JP7249155B2 - Storage battery management device and storage battery management method - Google Patents

Description

The present invention relates to a storage battery management device and a storage battery management method.

In recent years, there have been calls for measures against global warming and utilization of renewable energy, and along with this, storage batteries have become widespread in various situations. However, it is known that a storage battery deteriorates differently depending on the "usage" such as the amount and pattern of charge and discharge, and the "environmental conditions" such as the environmental temperature and installation location. Therefore, it is necessary to predict the deterioration under various usage and environmental conditions.

In predicting deterioration, it is common to examine the factors of deterioration and their degree of influence by actually deteriorating in a test, and to create a model. However, since testing takes time, it is not realistic to obtain data on all usage and environmental conditions. In addition, it is difficult to accurately evaluate the temperature of the storage battery, which is said to have a large influence on deterioration, because it changes from moment to moment due to charge/discharge, ambient temperature, and the like.

Therefore, instead of predicting deterioration through testing, a method of grasping the deterioration state is generally adopted by periodically charging and discharging the storage battery actually in use to its full capacity. However, this method forces the user to measure the capacity even when he/she wants to use the storage battery, thereby causing the user to lose the opportunity to use the storage battery. In addition, full charging and discharging may accelerate deterioration of the storage battery. Therefore, there is a demand for another method of grasping the state of deterioration.

Patent Documents 1 to 4 show the following matters related to the present invention. Patent Literature 1 shows a system for estimating deterioration of a storage battery based on history of current, voltage and temperature of the storage battery. In addition, Patent Document 2 discloses a device that acquires the temperature of a storage battery from the start of use at regular intervals, calculates the average temperature, and estimates the remaining life of the storage battery based on the calculated average temperature and the internal resistance value of the storage battery. show. Patent document 3 shows a system for estimating the temporal transition of the temperature of a storage battery for a plurality of charging/discharging patterns and selecting a charging/discharging pattern based on the estimation results. The system described in Patent Document 3 estimates the internal resistance, SOC (States Of Charge), and temperature of the storage battery based on basic information such as voltage, current, and storage battery. Further, Patent Document 4 discloses a device that repeatedly estimates the temperature of a storage battery at a predetermined cycle based on the amount of heat generated by the storage battery and the temperature immediately before charging the storage battery.

WO2016/208251 JP 2016-167336 A JP 2013-106476 A JP 2014-026913 A

As described above, there is a problem that the method of grasping the deterioration state by periodically charging and discharging the storage battery actually used to the full may accelerate the deterioration of the storage battery.

SUMMARY OF THE INVENTION It is an object of the present invention to provide a storage battery management device and a storage battery management method capable of accurately grasping and managing the deterioration state of a storage battery without accelerating the deterioration of the storage battery. With the goal.

In order to solve the above problems, one aspect of the present invention provides information representing a temperature estimation model of a storage battery whose variable is a value representing load intensity during charge/discharge, and an amount of deterioration of a storage battery whose variable is a cumulative value of the temperature of the storage battery. A storage unit that stores information representing an estimation model, an acquisition unit that acquires time-series data related to a charging rate of a storage battery, and a temperature that estimates the temperature of the storage battery using the temperature estimation model based on the time-series data. an estimating unit, a deterioration amount estimating unit that estimates the amount of deterioration of the storage battery using the deterioration amount estimation model based on the temperature of the storage battery estimated by the temperature estimating unit, and the deterioration estimated by the deterioration amount estimating unit The storage battery management device includes a deterioration amount prediction unit that predicts a future deterioration amount based on the amount .

Further, one aspect of the present invention is the storage battery management device, wherein the temperature estimation model includes, in addition to the value representing the load intensity during charging and discharging, the charging and discharging time, the storage battery temperature a certain time ago, the internal resistance, At least one of the outside air temperature, room temperature, amount of solar radiation, humidity, storage battery heat generation performance, and housing heat radiation performance is used as a variable.

Further, according to one aspect of the present invention, there is provided the storage battery management device, wherein the deterioration amount estimation model uses at least one of the accumulated charge/discharge amount and the accumulated usage time as variables in addition to the accumulated temperature value of the storage battery. .

Further, one aspect of the present invention is the above-described storage battery management device, wherein a predetermined amount is determined from the plurality of charge/discharge patterns based on the future deterioration amount predicted by the deterioration amount prediction unit for the plurality of charge/discharge patterns. It further includes a charge/discharge pattern selection unit that selects a charge/discharge pattern that satisfies a criterion.

Further, one aspect of the present invention is the storage battery management device described above, wherein the plurality of charge/discharge patterns for which the deterioration amount prediction unit predicts the future deterioration amount relate to economic efficiency, long-term usability, and environmental friendliness. Set based on priority.

Further, according to one aspect of the present invention, the storage unit stores information representing a temperature estimation model of a storage battery whose variable is a value representing load intensity during charge/discharge, and estimation of deterioration amount of the storage battery whose variable is an accumulated temperature value of the storage battery. Information representing the model is stored, the acquisition unit acquires time-series data related to the charging rate of the storage battery, and the temperature estimation unit estimates the temperature of the storage battery using the temperature estimation model based on the time-series data. Then, the deterioration amount estimation unit estimates the deterioration amount of the storage battery using the deterioration amount estimation model based on the temperature of the storage battery estimated by the temperature estimation unit, and the deterioration amount estimation unit estimates the deterioration amount estimation unit is a storage battery management method for predicting a future deterioration amount based on the estimated deterioration amount .

