Simulation device, operation management device, simulation method, and operation management method

Abstract

The present invention realizes a simulation of an operation plan for a storage battery, with which it is possible to reduce computational load relative to the prior art while ensuring accuracy. This device for simulating an operation of a storage battery provided with a plurality of module batteries each composed of a plurality of single batteries comprises a reference simulation execution unit that performs a reference simulation targeting at least one state index value on the assumption that at least one state index value of the plurality of module batteries changes the same in each of the plurality of module batteries when a predetermined operation plan of the storage battery is executed, and a correction processing unit that obtains an individual simulation result for the at least one state index value of each of the plurality of module batteries by correcting the simulation result for the at least one state index value obtained by the reference simulation with a correction amount corresponding to each of the plurality of module batteries.

Description

Simulation device, operation management device, simulation method, and operation management method

[0001] The present invention relates to the simulation of a storage battery including a plurality of module batteries.

[0002] There is already a known device that obtains an operation plan (charge / discharge plan) for a sodium-sulfur battery (NaS battery), which is a type of storage battery, and determines whether the state of the NaS battery is within an allowable range when the operation plan is executed, based on simulations of battery temperature and remaining capacity, and outputs guidance regarding the operation of the NaS battery (see, for example, Patent Document 1). The operation plan is created, for example, by a user of a power storage system including the NaS battery and its associated devices (control devices and auxiliary equipment such as heaters and fans). There is also already a known device that can calculate the charge / discharge conditions that can be adopted in a high-temperature operating secondary battery with a small amount of calculation. (See, for example, Patent Document 2).

[0003] As disclosed in Patent Documents 1 and 2, NaS batteries are usually configured by connecting a plurality of module batteries in series and in parallel. Each module battery is configured by incorporating a plurality of single cells into one housing container.

[0004] In a NaS battery, when over-discharge progresses, sodium polysulfide is generated on the anode side and sodium in the cathode is depleted, making subsequent charge / discharge impossible. Also, when over-charging progresses, the solid electrolyte is damaged, making subsequent charge / discharge impossible. Furthermore, when the temperature of a single cell becomes too high, phenomena such as the internal pressure of each single cell becoming abnormally high, or the negative electrode active material coming into direct contact with the positive electrode active material due to damage to the solid electrolyte and the temperature of each single cell rising abnormally may occur, and there is a possibility that the housing container will be damaged.

[0005] In order to be able to avoid such over-discharge, over-charging, and other problems, as described above, the guidance device disclosed in Patent Document 1 determines the feasibility of an operation plan created by a user of a power storage system prior to the execution of the plan.

[0006] In Patent Document 1, the internal resistance of the battery, the amount of heat dissipated by the module battery, and the thermal capacity of the module battery are used as parameters when simulating battery temperature and remaining capacity.

[0007] However, when setting the simulation parameters for each of the multiple battery modules constituting a single storage battery and performing the simulation according to the method disclosed in Patent Document 1, there is a problem that the amount of computation required is enormous and it takes a long time. The same applies when performing the simulation for each of the multiple storage batteries.

[0008] For example, if you want to set the simulation parameters for each of the n module batteries before running the simulation, you need to perform the following five-step process n times.

[0009] (1) Create multiple hypothetical parameter sets with different combinations of settings for each simulation parameter.

[0010] (2) For a standard operating pattern (charge / discharge schedule) targeting a predetermined test period, simulations are performed at predetermined time intervals using each set of provisional parameters.

[0011] (3) The battery is operated based on a standard operating pattern, and actual measured values ​​of the simulation target are obtained.

[0012] (4) For each set of provisional parameters, calculate the difference between the simulated values ​​(calculated values ​​obtained by simulation) and the measured values ​​at all simulation execution times during the test period.

[0013] (5) The setting values ​​in the provisional parameter set that have the smallest sum of the "mean squared difference between the simulated value and the measured value" at all times during the test period will be adopted as the simulation parameters.

[0014] On the other hand, there is a need to frequently and quickly change operational plans in order to use energy storage systems for bidding on the electricity market. Meeting this need requires short-duration simulations. To shorten the calculation time, one could consider, for example, using the results of a simulation for a standard single module battery, or the results of a simulation for a smaller number of module batteries than the actual number of module batteries, as representative results for the entire NaS battery. However, in such cases, the difference from the actual behavior of the NaS battery tends to become large, which is undesirable.

[0015] Furthermore, the calculation method disclosed in Patent Document 2 does not specifically mention that the NaS battery is composed of multiple module batteries, and therefore, individual calculations targeting each module battery are not disclosed.

[0016] Japanese Patent Publication No. 2008-210586, International Publication No. 2016 / 136445

[0017] This invention has been made in view of the above problems, and aims to provide a method for simulating the operation plan of a battery system composed of multiple module batteries, and an apparatus for realizing this method, which can reduce the amount of computation compared to conventional methods while ensuring accuracy.

[0018] To solve the above problems, a first aspect of the present invention is a device for performing operation simulations of a storage battery comprising a plurality of module batteries, each composed of one or more single cells, comprising: a reference simulation execution unit that performs a reference simulation targeting at least one state index value, assuming that at least one state index value changes in the same way in each of the plurality of module batteries when a predetermined operation plan for the storage battery is executed; and a correction processing unit that obtains individual simulation results for each of the plurality of module batteries by correcting the simulation results for the at least one state index value obtained by the reference simulation with a correction amount within a predetermined tolerance range set according to each of the plurality of module batteries.

[0019] A second aspect of the present invention is a simulation apparatus according to the first aspect, wherein the correction processing unit learns the relationship between feature quantities that characterize the operating state of the storage battery during a predetermined learning target period determined for the past operating period of the storage battery, and the deviation between the value obtained by the reference simulation for at least one state index value and the measured value in each of the plurality of module batteries, thereby creating a decision tree in advance in which the value of the correction amount is determined according to the conditions of the feature quantities, and determining the correction amount for each of the plurality of module batteries based on the decision tree.

[0020] A third aspect of the present invention is a simulation apparatus according to the second aspect, characterized in that the correction processing unit acquires the actual temperature, actual voltage, and actual current of each of the plurality of module batteries in advance for at least the predetermined learning target period, calculates and stores the feature quantities at predetermined time increments based on the actual temperature, actual voltage, and actual current, and sets the correction amount for each of the plurality of module batteries at a certain time within the target period of the operation plan to a value associated with the feature quantity at that time, which is assumed in the decision tree from the operation plan and the results of the reference simulation.

[0021] A fourth aspect of the present invention is a simulation apparatus according to the first aspect, characterized in that the correction processing unit determines the correction amount based on the moving average of the deviations between the value obtained by the reference simulation for at least one state index value and the measured value of each of the plurality of module batteries at multiple times within a predetermined period in the past.

[0022] A fifth aspect of the present invention is a simulation apparatus according to the first aspect, characterized in that, if the operating state of the storage battery assumed at a certain time during the target period of the operation plan is similar to the operating state at a certain time in the past, the correction processing unit sets the correction amount to the correction amount adopted at that time in the past.

[0023] A sixth aspect of the present invention is a simulation apparatus according to any of the first to fifth aspects, characterized in that, if the value to be set as the correction amount is greater than the predetermined allowable range, the correction processing unit sets the upper limit of the predetermined allowable range as the correction amount, and if the value to be set as the correction amount is less than the predetermined allowable range, the lower limit of the predetermined allowable range is set as the correction amount.

[0024] A seventh aspect of the present invention is a simulation apparatus according to any of the first to fifth aspects, characterized in that the correction processing unit sets the correction amount to 0 if the value to be set as the correction amount falls outside the predetermined allowable range.

[0025] An eighth aspect of the present invention is a simulation apparatus according to any one of the first to fifth aspects, characterized in that the at least one state index value includes the temperature of the module battery.

[0026] A ninth aspect of the present invention is a simulation apparatus according to the eighth aspect, characterized in that the correction processing unit obtains the individual simulation results for each of the multiple module batteries based on the relationship that the sum of the difference between the i-1th and ith calculation target time (i is a natural number of 2 or more) calculated by the reference simulation and the correction amount for the ith calculation target time for each of the multiple module batteries is equal to the temperature change between the i-1th and ith calculation target time in each of the multiple module batteries.

[0027] A tenth aspect of the present invention is a simulation apparatus according to any of the first to fifth aspects, characterized in that the at least one state index value includes the internal resistance of a module battery.

[0028] An eleventh aspect of the present invention is a simulation apparatus according to the tenth aspect, characterized in that the correction processing unit obtains the individual simulation results for the internal resistance of each of the plurality of module batteries based on the relationship that the sum of the internal resistance at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for the internal resistance of each of the plurality of module batteries is equal to the internal resistance of each of the plurality of module batteries at the i-th calculation target time.

[0029] A twelfth aspect of the present invention is a simulation device according to the tenth aspect, characterized in that, in any of the plurality of module batteries, if the ratio or difference between the individually simulated value of the internal resistance at a certain time and the value of the internal resistance at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in the module battery.

