WO2024071004A1

WO2024071004A1 - Battery energy storage system and management method for battery energy storage system

Info

Publication number: WO2024071004A1
Application number: PCT/JP2023/034636
Authority: WO
Inventors: 純川治; 宏明小西; 大輝小松
Original assignee: 株式会社日立製作所
Priority date: 2022-09-30
Filing date: 2023-09-25
Publication date: 2024-04-04
Also published as: JP2024051240A

Abstract

Provided are: a battery energy storage system that extracts a deterioration parameter for a material of secondary batteries constituting a BESS without stopping the operation of the entire BESS, thereby takes into account not only SOH diagnosis and prediction but also abnormal deterioration, and thus makes it possible for the owner of the BESS to suitably perform operation and maintenance of the BESS; and a management method for the battery energy storage system.　The battery energy storage system (BESS) comprises a state management unit that selects a battery bank to be diagnosed having less than 50% of total capacity from among a plurality of battery banks constituting the BESS and extracts a deterioration parameter for a material constituting at least one of battery cells, battery modules, and battery racks constituting the selected battery bank. The state management unit executes charging and discharging on the basis of a request from a grid when extracting the deterioration parameter.

Description

Battery energy storage system and method for managing battery energy storage system

The present invention relates to a battery energy storage system including a secondary battery and a method for managing the battery energy storage system.

In order to reduce _CO2 emissions, it is necessary to increase the ratio of renewable energy such as solar power generation and wind power generation as an energy source to cover electric power instead of fossil fuels. When a thermal power generator is disconnected from the grid, it becomes difficult to supply the adjustment power to stabilize the grid against fluctuations in demand. On the other hand, each renewable energy generator may be equipped with a battery energy storage system (hereinafter sometimes referred to as BESS) using a secondary battery to mitigate the fluctuation of its own power generation value. If the capacity of this battery can be utilized as an adjustment power, it can contribute to grid stability.

In addition, energy resource aggregation businesses that utilize the energy resources owned by electricity consumers, such as solar power generation, cogeneration, and BESS, are attracting attention. Aggregation businesses utilize a system known as a virtual power plant (VPP), which bundles together individual energy resources using advanced energy management technology based on IoT and makes them function as if they were a single power plant.

In recent years, BESS has also come to be used not only for adjustment capacity to stabilize the grid, but also for electricity trading between electricity retailers and consumers in the electricity market.

The above-mentioned BESS will experience degradation in its storage performance during its operation, and operation will become difficult and replacement will be necessary due to a certain drop in performance. Since consumers will need to bear the corresponding introduction and operating costs for the installation/replacement and operation of secondary batteries, it is desirable to diagnose the state of health (hereinafter referred to as SOH) of the secondary battery system during operation and operate it to extend the system's lifespan while predicting performance degradation of the secondary battery system.

Patent Document 1 discloses a method for providing a capacity estimation system, capacity estimation method, communication device, and computer program that enable remote grasping of the SOH of secondary battery elements for a BESS that can be used as a regulating power. Specifically, when it is determined that the appropriate conditions for a secondary battery in a specific bank extracted in the BESS to perform a process for estimating the full charge capacity of the bank are satisfied, a constant charge or discharge current (e.g., discharging at a constant rate from a charge state of 80% to 50%) is applied to the secondary battery in the bank to estimate the full charge capacity, and the full charge capacity is estimated based on the battery voltage and current at that time.

Patent Document 2 discloses a technology for predicting revenues and indicating the risks involved in electricity trading using a BESS. Specifically, the technology discloses a method for preparing a model formula for predicting the deterioration of the battery capacity that the BESS can take in and out, using arguments such as the current and temperature conditions applied to the secondary battery system during electricity trading and the charging rate conditions at that time, and presenting an optimal trading solution for multiple electricity markets, taking into account the electricity buying and selling price at the time of trading and the cost loss due to battery capacity deterioration associated with trading.

JP 2020-20654 A International Publication No. 2021/044132 JP 2016-82728 A

However, in

Patent Documents

1 and 2, the remaining capacity and resistance of the secondary battery in the BESS are diagnosed as the SOH, and the SOH trend prediction for the usage conditions and period of use is aimed at realizing optimal operation of the BESS. However, although this method can predict the SOH continuously, it is difficult to predict discontinuous SOH changes (abnormal deterioration) or to grasp the signs of such changes. Examples of discontinuous SOH diagnosis include a reversal of the ratio of positive and negative electrode capacities during operation, a short circuit that occurs inside the battery, and depletion of electrolyte in the battery, but all of these are based on the deterioration of the materials that make up the battery, and it is difficult to grasp the behavior of the battery using only the SOH diagnosis disclosed in

Patent Documents

1 and 2. Therefore, it is necessary to diagnose the material-based deterioration of the secondary battery that constitutes the BESS as a feature.

As a method for diagnosing the deterioration of the material base from a secondary battery system in operation, Patent Document 3 and the like can be mentioned. In Patent Document 3, after obtaining the open circuit voltage (OCV, Open Circuit Voltage)-state of charge (SOC, State of Charge) curve of the battery from the voltage behavior when charging and discharging at an extremely low rate and applying a pulsed current, the open circuit potential (OCP, Open Circuit Potential)-SOC curve of the positive electrode and the negative electrode already obtained as a reference and the state quantity of the material base are used to reproduce the SOC-OCV curve, so that the values of the charge and discharge state parameters and the internal resistance parameters of the positive electrode and the negative electrode can be set. This makes it possible to grasp the utilization rate (mp, mn), capacity deviation (d _p , d _n ), and internal resistance (r ₀ , a _p , a _n ) of the active material associated with the deterioration of the material inside the battery. However, this method is performed in a laboratory in a properly controlled charging and discharging environment, and applying this method to a BESS requires that the BESS's operation be stopped, which carries a risk of disrupting the business of the BESS owner (e.g., consumer).

The present invention aims to provide a battery energy storage system and a management method thereof that can extract degradation parameters of the materials in the secondary batteries that make up the BESS without stopping the operation of the entire BESS, and thus enable BESS owners to operate and maintain the BESS appropriately by considering abnormal degradation as well as diagnosing and predicting the SOH.

In order to solve the above problems, the battery energy storage system including the secondary battery according to the present invention is characterized in that it has a status management unit that selects a battery bank that is the subject of diagnosis and has 50% or less of the total capacity from among the multiple battery banks that make up the battery energy storage system, extracts deterioration parameters of materials that make up at least one of the battery cells, battery modules, and battery racks that make up the selected battery bank, and performs charging and discharging based on a request from the grid when the status management unit extracts the deterioration parameters.

The method for managing a battery energy storage system including a secondary battery according to the present invention is characterized by including the steps of: selecting a battery bank with 50% or less of the total capacity to be diagnosed from among a plurality of battery banks constituting the battery energy storage system; transmitting a pulsed charge/discharge command to a power conditioner connected to the selected battery bank, and transmitting a charge/discharge command corresponding to a charge/discharge plan from the grid to a power conditioner of a battery bank not to be diagnosed; obtaining a curve showing the relationship between the charging rate and battery state of the battery rack constituting the battery bank to be diagnosed from the voltage behavior at the time of the pulsed charge/discharge command; and extracting from the obtained curve a deterioration parameter of the material constituting at least one of the battery cells, battery modules, and battery racks constituting the selected battery bank.