According to each aspect of the present invention, it is possible to accurately grasp and manage the deterioration state of the storage battery without accelerating the deterioration of the storage battery.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the structural example of the storage battery management system which concerns on embodiment of this invention. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. FIG. 2 is a flowchart showing an operation example of the storage battery management device 1 shown in FIG. 1; FIG. FIG. 2 is a flowchart showing an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. It is a figure which shows the Example of the storage battery management apparatus 1 shown in FIG. It is a figure which shows the Example of the storage battery management apparatus 1 shown in FIG. FIG. 2 is a flowchart showing an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG. 2 is a schematic diagram for explaining an operation example of the storage battery management device 1 shown in FIG. 1; FIG.

Embodiments of the present invention will be described below with reference to the drawings. FIG. 1 is a block diagram showing a configuration example of a storage battery management system 10 according to an embodiment of the present invention. A storage battery management system 10 shown in FIG.

The storage battery management device 1 is a computer, and internally includes hardware such as a CPU (central processing unit), a storage device, an input/output device, a communication device, etc. The CPU executes programs (software) stored in the storage device. It works by doing The storage battery management device 1 includes a communication unit 11 , a temperature estimation unit 12 , a deterioration amount estimation unit 13 , a deterioration amount prediction unit 14 , a charge/discharge pattern selection unit 15 and a storage unit 16 . These communication unit 11, temperature estimation unit 12, deterioration amount estimation unit 13, deterioration amount prediction unit 14, charge/discharge pattern selection unit 15, and storage unit 16 are functional blocks configured by combining hardware and software. be. The storage unit 16 also stores (information representing) a temperature estimation model 161, (information representing) a deterioration amount estimation model 162, (information representing) a storage battery usage pattern 163, (information representing) an external environment 164, and basic performance (Information representing) 165 is stored.

The power storage device 2 includes a control unit 21 , a communication unit 22 , a charge/discharge control unit 23 and a storage battery 24 . Control unit 21 controls communication unit 22 and charge/discharge control unit 23 . The communication unit 22 functions as a wired or wireless communication terminal, and in accordance with an instruction from the control unit 21, directly or via another device, directs the storage battery 24 to the storage battery management device 1 via the communication network 3. Information representing a usage pattern (referred to as a storage battery usage pattern) is transmitted. The information representing the storage battery usage pattern is information that indicates the usage state of the storage battery 24, and indicates, for example, load intensity during charge/discharge of the storage battery 24, charge/discharge time, storage battery temperature a certain time ago, internal resistance, time, and the like. Contains information. The values that represent the load intensity during charging and discharging are the charging and discharging current value, the rate of change of the charging and discharging current, the charging and discharging voltage value, the rate of change of the charging and discharging voltage, the rate of change of the SOC (the amount of change in the charging rate per unit time). ), etc., and is a value of a physical quantity at which the degree of deterioration of the storage battery increases when the value is large. It should be noted that the power storage device 2 of the present embodiment transmits at least one piece of information representing at least the load intensity during charge/discharge to the storage battery management device 1 as the storage battery usage pattern. In addition, the power storage device 2 may transmit information representing charge/discharge current, charge/discharge voltage, charge/discharge time, and internal resistance to the storage battery management device 1 as the storage battery usage pattern. However, the power storage device 2 of the present embodiment may include information indicating the storage battery temperature that is not transmitted to the storage battery management device 1 . The other device is, for example, a HEMS (Home Energy Management System) controller.

In accordance with instructions from the control unit 21, the charge/discharge control unit 23 converts the input AC power into DC power to charge the storage battery 24, or converts the DC power discharged by the storage battery 24 into AC power and outputs it. or Further, the charge/discharge control unit 23 measures, for example, charge/discharge current and charge/discharge voltage, estimates SOC, measures storage battery temperature, measures or estimates internal resistance, and performs measurement or estimation. The result obtained is output to the control unit 21 . The storage battery 24 is a secondary battery such as a lithium ion battery. Note that the power storage device 2 may be of a type in which the input/output of the charge/discharge control unit 23 is connected to a distribution board or indoor wiring (outlet) in the house to enable system cooperation, or only the input is connected to the distribution board or the indoor wiring (outlet). A type (emergency power supply, uninterruptible power supply, etc.) that is connected to indoor wiring and is not connected to the output may be used. The power storage device 2 may be installed indoors or outdoors.

The communication network 3 is a wired or wireless public communication network, the Internet, a wired or wireless local communication network, or a combination thereof.

The information providing device 4 is a server that provides past, present or future weather information via the communication network 3 . The information providing device 4 provides, for example, weather information such as temperature, humidity, and amount of solar radiation for each region via the communication network 3 .

Returning to the description of the storage battery management device 1, the communication unit 11 (acquisition unit) functions as a wired or wireless communication terminal, and the communication unit 22 of the power storage device 2 periodically transmits data via the communication network 3. It receives information representing the storage battery usage pattern of the storage battery 24 and receives weather information transmitted by the information providing device 4 . The information representing the storage battery usage pattern received by the communication unit 11 is stored in the storage unit 16 as the storage battery usage pattern 163 in association with, for example, preset identification information of the power storage device 2 . Further, the weather information received by the communication unit 11 is stored in the storage unit 16 as the external environment 164 in association with, for example, the identification information of the region set in advance.

