery 12 Voltage measurement part 13 Current measurement part 14 Battery temperature measurement part 21 Measurement value collection part 22 SOC calculation part 23 Internal resistance value calculation part 24 Total data preparation Unit 25 storage unit 26 transmission unit 27 charge / discharge depth calculation unit 28 charge / discharge power amount calculation unit 29 capacity maintenance rate estimation unit 31 reception unit 32 database 33 deterioration rate calculation unit 34 deterioration rate update unit 35 deterioration prediction unit 50 display device N network

Claims (8)

A database that stores predicted deterioration rates corresponding to a plurality of storage battery usage conditions;
A deterioration rate update unit that updates the deterioration rate predicted value using the storage battery or other storage battery usage conditions and the deterioration rate data in the usage conditions that are tabulated every predetermined period;
A storage battery state monitoring apparatus comprising: a deterioration prediction unit that performs deterioration prediction of the storage battery using the deterioration rate prediction value.   The plurality of use conditions of the storage battery include at least two or more of battery temperature, SOC, and charge / discharge depth of the storage battery, and the use condition and the deterioration rate data in the use condition are charge / discharge cycles of the storage battery or another storage battery. The storage battery state monitoring device according to claim 1, wherein the storage battery state monitoring device is tabulated every time.   The plurality of use conditions of the storage battery include at least one of the battery temperature, SOC, and charge / discharge depth of the storage battery, and the charge / discharge power amount, and the use conditions and deterioration rate data in the use conditions are aggregated in units of years. The storage battery state monitoring device according to claim 1.   The deterioration rate of the said storage battery or another storage battery is further provided with the deterioration rate calculation part which calculates from the difference of the internal resistance in one period of a storage battery, and the internal resistance in the period before the said one period. The storage battery state monitoring apparatus according to any one of Items 1 to 3.   A deterioration rate calculating unit that calculates a deterioration rate of the storage battery or another storage battery from a difference between a battery capacity maintenance rate in one period of the storage battery and a battery capacity maintenance rate in a period before the one period; The storage battery state monitoring apparatus according to any one of claims 1 to 3.   The deterioration rate calculation unit calculates a deterioration rate of a plurality of periods having the same use condition, and the deterioration rate update unit corresponds to the same use condition by using the calculated deterioration rates of the plurality of periods. 6. The storage battery state monitoring apparatus according to claim 4, wherein the deterioration rate predicted value is updated by Bayes' theorem.   The deterioration rate calculation unit calculates a deterioration rate for a plurality of periods having the same use condition, and the deterioration rate update unit uses the calculated deterioration rates for the plurality of periods to approximate use of the same use condition. The deterioration rate prediction value corresponding to the condition is updated by Bayes' theorem, and the updated deterioration rate prediction value is registered as the deterioration rate prediction value corresponding to the same use condition. Storage battery status monitoring device. Based on data including charge / discharge current, voltage, and temperature measured from the storage battery, a total data creation unit that creates total data regarding the use condition and deterioration rate of the storage battery for each predetermined period;
A database that stores predicted deterioration rates corresponding to a plurality of storage battery usage conditions;
A degradation rate update unit that updates the degradation rate prediction value using aggregated data relating to usage conditions and degradation rates of storage batteries that are aggregated every predetermined period;
A storage battery device comprising: a deterioration prediction unit that performs deterioration prediction of the storage battery using the deterioration rate prediction value.