According to the present invention, by extracting deterioration parameters of the materials of the secondary batteries that make up the BESS without stopping operation of the entire BESS, it is possible to provide BESS owners with a battery energy storage system and a management method thereof that can not only diagnose and predict SOH, but also take abnormal deterioration into consideration, thereby enabling them to operate and maintain the BESS appropriately.
For example, it is possible to obtain parameters that are indicators of material deterioration of the secondary batteries that make up the BESS, and from the time series changes in these parameters, it is possible to predict future changes in battery capacity and resistance, as well as to grasp signs of specific performance degradation. This makes it possible to provide highly reliable operating methods and timely maintenance instructions for a wide variety of secondary batteries, thereby minimizing the lifetime costs of BESS owners and maximizing the profits that can be obtained by utilizing the BESS.
Problems, configurations and effects other than those described above will become apparent from the following description of the embodiments.

FIG. 1 is a diagram showing the configuration of a battery energy storage system (BESS) according to an embodiment of the present invention. FIG. 2 is a diagram showing an example of a form of current distribution to a battery bank constituting a BESS according to the first embodiment of the present invention. FIG. 4 is a diagram showing another example of a form of current distribution to the battery bank constituting the BESS according to the first embodiment of the present invention. 4A and 4B are diagrams showing an example of the input/output current to and from a battery bank in diagnosis mode and the voltage behavior of the battery cells in the bank at that time, in which FIG. 4(a) is an example of the charge/discharge waveforms (left axis) of a battery bank in diagnosis mode and the battery voltage behavior at that time (right axis), FIG. 4(b) is an example of the battery voltage behavior immediately after pulse discharge, FIG. 4(c) shows the DOD dependence of the open circuit voltage (OCV) and its capacity derivative (dV/dQ), and FIG. 4(d) shows the DOD dependence of DC resistance at each time. FIG. 13 is a diagram illustrating the process of extracting parameters indicative of material degradation from battery cells in a battery bank in a diagnostic mode. FIG. 2 is a diagram showing a management procedure of the BESS according to the first embodiment of the present invention. FIG. 4 is a diagram showing another example of a form of current distribution to the battery bank constituting the BESS according to the first embodiment of the present invention. FIG. 2 is a diagram showing a mechanism for transmitting a diagnosis result or an abnormality alert to a BESS owner or operator based on the diagnosis result of the BESS according to the first embodiment of the present invention. FIG. 2 is a diagram showing a virtual operation of a plurality of BESSes using parameters obtained by the BESS according to the first embodiment of the present invention. FIG. 1 is a diagram showing an example in which a plurality of BESSes are applied to natural energy power generation using parameters obtained by the BESS according to the first embodiment of the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS An embodiment for carrying out the present invention will be described in detail with reference to the accompanying drawings.
FIG. 1 is a diagram showing the configuration of a battery energy storage system (BESS) according to an embodiment of the present invention. The battery energy storage system 15 has a configuration in which a plurality of

battery banks

1a, 1b, 1c, 1d, ..., 1n are connected in parallel, and each battery bank 1 has a configuration in which an output converter (Power Conditioning System: hereinafter referred to as PCS) 2a, 2b, 2c, 2d, ..., 2n and a plurality of

battery racks

3a, 3b, 3c, 3d, ..., 3n are connected. In the battery rack 3, a plurality of battery modules are connected in series and/or parallel, and the battery module is configured by connecting battery cells, which are the smallest units, in series and/or parallel. Here, the battery banks 1 are connected in parallel, and the number of parallel connections varies depending on the installation location and use of the BESS. In the battery bank 1, a group of batteries in multiple hierarchical levels is connected in series or parallel. That is, in the battery bank, a plurality of battery racks 3 are connected in series and/or parallel, and the battery racks 3 further have a configuration in which a plurality of battery modules are connected in series and/or parallel. Furthermore, the battery module is configured with a series/parallel connection of multiple battery cells. In the BESS 15, a battery management system (BMS, also referred to as a battery management unit BMU) is arranged to diagnose the state of the battery group in each layer and transmit the information to the upper control unit 6. In this specification, the reference characters 1a to 1n are used to indicate a specific battery bank, and the reference character 1 is used to indicate a general battery bank. The same is true for the PCS, the battery rack, and the rack BMS. FIG. 1 shows a rack BMS 4 for monitoring the state of the battery rack 3. In addition, in FIG. 1, the upper control unit 6 is present between each of the multiple battery banks 1 and the system (grid) 5, but here, a system BMS 7 is present to determine the standby state of charge (SOC) of each battery bank 1 and the current distribution rate to each PCS 2 and issue a command, taking into account the state of the battery rack 3 in each battery bank 1. The rack BMS 4 and the system BMS 7 are known by various names, and may be called SBMU (System Battery Management Unit) or MBMU (Master Battery Management Unit), respectively. Battery management systems such as the system BMS 7 and the rack BMS 4 are collectively referred to as a status management unit. Here, the rack BMS 4, the system BMS 7, and the upper control unit 6 are realized by, for example, a processor such as a CPU (not shown), a ROM for storing various programs, a RAM for temporarily storing data in the calculation process, and a storage device such as an external storage device, and the processor such as the CPU reads and executes various programs stored in the ROM, and stores the calculation results, which are the execution results, in the RAM or the external storage device.

The upper control unit 6 receives information from the grid 5 and the energy management system (EMS) 8 connected to the grid 5, determines the charge/discharge current pattern for each battery bank 1, and transmits this to the PCS 2 of each battery bank 1. Among these, the system BMS 7 has the function of isolating battery racks 3 whose SOC or SOH deviates based on information on the state of charge (SOC) and state of health (SOH) measured by the rack BMS 4 of each battery bank 1, and determining the current distribution (Current Dispatch) ratio to be passed to each battery bank 1. The simplest method is to divide the current value required by the grid 5 proportionally among the number of battery banks that make up the battery bank, and issue charge/discharge commands equally.

The SOC and SOH of the battery racks 3 that make up the BESS 15 are measured by the rack BMS 4, but there is a limit to the detection accuracy, and there is a risk that the measured values will diverge greatly from the actual values due to malfunctions in the BMS itself, so it is necessary to periodically diagnose the condition of each battery rack 3. The simplest way to do this is to stop all operation of the BESS 15 and diagnose the performance of the

constituent battery racks

3a, 3b, 3c, 3d, ... 3n using an external power source, but this means stopping operation of the BESS 15 during the diagnosis, which will miss opportunities for grid stabilization and power trading using the BESS 15.

Next, consider performing a diagnosis on a specific battery bank 1. For example, to precisely diagnose the SOC and SOH in the battery rack 3n in the battery bank 1n shown on the far right of Figure 1, it is possible to stop input/output commands to the relevant battery bank 1n and apportion them among the

remaining battery banks

1a, 1b, 1c, 1d, ..., 1n-1. In this case, the SOC and SOH can be diagnosed by supplying power from an external source to the stopped battery bank 1n and repeating a specific charge/discharge, but connecting the external power source and performing charge/discharge tests requires a lot of human and time resources, and furthermore, there is a risk that long-term diagnosis will relatively increase the load on the

other battery banks

1a, 1b, 1c, 1d, ..., 1n-1.

In addition, while it is possible to predict the tendency for continuous performance changes by simply diagnosing the SOC and SOH, this does not provide information on the deterioration of the materials that make up the battery, making it difficult to predict or anticipate discontinuous performance changes caused by material deterioration.

Below, an embodiment of the present invention will be explained using the drawings.