[0030] A thirteenth aspect of the present invention is a simulation apparatus according to any of the first to fifth aspects, characterized in that the at least one state index value includes at least one of the discharge depth at the end of discharge, which is the discharge depth when the module battery reaches the discharge stop voltage, and the charge depth at the end of charge, which is the discharge depth when the module battery reaches the charge stop voltage.

[0031] A fourteenth aspect of the present invention is a simulation apparatus according to the thirteenth aspect, wherein the at least one state index value includes the discharge end-stop depth, and the correction processing unit obtains the individual simulation results for the discharge end-stop depth of each of the plurality of module batteries based on the relationship that the sum of the discharge end-stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for each of the plurality of module batteries is equal to the discharge end-stop depth at the i-th calculation target time for each of the plurality of module batteries.

[0032] A fifteenth aspect of the present invention is a simulation device according to the thirteenth aspect, characterized in that, in any of the plurality of module batteries, if the ratio or difference between the individually simulated value for the discharge end-side stop depth at a certain time and the value of the discharge end-side stop depth at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in the module battery.

[0033] A sixteenth aspect of the present invention is a simulation apparatus according to the thirteenth aspect, wherein the at least one state index value includes the charging end-stop depth, and the correction processing unit obtains the individual simulation results for the charging end-stop depth of each of the plurality of module batteries based on the relationship that the sum of the charging end-stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for each of the plurality of module batteries is equal to the charging end-stop depth at the i-th calculation target time for each of the plurality of module batteries.

[0034] A 17th aspect of the present invention is a device for managing the operation of a storage battery comprising a plurality of module batteries, each composed of a plurality of single cells, comprising: an operation feasibility determination unit that determines whether or not to operate the storage battery according to the operation plan based on the results of a simulation in a simulation device according to any of the first to fifth aspects; and a charge / discharge management unit that sends the operation plan, which has been determined to be operational by the operation feasibility determination unit, to a control device of the storage battery, and causes the control device to operate the storage battery according to the operation plan.

[0035] An eighteenth aspect of the present invention is an operation management device according to the seventeenth aspect, characterized in that, when the at least one state index value includes the internal resistance of the plurality of module batteries, if the ratio or difference between the individually simulated value of the internal resistance at a certain time and the value of the internal resistance at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in any of the plurality of module batteries.

[0036] A 19th aspect of the present invention is an operation management device according to the 17th aspect, characterized in that, when the at least one state index value includes the discharge depth at the end of discharge, which is the discharge depth when the plurality of module batteries reach the discharge stop voltage, if the ratio or difference between the value of the individual simulation for the discharge depth at a certain time and the value of the discharge depth at that time is greater than a predetermined threshold, a failure is estimated to have occurred in one of the plurality of module batteries.

[0037] A 20th aspect of the present invention is a method for simulating the operation of a storage battery comprising a plurality of module batteries, each composed of a plurality of single cells, comprising: a reference simulation step of performing a reference simulation targeting at least one state index value, assuming that when a predetermined operation plan for the storage battery is executed, at least one state index value changes in the same way in each of the plurality of module batteries; and a correction processing step of obtaining individual simulation results for each of the plurality of module batteries by correcting the simulation results for the at least one state index value obtained from the reference simulation with a correction amount within a predetermined allowable range set according to each of the plurality of module batteries.

[0038] A 21st aspect of the present invention is a simulation method according to the 20th aspect, further comprising a decision tree creation step of creating a decision tree in advance in which the value of the correction amount is determined according to the conditions of the feature amount by learning the relationship between a feature quantity that characterizes the operating state of the storage battery during a predetermined learning target period determined for the past operating period of the storage battery, and the deviation between the value obtained by the reference simulation for at least one state index value and the measured value in each of the plurality of module batteries, wherein in the correction processing step, the correction amount for each of the plurality of module batteries is determined based on the decision tree.

[0039] A 22nd aspect of the present invention is a simulation method according to the 21st aspect, characterized in that, in the decision tree creation step, the actual temperature, actual voltage, and actual current of each of the plurality of module batteries are acquired in advance for at least the predetermined learning target period, the feature quantities are calculated and accumulated at predetermined time increments based on the actual temperature, the actual voltage, and the actual current, and in the correction processing step, the correction amount for each of the plurality of module batteries at a certain time within the target period of the operation plan is set to a value that corresponds to the feature quantity at that time, which is assumed in the decision tree from the operation plan and the results of the reference simulation.

[0040] A 23rd aspect of the present invention is a simulation method according to the 20th aspect, characterized in that, in the correction processing step, the correction amount is determined based on the moving average of the deviations between the value obtained by the reference simulation for at least one state index value at multiple times within a predetermined period in the past and the measured values ​​of each of the multiple module batteries.

[0041] A 24th aspect of the present invention is a simulation method according to the 20th aspect, characterized in that, in the correction processing step, if the operating state of the storage battery assumed at a certain time during the target period of the operation plan is similar to the operating state at a certain time in the past, the correction amount is set to the correction amount adopted at that time in the past.

[0042] A 25th aspect of the present invention is a simulation method according to any of the 20th to 24th aspects, characterized in that, in the correction processing step, if the value to be set as the correction amount is greater than the predetermined allowable range, the upper limit of the predetermined allowable range is set as the correction amount, and if the value to be set as the correction amount is less than the predetermined allowable range, the lower limit of the predetermined allowable range is set as the correction amount.

[0043] The 26th aspect of the present invention is a simulation method according to any one of the 20th to 24th aspects, wherein in the correction processing step, when the value to be set as the correction amount is outside the predetermined allowable range, the correction amount is set to 0.

[0044] The 27th aspect of the present invention is a simulation method according to any one of the 20th to 24th aspects, wherein the at least one state index value includes the temperature of the module battery.

[0045] The 28th aspect of the present invention is the simulation method according to claim 27, wherein in the correction processing step, the difference value between the (i - 1)-th and i-th calculation target times (i is a natural number of 2 or more) calculated by the reference simulation, and the correction amount for the i-th calculation target time for each temperature of the plurality of module batteries, the sum of which is equal to the temperature change between the (i - 1)-th and i-th calculation target times in each of the plurality of module batteries, based on this relationship, the individual simulation results for each temperature of the plurality of module batteries are obtained.

[0046] The 29th aspect of the present invention is a simulation method according to any one of the 20th to 24th aspects, wherein the at least one state index value includes the internal resistance of the module battery.

[0047] The 30th aspect of the present invention is the simulation method according to the 29th aspect, wherein in the correction processing step, the internal resistance at the i-th calculation target time (i is a natural number) calculated by the reference simulation, and the correction amount for each internal resistance of the plurality of module batteries, the sum of which is equal to the internal resistance at the i-th calculation target time in each of the plurality of module batteries, based on this relationship, the individual simulation results for each internal resistance of the plurality of module batteries are obtained.

[0048] The 31st aspect of the present invention is the simulation method according to the 29th aspect, wherein, in any one of the plurality of module batteries, when the ratio or difference between the value of the individual simulation of the internal resistance at a certain time and the value of the internal resistance at that time is greater than a predetermined threshold value, it is estimated that a failure has occurred in the module battery.

[0049] The 32nd aspect of the present invention is the simulation method according to any one of the 20th to 24th aspects, wherein the at least one state index value includes at least one of a discharge end-side stop depth which is a depth of discharge when the module battery reaches the discharge stop voltage and a charge end-side stop depth which is a depth of discharge when the module battery reaches the charge stop voltage.

[0050] The 33rd aspect of the present invention is the simulation method according to the 32nd aspect, wherein the at least one state index value includes the discharge end-side stop depth, and in the correction processing step, based on the relationship that the sum of the discharge end-side stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for the discharge end-side stop depth of each of the plurality of module batteries is equal to the discharge end-side stop depth at the i-th calculation target time in each of the plurality of module batteries, the individual simulation results for the discharge end-side stop depth of each of the plurality of module batteries are obtained.

[0051] The 34th aspect of the present invention is the simulation method according to the 32nd aspect, wherein, in any one of the plurality of module batteries, when the ratio or difference between the value of the individual simulation of the discharge end-side stop depth at a certain time and the value of the discharge end-side stop depth at that time is greater than a predetermined threshold value, it is estimated that a failure has occurred in the module battery.

[0052] A 35th aspect of the present invention is a simulation method according to the 32nd aspect, characterized in that the at least one state index value includes the charging end-stop depth, and in the correction processing step, the individual simulation results for the charging end-stop depth of each of the plurality of module batteries are obtained based on the relationship that the sum of the charging end-stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for each of the plurality of module batteries is equal to the charging end-stop depth at the i-th calculation target time for each of the plurality of module batteries.