Moreover, the temperature estimation model 161 stored in the storage unit 16 is a mathematical model for estimating the storage battery temperature using the value representing the load intensity during charge/discharge as a variable. The temperature estimation model 161 can be a function (function f) for estimating the storage battery temperature using the storage battery usage pattern, the external environment, and the basic performance as variables, as shown in Equation (1), for example. Hereinafter, the estimated battery temperature will be referred to as an estimated battery temperature. As shown in FIG. 2, the storage battery usage pattern with the function f as a variable is time, SOC (state of charge), absolute value of SOC time change rate (hereinafter referred to as SOC change rate), one frame (predetermined time ) contains information representing the previous estimated battery temperature, current (charge/discharge current), voltage (charge/discharge voltage), charge/discharge duration, and internal resistance. FIG. 2 schematically shows an operation example when the temperature estimator 12 calculates the estimated storage battery temperature using equation (1). An example of operation of the temperature estimator 12 will be described later. Also, the external environment, which is a variable of the function f, includes information representing outside air temperature, room temperature, amount of solar radiation, and humidity. Further, the basic performance, which is a variable of the function f, includes information representing storage battery heat generation performance and housing heat radiation performance. The temperature estimation model 161 is prepared, for example, as a different function for each feature of the storage battery 24 and the product type of the power storage device 2 .

In equation (1), of the storage battery usage pattern shown in FIG. 2, the SOC change rate and the estimated storage battery temperature one frame before are essential, but the current, voltage, charge/discharge duration, and internal resistance are not essential. However, by adding these pieces of information, the temperature estimation model 161 can increase the estimation accuracy. Although the external environment and the basic performance may be omitted from the equation (1), the temperature estimation model 161 can improve the estimation accuracy by using these variables.

A function f representing the temperature estimation model 161 is constructed from a plurality of field data using machine learning or the like. For example, the function f is constructed using machine learning or the like based on the information acquired from the power storage device 2 capable of transmitting the storage battery usage pattern including the storage battery temperature. Regarding the temperature estimation model 161, linear regression is the simplest and best. Construct using a combination, etc. For example, when obtaining the deterioration state of a household storage battery, the storage battery management device 1 acquires charging/discharging data as time-series data from the time when the storage battery in question starts to be used.

The deterioration amount estimation model 162 stored in the storage unit 16 is a mathematical model for estimating the deterioration amount of the storage battery using the accumulated value of the estimated storage battery temperature estimated using the temperature estimation model 161 as a variable. The deterioration amount estimation model 162 can be a function (function g) that estimates the amount of deterioration of the storage battery using the cumulative charge/discharge amount, the cumulative usage time, and the cumulative exposure temperature as variables, as shown in Equation (2), for example. can. The cumulative charge/discharge amount, which is the variable of the function g, is the total value of the charge power amount and the discharge power amount. The accumulated usage time, which is a variable of the function g, is the time from the time when the storage battery 24 is started to be used to the time when the deterioration amount is estimated, and is the total time including the standby time. The cumulative exposure temperature, which is a variable of the function g, is the cumulative value of the estimated battery temperature estimated using the temperature estimation model 161 (the time integral value of the estimated battery temperature), and is expressed by Equation (3). The amount of deterioration obtained by the function g is a numerical value representing the degree of deterioration of the storage battery, and can be SOH (States Of Health) or the like. SOH is the capacity retention ratio expressed by the ratio of the initial full charge capacity to the full charge capacity when deteriorated, and the resistance expressed by the ratio of the initial internal resistance value and the internal resistance value when deteriorated. Expressed as a rate of increase. The deterioration amount estimation model 162 is prepared, for example, as a different function for each feature of the storage battery 24 and the product type of the power storage device 2 .

The function g representing the deterioration amount estimation model 162 is constructed from field data using machine learning or the like, using, for example, the storage battery usage pattern and the deterioration amount due to full charging/discharging as teacher data. Like the temperature estimation model 161, the deterioration amount estimation model 162 may be the simplest linear regression. It is constructed using a generalized linear model or a combination thereof. For example, when obtaining the deterioration state of a household storage battery, it is assumed that charging/discharging data has been acquired as time-series data since the storage battery was started to be used.

Further, the storage battery usage pattern 163 includes information on the storage battery usage pattern for each power storage device 2 and information indicating a plurality of assumed typical storage battery usage patterns. External environment 164 includes external environment information for each power storage device 2 or for each predetermined area, and information indicating a plurality of assumed typical external environments. The basic performance 165 includes pre-registered basic performance information for each power storage device 2 (information such as storage battery heat generation performance, housing heat dissipation performance, rated capacity, rated output, etc.).