The configuration and procedure for solving the above problem will be described with reference to Figs. 2 to 6. Fig. 2 is a diagram showing an example of the current distribution to the battery banks constituting the BESS according to the first embodiment of the present invention. Here, the battery in the battery bank 1n on the far right side of the figure is the subject of diagnosis. In this case, the corresponding battery bank 1n is in diagnosis mode, and the

other battery banks

1a, 1b, 1c, 1d, ..., 1n-1 (not shown) are in diagnosis support mode. In the diagnosis mode, a characteristic charge/discharge pattern for diagnosis is issued from the PCS 2 in the battery bank 1n, and for the

other battery banks

1a, 1b, 1c, 1d, ..., 1n-1 (not shown), the charge/discharge pattern for the battery in diagnosis mode is subtracted from the charge/discharge pattern from the grid (system) 5, and this is matched with the

battery banks

1a, 1b, 1c, 1d, ..., 1n-1 (not shown) in diagnosis support mode, so that diagnosis can be automatically performed on any battery bank without an external power source.

Figure 3 is a diagram showing another example of the form of current distribution to the battery banks that make up the BESS according to the first embodiment of the present invention. Here, multiple battery banks 1 are selected to be in diagnostic mode, and electricity is transferred by accommodating charge and discharge current between each of the battery banks 1. As an example, Figure 3 shows a case where two battery banks, battery bank 1n-1 and battery bank 1n, are in diagnostic mode. In this case, multiple battery banks can be diagnosed without exchanging power with the outside. Note that, although Figure 3 shows a case where two battery banks are in diagnostic mode, this is not limited to the above, and multiple battery banks may be in diagnostic mode with an upper limit of 50% of the total capacity of the entire BESS 15.

FIG. 4 shows an example of charge/discharge waveforms for a battery bank in diagnosis mode, and an example of a charging rate and battery state (OCV, resistance) curve obtained by the analysis. FIG. 4(a) plots the charge/discharge current waveform (left axis) for a battery bank 1 that is fully charged (SOC=100%) and the battery voltage (right axis) at that time. In the battery module, battery rack 3, and battery bank 1 in the BESS 15, multiple battery cells are connected in multiple series and in parallel, so the voltage of the battery group is diverse. In FIG. 4, values normalized by the number of cells connected in series are used. The current value on the left axis of FIG. 4(a) is positive for the current in the direction of charging the battery and negative for the current in the direction of discharging from the battery. In general, when obtaining the battery capacity, a constant charge or discharge current is continuously applied and the battery voltage is measured to obtain a curve showing the relationship between the charge amount and voltage of the battery. However, while this method can determine the performance change of the entire battery, it is difficult to estimate the deterioration state of each material that constitutes the battery. Although the method of estimating the deterioration state of each material will be described in detail later, a curve showing the correlation between the open circuit voltage (OCV) or resistance value and the state of charge (SOC) of the battery is obtained, and the deterioration state of each material can be obtained by analyzing this curve. FIG. 4(a) shows an example of the charge/discharge current for obtaining the curve. Specifically, a pattern of repeating pulsed discharge and pause is used. There is no particular regulation for the allowable range of the pulsed discharge current, but a high rate is desirable from the viewpoint of diagnostic speed. However, a high discharge rate may promote deterioration due to heat generation inside the battery. From the above viewpoint, the allowable range of the pulsed discharge current is 0.1C to 3C, preferably 0.5C to 1.5C. Here, 1C indicates the magnitude of the current when charging or discharging the amount of electricity that the current can charge and discharge at a constant current value over one hour. The right axis of FIG. 4(a) shows the battery voltage waveform when this pulsed discharge current is applied. The inset in the figure shows the change in the battery voltage during one pulsed discharge and the subsequent pause state. When a pulsed discharge current is applied, an overvoltage (ΔV) derived from the resistance component in the battery is applied, so the battery voltage drops instantaneously, and then the voltage is relaxed when the battery is at rest. Figure 4(b) shows this battery voltage behavior in more detail. The pulse discharge starts at the point indicated by the ▼ mark in the figure, and an example is shown in which the battery is discharged for 72 seconds at a 1C rate (the amount of current required to discharge a fully charged amount of electricity over one hour). Here, the battery voltage is measured after 1 second and 10 seconds, and the difference between this and the voltage value immediately before the start of discharge is detected as the overvoltage (ΔV _1sec , ΔV _10sec ). In addition, the difference between the voltage immediately before the end of discharge and the voltage at the time of rest thereafter is detected as the overvoltage (ΔV _72sec ). By dividing this overvoltage by the discharge current, the DC resistance of the battery at the corresponding SOC can be obtained.

Using the above measurement results, it is possible to obtain the following curves showing the battery state in the battery bank to be diagnosed: (i) an open circuit voltage (OCV) vs. depth of discharge (DOD) curve, (ii) a voltage derivative of capacity (dV/dQ) vs. DOD curve, and (iii) a direct current resistance vs. DOD curve. Here, DOD is obtained by subtracting the SOC value from 100%, with a fully charged state (SOC 100%) being DOD=0% and a fully discharged state being DOD(SOC 100%)=100%.
(i) OCV-DOD curve: The open circuit voltage (OCV) at the corresponding charge rate (DOD) can be obtained from the voltage after relaxation after discharge (e.g., Insert ▼) in Figure 4(a). The curve is obtained by plotting the OCV under multiple SOC conditions.
(ii) dV/dQ-DOD curve: The derivative of OCV (dV/dQ) at a particular SOC is calculated by dividing the change in SOC (DOD) (ΔSOC (ΔDOD), in capacity units) at a particular SOC (DOD) region by the corresponding change in open circuit voltage (ΔOCV), and plotted against DOD to obtain a curve.
(iii) DC resistance-DOD curve: For each DOD, the overvoltage (ΔV _1sec , ΔV _10sec , ΔV _72sec ) shown in Figure 4(b) is divided by the pulsed discharge current value (I _discharge ) to obtain the DC resistance R _1s , R _10s , R _72s for each discharge time. Equation (1) shows the calculation formula for R _1s .
R1s (SOC) = {ΔOCV1s} / I _discharge ... (1)
Figure 4(c) shows the open circuit voltage (OCV) versus state of charge (SOC) and the value (dV/dQ) differentiated on the horizontal axis of the graph, and Figure 4(d) shows the degree of discharge (DOD) dependency of DC resistance values (R _1s , R _10s , R _72s ) at discharge times of 1 sec, 10 sec, and 72 sec. DOD is the value obtained by subtracting SOC from 100. Here, the discharge times used for measuring DC resistance are 1 sec, 10 sec, and 72 sec, but results from discharge times other than these can also be used in the analysis.
In FIG. 4(a), the battery is discharged from the fully charged state at SOC 2% intervals, and the subsequent relaxation process is measured, but this SOC range is variable. For example, in the case of DOD 30-40% in FIG. 4(c) and FIG. 4(d), there are inflection points and peaks in OCV, dV/dQ, and R _{72 sec} , and it is possible to measure only this region with a narrow SOC range and measure other regions with a wider range. In addition, it is not necessary to perform measurement in the section from SOC 100% to 0% (DOD 0 to 100%), and diagnosis may be performed including a DOD region that shows characteristic behavior. Furthermore, it is not necessary to complete the diagnosis at once, and an interval is inserted between the measurement in the high SOC (low DOD) region and the measurement in the low SOC (high DOD) region, and during the interval, the battery can respond to charge/discharge commands from the grid 5 as a normal BESS 15. In addition, the current value during pulse discharge in the diagnosis mode does not need to be constant during diagnosis, and can be changed.