[0053] A 36th aspect of the present invention is a method for managing the operation of a battery storage system comprising a plurality of module batteries, each composed of a plurality of single cells, comprising: an operation feasibility determination step of determining whether or not to operate the battery storage system according to the operation plan based on the results of a simulation method according to any of the 20th to 24th aspects; and a charge / discharge management step of sending the operation plan, which was determined to be operational in the operation feasibility determination step, to a control device of the battery storage system and causing the control device to operate the battery storage system according to the operation plan.

[0054] A 37th aspect of the present invention is an operation management method according to the 36th aspect, characterized in that, when the at least one state index value includes the internal resistance of the plurality of module batteries, if the ratio or difference between the individually simulated value of the internal resistance at a certain time and the value of the internal resistance at that time in any of the plurality of module batteries is greater than a predetermined threshold, it is estimated that a failure has occurred in that module battery.

[0055] A 38th aspect of the present invention is an operation management method according to the 36th aspect, characterized in that, when the at least one state index value includes the discharge end-stop depth, which is the discharge depth when the plurality of module batteries reach the discharge stop voltage, if the ratio or difference between the value of the individual simulation for the discharge end-stop depth at a certain time and the value of the discharge end-stop depth at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in any of the plurality of module batteries.

[0056] According to the first to ninth and twentieth to twenty-eighth aspects of the present invention, it is possible to perform simulations that take into account the individual differences of multiple module batteries with less computational effort than conventional methods.

[0057] Furthermore, according to the first to thirty-eighth aspects of the present invention, it is possible to perform simulations that take into account the individual differences of each of the multiple module batteries, without having to individually set parameters for each of the multiple module batteries and perform simulations.

[0058] This is a block diagram showing the relationships between various devices involved in the simulation of charge and discharge operations. This is a schematic diagram showing the electrical connection configuration of multiple single cells 13c within the containment container 11a. This is a diagram showing the flow of the simulation process, along with the preceding and succeeding processes. This is a diagram showing the flow of the reference simulation. This is a diagram showing the procedure for the learning process. This is a diagram illustrating a decision tree obtained through the learning process. This is a diagram showing the procedure for determining the value of the correction amount Te_m(i) for the calculation target time i in an individual simulation. This is a diagram illustrating a decision tree consisting of multiple layers created using SOC as a feature. This is a diagram illustrating a decision tree in which layers based on non-continuous features are interposed between layers based on continuous features. This is a graph schematically showing the relationship between discharge depth and voltage in a NaS battery.

[0059] <First Embodiment> <Overview> Figure 1 is a block diagram showing the relationship between an energy storage system 1 that performs charging and discharging operations according to a pre-set operating pattern (also referred to as an operating plan or charge / discharge plan) and various devices related to the simulation of charging and discharging operations based on the created operating pattern.

[0060] The energy storage system 1 comprises a storage battery 11 and a control device 12 that controls the charging and discharging operations of the storage battery 11.

[0061] The storage battery 11 comprises a plurality of module batteries 11(1) to 11(n) (where n is a natural number). Each module battery 11(1) to 11(n) is generally composed of a battery assembly 13 formed by connecting a plurality of single cells 13c in series and parallel, a heater 14 for heating and maintaining the temperature of the battery assembly 13, a fan 15 for dissipating heat from the battery assembly 13, and a temperature sensor 16 for measuring the temperature of the module batteries 11(1) to 11(n), all housed in a single container 11a. The storage battery 11 is then constructed by connecting the battery assemblies 13 of each module battery 11(1) to 11(n) in series.

[0062] The single cell 13c is, for example, a sodium-sulfur cell (NaS cell) with sulfur as the positive electrode active material and metallic sodium as the negative electrode active material. Alternatively, the single cell 13c may be a lithium-ion cell. In the following explanation, we will mainly use the case where the single cell 13c is an NaS cell as an example.

[0063] Figure 2 schematically shows the electrical connection configuration of multiple single cells 13c within the housing container 11a. As described above, in each of the module batteries 11(1) to 11(n), multiple single cells 13c are housed in a single housing container 11a to constitute a battery assembly 13. Note that the heater 14, fan 15, and temperature sensor 16 are not shown in Figure 2.

[0064] The battery assembly 13 is composed of one or more blocks 13b, each consisting of several individual cells 13c. The housing 11a is also provided with a positive external terminal 60 and a negative external terminal 62 on its exterior, and one or more blocks 13b are connected in series from the positive external terminal 60 to the negative external terminal 62. Each block 13b is formed by connecting two or more strings 13s, each consisting of two or more individual cells 13c connected in series, in parallel.

[0065] The battery 11 is used by the owner of the energy storage system 1 (energy storage business operator), or by power generators, transmission and distribution companies, retail electricity companies, etc., for adjusting the supply and demand of electricity with the external commercial power grid, or further for generating revenue through the buying and selling of electricity in the electricity market (e.g., wholesale electricity market, capacity market, supply and demand adjustment market).

[0066] The operation pattern creation device 2 is a device responsible for creating operation patterns for performing charge and discharge operations in the battery 11. The operation pattern creation device 2 generates operation pattern data that describes the contents of the created operation pattern and sends this to the battery simulation device 3. The operation pattern data describes the target operation period, the charge and discharge periods and output during the operation period, etc. Furthermore, the timing of the ON / OFF operation of the heater 14 and fan 15 may also be described.

[0067] The battery simulation device 3 is a device that simulates the charging and discharging operation when the operating pattern created in the operating pattern creation device 2 is applied to the battery 11. More specifically, the battery simulation device 3 simulates various state indicator values ​​(more precisely, their changes over time) that represent the state of each module battery 11(1) to 11(n) of the battery 11 when the charging and discharging operation is performed, over the target period of the operating pattern (also referred to as the operating output time), and outputs the time-series data.

[0068] The state indicator values ​​subject to simulation include, at a minimum, the temperature (battery temperature) and remaining capacity (SOC) of each module battery 11(1) to 11(n).

[0069] The data output by the battery simulation device 3 (simulation result data) is used to determine, in advance of executing the operation pattern, whether or not it is possible to actually execute the charge and discharge operation based on the created operation pattern in the battery 11, or more specifically, whether or not each of the module batteries 11(1) to 11(n) that make up the battery 11 will operate as expected in the created operation pattern.

[0070] However, in this embodiment, the battery simulation device 3 includes a reference simulation execution unit 31 and a correction processing unit 32. The simulation for at least some of the various state index values ​​described above is performed in two stages: a reference simulation in the reference simulation execution unit 31, and individual simulations for each of the module batteries 11(1) to 11(n) by correcting the results of the reference simulation in the correction processing unit 32.

[0071] To more reliably achieve the simulation's objective of determining the feasibility of charge and discharge operations based on operational patterns, higher simulation accuracy is desirable. However, since the battery 11 is composed of multiple module batteries 11(1) to 11(n), in order to ensure accuracy, it is necessary to perform a process to set simulation parameters for all of these module batteries 11(1) to 11(n), and then calculate the state index values ​​targeted by the simulation at predetermined time intervals over the entire operational output time. However, this approach results in an enormous amount of computation and is time-consuming.

[0072] Furthermore, even if the configuration is the same, there are slight individual differences between the individual module batteries 11(1) to 11(n), which can lead to differences in their charge and discharge behavior. Therefore, there are some state indicators for which it is desirable to perform simulations that take individual differences into account. Battery temperature is one example of this. For this reason, applying the same simulation parameters uniformly to simulations targeting each module battery 11(1) to 11(n), or applying a single simulation result to all module batteries 11(1) to 11(n), is not necessarily appropriate in terms of accuracy.

[0073] The two-stage simulation performed by the battery simulation device 3 takes these points into consideration. In general terms, first, a baseline simulation is performed targeting the state index values, assuming that the state index values ​​to be simulated will change in the same way in each of the module batteries 11(1) to 11(n) when the operation plan is executed.

[0074] Once a reference simulation value (reference simulation value) is obtained for each of the module batteries 11(1) to 11(n), an individual simulation is performed for each of the module batteries 11(1) to 11(n) based on the reference simulation value. Details of the simulation in the battery simulation device 3 will be described later.

[0075] The battery operation management device 4 is a device for managing the operation of the battery 11. The battery operation management device 4 comprises an operation feasibility determination unit 41 and a charge / discharge management unit 42.

[0076] The operation feasibility determination unit 41 acquires individual module simulation result data from the battery simulation device 3, which consists of the results of individual simulations for each of the module batteries 11(1) to 11(n) and the operation patterns that were the subject of the simulations. Based on this individual module simulation result data, it determines whether or not it is possible to perform the charge and discharge operation in the battery 11 based on the operation patterns that were the subject of the simulations.

[0077] The results of the determination are notified to the operation pattern creation device 2 as appropriate. If it is determined that execution of a certain operation pattern is "not" and this result is notified to the operation pattern creation device 2, the operation pattern creation device 2 will appropriately modify the operation pattern in question or recreate a new operation pattern.

[0078] The charge / discharge management unit 42 sends data of the operation pattern that the operation feasibility determination unit 41 has determined to be "permissible" (operation feasible pattern data) to the control device 12 of the energy storage system 1.