In addition, the temperature estimating unit 12 stores the storage battery usage pattern of each storage battery 24 acquired by the communication unit 11 (time-series data related to the charging rate, etc.), the information representing the external environment provided from the information providing device 4, etc. in the storage unit. 16, and the temperature of each storage battery 24 is estimated using the temperature estimation model 161 based on them. Here, an operation example of the temperature estimator 12 will be described with reference to FIG. FIG. 2 shows the storage battery usage pattern, external environment, basic performance and estimated storage battery temperatures _{T 1} , T ₂ , T ₃ , T ₄ and T at times t ₀ , t 1 , t ₂ , t ₃ , t ₄ and t ₅ . An example of the relationship between ₅ and _T6 is shown. In this example, it is assumed that the power storage device 2 is installed indoors. Also, the times t ₀ , t ₁ , t ₂ , t ₃ , t ₄ and t ₅ are assumed to be at intervals of 10 minutes, for example. It is also assumed that charging and discharging are continuously performed from time _t0 to time _t5 .

First, the temperature estimator 12 estimates the estimated storage battery temperature _T1 at time _t0 as follows. That is, the _temperature estimator 12 first assumes that the estimated storage battery temperature T0 one frame (10 minutes before) the time _t0 corresponds to, for example, room temperature. Also, the temperature estimator 12 assumes the SOC change rate at time _t0 as an average value for the last few frames. Then, the temperature estimating unit 12 calculates the SOC (80%) at time t ₀ , the assumed SOC change rate, the assumed one frame previous estimated storage battery temperature T ₀ , the charging/discharging current I ₀ and voltage V ₀ , the charging/discharging duration time (0 minutes), internal resistance R ₀ , outside air temperature T ₀₀ , room temperature T ₁₀ , amount of solar radiation p ₀ , humidity m ₀ , storage battery heat generation performance a, and housing heat radiation performance b are applied to the temperature estimation model 161 of equation (1). Substitute to estimate the estimated battery temperature _T1 at time _t0 .

Next, the _temperature estimator 12 estimates the estimated battery temperature _T2 at time _t1 using the estimated battery temperature T1 one frame before (time _t0 ) as follows. That is, the temperature estimator 12 calculates the SOC (85%) at time t ₁ , the SOC change rate (|(85−80)/(t ₁ −t ₀ )|), the estimated storage battery temperature T ₁ one frame before, the charge/discharge current I ₁ and voltage V ₁ , charge/discharge duration (t ₁ −t ₀ ), internal resistance R ₁ , outside temperature T ₀₁ , room temperature T _I1 , amount of solar radiation p ₁ , humidity m ₁ , storage battery heat generation performance a and housing Estimated storage battery temperature _T2 at time _t1 is estimated by substituting body heat radiation performance b into temperature estimation model 161 of equation (1).

Similarly, the temperature estimating unit 12 estimates the estimated battery temperatures T ₃ to T ₆ at times t ₂ to t ₅ using the estimated battery temperatures T ₂ to T ₅ one frame before.

In the example shown in FIG. 2, the charge state is from time _t0 to time _t4 , and the discharge state is from time _t4 to time _t5 . In the example shown in FIG. 2, the charge state and the discharge state are distinguished by the positive and negative polarities of the charge/discharge duration. Note that the SOC change rate may be divided into two terms by distinguishing between charging and discharging.

Further, the deterioration amount estimation unit 13 estimates the deterioration amount of the storage battery using the deterioration amount estimation model 162 based on the estimated storage battery temperature of the storage battery estimated by the temperature estimation unit 12 . An operation example of the deterioration amount estimation unit 13 will be described with reference to FIG. FIG. 3 is a flowchart showing an operation example of the deterioration amount estimation unit 13. As shown in FIG. When the process shown in FIG. 3 is started, the deterioration amount estimating unit 13 first acquires (or acquires) data indicating the charge/discharge amount of each frame from the start time of use of the storage battery 24 whose deterioration amount is estimated from the storage unit 16 to the present time. (calculated based on the obtained data) (step S11). The charge/discharge amount can be calculated, for example, based on the SOC and the rated capacity, and the calculated value can be stored in the storage unit 16 as the storage battery usage pattern 163 .

Next, the deterioration amount estimator 13 adds all the data obtained in step S11 to calculate accumulated charge/discharge amount data (steps S12 and S13). Further, the deterioration amount estimator 13 adds all the data obtained in step S11 to calculate cumulative usage time data (steps S14 and S15). Further, the deterioration amount estimation unit 13 instructs the temperature estimation unit 12 to estimate the estimated storage battery temperature, adds the estimated storage battery temperature of each frame estimated by the temperature estimation unit 12, and calculates the cumulative exposure temperature data ( Steps S16-S17).

Next, the deterioration amount estimation unit 13 substitutes each data into the deterioration amount estimation model 162 of Equation (2) to estimate deterioration amount data (steps S18 and S19).

As the deterioration amount estimation model ₁₆₂ , for example, as shown in FIG _. A model for estimating the amount of deterioration deg ₂ of the frame may be used based on .

According to the deterioration amount estimating unit 13 of the present embodiment, since it is not necessary to measure the deterioration amount due to full charging and discharging, the deterioration state of the storage battery can be accurately grasped and managed without accelerating the deterioration of the storage battery. In addition, it does not lead to loss of opportunity for the user, and online monitoring on the data server is also possible.

Further, the deterioration amount prediction unit 14 predicts the future deterioration amount based on the deterioration amount estimated by the deterioration amount estimation unit 13 . An operation example of the deterioration amount prediction unit 14 will be described with reference to FIG. FIG. 4 is a flowchart showing an operation example of the deterioration amount prediction unit 14. As shown in FIG. In the process shown in FIG. 4, the deterioration amount prediction unit 14 estimates the past deterioration amount of the storage battery 24 by dividing it into n sections for each predetermined time period, and calculates the approximate curve from the n deterioration amounts obtained for each section. is calculated to predict the amount of future deterioration.