Figure 5 shows an example of a process for acquiring parameters reflecting material degradation from the correlation between the battery state and the charging rate obtained in Figure 4. Figure 5 shows the steps: Step 0 (preparation stage): acquiring positive and negative electrode data, Step 1: acquiring the battery state and charging rate state, Step 2: curve fitting using the battery state parameters, and Step 3: deriving the parameters.
Step 0 (preparation): Obtaining positive and negative electrode data
Before obtaining the correlation between the battery state and the charging rate by the charge/discharge control shown in FIG. 4 from the BESS 15 in operation, in step 0 (preparation stage), the relationship between the capacity, electrode potential, and resistance of the positive and negative electrodes used in the batteries in each battery bank (hereinafter referred to as a single-electrode curve per unit active material) is obtained. In FIG. 5, as an example, a single-electrode curve per unit active material is shown when a ternary material (LiNi _x Co _y Mn _Z O ₂ ) made of lithium-(nickel-cobalt-manganese) oxide is applied to the positive electrode and a graphite material is applied to the negative electrode. In FIG. 5, the horizontal axis (q _p , q _n ) indicates the discharge capacity per unit weight of the active material, the vertical axis (left axis) indicates the electrode potential (V _p (q _p ) and V _n (q _n )) for each discharge state, and the vertical axis (right axis) indicates the resistance value per unit weight. These single-electrode curves per unit active material can be obtained using a three-electrode electrochemical cell that combines a working electrode obtained by disassembling and acquiring a battery cell actually used in each battery bank 1 with a reference electrode and a counter electrode made of lithium metal. Also, single-electrode curves per unit active material can be obtained in advance for a plurality of active material materials to prepare a database, and if the database contains material data similar to the positive and negative electrode materials of the batteries in each battery bank 1, this can be used. Even if the material of the secondary battery cell is unknown, if single-electrode data that allows appropriate fitting can be selected in step 2 described below, this can be used as the single-electrode data as it is.

Step 1: Get battery status and charging rate
Using FIG. 4 and the above-mentioned method, the relationship of OCV, dV/dQ, and DC resistance to the depth of discharge (DOD) of the battery is obtained as curves.

Step 2: Curve fitting with battery state parameters
By comparing the capacity dependence of the capacity and resistance of each electrode obtained in step 0 of FIG. 5 with the discharge rate dependence of the state of the target battery bank 1 (capacity, dV/dQ, DC resistance) obtained in step 1, it is possible to quantitatively evaluate parameters reflecting the state of material deterioration inside the battery.
There are various factors that can cause "material degradation," which is the deterioration of the charging rate of the positive and negative electrodes and the capacity of the battery during operation, but these can be broadly categorized as follows, as is well known: i) a decrease in the utilization rate of the positive and negative electrode active materials, and iii) a capacity shift due to the formation of a coating on the surface of the active material of the electrolyte. Here, the utilization rate of the positive and negative electrodes is defined as the ratio of the weight of the active material that can contribute to the battery reaction to the weight of the active material contained in the electrode.
The capacity deviation corresponds to the amount of electricity [Ah] of lithium ions taken into the film. As is known, the utilization rate and capacity deviation in the positive electrode (subscript p) and negative electrode (subscript n) made of a single active material are respectively m _p , m _{n ,} δ _p , and δ _n , and the capacity per weight, q _p and q _n , which are characteristic values specific to the active material, are combined to give the following basic formula. Here, Q _cell is the discharge capacity of the battery, and V _cell (Q _cell ) is the battery voltage for the discharge capacity Q _cell . Formula (2) means that the discharge capacity of the battery is expressed in the form of the product of the utilization rate and unit capacity of the active material of the positive and negative electrodes minus the capacity deviation, and formula (3) means that the battery voltage is expressed as the difference between the potentials of the corresponding positive and negative electrodes.
Q _cell = m _p × q _p - δ _p = m _n × q _n - δ _{n ...} (2)
V _cell (Q _cell )=V _p (q _p )−V _n (q _n ) (3)
The ◯ plot (Cell exp) in the diagram shown in step 2 of FIG. 5 indicates the actual measured value of the voltage value (OCV) with respect to the discharge capacity. Pos. and Neg. in the diagram indicate the corresponding positive and negative electrode monopolar curves, respectively, and Cell in FIG. 5 is shown as the difference (Pos.-Neg.) between the positive and negative electrode monopolar curves. As shown in formula (2), Pos. in FIG. 5 is determined by expanding the monopolar curve per unit active material obtained in step 0 in the horizontal axis direction by the utilization rate (m _p ) of the active material, and further translating it by the capacity deviation (δ _p ). Similarly, the monopolar curve Neg. of the negative electrode is also determined by expanding the horizontal axis of the curve per unit active material by the utilization rate (m _n ) and translating it by the capacity deviation (δ _n ). Although these values (m _p , m _n , δ _p , δ _n ) cannot be measured directly, the positive and negative electrode unipolar curves (Pos., Neg.) in FIG. 5 can be reproduced using these values and the unipolar curve per unit active material obtained in step 0, and further, the voltage-capacity curve per battery (Cell) can be reproduced by subtracting these voltages. By determining m _p , m _n , δ _p , δ _n so as to minimize the difference between this and the actual measured value, the deterioration parameters for each material can be indirectly determined.

Step 3: Derive the degradation parameters
As in step 0, a single-electrode curve per unit active material is obtained in advance, and a discharge curve (OCV-capacity curve) of the entire battery is predicted from the degradation parameters and single-electrode data. Then, the degradation parameters, for example, m _p , m _n , δ _p , and δ _n can be derived by performing curve fitting to minimize the difference by comparing the predicted curve with the actual measured value of the discharge curve of the battery during operation.
Here, in FIG. 5, the OCV-DOD curve in FIG. 4(c) is used as the measured voltage-capacity curve, but by using the measured dQ/dV-DOD curve and further performing curve fitting using the vertical axis of the monopolar curve per unit active material as the capacity differential, m _p , m _n , δ _p , and δ _n can be obtained in the same manner.
Similarly, by using the DOD dependence of resistance obtained as shown in FIG. 4(c) and the resistance-capacity curve (r _p (q _p )-q _p ) of the single-electrode measurement in FIG. 5, the deterioration parameters R ₀ , a _p , and a _n related to resistance can be obtained. Here, R ₀ is the sum of the Li conductive resistance and various electronic conductive resistances in the electrolyte inside the battery cell, a _p is the resistance increase rate due to the alteration of the surface of the positive electrode active material, and a _n is the resistance increase rate due to the alteration of the surface of the positive electrode active material.

The battery groups from which degradation parameters can be obtained using the above method are either battery cells, battery modules, or battery racks 3, and vary depending on the measurement target of the SOC or DOD-dependent data of the battery state obtained in Figure 4. That is, when the battery rack average voltage and current data is used in Figure 4, the obtained degradation parameters indicate the average material degradation of the battery rack. Similarly, when the battery module average voltage and current data is used, the averaged material degradation of the module can be obtained, and when the individual cell voltage and current data is used, the degradation parameters for each cell can be obtained. It is possible to freely select which layer of degradation information to obtain. When placing importance on accuracy and degree of dispersion, the cell layer can be used as the priority, and when placing importance on the cost and load associated with diagnosis, the battery rack layer can be used as the priority.