[0079] The operation pattern creation device 2, the battery simulation device 3, and the battery operation management device 4 are all realized by executing a predetermined program on a general-purpose or dedicated computer that has a control unit equipped with a CPU, ROM, RAM, etc., an input operation unit such as a mouse, keyboard, or touch panel, a display or other display unit, a storage unit such as a hard disk or SSD, etc. Therefore, the components of each device, such as the reference simulation execution unit 31 and correction processing unit 32 of the battery simulation device 3, and the operation feasibility determination unit 41 and charge / discharge management unit 42 of the battery operation management device 4, are functional components that are virtually realized by the computer.

[0080] Furthermore, the battery simulation device 3 and the battery operation management device 4 may be implemented by a single computer, or the operation pattern creation device 2 and the battery simulation device 3 may be implemented by a single computer. Alternatively, the operation pattern creation device 2, the battery simulation device 3, and the battery operation management device 4 may all be implemented by a single computer. In addition, the operation feasibility determination unit 41 of the battery operation management device 4 may be provided in the battery simulation device 3 instead of the battery operation management device 4.

[0081] When the control device 12 of the energy storage system 1 obtains operational pattern data from the charge / discharge management unit 42 of the battery operation management device 4, it controls the charging and discharging of the battery 11 based on the contents of that data. At the same time, the control device 12 also controls the ON / OFF status of the heaters 14 and fans 15 of each of the module batteries 11(1) to 11(n).

[0082] Furthermore, in the battery 11, the temperature sensor 16 measures the temperature (actual temperature) of each module battery 11(1) to 11(n) regardless of whether it is operating (charging / discharging) or in standby mode. The measurement results from the temperature sensor 16 are sequentially sent to the correction processing unit 32 of the battery simulation device 3. In addition, the real-time voltage and current values ​​(actual voltage and actual current) of each module battery 11(1) to 11(n) are also sequentially sent to the correction processing unit 32 of the battery simulation device 3. These actual temperature, actual voltage, and actual current values ​​are used in the correction processing in the correction processing unit 32.

[0083] <Simulation Details> Next, the content of the simulation performed by the battery simulation device 3 will be explained. In this embodiment, as disclosed in Patent Document 1, the battery temperature and SOC are assumed to be the state indicator values ​​that are the subject of the simulation, and of these, the battery temperature is assumed to be the subject of a two-stage simulation. On the other hand, regarding SOC, since individual differences are unlikely to occur as long as each module battery 11(1) to 11(n) is operating normally, the results of the reference simulation will be used as the simulation results for each of the module batteries 11(1) to 11(n).

[0084] (Reference Simulation) The reference simulation is performed using the same procedure as conventional simulations, with pre-set simulation parameters (reference parameters) such as the internal resistance of the battery, the heat dissipation amount of the module battery, and the thermal capacity of the module battery. The reference parameters are stored in a memory unit (not shown) of the battery simulation device 3.

[0085] In the reference simulation, the SOC and battery temperature of all module batteries 11(1) to 11(n) are assumed to change in the same way, without distinguishing between individual module batteries 11(1) to 11(n), and each is represented by a single simulation value.

[0086] The final simulation results obtained are used as the baseline simulation value for battery temperature and the simulation value for SOC.

[0087] (Individual Simulations) On the other hand, individual simulations targeting battery temperature are performed based on the following equation (1).

[0088] Tsc_m(i) = Tsc_m(i-1) + (Tss(i) - Tss(i-1)) + Te_m(i) ... (1) In equation (1), Tsc_m(i) is the simulated value (individual simulated value) of the battery temperature at the i-th calculation target time (i is a natural number of 2 or more) in the operational output time of module battery 11(m), which is any one of module batteries 11(1) to 11(n) (m is a natural number between 1 and ≤ m ≤ n); Tss(i) is the reference simulated value of the battery temperature at time i obtained in the reference simulation; Te_m(i) is the correction amount of the battery temperature of module battery 11(m) at time i; For simplicity, the i-th calculation target time will also be referred to as calculation target time i from here on.

[0089] More specifically, equation (1) is based on the relationship that the difference value Tss(i) - Tss(i-1), which corresponds to the temperature change between calculation target time i-1 and calculation target time i, obtained from the results of a reference simulation and corresponding to a certain time step Δt, is equal to the change in simulated temperature Tsc_m(i) - Tsc_m(i-1) of the module battery 11(m) between calculation target time i-1 and calculation target time i, and is obtained by adding a correction amount Te_m(i). In this embodiment, individual simulated values ​​of the battery temperature of the module battery 11(m) at each calculation target time i are obtained by correcting the results of the reference simulation sequentially from i=1 based on this relationship.

[0090] In such cases, the correction amount Te_m(i) for a certain module battery 11(m) is determined based on the results of past reference simulations and the measured temperature of the module battery 11(m), which are stored in advance in a memory unit (not shown) of the battery simulation device 3.

[0091] Specifically, it is determined based on the deviation ΔTm(j) between the time change of the battery temperature in the reference simulation and the time change of the measured temperature (actual temperature) of the module battery 11(m) for a certain past time j, calculated by equation (2) below. Note that time j is assumed to be in the past than the calculation target time i.

[0092] ΔTm(j) = (Tm_m(j) - Tm_m(j-1)) - (Tss(j) - Tss(j-1)) ... (2) In equation (2), Tm_m(j) is the actual temperature of the module battery 11(m) at past time j.

[0093] In order to enable the calculation of the deviation ΔTm(j), in this embodiment, the correction processing unit 32 acquires the temperature (actual temperature) of each module battery 11(1) to 11(n) from the temperature sensor 16 provided in each module during the operation of the storage battery 11 (during the operating period). The correction processing unit 32 also acquires operational pattern data from the charge / discharge management unit 42. As a result, the reference simulation value described in the operational pattern data is stored as Tss(j).

[0094] The correction processing unit 32 uses the past actual temperature data obtained in this way and the calculated values ​​from a reference simulation performed on the time when the data was measured to continuously calculate the deviation ΔTm(j) according to equation (2) at time step Δt and store it in a storage unit (not shown).

[0095] In one exemplary embodiment, the correction processing unit 32 obtains the moving average value of the stored deviation ΔTm(j) over a predetermined time as the correction amount Te_m(i).

[0096] Furthermore, if the operating state of the battery 11 at the calculation target time i, which is assumed based on the operation pattern targeted for simulation, is similar to the actual operating state of the battery 11 at a past time j, then instead of adopting the moving average value described above, the correction amount Te_m(j) adopted at time j may be obtained as the correction amount Te_m(i) at the calculation target time i.

[0097] In such cases, the determination of similarity in operating states can be made based on the degree of difference between the assumed value for time i and the actual value for past time j regarding the charge / discharge power, state of charge (SOC), battery temperature, or state of degradation (SOH) of the storage battery 11. To enable this determination, the correction processing unit 32 acquires measured values ​​(actual voltage and actual current) of voltage and power from each of the module batteries 11(1) to 11(n).

[0098] <Processing Flow> Figure 3 is a schematic diagram showing the simulation processing flow, along with the preceding and succeeding processes.

[0099] First, an operation pattern for the battery 11 is created in advance in the operation pattern creation device 2, and the reference simulation execution unit 31 of the battery simulation device 3 acquires operation pattern data from the operation pattern creation device 2, which describes the contents of the said operation pattern (step S1).

[0100] An operational pattern is created for a specific operational output time. Typically, this operational output time is divided into multiple "frames," and the operational pattern is set for each frame. One frame is, for example, 30 minutes long.

[0101] The reference simulation execution unit 31, which has acquired the operational pattern data, performs a reference simulation based on the contents of that description (step S2). The reference simulation is performed to obtain a single simulation result that will serve as the basis for the simulations for each individual module battery 11(1) to 11(n).

[0102] Figure 4 shows the flow of a reference simulation. This reference simulation is based on the battery simulation disclosed in Patent Document 1. Figure 4 illustrates a simulation targeting a particular operating output time. If the operating pattern is divided into multiple "frames," the reference simulation shown in Figure 4 is performed for each frame. Hereafter, the explanation will be given using the case where the operating pattern is set on a frame-by-frame basis as an example.

[0103] First, initial values ​​are set for various variables used in the simulation (step S101). The variables targeted for initial value setting are: operating output time: T; time step: Δt; operating output: Pn; remaining capacity: SOC; battery temperature: Temp.

[0104] More specifically, the operating output time T is 30 minutes, which is the length of the time frame. The time step Δt is set to approximately 10 seconds to 10 minutes. The operating output Pn is set to the charge / discharge power value of the time frame described in the operating pattern.

[0105] The initial value of the remaining capacity (SOC) is set based on the discharge depth control value at the start of the operation pattern being simulated. In this embodiment, the remaining capacity (SOC) is assumed to be 100% - discharge depth (control value).

[0106] The initial value of the battery temperature Temp is set in the range of 305°C to 360°C, according to the initial value of the remaining capacity SOC, that is, according to the degree of charging and discharging in the storage battery 11.