In the process shown in FIG. 4, the deterioration amount prediction unit 14 first calculates the charge/discharge amount in the first section from time t ₀ to t ₁ (where time t ₀ to t ₁ is a time for a plurality of frames). is acquired (or calculated based on the acquired data) (step S21). Next, the deterioration amount prediction unit 14 instructs the deterioration amount estimation unit 13 to estimate the deterioration amount as described with reference to FIG. 3 (steps S12 to S19). The deterioration amount prediction unit 14 estimates each deterioration amount for each section until the processing of all n sections is completed (steps S20, S21, S12 to S19 and S30).

Next, the deterioration amount prediction unit 14 calculates an approximate curve of the deterioration amount from the n deterioration amount values, predicts the future deterioration amount based on the calculated approximate curve, and determines the time when the deterioration amount reaches a predetermined value. is predicted to be the end of life (step S31).

According to the deterioration amount prediction unit 14 of the present embodiment, since long-term deterioration amount prediction is possible, it is also possible to predict the number of years of service life and to make an appropriate maintenance plan for preventing failures.

In addition, the charge/discharge pattern selection unit 15 is activated according to a user's instruction or at regular intervals, and is based on a plurality of charge/discharge patterns and temperature histories estimated by the temperature estimation unit 12 for the plurality of charge/discharge patterns. Then, based on the future deterioration amount predicted by the deterioration amount prediction unit 14, a charge/discharge pattern that satisfies a predetermined standard is selected from a plurality of charge/discharge patterns, and the selected charge/discharge pattern is used as a recommended charge/discharge pattern. The information is presented to the person or notified to the power storage device 2 . By providing the power storage device 2 with a function of operating according to the charge/discharge pattern selected by the charge/discharge pattern selection unit 15, the charge/discharge pattern in the power storage device 2 can be controlled to reduce the amount of deterioration. The charge/discharge pattern includes a storage battery usage pattern for a certain period (a plurality of frames) set so that the load on the storage battery (SOC change rate, number of charge/discharge cycles, etc.) is different. The charge/discharge pattern is stored in the storage unit 16 as, for example, a storage battery usage pattern 163 . The predetermined criteria are, for example, taking into consideration the history of the user's past power consumption and the amount of power generated by photovoltaic power generation, etc., and as shown in FIG. It is set in consideration of balance. The balance of economic effects means, for example, the minimization of deterioration, the degree of effective use including conversion efficiency from the amount of discharge/charge during the entire period, profit from electricity sales during the FIT (feed-in tariff) period, This is the equilibrium state of the electricity consumption reduction effect by discharging. FIG. 5 shows an example of the relationship between the storage battery usage rate (horizontal axis) and the economic effect (vertical axis). The storage battery usage rate is represented by the integrated value of the ratio of the amount of one charge/discharge to the amount of full charge/discharge. The economic effect is higher toward the top, and the curves differ from the viewpoint of electricity charges and the viewpoint of long-term use of the storage battery (1-deterioration amount). By formulating a charging/discharging plan based on the charging/discharging pattern selected by the charging/discharging pattern selector 15, for example, it is possible to perform an operation that suppresses deterioration.

Here, an example of the operation of the charge/discharge pattern selector 15 will be described in detail with reference to FIGS. 8 to 17. FIG. In this example, the charge/discharge pattern selection unit 15 selects a plurality of charge/discharge patterns for which the deterioration amount prediction unit 14 predicts the future amount of deterioration according to economic efficiency, long-term usability, and environmental performance selected by the user. It is set based on the order of priority. FIG. 8 is a flowchart showing an operation example of the charge/discharge pattern selector 15. As shown in FIG. 9 to 17 are schematic diagrams for explaining an operation example of the charge/discharge pattern selector 15. FIG.

When the process shown in FIG. 8 starts, the charge/discharge pattern selection unit 15 first calculates a model power usage amount and a model power generation amount based on the past history (step S101). The model power usage amount and the model power generation amount are based on the past history of a consumer (for example, a detached house or a collective housing) equipped with the power storage device 2 and equipped with solar power generation equipment or power demand equipment. This is the typical power consumption and power generation assumed for FIG. 9 schematically shows an example of model power consumption and model power generation. The horizontal axis is the time from 0:00 to 24:00, the vertical axis is the amount of power, the polygonal line represents the amount of power generated, and the bar graph represents the amount of power used.