FIG. 6 is a diagram showing the management procedure of the BESS according to the first embodiment of the present invention. FIG. 6 illustrates an example of the process for obtaining the deterioration parameters of the battery in a specific battery bank 1 from the BESS 15 in operation by the above-mentioned method. In this embodiment, the battery bank 1 is selected periodically, and the deterioration parameters of the secondary battery are obtained through the above-mentioned process, but the frequency and the diagnosis order of the battery banks 1 are arbitrary. In particular, when there is no significant difference in the deterioration state of each

battery bank

1a, 1b, 1c, 1d, ... 1n, the battery bank 1 to be diagnosed can be selected in a predetermined order and frequency, and the diagnosis mode can be set as shown in FIG. 2 and FIG. 3. In addition, when the simple SOC and SOH diagnosis by the rack BMS 4 in the BESS 15 reveals the presence of a battery rack 3 that is preferentially deteriorating, the battery bank 1 including that battery rack 3 can be preferentially set to the diagnosis mode.

In step S101 of FIG. 6, the rack BMS 4 in the BESS 15 or a lower battery controller (not shown) can be used to easily diagnose the SOC and SOH of each

battery rack

3a, 3b, 3c, 3d, 3n-1, 3n and the battery modules and cells that compose them. This diagnostic data is automatically acquired for all batteries in the BESS. Here, the lower battery controller is implemented, for example, in the battery module in the battery rack 3. The lower battery controller measures the battery state (charging rate, deterioration degree, voltage, temperature, etc.) in the battery module and transmits the measurement information to the upper rack BMS 4 or system BMS 7. It is the system BMS 7 that determines the current distribution to each

battery bank

1a, 1b, 1c, 1d, ... 1n, and the battery state for that current is measured by the controller in the battery module, and the measurement result is transmitted to the system BMS 7 by the rack BMS 4. The function of diagnosing the deterioration state can be implemented in either the rack BMS 4 or the system BMS 7, but it is preferable to implement it in the system BMS 7.

In step S102, the diagnostic data of the simplified diagnosis obtained in step S101 is compared with a predetermined battery bank diagnostic plan to confirm whether or not there is a target battery bank that should be diagnosed at the appropriate time.
In step S103, if there is one battery bank to be diagnosed, that battery bank is selected; if there are multiple battery banks, the one with the most significant deterioration is selected, and the battery bank to be diagnosed is determined.
In step S104, the target battery bank is determined, and the charging rate of the battery bank to be diagnosed is obtained. In step S105, the charging rates of the other battery banks are obtained. After that, or in parallel with that, an input/output plan (needs) from the grid 5 is obtained (step S106), and a future charging/discharging plan for each battery bank is determined (step S107). A pulse charging/discharging pattern as shown in FIG. 4 is set and instructed to the PCS2 of the battery bank in the diagnosis mode, and a charging/discharging pattern obtained by subtracting the charging/discharging plan of the diagnosis mode from the input/output plan from the grid 5 is divided and instructed to the PCS2 of the other battery banks. At this time, in step S108, it is confirmed that the charging/discharging range of the other battery banks in the diagnosis support mode does not exceed the allowable range. Here, the allowable range refers to a charge/discharge allowable range that is set in advance to minimize deterioration of the constituent batteries and ensure safety. If this range is exceeded, the risk of unexpected deterioration and unsafe risks increases. Therefore, if the allowable range is exceeded, the conditions and timing of the diagnosis mode and the diagnosis support mode are reviewed. In step S108, if the charge/discharge plan is within the allowable range, the set charge/discharge plan can be executed to perform pulse charge/discharge in the bank to be diagnosed (step S109) and charge/discharge response to grid needs by other battery banks in the diagnosis support mode (step S110). After that, if the required SOC-battery state (OCV, resistance) curves can be obtained using the procedure described in FIG. 4 above (step S111), each battery bank returns to the normal charge/discharge mode. In parallel with this, deterioration parameters of the batteries in the target battery bank are obtained using the procedure described in FIG. 5 above (step S112).

The deterioration parameters obtained by the above-mentioned procedure include m _p , m _n , δ _p , δ _n , R ₀ , _ap , and _an . The present invention also includes an optimal operation and maintenance method for the BESS 15 using these deterioration parameters. An example of this method will be described below.
By using the deterioration parameters, it is possible to predict the change over time in the SOH of the battery cells, modules, and battery racks that constitute the BESS 15. This function can be arranged either as an internal function of the BESS in FIG. 1 or as an external function that manages the BESS, but below, as an example, an example of predicting the change over time in the SOH inside the BESS is shown. The SOH prediction part can be executed by the system BMS 7 in FIG. 1. The system BMS 7 includes a model calculation unit that diagnoses the SOH using a deterioration prediction formula, but here, the assumed current distribution pattern is analyzed to extract deterioration acceleration factors that affect battery deterioration (e.g., current, center SOC, ΔSOC, temperature, duty ratio (=current application time/(current application time+non-application time))), and the deterioration of the battery when charging and discharging are repeated using the specified current distribution pattern is predicted. There are several methods for predicting battery deterioration, such as a method of predicting from an empirical formula for the target battery and a method of predicting from a physical model formula that takes into account the deterioration of each material inside the battery. In the latter, the deterioration acceleration factors extracted are input into an equation (deterioration prediction equation) showing the time dependency of the parameters indicating the deterioration of each material, thereby predicting the change over time of each deterioration parameter, and the battery performance (capacity, resistance) is calculated from the predicted future deterioration parameter values to predict the SOH, etc., and information on the remaining life of the BESS15 is obtained.

Fig. 7 is an example of a block diagram of a deterioration model calculation unit. The deterioration model calculation unit according to this embodiment is implemented in the system BMS 7 of Fig. 2 or Fig. 3, and includes an input 51, an internal deterioration parameter calculation block 52 (a deterioration parameter rate map reference unit 521, a deterioration parameter calculation unit 522), a deterioration parameter prediction formula correction block 53, a capacity and internal resistance calculation block 54, an SOH calculation block 55, and an output 56. Here, the internal deterioration parameter calculation block 52 (a deterioration parameter rate map reference unit 521, a deterioration parameter calculation unit 522), the capacity and internal resistance calculation block 54, and the SOH calculation block 55 are realized by, for example, a processor such as a CPU (not shown), a ROM for storing various programs, a RAM for temporarily storing data in the calculation process, and a storage device such as an external storage device, and the processor such as a CPU reads out and executes various programs stored in the ROM, and stores the calculation results, which are the execution results, in the RAM or the external storage device.
(Process S1)
The input 51 analyzes an expected current distribution pattern and extracts and inputs characteristic quantities (for example, current, median SOC (standby SOC), ΔSOC, and temperature) that affect battery degradation.
(Process S2)
In the internal deterioration parameter calculation block 52, the characteristic quantities of the input 51 are input into an equation (deterioration prediction equation) showing the time dependency of parameters (deterioration parameters) showing the deterioration of each material inside the battery, thereby predicting the change over time of each deterioration parameter. Here, the deterioration parameters are not limited, but may include the utilization efficiency (m _p , m _n ) of the active material used in the positive and negative electrodes, the amount of lithium ion deactivation due to the formation of a coating on the positive and negative electrode surfaces (δ _p , δ _m ), the ohmic resistance (R _o ) of the battery components, and the resistivity (a _p , a _n ) of the positive and negative electrode materials. In addition, the equation expressing the time dependency of the deterioration parameters is not limited to one. An example is shown in equation (4).