[0107] Next, the number of loops N executed in the latter half of the simulation is calculated using the formula N = T / Δt (step S102).

[0108] Next, with n set to 1 (step S103), the battery current In is determined from the operational output Pn (step S104).

[0109] Furthermore, using the battery current In value calculated in step S104, the time step Δt set as the initial value in step S101, and the most recent remaining capacity SOC value, the remaining capacity SOC at the point when time has advanced by Δt is calculated using the formula SOC = SOC - In × Δt (step S105).

[0110] Furthermore, the battery temperature Temp at a time interval of Δt is calculated using the following formula: Temp = Temp + (In × In × r) × Δt / C, based on the value of the battery current In calculated in step S104, the time step Δt set as an initial value in step S101, the value of the most recent battery temperature Temp, and the internal resistance r, heat dissipation loss, and heat capacity C, which are set as reference parameters in advance as described above (step S106). Note that the order of steps S105 and S106 may be reversed, and both may be performed in parallel.

[0111] Next, n = n + 1 is set (step S107), and if n > N is not true for the new n (NO in step S108), then steps S105 to S107 are repeated at time intervals Δt.

[0112] On the other hand, if n > N (YES in step S108), the latest calculated values ​​of the remaining capacity (SOC) and the battery temperature (Temp) are output as the result of the reference simulation (step S109).

[0113] The results of the standard simulation obtained in this manner, along with the operational patterns targeted for the simulation, are passed to the correction processing unit 32 as standard simulation result data and used for individual simulations.

[0114] The reference simulation result data, which describes the results of the reference simulation, is passed to the correction processing unit 32. The correction processing unit 32, having acquired the reference simulation result data, acquires a correction amount Te_m(i) for each of the module batteries 11(1) to 11(n) for all calculation target time i within the operating output time (step S3), in order to enable individual simulations based on equation (1) described above.

[0115] Once the correction amount Te_m(i) is obtained for all calculation target time i, the correction processing unit 32 individually determines for each of the module batteries 11(1) to 11(n) whether the obtained correction amount Te_m(i) is within the acceptable range (between the upper and lower correction limits) for the correction amount, which is stored in a predetermined storage unit (not shown) of the battery simulation device 3 (step S4).

[0116] This determination is made to prevent excessive correction from being applied during individual simulations due to noise or abnormal measurement values ​​caused by some factor. This approach is particularly effective in the early stages of simulation execution when an appropriate correction amount cannot be obtained because there is little accumulated actual values ​​(operational history data) acquired from the battery 11.

[0117] If the acquired correction amount Te_m(i) is determined to be within the acceptable range (YES in step S4), the correction processing unit 32 uses the value of the correction amount Te_m(i) to calculate the value of Tsc_m(i) of the module battery 11(m) according to equation (1) (step S5a). On the other hand, if the acquired correction amount Te_m(i) is outside the acceptable range (NO in step S4), the correction processing unit 32 replaces the value of the correction amount Te_m(i) with an upper limit correction amount if it is excessively large, and with a lower limit correction amount if it is less than the acceptable range, and then calculates the value of Tsc_m(i) of the module battery 11(m) according to equation (1) (step S5b).

[0118] Alternatively, if the acquired correction amount Te_m(i) falls outside the acceptable range, no correction may be performed, and the result of the reference simulation may be used as is for the calculation time i. In other words, the correction amount Te_m(i) may be set to 0.

[0119] This means that individual simulations of the battery temperature over the entire operating output time described in the operating pattern have been performed for all of the module batteries 11(1) to 11(n).

[0120] The correction processing unit 32 generates individual module simulation result data, which describes the results of the individual simulations together with the operation pattern that was the subject of the simulation and the results of the reference simulation on which the individual simulations were based, and passes this data to the battery operation management device 4.

[0121] In the battery operation management device 4, which has acquired the individual module simulation result data, the operation feasibility determination unit 41 makes a determination as to whether the battery 11 is operational (step S6).

[0122] The operation feasibility determination unit 41 refers to the description content of the individual module simulation result data and determines whether or not there are modules that cannot be operated, thereby determining whether or not the battery 11 can be operated (charged / discharged) based on the operation pattern that was the subject of the simulation.

[0123] Specifically, for each of the module batteries 11(1) to 11(n), it is determined, based on the description in the individual module simulation result data, whether or not the operating conditions (for example, the allowable range of battery temperature and SOC) may be exceeded at some point during the operation output time (at any of the calculation target time i).

[0124] If even one of the module batteries 11(1) to 11(n) deviates from the operating condition range, it is determined that the battery 11 cannot be operated because there is an unusable module.

[0125] If the operating conditions are met for all module batteries 11(1) to 11(n) throughout the entire operating output time, there are no unoperable modules and all module batteries 11(1) to 11(n) are operational. Therefore, it is determined that the operation of the battery 11 is "possible" based on the operation pattern data that was the subject of the simulation.

[0126] The judgment result (operation feasibility judgment result) is notified to the operation pattern creation device 2. If there are modules that cannot be operated and operation is determined to be "not" possible, the operation pattern creation device 2 will modify or recreate the operation pattern data.

[0127] On the other hand, if operation is determined to be "possible," the operation pattern data (operation-possible pattern data) is passed from the charge / discharge management unit 42 to the control device 12 of the energy storage system 1. The control device 12, having acquired the operation-possible pattern data, controls the battery 11 according to its description, thereby putting the battery 11 into operation (actual operation) (step S7).

[0128] Furthermore, during actual operation, the correction processing unit 32 acquires actual temperature, actual current, and actual voltage data (actual data) for each of the module batteries 11(1) to 11(n) and records them in a storage unit (not shown) of the battery simulation device 3 (step S8). In addition, the reference simulation values ​​described in the operational pattern data are also recorded as Tss(j) in a storage unit (not shown) of the battery simulation device 3. These actual data and reference simulation values ​​are used to calculate the deviation ΔTm(j) based on equation (2) and to accumulate the correction amount Te_m(j). These will be used when executing a new simulation.

[0129] As described above, in the simulation of the battery operation patterns performed in this embodiment, the simulation parameters for each module battery 11(1) to 11(n), as in the conventional method, are not set. Therefore, the amount of computation required for such settings (let's call it a) is reduced. Although the amount of computation required for individual simulations (let's call it b) is necessary, since a > b, according to this embodiment, it is possible to perform a simulation that takes into account the individual differences of each module battery 11(1) to 11(n) that constitute the battery 11 with less computation than the conventional method. In other words, it is possible to perform a simulation that takes into account the individual differences of each module battery 11(1) to 11(n) without setting parameters individually for each module battery 11(1) to 11(n) and performing a simulation.

[0130] However, if, at many calculation time points i, the actual temperatures of each of the module batteries 11(1) to 11(n) are above the reference simulation value Tss(i), causing the correction amount Te_m(i) to be biased towards the positive side, or if the actual temperatures of each of the module batteries 11(1) to 11(n) are below the reference simulation value Tss(i), causing the correction amount Te_m(i) to be biased towards the negative side, it is preferable to redo the reference simulation from the viewpoint of maintaining the accuracy of the simulation.

[0131] <Second Embodiment> <Determination of Correction Amount Based on Decision Tree> In this embodiment, the value of the correction amount Te_m(i) required for individual simulations based on equation (1), which in the first embodiment used the moving average of the deviation ΔTm(j) calculated by equation (2) or values ​​from similar past operating states, is determined based on the results of a learning process targeting a predetermined learning target period defined for the past operating period of the battery 11. In other words, this embodiment differs from the first embodiment in how the correction amount Te_m(i) for the correction processing in the correction processing unit 32 is set. Therefore, the configuration of the energy storage system 1, operation pattern creation device 2, battery simulation device 3, and battery operation management device 4 shown in the first embodiment, as well as the processing other than the setting of the correction amount Te_m(i), are almost the same in this embodiment, so the details of these devices and processes are omitted.

[0132] Figure 5 shows the steps of the learning process. Figure 6 is an example of a decision tree obtained through the learning process.

[0133] In general terms, the learning process learns combinations of the deviation ΔTm(j) values ​​of each module battery 11(1) to 11(n) at all time points j for each time step Δt of the learning period, and various feature quantities that characterize the operating state of the battery 11 (each module battery 11(1) to 11(n)). Based on the learned content, a decision tree consisting of multiple levels with progressively set sorting conditions is created. The learned content and the data of the created decision tree are stored in a memory unit (not shown) of the battery simulation device 3. During the simulation, the value of the correction amount Te_m(i) for the target time point i of the simulation is determined based on the decision tree defined in the learning process.

[0134] Examples of learning periods include one day, ten days, one month, and one year. Alternatively, the learning period may be set to the time it takes for the battery to reach 1000 charge-discharge cycles or the time it takes for the discharged energy to reach 100 MWh. The learning period may also be extended according to the elapsed time since the initial operation of the battery 11.