Next, the charge/discharge pattern selector 15 allows the user to select the order of priority among "economy," "long-term usability," and "environmental friendliness" using an information terminal or the like (step S102). Next, the charge/discharge pattern selection unit 15 sets the highest priority item and the remaining two items in the predetermined charge/discharge pattern table based on the rule for changing the priority order. For all charge/discharge patterns, The deterioration prediction unit 14 predicts deterioration based on the model power consumption and the model power generation (step S103). Here, the charging/discharging pattern table is, for example, as outlined as the charging/discharging pattern table 151 in FIGS. Correspondingly, based on the model power consumption and model power generation, it is information that summarizes rules indicating how to set the charge/discharge pattern. 10 and 11 show one charge/discharge pattern table 151 divided into two figures. The charge/discharge pattern table 151 shown in FIGS. 10 and 11 has 13 types of charge/discharge pattern settings corresponding to 13 types of combinations of priorities relating to "economy," "long-term usability," and "environmental friendliness." Contains rules. In the charge/discharge pattern table 151, for example, No. In the charge/discharge pattern of 1, the priorities of "economic efficiency", "long-term usability" and "environmental performance" are all "1" (1 is the highest priority, 2 is the second priority, and 3 is the lowest priority). Yes, and balanced. In this case, the generated power is first used for self-consumption, and the rest is sold. In addition, slow and constant charge is performed during the entire midnight power period, and discharging is performed during the rest of the time period. No. FIG. 12 shows an example of a charge/discharge pattern set in the balance type of No. 1. In FIG. Based on the model power generation amount and the model power consumption shown in FIG. 9, FIG. 1 shows an example in which the charged power amount, the purchased power amount, the self-consumed solar power amount, the sold power amount, and the discharged power amount are set according to the balance type rule of No. 1. 12 to 17, the horizontal and vertical axes are the same times and power amounts as in FIG. The deterioration amount prediction unit 14 predicts deterioration, for example, based on the time change (charge/discharge pattern) of the charged power amount and the discharged power amount shown in FIG. 12 .

In the present embodiment, when "economy" is the top priority, the basic idea is to sell all of the photovoltaic power generated and charge the battery during the night when the electricity rate is low. In addition, when "long-term usability" is given top priority, the basic idea is to charge and discharge the battery once a day or less, and to charge the battery slowly over as long a period of time as possible. In addition, when "environmental friendliness" is given the highest priority, the basic principle is to charge and self-consume with photovoltaic power.

Note that in the charge/discharge pattern table 151, No. 2 is "1", "1" and "3" in the priorities of "economic efficiency", "long-term usability", and "environmental performance", and is economical / long-term usability compatible type, power generation All power is sold, late-night power is charged at a slow and constant rate, and the rest of the time is discharged. Also, No. 3 is "1", "2" and "2" in the priority order of "economy", "long-term usability", and "environmental friendliness"; Power selling, late-night power all hours are slow charging, and the rest of the time is discharging. Also, No. 4 is "economic efficiency", "long-term usability", and "environmental performance", each priority is "1", "2" and "3", economic efficiency / long-term usability consideration type, All generated power is sold, late-night power is charged at a slow rate, and the rest of the time is discharged. Also, No. 5 is "economic efficiency", "long-term usability", and "environmental efficiency", each priority is "1", "3" and "1", economic / environmental compatibility is emphasized, power generation Self-consumption, charging for the rest, purchasing power for the rest, and discharging for the rest of the time. Also, No. 6 is "1", "3" and "2" in the priority of "economic efficiency", "long-term usability", and "environmental performance", and is economically oriented/environmentally conscious type, power generation Self-consumption, remaining electricity sold, charging for a short time with late-night electricity, and discharging during the rest of the time. Also, No. 7 is "2", "1" and "2" in the priority order of "economy", "long-term usability", and "environmental friendliness"; The remaining power is sold, and the remaining time is discharged. Also, No. 8 is "economy", "long-term usability", and "environmental", with the respective priorities of "2", "1" and "3", long-term usability-oriented/economy-considered type; Charging with power generation, self-consumption with the rest, selling the rest, and discharging during the rest of the time. Also, No. 9 is "2", "2" and "1" in the priority order of "economic efficiency", "long-term usability", and "environmental friendliness". The rest is charged, and the rest is sold. If it is predicted that the next day's power generation is likely to be low, the battery is charged during a short period of time at night and discharged during the remaining hours. Also, No. 10 is "2", "3" and "1" in the priorities of "economic efficiency", "long-term usability", and "environmental efficiency", and is environment-oriented/economic consideration type, power generation If it is predicted that the next day's power generation will be low, you can use slow charging during all hours of late-night electricity and discharge during the remaining hours. be. Also, No. 11 is "economic efficiency", "long-term usability" and "environmental performance", each priority is "3", "1" and "1", long-term usability / environmental compatibility is emphasized, Generated power is self-consumed, the remaining power is sold, late-night power is charged at a slow rate, and the rest of the time is discharged. Also, No. 12 is "3", "1" and "2" in the priority order of "economy", "long-term usability", and "environmental", and is a long-term usability-oriented type, and is a constant-speed power generation type. , the rest is self-consumption, the rest is sold, and the rest is discharged. And no. 13 is "economy", "long-term usability", and "environmental", each priority is "3", "2" and "1", environment-oriented / long-term usability consideration type, Self-consumption of power generation, constant-speed charging with the rest, and discharging in the remaining time zone by selling the rest.

For example, in step S102, when the user selects to give top priority to "economy", in step S103, the charge/discharge pattern selection unit 15 determines that the priority of "economic efficiency" is set to " 1”. 1 to No. A charge/discharge pattern is set based on six types of rules 6, and degradation prediction is performed by the degradation amount prediction unit 14 for all charge/discharge patterns. 13 to 17 are based on the model power generation amount and the model power consumption shown in FIG. 2 to No. 6 shows an example in which the charging power amount, the power purchasing power amount, the power amount for self-consumption of sunlight, the power selling power amount, and the discharging power amount are set according to each rule of No. 6. Note that the examples shown in FIGS. 12 to 17 are examples of power consumption when economic efficiency is given top priority, and the time period for low power rates is assumed to be 0:00 AM to 5:00 AM.