Where t is time,
a ₀ , a _m , k _n (m: integer from 1 to M, N is arbitrary): Values that depend on the deterioration acceleration factors of the battery. Here, a ₀ , a _m , and k _n are also values that depend on the operating conditions of the battery (current, center SOC, ΔSOC, T _batt , Duty), and the g, h, and l functions are those that are expressed as functions. The form of the functions varies depending on the type of battery.
_a0 = g (I, SOC, ΔSOC, _Tbatt, Duty)
a _m =h (I, SOC, ΔSOC, T _batt, Duty)
_kn = l (I, SOC, ΔSOC, T _batt, Duty)
Here, I is the current, SOC is the center SOC (standby SOC), ΔSOC is the SOC range during charging and discharging, and T _batt is the battery temperature.

The g, h, and l functions can be expressed as a product of the powers of the respective factors, α×(I^β)×(SOC^γ)×(ΔSOC^δ)×exp(-η/T _batt ), or as a simple linear expression, (α+β×I+γ×SOC+δ×ΔSOC+η×T _batt ), where α, β, γ, δ, and η are coefficients (the specific values are determined by fitting).
(Process S2′)
The above-mentioned functions themselves and the coefficients therein are set in advance before operation and implemented in the deterioration parameter rate map reference unit 521. As a result, if there is a discrepancy between the predicted value of the deterioration parameter obtained by the deterioration parameter calculation unit 522 ("predicted" in the figure) and the deterioration parameter during operation obtained by the method of Figures 4 to 6 ("diagnosed" in the figure), the coefficients are appropriately changed in the deterioration parameter prediction formula correction block 53 to make the predicted value coincide with the actual measured value (process S2' in Figure 7). In this way, by updating the deterioration prediction formula using the deterioration parameters obtained from a specific battery bank in the BESS 15 during operation, it is possible to improve the prediction accuracy and predict specific performance degradation.
(Process S3)
A capacity and internal resistance calculation block 54 calculates the battery performance (capacity, resistance) from the future deterioration parameter values predicted in step S2, and a SOH calculation block 55 calculates remaining SOH life information.

The predictions of the capacity and internal resistance obtained through the processes S1, S2, S2', and S3 indicate future predictions that reflect the actual operating conditions in the battery bank. Normally, the degradation parameters, battery capacity, and internal resistance change according to a model formula that is constructed in advance at the time of design. On the other hand, as described above, when a deviation is observed between the predicted value by the model and the actual measured value, the coefficients of the degradation parameter prediction formula are updated. However, when the deviation is so large that the deviation cannot be eliminated by updating the coefficients, it suggests that an unusual performance degradation that was not initially expected has started. In that case, the function system of the degradation parameter prediction formula can be changed. At the same time, an alert that the risk of an unusual performance change is high is notified to the operation manager of the BESS 15 via the monitor system via the system BMS 7, and the maintenance timing of the BESS 15 can be promoted earlier.
By clarifying the replacement timing of the battery rack 1 in the BESS 15 taking into account specific performance degradation, planned maintenance can be performed without interrupting the operation of the BESS 15.

Furthermore, by using the present invention to store the operation history of the BESS 15 and the degradation parameters, SOH, and SOC information of the batteries in each battery bank at that time in a database for multiple projects using the same BESS 15, it is possible to construct a degradation prediction formula for the BESS 15 for any operating conditions. When the degradation record of a newly installed BESS 15 deviates from the degradation prediction formula in this database, it is determined that a unique performance change has occurred, and this can be sent as an alert via the system BMS 7 and the monitor system to the BESS 15 operation manager.

In another aspect of this embodiment, the deterioration pattern progressing in the battery can be estimated from the change over time of the obtained deterioration parameters, and based on that, it becomes possible to grasp in advance the material deterioration that leads to an increase in safety risk. For example, among the deterioration parameters, m _n and δ _n indicate the amount of lithium ion deactivation due to the use of active material in the negative electrode and the formation of a film occurring at the negative electrode-electrolyte interface. When the reactive active points in the negative electrode become limited due to a decrease in m _n and an increase in δ _n , the risk that lithium ions are not properly inserted (intercalated) into the active material on the negative electrode surface due to the concentration of charge and discharge current increases, and lithium metal is precipitated on the surface. By preparing a database that accumulates the behavior of these m _n and δ _n and the probability of occurrence of unsafe demonstrations through advance consideration, it becomes possible to perform a sign diagnosis of unsafe phenomena of BESS15 from the behavior prediction of m _n and δ _n obtained in process S2. When a sign of unsafe phenomena is confirmed, it becomes possible to convey the increase in unsafe risk as an alert to the owner or operator of BESS15 via a monitor related to the operation of BESS15. The risk of unsafety can be mitigated by reflecting this in planned battery rack replacement or by adjusting the charge/discharge current to each battery bank to mitigate the rate of deterioration of m _n and δ _n . Here, only m _n and δ _n are mentioned, but by finding correlations between the above-mentioned other parameters, such as the utilization efficiency of the positive electrode active material (m _p ), the amount of lithium ion deactivation due to the formation of a coating on the positive electrode surface (δ _p ), the ohmic resistance of the battery components (R _o ), and the resistivities of the positive and negative electrode materials (a _p , a _n ), and safety events, other parameters can be similarly utilized for safety prediction and management.

FIG. 8 is a diagram showing a mechanism for transmitting diagnostic results and abnormality alerts to the owner or operator of the BESS based on the diagnostic results of the BESS according to the first embodiment of the present invention. FIG. 8 shows an example of a flow for transmitting the deterioration state and deterioration prediction diagnosed using this embodiment, as well as specific performance degradation and increased safety risk to the owner or operator of the BESS. The BESS in FIG. 8 is the same as the BESS 15 in FIG. 2 and FIG. 3, but FIG. 8 shows information exchanged between each part. The system BMS transmits a charge/discharge plan for each battery bank to the battery bank, and transmits charge/discharge commands for system stabilization or battery diagnosis. The rack BMS in the battery bank measures the time series data of voltage, current, and temperature, SOH, and SOC data of each battery rack that has been charged and discharged in response to a battery command, and transmits them to the system BMS. The system BMS derives deterioration parameter information based on the information transmitted from the rack BMS, and updates the coefficients of the deterioration prediction formula in the deterioration model calculation unit. In addition, the system transmits operation history and deterioration data to a deterioration/safety database installed on the cloud or on-premise, and expands the database with data from other BESSs. At this time, if there is a discrepancy between the deterioration prediction formula in the deterioration database and the deterioration information transmitted from the system BMS, it is determined that specific deterioration is progressing, and an alert is transmitted to the system BMS. The system BMS transmits the time series data of the deterioration parameters obtained by the deterioration parameter extraction unit and the remaining life derived therefrom to a BESS monitor system that can be viewed by the BESS owner or operator. Furthermore, when a specific performance deterioration or an increase in safety risk is diagnosed, an alert can be displayed on the monitor. Here, the deterioration model calculation unit and the deterioration parameter extraction unit that constitute the system BMS are realized by, for example, a processor such as a CPU (not shown), a ROM that stores various programs, a RAM that temporarily stores data in the calculation process, and a storage device such as an external storage device, and the processor such as the CPU reads and executes various programs stored in the ROM, and stores the calculation results that are the execution results in the RAM or the external storage device.