[0135] Features do not necessarily have to be quantitative values ​​represented by continuous values; they may be determined alternately or qualitatively. Examples of such features include the operating state of the battery 11 (discharge, charge, stop), charge / discharge power, SOC (or depth of discharge DOD), actual temperature, operating state of the heater 14 and fan 15 (on / off), number of charge / discharge cycles, number of cells lost function, and charge / discharge history index value. Charge / discharge power may be represented as a feature that reflects the operating state by representing it as a positive value during discharge, a negative value during charging, and 0 when charge / discharge is stopped. Alternatively, decision trees may be created individually for each case: during discharge, during charging, and when charge / discharge is stopped.

[0136] The inclusion of operating state and charge / discharge power as feature quantities takes into account that the amount of heat generated in module batteries 11(1) to 11(n) differs depending on these factors.

[0137] The reason for including SOC (or DOD) as a feature is that the internal resistance of the module batteries 11(1) to 11(n) and the chemical reactions occurring within the single cell 13c differ depending on the SOC (or DOD) value, and therefore the amount of heat generated differs. In the range of DOD where the amount of heat generated is large, the temperature rise tends to be larger, and the difference between the simulated value and the measured value also tends to be larger.

[0138] The reason for including the operating states of the heater 14 and fan 15 as features is to take into account that the way the temperature of the module batteries 11(1) to 11(n) rises differs when they are operating and when they are not.

[0139] Furthermore, the number of cells that have lost their function refers to the number of individual cells 13c that have stopped operating due to malfunction, abnormality, or other reasons after the battery 11 has been put into use.

[0140] Furthermore, examples of charge / discharge history index values ​​include the cumulative value of charge / discharge power over a predetermined period of time in the past (≒ SOC change), where charging power is negative and discharging power is positive, and the elapsed time since the start of discharge (or charge).

[0141] In order to enable such learning processing and setting of the correction amount Te_m(i), the correction processing unit 32, as in the first embodiment, acquires actual temperature, actual voltage, and actual current from each of the module batteries 11(1) to 11(n). Then, for each time step Δt, these measured values ​​and the values ​​of the feature quantities calculated based on these measured values ​​are stored in a storage unit (not shown) of the battery simulation device 3. This data will be used for future simulations. In addition, some feature quantities are identified based on the description content of the operational pattern data, and may also be acquired from the control device 12 of the energy storage system 1.

[0142] The learning process can be started at any appropriate time after the battery 11 first starts operating, and the simulation can be run as long as the learning process is performed. However, immediately after the battery 11 starts operating, there is not enough time for the learning target period, and therefore the amount of learning target data accumulated is small. As the total operating time of the battery 11 increases and a sufficient time for the learning target period is secured, the accuracy of the simulation improves.

[0143] As shown in Figure 5, in the learning process using a decision tree, the correction processing unit 32 first reads the value of a feature at a past time j included in the learning period from the storage unit (not shown) of the battery simulation device 3 as the j-th learning target data (step S11).

[0144] Then, the correction processing unit 32 calculates the deviation ΔTm(j) based on equation (2) using the value described in the j-th data to be learned that was read out (step S12).

[0145] The correction processing unit 32 also identifies the decision tree node corresponding to the feature value of the j-th data to be learned, and records the value of the deviation ΔTm(j) calculated based on equation (2) in the storage unit, associating it with each node (step S13). As a result, the value of the deviation ΔTm(j) corresponding to the j-th data to be learned is assigned to one of the termination nodes.

[0146] In Figure 6, for simplicity, an example is shown where a decision tree is defined using the operating state, SOC, and fan 15 operating state from the features described above. In the decision tree shown in Figure 6, the deviation ΔTm(j) is distributed based on whether the operating state is discharged or not at the top level (root node), whether the SOC is less than 50% or not at the next level, and whether the fan 15 is on or not at the final level. As a result, eight termination nodes are set.

[0147] For example, if the j-th data point to be studied is from a time when the battery 11 was discharged, the SOC was 50% or more, and the fan 15 was not operating, then its deviation ΔTm(j) will be allocated to the fourth final node from the left.

[0148] If there is further training data that has not been sorted (recorded) based on this decision tree (NO in step S14), steps S11 to S13 are repeated.

[0149] Once all the training data has been sorted (recorded) (YES in step S14), the average of the corresponding (sorted) deviations ΔTm(j) is calculated as the average deviation ΔTd(Ne) for each completion node Ne, and this is recorded in association with each completion node Ne (step S15). Figure 6 also shows the calculation results of the average deviation ΔTd(Ne) for each completion node Ne.

[0150] Figure 7 shows the procedure for determining the value of the correction amount Te_m(i) for the calculation target time i in an individual simulation.

[0151] The correction processing unit 32 first reads out the value of the feature used for distribution based on the decision tree at the calculation target time i (expected value) as the i-th feature (step S21). Specifically, this expected value is set based on the description of the operation pattern data included in the reference simulation data. When using the decision tree shown in Figure 6, the operating state of the battery 11 assumed at the calculation target time i, the SOC value at the calculation target time i calculated in the reference simulation, and the operating state of the fan 15 at the calculation target time i are read out as the i-th feature.

[0152] Then, the correction processing unit 32 identifies the node corresponding to the i-th feature that was read out, according to a predetermined decision tree, reads out the value of the mean deviation ΔTd(Ne) associated with the corresponding termination node Ne (step S22), and sets that value as the correction amount Te_m(i) (output) (step S23).

[0153] For example, if at a certain calculation time i the battery is in a charged state, the SOC value obtained from the reference simulation is 45%, and the fan 15 is operating, then these feature quantities will be associated with the fifth termination node Ne from the left in Figure 6. Therefore, the correction processing unit 32 sets the average deviation ΔTd(Ne) of 0.04 associated with that termination node as the value of the correction amount Te_m(i) at the calculation time i.

[0154] Furthermore, the concept of the acceptable range in the first embodiment can also be applied to the value of the correction amount Te_m(i) obtained in this embodiment. However, in this embodiment, since the correction amount Te_m(i) is set to the value of the average deviation ΔTd(Ne) associated with each termination node, it is determined whether or not the value of the average deviation ΔTd(Ne) falls outside the applicable range.

[0155] In other words, if the value of the average deviation ΔTd(Ne) is greater than the acceptable range, it is replaced with an upper limit correction amount, and if it is less than the acceptable range, it is replaced with a lower limit correction amount.

[0156] Alternatively, if the value of the average deviation ΔTd(Ne) falls outside the acceptable range, ΔTd(Ne) may be set to 0.

[0157] Even when determining the value of the correction amount Te_m(i) as in this embodiment, it is possible to perform a simulation that takes into account the individual differences of each module battery 11(1) to 11(n) constituting the storage battery 11 with less computation than conventional methods, similar to the first embodiment. Alternatively, it is possible to perform a simulation that takes into account the individual differences of each module battery 11(1) to 11(n) without individually setting parameters for each module battery 11(1) to 11(n) and performing a simulation.

[0158] Furthermore, the operational patterns determined to be operational based on this embodiment may be simulated again using the method of the first embodiment or a conventional method to determine whether they are operational again or to further optimize the operational patterns. For example, based on the results of the simulation using the decision tree performed in this embodiment, a preliminary determination of operational feasibility and optimization of the operational patterns may be made, and then a simulation may be performed again using the method of the first embodiment or a conventional method, and a final determination of operational feasibility may be made based on the results.

[0159] In such cases, the operational pattern can be more optimally optimized, allowing for a more accurate determination of whether or not operation is feasible. Furthermore, if the initial determination of feasibility of operation is negative, the total time required to create the operational pattern is likely to be reduced compared to the method of creating an operational pattern again using a decision tree. Moreover, when using the energy storage system 1 for the purpose of generating revenue through electricity trading in the electricity market, it may be possible to derive an operational plan that is expected to yield higher profits.

[0160] <Creating a decision tree with a continuous threshold> When creating a decision tree to determine the value of the correction amount Te_m(i), features whose values ​​change continuously (take continuous values) may be used. Figure 8 is an example of a multi-layered decision tree created using SOC, one such feature.

[0161] In the decision tree shown in Figure 8, the top level (root node) assigns a deviation ΔTm(j) based on whether the SOC is less than 62% or not. If the SOC is less than 62%, the next level assigns a deviation ΔTm(j) based on whether the SOC is less than 37% or not. On the other hand, if the SOC is not less than 62% (i.e., 62% or more), the next level assigns a deviation ΔTm(j) based on whether the SOC is (62% or more) less than 92% or not. As a result, four termination nodes Ne are set.

[0162] When creating a decision tree using features that take continuous values ​​in this way, it is preferable to set the threshold so that the impurity (variability) of the deviation ΔTm(j) values ​​that apply to each node is small. This impurity can be expressed, for example, by the variance (or standard deviation) of the corresponding deviation ΔTm(j) values.