Next, the charge/discharge pattern selection unit 15 calculates how much the power charge will be reduced from the current state in each charge/discharge pattern based on the charge/discharge time period, the power charge unit price, and the power sales unit price ( step S104). Next, the charge/discharge pattern selection unit 15 provides the user with the deterioration prediction and the calculation result of the power rate reduction amount in each charge/discharge pattern as one of the judgment materials for the charge/discharge pattern selection (step S105). In step S105, the charge/discharge pattern selection unit 15 provides, for example, as shown in FIGS. 12 to 16, a graph showing each charge/discharge pattern and information showing the electricity cost and the life expectancy of the storage battery (LIB). Next, the charge/discharge pattern selection unit 15 allows the user to select a charge/discharge pattern to be used from the plurality of provided charge/discharge patterns (step S106).

Regarding the start and end times of late-night hours (including short periods) and the speed of constant-rate charging, fixed values are set in advance in the system, but past usage history and power generation results Therefore, for example, if the amount of electricity used compared to the amount of electricity generated + the amount of charge is small, the amount of charge is suppressed by reducing the amount of charge per unit time while maintaining the charging time. Since it can be suppressed, it contributes to long life and economic efficiency, and if there is a lot of power consumption during the daytime and the amount of power generation is small, in addition to charging from power generation, charging at night can also be performed. A machine learning control function suitable for the user, such as driving that contributes to economic efficiency, may be added.

In addition, for system simplification, there are four patterns: "No. 1 balance type", "No. 3 economy type", "No. 7 long-term use type", and "No. 9 environment type". may be presented to the user from the beginning and selected from among them.

Further, using the charge/discharge pattern selection unit 15, it is possible to construct a deterioration suppression operation as follows. First, simulations of several charge/discharge patterns are prepared. For this simulation, it is better to prepare various patterns considering the rate and total capacity under actual use. Here, each simulation pattern is given to the charge/discharge pattern selector 15 . By doing so, from the long-term deterioration prediction based on the storage battery temperature pattern under actual use in each simulation, when the emphasis is placed on minimizing the amount of deterioration among the predetermined criteria, the charge/discharge pattern selection unit 15 It can be said that the selected pattern is effective for deterioration suppression operation.

As described above, according to the present embodiment, by modeling the storage battery temperature and the amount of deterioration from actual usage data, (a) the storage battery temperature that has a large influence on deterioration is estimated, and (b) the estimated storage battery temperature is used. It is also possible to estimate the amount of deterioration of the storage battery. In addition, by using the storage battery management device 1 as a storage battery deterioration evaluation system, deterioration of the storage battery can be predicted from simulation patterns of environment and usage without testing. Furthermore, by incorporating the storage battery deterioration evaluation system into the storage battery system under actual use, it is possible to evaluate the amount of deterioration and predict deterioration that reflects the interaction of complex deterioration factors without losing the opportunity for users to use the storage battery. It can be carried out.

Further, according to the present embodiment, it is possible to estimate the time-series data of the storage battery temperature using the usage pattern of the storage battery. In addition, by using the time-series estimation result of the storage battery temperature, it is possible to accurately analyze the deterioration of the storage battery according to the actual usage situation without impairing the user's opportunity to use the storage battery and without accelerating the deterioration of the storage battery. becomes possible. In addition, according to the present embodiment, it is possible to build a model (both the temperature of the storage battery and the amount of deterioration) based on actual field data instead of a preliminary test of the storage battery. Therefore, it is possible to reflect the interaction of complicated deterioration factors in actual use. In addition, in the method of estimating temperature according to the present embodiment, for example, household storage batteries have the characteristic of being charged and discharged once a day. can do.

Next, an example of estimating the temperature of a domestic storage battery according to this embodiment will be described with reference to FIGS. 6 and 7. FIG. First, the temperature estimation model 161 was constructed by the following procedures (1) to (3), and the storage battery temperature was estimated using the temperature estimation model 161 constructed by the procedure (4).

(1) Two houses were used as storage battery temperature observation houses. We used data for several days including four seasons. The two residences differ in annual average temperature by about 10° C. and also in charging/discharging patterns. For the outside air temperature, 10-minute data of the relevant area was used from the database of the Japan Meteorological Agency.

(2) The data obtained in (1) were divided into data for model construction and data for verification (ratio of about 4:1).

(3) Of (2), using data for model construction, regression analysis was performed assuming normal distribution in the population (reason for selection of analysis method: simplification). At least one or more items listed in FIG. 2 are used as variables. The results are shown in FIG. In FIGS. 6 and 7, the horizontal axis represents date and time (time), and the vertical axis represents cell temperature (storage battery temperature), showing measured values and estimated values. It should be noted that broken vertical lines are shown when the horizontal axis is discontinuous, and the order of the intervals is not necessarily chronological.