The diagnosis/prediction of life and safety based on the deterioration parameters obtained in the present invention can also be utilized when virtually operating a plurality of BESSs. Fig. 9 is a diagram showing the virtual operation of a plurality of BESSs using the parameters obtained by the BESS according to the first embodiment of the present invention.
As shown in FIG. 9, the virtual power storage management system 100 is composed of a first layer L1, a second layer L2, and a third layer L3. The first layer L1 is a layer mainly targeted at VPP operators (virtual power plant operators) and resource aggregators. The second layer L2 is a layer mainly targeted at resource aggregators, and is a layer that manages multiple BESSs distributed in each region. The third layer L3 is a layer mainly targeted at battery owners (consumers) who own one or multiple BESSs (physical BESSs), such as individual owners who aim to store electricity in commercial facilities or homes. In the figure, there are two second management units in the L2 layer below the L1 layer, and two third management units in the L3 layer below that, but there is no limit to the number of branches, and there may be one or more than three. In addition, the division boundaries of multiple management units existing in the same layer may be physical constraints (division by region or owner unit) or functional constraints (current scale to be handled, etc.).
The first level L1, the second level L2, and the third level L3 may be managed by different administrators or by the same administrator.

The first management unit 10 exchanges responses regarding whether or not a transaction can be made with the trading market 200. In addition to the first management unit 10, the trading market 200 also includes the electricity transmission and distribution business operator 310, the retail business operator 320, the power generation business operator 330, and the like, and electricity is traded according to the electricity supply and demand needs of these businesses. For example, when there is a surplus of electricity at each business operator, the BESS owner and manager charges the BESS, and conversely, when there is a shortage of electricity, the BESS is discharged, thereby conducting a trade of compensation to stabilize the power grid. Here, the first management unit 10 in the first layer L1 has the function of selecting the electricity trading needs that can be met based on the SOC of the virtual BESS, which is the average of the SOC of the individual physical BESSs, and issuing charging and discharging commands to the lower layers (L2 and L3 layers).

The second management unit 20 in the second layer L2 has a function to determine the SOC in the standby state of each physical BESS in consideration of the characteristics (lifespan, input/output) of the physical BESS arranged in the third layer L3, and a function to distribute the charge/discharge pattern of each physical BESS based on the charge/discharge command.

The third management unit 30 in the third layer L3 has the function of diagnosing the battery status (SOC, remaining life, resistance, allowable power, etc.) from the operation data (time series data of voltage, current, and temperature) of the battery owner's BESS, and transmitting this information to the second layer L2 in a consolidated manner.

The details of each management department are explained below.

The first management unit 10 has, as processing units, a trading management unit 11 that manages trading with the trading market 200, a service management unit 12 that manages a menu of power trading services (e.g., frequency adjustment, peak shift, peak cut) that can be provided by the virtual BESS, a service resource management unit 13 that manages the correspondence between the power trading services and the virtual BESS to be used, and a virtual BESS control unit 14 that issues control commands to the distributed BESS control unit 21 of the second management unit 20.

The database 17 of the first management unit 10 stores various information in the processing unit, such as SLA (Service Level Agreement) information 171, which is the default conditions for the energy trading service and the service and maintenance conditions for each contract, trading service information 172, which is a menu of energy trading services that can be provided by the virtual BESS, service resource information 173, which is the correspondence between the energy trading service and the virtual BESS to be used, and virtual BESS_SOC information 174, which is information on the total SOC (the total electrical capacity Ah actually charged compared to the total electrical capacity Ah that can be exchanged) of the physical BESS present in the third layer L3.

The second management unit 20 has a distributed BESS control unit 21 that takes into consideration the characteristics (SOC, remaining life, resistance, allowable power, etc.) of the physical BESSes arranged in the third management unit 30 and distributes and controls the standby SOC of each BESS until a charge/discharge command is received and the charge/discharge pattern of each BESS when a charge/discharge command is received, and distributed BESS_SOC information 27 that contains distribution information of the SOC of the physical BESSes managed by each distributed BESS control unit (e.g., the electric capacity of BESSes with an SOC in the range of 〇% to △% among all physical BESSes is xx Ah).

The third management unit 30 has a BESS 32 which is a physical BESS owned by the battery owner, a BESS control unit 31 which compiles information from a lifespan diagnosis unit 321 of the BESS 32 under the management of the battery owner and transmits it to the second management unit 20, and physical BESS_SOC information 37 which is SOC information of the BESS owned by the battery owner. In addition to the battery itself, the BESS 32 has a lifespan diagnosis unit 321 and SOH information 322. The lifespan diagnosis unit 321 is a part which diagnoses the remaining lifespan of each individual BESS, and diagnoses the battery state (remaining lifespan, resistance) from the operating data (time series data of voltage, current, temperature) of the battery owner's BESS 32. SOH is an abbreviation for State of Health, and is an index which indicates the health and deterioration state. The ratio of the remaining battery capacity (Ah) after a certain period of time to the initial battery capacity (Ah) is indicated as SOHQ or SOHC, and the ratio of the increase in resistance (Ω) after a certain period of time to the initial resistance (Ω) is indicated as SOHR, and these are usually expressed as percentages.

Here, the relationship between the virtual BESS control unit 14, the distributed BESS control unit 21, and the BESS control unit 31 will be described.
(Second layer L2 → First layer L1)
The virtual BESS control unit 14 receives, from the distributed BESS control unit 21 , SOC information of the entire physical BESS under the management of the distributed BESS control unit 21 .
(First layer L1 → Second layer L2)
The virtual BESS control unit 14 responds to transaction needs, determines appropriate current distribution commands to the L2 layer, and transmits them to the distributed BESS control unit 21.
(Third layer L3 → Second layer L2)
The BESS control unit 31 transmits battery state information such as SOH and SOC information of the physical BESS owned by the battery owner to the distributed BESS control unit 21 .
(Second layer L2 → Third layer L3)
The distributed BESS control unit 21 determines an SOC and a current distribution command for equalizing the life spans of the physical BESSes managed by one distributed BESS control unit 21 , and transmits the SOC and a current distribution command to a BESS control unit 31 .

In FIG. 9, each BESS 32 contains the configuration of the present invention (e.g., FIG. 2, FIG. 3), and the lifespan diagnosis unit 321 is updated appropriately based on the obtained degradation parameters, enabling highly accurate degradation prediction. Furthermore, by sending the degradation prediction and unsafety risk diagnosis results to the BESS control unit 31 and distributed BESS control unit 21, the results can be reflected in the current distribution and SOC setting control between multiple BESSes, contributing to improved reliability of the system as a whole.

FIG. 10 is a diagram showing an example of a case where multiple BESSes are applied to natural energy power generation using parameters obtained by the BESS according to the first embodiment of the present invention. As shown in FIG. 10, a field is shown in which, for example, a wind power generation device and a solar power generation device are connected to a grid as natural energy power generation. BESSA, BESSB, and BESSC are connected to the grid, and a charge/discharge management unit manages these three BESSes. Here, the charge/discharge management unit is realized by, for example, a processor such as a CPU (not shown), a ROM that stores various programs, a RAM that temporarily stores data in the calculation process, and a storage device such as an external storage device, and the processor such as the CPU reads and executes the various programs stored in the ROM, and stores the calculation results that are the execution results in the RAM or the external storage device.