[0163] Figure 9 illustrates a decision tree in which layers based on non-continuous features are interposed between layers based on continuous features. In the decision tree shown in Figure 9, a layer based on the operating state of fan 15 is interposed after the decision tree shown in Figure 8 when the SOC is less than 62%. The layers after this layer are the same as those in the decision tree shown in Figure 8. As a result, six termination nodes Ne are set.

[0164] When setting a threshold for a hierarchy based on features that take continuous values ​​to minimize impurity, depending on the size of the set threshold, it may be possible to perform more appropriate sorting by interposing a hierarchy based on features that do not take continuous values, as shown here.

[0165] Figure 9 illustrates a case where the same feature (specifically, SOC) is used as a branching condition at different levels, and the same threshold is used at lower levels regardless of the branching result at higher levels. However, when constructing a regression tree to minimize the aforementioned impurity, the branching conditions at each level usually differ from node to node.

[0166] <Addressing Degradation Over Time> When the battery 11 is operated continuously, the characteristics of individual module batteries 11(1) to 11(n) change due to degradation over time. Therefore, if a decision tree created with the initial operation of the battery 11 or a period close to it set as the learning target period is used continuously, the risk of the difference between the simulated values ​​and the measured values ​​becoming larger will gradually increase. It is preferable to perform the learning process for creating the decision tree while taking this degradation over time into consideration.

[0167] (Example 1) For example, the learning period may be reset at successive intervals and the decision tree may be updated. By performing simulations based on a new decision tree that reflects the deterioration over time, the difference between the simulated values ​​and the measured values ​​can be kept small.

[0168] (Example 2) For example, multiple (new and old) learning periods of the same length, such as every month, may be set sequentially, and a decision tree may be created for each period using the same features. When setting the correction amount Te_m(i), the node corresponding to the i-th feature may be identified in each decision tree, the value of the mean deviation ΔTd(Ne) associated with the corresponding termination node Ne may be read, and the average of the mean deviations ΔTd(Ne) obtained from each decision tree may be used as the value of the correction amount Te_m(i). In this case, the maximum number of decision trees to use may be kept constant, and if the number of decision trees exceeds the maximum number when a newly created decision tree is added, the oldest decision tree may be excluded.

[0169] (Example 3) When using multiple decision trees, both old and new, as in Example 2, the old decision tree may be corrected before use. Specifically, when the average value of the output of the root node of the old decision tree (the average value of all deviations ΔTm(j) used to create the decision tree) is set as Ave_old, and the average value of the output of the root node of the latest decision tree (the average value of all deviations ΔTm(j) used to create the decision tree) is set as Ave_new, the average deviation ΔTd(Ne) associated with each termination node Ne of the old decision tree may be corrected using the following equation (3).

[0170] Corrected ΔTd(Ne) = Uncorrected ΔTd(Ne) + (Ave_new - Ave_old) ... (3) (Example 4) In the case of using multiple decision trees, both old and new, as in Example 2, the weighted average of the mean deviations ΔTd(Ne) obtained by increasing the weighting coefficient of the mean deviation ΔTd(Ne) obtained from the new decision tree and decreasing the weighting coefficient of the mean deviation ΔTd(Ne) obtained from the old decision tree may be used as the value of the correction amount Te_m(i). In this case, since the number of data to be learned is secured, the change in the state of the battery 11 can be reflected in the value of the correction amount Te_m(i) while ensuring the stability of the value applied as the correction amount Te_m(i).

[0171] <Third Embodiment> <Expansion of Individual Simulation Scope> In the first and second embodiments, a two-stage simulation consisting of a reference simulation and an individual simulation is employed for the battery temperature of each module battery 11(1) to 11(n). This reduces the amount of computation compared to conventional methods while enabling simulations that take into account the individual differences of each module battery 11(1) to 11(n).

[0172] In this embodiment, the correction concept used in individual simulations is extended to several state indicator values ​​other than battery temperature. Simulating these state indicator values, which were previously considered common to all module batteries 11(1) to 11(n), now takes into account the individual differences of each module battery 11(1) to 11(n). In general terms, by adding a correction amount for each module battery 11(1) to 11(n) to the value obtained from the reference simulation, simulation values ​​that take individual differences into account are obtained. This makes it possible to perform simulations that take into account the individual differences of each module battery 11(1) to 11(n) for state indicator values ​​other than battery temperature, without having to individually set parameters for each module battery 11(1) to 11(n) and perform simulations, as in the first and second embodiments. Furthermore, by including state indicator values ​​other than battery temperature in the simulation, the state of the storage battery 11 can be simulated in more detail.

[0173] In this embodiment as well, similar to the first and second embodiments, the simulation results are described in individual module simulation result data and passed to the operational feasibility determination unit 41. The results of the individual simulations relating to state index values ​​other than battery temperature, described in the data, may be used to determine whether the storage battery 11 is operational or not.

[0174] Examples of state indicator values ​​that can be simulated in this way include internal resistance, discharge end-stop depth, and charge end-stop depth. These will be explained in detail below. Note that the types of features to be learned and the learning period may be the same as or different from those used for learning battery temperature. Furthermore, all or part of battery temperature, internal resistance, discharge end-stop depth, and charge end-stop depth may be simulated in parallel.

[0175] (Internal Resistance) Individual simulations of the internal resistance of module battery 11(m), which is any one of module batteries 11(1) to 11(n), are performed based on the following equation (4).

[0176] Rsc_m(i) = Rss(i) + Re_m(i) ... (4) In equation (4), i is a natural number, and Rsc_m(i): Simulation value of the internal resistance of module battery 11(m) at calculation time i (individual simulation value); Rss(i): Reference simulation value of the internal resistance at calculation time i based on the reference simulation result; Re_m(i): Correction amount of the internal resistance of module battery 11(m) at calculation time i.

[0177] In addition, Rss(i) has conventionally been a fixed value in battery temperature simulations, but it may be calculated based on the calculation target time i using a reference simulation, or it may be set as a function of Tss(i), which is a reference simulation value for battery temperature, and the depth of discharge, or it may be a fixed value as in the conventional case. In any case, its value is written to the reference simulation data in the reference simulation execution unit 31 and is referenced by the correction processing unit 32 during individual simulations.

[0178] The correction amount Re_m(i) is determined based on the deviation ΔRm(j) calculated by the following equation (5).

[0179] ΔRm(j) = Rm_m(j) - Rss(j) ... (5) In equation (5), Rm_m(j) is the calculated value of the internal resistance of the module battery 11(m) based on the actual temperature at past time j.

[0180] In setting the correction amount Re_m(i) based on the deviation ΔRm(j), for example, as in the first embodiment, a moving average of the accumulated deviation ΔRm(j) may be adopted, or if the operating state of the battery 11 assumed at the calculation target time i is similar to the actual operating state of the battery 11 at a past time j, the correction amount Re_m(j) adopted at time j may be obtained as the correction amount Re_m(i). Alternatively, as in the second embodiment, it may be determined using a decision tree created based on the deviation ΔRm(j) data during a certain learning target period. The feature quantities and learning target period used in this case may be the same as or different from those used when learning about battery temperature.

[0181] (End-of-discharge and end-of-charge depth) First, the end-of-discharge depth and end-of-charge depth will be explained based on Figure 10. Figure 10 is a schematic graph showing the relationship between the end-of-discharge depth and voltage in a NaS battery.

[0182] As shown in Figure 10, the voltage (charge / discharge voltage) of the NaS battery is kept at a generally constant value V0 except near the end of discharge where the depth of discharge is maximum and near the end of charging where the depth of discharge is minimum. However, it decreases near the end of discharge and increases near the end of charging. Based on these characteristics, the charge / discharge voltages of each module battery 11(1) to 11(n) of the storage battery 11 are predetermined to be the voltage at which the end of discharge is considered to have been reached and further discharge is stopped (discharge stop voltage) VL, and the voltage at which the end of charging is considered to have been reached and further charging is stopped (charge stop voltage) VH.

[0183] The discharge depth at which the voltage of each module battery 11(1) to 11(n) reaches the discharge stop voltage VL, and the discharge depth at which it reaches the charge stop voltage VH, are referred to as the discharge end stop depth and the charge end stop depth, respectively.

[0184] However, as shown in Figure 10, the voltage behavior near the end of discharge and near the end of charge in each module battery 11(1) to 11(n) is not necessarily uniform. Therefore, the values ​​of the end-of-discharge shutdown depth and the end-of-charge shutdown depth in each module battery 11(1) to 11(n) can usually differ. For this reason, individually simulating the end-of-discharge shutdown depth and the end-of-charge shutdown depth for each module battery 11(1) to 11(n) is significant in order to operate the battery 11 to its maximum capacity without causing a shutdown of charge and discharge in each module battery 11(1) to 11(n).

[0185] Individual simulations of the discharge end-stop depth of module battery 11(m), which is any one of module batteries 11(1) to 11(n), are performed based on the following equation (6a).