(4) Using the model formula obtained in (3), the storage battery temperature was estimated for the verification data described in (2). The results are shown in FIG. In addition, since some dates were taken out for verification this time, it is difficult to specify the time when the storage battery temperature has settled down. Therefore, the observed value was used for the "storage battery temperature one frame before the current time" at the first point where the data became discontinuous. The coefficient of determination is about 95%, which is sufficient for estimation accuracy.

In the example described above, the time interval is set to 10 minutes, but it does not necessarily have to be 10 minutes. However, if the interval is too short, the change in SOC will be small, and if it is too long, the switching of charging and discharging will overlap, and there is a possibility that an accurate value cannot be obtained. It is good to decide the interval in consideration of the rate. Since it is necessary to assume the temperature of the storage battery at the very beginning of the analysis, the starting point should be the part where the battery has not been charged or discharged for a long time and the temperature is considered to be the same as the room temperature. is good. Self-heating from the storage battery has a greater effect on the storage battery temperature than the outside air temperature, and even if the assumed storage battery temperature at the starting point in the previous section deviates slightly, the error will be absorbed within one day's worth of analysis. . The reason for this is that the capacity of the current mainstream household storage batteries is less than the amount of electricity consumed by households in a day, so charging and discharging are completed in one day, standby time always occurs, and the temperature is reset. This is because that. Therefore, even if the data is discontinuous, it can be estimated by setting the initial temperature to be about room temperature at the timing of the waiting time.

There is no problem even if the data is missing for about one frame. The previous battery temperature assumption must be processed. If the storage battery is placed indoors, it is not greatly affected by the outside air temperature, so the corresponding term may be removed from the model formula. However, if it is not excluded, it can be used with good accuracy even when placed outdoors, but the degree of influence of each parameter differs from that of indoor placement.
In constructing the model this time, two residences were used, but in order to further improve the accuracy, the following should be considered.

1) Relationship between various regions and battery temperature. 2) Relationship between various charging/discharging (lifestyle) patterns and storage battery temperature. 3) Continuity of days (it should be as consecutive as possible). 4) Number of data (as large as possible is better). 5) Relationship between room temperature and storage battery temperature during standby. 6) Relationship between outside temperature and room temperature. 7) Heat dissipation of the housing.

Although the embodiments of the present invention have been described above with reference to the drawings, the specific configuration is not limited to the above-described embodiments, and designs and the like are included within the scope of the present invention.

10 storage battery management system 1 storage battery management device 2 power storage device 3 communication network 4 information providing device 11 communication unit 12 temperature estimation unit 13 deterioration amount estimation unit 14 deterioration amount prediction unit 15 charge/discharge pattern selection unit , 16 storage unit, 161 temperature estimation model, 162 deterioration amount estimation model, 163 storage battery usage pattern, 164 external environment, 165 basic performance, 21 control unit, 22 communication unit, 23 charge/discharge control unit, 24 storage battery

Claims (6)

a storage unit that stores information representing a storage battery temperature estimation model whose variable is a value representing load intensity during charge/discharge and information representing a storage battery deterioration amount estimation model whose variable is a cumulative value of storage battery temperature;
an acquisition unit that acquires time-series data related to the charging rate of the storage battery;
a temperature estimation unit that estimates the temperature of the storage battery using the temperature estimation model based on the time-series data;
a deterioration amount estimation unit that estimates the amount of deterioration of the storage battery using the deterioration amount estimation model based on the temperature of the storage battery estimated by the temperature estimation unit ;
a deterioration amount prediction unit that predicts a future deterioration amount based on the deterioration amount estimated by the deterioration amount estimation unit;
A storage battery management device. In addition to the value representing the load intensity during charging and discharging, the temperature estimation model includes the charging and discharging time of the storage battery, the storage battery temperature a certain time ago, the internal resistance, the outside temperature, the room temperature, the amount of solar radiation, the humidity, the storage battery heat generation performance, or The storage battery management device according to claim 1, wherein at least one of housing heat dissipation performance is used as a variable. The storage battery management device according to claim 1 or 2, wherein the deterioration amount estimation model uses at least one of a cumulative charge/discharge amount and a cumulative usage time as a variable in addition to the cumulative temperature value of the storage battery. A charge/discharge pattern selection unit that selects a charge/discharge pattern that satisfies a predetermined criterion from the plurality of charge/discharge patterns based on the future deterioration amount predicted by the deterioration amount prediction unit for the plurality of charge/discharge patterns. The storage battery management device according to any one of claims 1 to 3 . 5. The storage battery management device according to claim 4 , wherein the plurality of charge/discharge patterns for which the deterioration amount prediction unit predicts the future deterioration amount are set based on priorities relating to economy, long-term usability, and environmental friendliness. . The storage unit stores information representing a storage battery temperature estimation model whose variable is a value representing load intensity during charge/discharge, and information representing a storage battery deterioration amount estimation model whose variable is a cumulative value of storage battery temperature,
The acquisition unit acquires time-series data related to the charging rate of the storage battery,
estimating the temperature of the storage battery using the temperature estimation model based on the time-series data by the temperature estimation unit;
estimating the amount of deterioration of the storage battery by the deterioration amount estimating unit using the deterioration amount estimation model based on the temperature of the storage battery estimated by the temperature estimating unit ;
A deterioration amount prediction unit predicts a future deterioration amount based on the deterioration amount estimated by the deterioration amount estimation unit.
Storage battery management method.

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