As shown in FIG. 10, the charge/discharge management unit is a battery status management unit for charging/discharging the BESS for multi-use energy management, and selects the BESS in consideration of the requested load pattern. In the example shown in FIG. 10, the charge/discharge management unit takes into consideration the charge amount of each, and causes BESSA to operate as the BESS for discharging, BESSB to operate as the BESS for charging, and BESSC to operate as the BESS for charging/discharging. In addition, the screen display 40 has battery performance on the vertical axis and years of use on the horizontal axis, and displays the performance of BESSA, BESSB, and BESSC in a manner that is easily visible to the administrator.

As described above, according to this embodiment, by extracting the deterioration parameters of the materials of the secondary batteries that make up the BESS without stopping the operation of the entire BESS, it is possible to provide the BESS owner with a battery energy storage system and a management method thereof that can appropriately operate and maintain the BESS by taking into account not only SOH diagnosis and prediction but also abnormal deterioration.

In addition, it is possible to obtain parameters that are indicators of material degradation of the secondary batteries that make up the BESS, and from the time series changes in these parameters, it is possible not only to predict future trends in battery capacity and resistance, but also to grasp signs of specific performance degradation. This makes it possible to output and visualize reliable operating methods and timely maintenance instructions for a wide variety of secondary batteries, thereby minimizing the lifetime costs for BESS owners and maximizing the profits that can be obtained by using the BESS.

The present invention is not limited to the above-described embodiment, but includes various modifications. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to having all of the configurations described.

1a, 1b, 1c, 1d, 1n-1, 1n...

Battery banks

2a, 2b, 2c, 2d, 2n-1, 2n... PCS
3a, 3b, 3c, 3d, 3n-1, 3n...

Battery rack

4a, 4b, 4c, 4d, 4n-1, 4n... Rack BMS
5... Grid 6... Upper control unit 7... System BMS
8. Energy Management System (EMS)
10... First management unit 11... Transaction management unit 12... Service management unit 13... Service resource management unit 14... Virtual BESS control unit 15... Battery energy storage system (BESS)
17...database 20...second management unit 21...distributed BESS control unit 211...standby SOC/current distribution determination unit 212...physical model calculation unit 27...distributed BESS_SOC information 30...third management unit (BESS holder)
31...BESS control unit 32...BESS (physical BESS)
37...Physical BESS_SOC information 40...Screen display 51...Input 52...Internal deterioration parameter calculation block 53...Deterioration parameter prediction formula correction block 54...Capacity and internal resistance calculation block 55...SOH calculation block 56...Output 100...Virtual power storage management system 171...SLA information 172...Transaction service information 173...Service resource information 174...Virtual BESS_SOC information 200...Trading market 310...Power transmission and distribution company 320...Retailer 321...Life diagnosis unit 322...SOH information 330...Power generation company 521...Deterioration parameter rate map reference unit 522...Deterioration parameter calculation unit L1...First layer L2...Second layer L3...Third layer

Claims

A battery energy storage system including a secondary battery,
a state management unit that selects a battery bank that is a diagnosis target and has a total capacity of 50% or less from among a plurality of battery banks that constitute the battery energy storage system, and extracts deterioration parameters of materials that constitute at least one of a battery cell, a battery module, and a battery rack that constitute the selected battery bank;
A battery energy storage system characterized in that, when the state management unit extracts deterioration parameters, charging and discharging are performed based on requests from a grid.
2. The battery energy storage system of claim 1,
A battery energy storage system, wherein the state management unit extracts the deterioration parameters from a battery voltage response to a pulsed charge/discharge current applied to the battery bank to be diagnosed.
2. The battery energy storage system of claim 1,
A battery energy storage system characterized in that the status management unit outputs an operation and/or maintenance plan for the battery energy storage system using the extracted deterioration parameters.
3. The battery energy storage system of claim 2,
A battery energy storage system, characterized in that the pulsed charge/discharge current to the battery bank to be diagnosed also serves as part of the charge/discharge current required from the grid to the battery energy storage system.
3. The battery energy storage system of claim 2,
A battery energy storage system, characterized in that the charge/discharge current of a battery bank not subject to diagnosis is determined by subtracting the charge/discharge current to the B battery bank subject to diagnosis from a charge/discharge plan from the grid.
3. The battery energy storage system of claim 2,
There are multiple battery banks to be diagnosed,
A battery energy storage system characterized in that the state management unit extracts the deterioration parameters by applying a pulsed charge/discharge current in both directions between the multiple battery banks.
A battery energy storage system according to any one of claims 1 to 6,
The state management unit extracts the deterioration parameters by obtaining a curve showing a relationship between a charging rate of the battery bank and a battery state;
A battery energy storage system, characterized in that the battery condition obtained from the battery bank to be diagnosed is open circuit voltage and/or DC resistance.
A method for managing a battery energy storage system including a secondary battery, comprising:
selecting a battery bank having a total capacity of 50% or less as a diagnosis target from a plurality of battery banks constituting a battery energy storage system;
transmitting a pulsed charge/discharge command to a power conditioner connected to the selected battery bank, and transmitting a charge/discharge command corresponding to a charge/discharge plan from the grid to a power conditioner of a battery bank not being diagnosed;
obtaining a curve showing the relationship between the charging rate and the battery state of the battery rack constituting the battery bank to be diagnosed from the voltage behavior when a pulse-like charge/discharge command is issued;
A method for managing a battery energy storage system, comprising a step of extracting deterioration parameters of materials constituting at least one of the battery cells, battery modules and battery racks constituting the selected battery bank from the obtained curve.
9. A method for managing a battery energy storage system according to claim 8, characterized in that at least one of the following is extracted as deterioration parameters of secondary batteries constituting the battery bank to be diagnosed, based on a correlation curve of the open circuit voltage, DC resistance, and charging rate obtained from the battery bank to be diagnosed: positive electrode active material utilization rate m p , negative electrode active material utilization rate m n , amount of lithium ion loss associated with film formation on the positive electrode surface δ p , amount of lithium ion loss associated with film formation on the negative electrode surface δ n , high frequency resistance R 0 inside the battery, rate of resistance increase of the positive electrode active material surface ap , and rate of resistance increase of the negative electrode active material surface an .
The method for managing a battery energy storage system according to claim 8, further comprising a step of updating a deterioration prediction formula for the deterioration parameters of the battery bank to be diagnosed based on the deterioration parameters.
The method for managing a battery energy storage system according to claim 10, characterized in that the remaining life and the recommended timing for battery replacement for each battery bank are presented by using a deterioration prediction formula for the deterioration parameters updated for each battery bank.
The method for managing a battery energy storage system according to claim 10, characterized in that a charge/discharge plan for the battery banks in the BESS is updated using a deterioration prediction equation for the deterioration parameters updated for each battery bank so that the performance degradation of each battery bank is equalized.
The method for managing a battery energy storage system according to claim 10, characterized in that the risk of an unsafe event that may occur within the BESS is output using a deterioration prediction equation for the deterioration parameters updated for each battery bank.
The battery energy storage system management method according to claim 8 is a virtual power storage management method for adjusting power supply and demand based on commands from the grid using battery energy storage systems and virtual battery energy storage systems that are integrated and controlled as a group, and is characterized in that the method integrates and manages deterioration parameter information extracted from each battery energy storage system, determines at least one of standby SOC conditions, charge/discharge current conditions, and maintenance plans for multiple battery energy storage systems, and transmits the determined items to a control unit that constitutes the battery energy storage system.