[0186] DLsc_m(i) = DLss(i) + DLe_m(i) ... (6a) In equation (6a), i is a natural number, and DLsc_m(i): Simulation value (individual simulation value) of the discharge end-stop depth of module battery 11(m) at calculation time i; DLss(i): Reference simulation value of the discharge end-stop depth at calculation time i based on the reference simulation result; DLe_m(i): Correction amount of the discharge end-stop depth of module battery 11(m) at calculation time i.

[0187] Similarly, individual simulations of the end-of-charge stop depth for any one of the module batteries 11(1) to 11(n), namely module battery 11(m), are performed based on the following equation (6b).

[0188] DHsc_m(i) = DHss(i) + DHe_m(i) ... (6b) In equation (6b), i is a natural number, and DLsc_m(i): Simulation value (individual simulation value) of the charging end stop depth of module battery 11(m) at the calculation target time i; DHss(i): Reference simulation value of the charging end stop depth at the calculation target time i based on the reference simulation result; DHe_m(i): Correction amount of the charging end stop depth of module battery 11(m) at the calculation target time i.

[0189] DLss(i) and DHss(i) may be calculated based on the calculation time i using a reference simulation, or they may be fixed values. In either case, the values ​​are written to the reference simulation data in the reference simulation execution unit 31 and referenced by the correction processing unit 32 during individual simulations.

[0190] The correction amounts DLe_m(i) and DHe_m(i) are determined based on the deviations ΔDLm(j) and ΔDHm(j) calculated by equations (7a) and (7b) below, respectively.

[0191] ΔDLm(j) = DLm_m(j) - DLss(j) ... (7a) ΔDhm(j) = DHm_m(j) - DHss(j) ... (7b) In equations (7a) and (7b), DLm_m(j): The value of the depth of discharge when the module battery 11(m) reaches the discharge stop voltage VL either before or after time j, and at the most recent time; DHm_m(j): The value of the depth of discharge when the module battery 11(m) reaches the charge stop voltage VH either before or after time j, and at the most recent time;

[0192] When setting the correction amount DLe_m(i) based on the deviation ΔDLm(j) and the correction amount DHe_m(i) based on the deviation ΔDHm(j), for example, as in the first embodiment, the moving average value of the accumulated deviations ΔDLm(j) and ΔDHm(j) may be adopted. Alternatively, if the operating state of the battery 11 assumed at the calculation target time i is similar to the actual operating state of the battery 11 at a past time j, the correction amounts DLe_m(j) and DHe_m(j) adopted at time j may be obtained as the correction amounts DLe_m(i) and DHe_m(i). Or, as in the second embodiment, the correction amounts may be determined using a decision tree created based on the data of deviations ΔDLm(j) and ΔDHm(j) during a certain learning target period. The feature quantities and learning target period used in this case may be the same as or different from those used when learning about battery temperature.

[0193] In the case of lithium-ion batteries, the battery voltage tends to increase with increasing charge level, but the dependence between charging and discharging differs, and this dependence also changes depending on the battery's degradation state. Therefore, even when a battery system is composed of multiple lithium-ion batteries, it is worthwhile to individually simulate the discharge end-stop depth and charge end-stop depth for each lithium-ion battery, similar to the case of NaS batteries.

[0194] <Fourth Embodiment> In this embodiment, the simulation results of the internal resistance described in the third embodiment are used to estimate failures in each of the module batteries 11(1) to 11(n). Examples of failures to be estimated include failures (loss of function) of the single cell 13c and failures of the fan 15.

[0195] If each of the module batteries 11(1) to 11(n) is composed of a battery array 13 in which a large number of single cells 13c are connected, for example, as shown in Figure 2, then if any single cell 13c fails in a module battery 11(m), current will no longer flow to the string 13s to which the affected single cell 13c belongs. As a result, the module battery 11(m) will perform charging and discharging operations with fewer strings 13s than in a normal state. Consequently, the internal resistance of the module battery 11(m) will increase significantly beyond what would be expected due to deterioration over time.

[0196] Furthermore, if the fan 15 fails in a certain battery module 11(m), the inside of the housing 11a of that battery module 11(m) will not be cooled, and the temperature will rise. As a result, the temperature of each individual cell 13c will rise uniformly, and their resistance will increase. In this case as well, the internal resistance of the battery module 11(m) will increase significantly beyond what would be expected due to deterioration over time.

[0197] These failure estimations are performed, for example, in the operation feasibility determination unit 41 of the battery operation management device 4, which acquires individual module simulation result data describing the results of individual simulations related to internal resistance. In this case, the operation feasibility determination unit 41 estimates failures in conjunction with determining the feasibility of operation for the operation pattern to be judged.

[0198] Specifically, the operational feasibility determination unit 41 calculates a ratio Rm_m(k) / Rsc_m(k) or a difference Rm_m(k)-Rsc_m(k) between the individual simulation value Rsc_m(k) of the internal resistance of module battery 11(m), which is any one of module batteries 11(1) to 11(n), at a certain time k obtained from equation (4), and the internal resistance value of module battery 11(m) at that time k (calculated value based on actual temperature) Rm_m(k). This calculation does not necessarily have to be performed at every time step Δt, and may be performed at time intervals larger than the time step Δt.

[0199] If the ratio Rm_m(k) / Rsc_m(k) or the difference Rm_m(k)-Rsc_m(k) calculated in this manner becomes larger than a predetermined threshold value, the operational feasibility determination unit 41 estimates that a malfunction has occurred in the module battery 11(m).

[0200] If a malfunction is suspected, the operational feasibility determination unit 41 notifies the operational pattern creation device 2 of the suspected malfunction, along with the operational feasibility determination result.

[0201] Alternatively, the fault estimation may be performed in another device, such as the control device 12 of the energy storage system 1.

[0202] If it is estimated that a failure has occurred in any of the module batteries 11(1) to 11(n), the result of this estimation is preferably notified to the battery simulation device 3, and the parameters for the reference simulation are reset in the reference simulation execution unit 31. This makes it possible to perform a simulation that eliminates the effects of the failure, thereby preventing a decrease in simulation accuracy due to the failure.

[0203] Furthermore, the estimation of failures of the module batteries 11(1) to 11(n) based on the simulation results of internal resistance as described above may be performed in the battery simulation device 3 (in the correction processing unit 32 or other components).

[0204] <Modification> In the above embodiment, each of the module batteries 11(1) to 11(n) constituting the storage battery 11 is composed of a battery assembly 13 in which multiple single cells 13c are connected. However, instead, each of the module batteries 11(1) to 11(n) may be composed of a single cell 13c.

[0205] In the above-described embodiment, a reference simulation is performed using a single reference parameter for all of the module batteries 11(1) to 11(n) that constitute the storage battery 11. Alternatively, the module batteries 11(1) to 11(n) may be divided into p groups (where p is a natural number of 2 or more) that are similar in state (number of failures of single cells 13c, number of charge / discharge cycles, etc.) or state index values ​​(battery temperature, SOC, etc.), a reference parameter may be set for each group, a reference simulation may be performed, and individual simulations of each module battery 11(1) to 11(n) may be performed based on the results.

[0206] The recording of the deviation ΔTm(j) of the battery 11 during its operating period may cover all time points j for each time step Δt during the operating period, or it may cover only a portion of the period. When the operational profitability of the battery 1 is important, such as when the battery 1 is used for the purpose of generating revenue through the buying and selling of electricity in the electricity market, it is preferable to use the deviation ΔTm(j) at time points j within a predetermined time period when the temperature of the battery 11 is close to the upper and lower limits of the operating temperature range.

Claims

1. A simulation device for performing operation simulations of a storage battery comprising a plurality of module batteries, each composed of one or more single cells, comprising: a reference simulation execution unit that performs a reference simulation targeting at least one state index value, assuming that at least one state index value changes in the same way in each of the plurality of module batteries when a predetermined operation plan for the storage battery is executed; and a correction processing unit that obtains individual simulation results for each of the plurality of module batteries by correcting the simulation results for the at least one state index value obtained by the reference simulation with a correction amount within a predetermined tolerance range set according to each of the plurality of module batteries.

2. A simulation apparatus according to claim 1, wherein the correction processing unit learns the relationship between a feature quantity that characterizes the operating state of the storage battery during a predetermined learning target period determined for the past operating period of the storage battery, and the deviation between the value obtained by the reference simulation for at least one state index value and the measured value in each of the plurality of module batteries, thereby creating a decision tree in advance in which the value of the correction amount is determined according to the conditions of the feature quantity, and determines the correction amount for each of the plurality of module batteries based on the decision tree.

3. A simulation apparatus according to claim 2, wherein the correction processing unit acquires the actual temperature, actual voltage, and actual current of each of the plurality of module batteries in advance for at least the predetermined learning target period, calculates and stores the feature quantities at predetermined time increments based on the actual temperature, actual voltage, and actual current, and sets the correction amount for each of the plurality of module batteries at a certain time within the target period of the operation plan to a value associated with the feature quantity at that time, which is assumed in the decision tree from the operation plan and the results of the reference simulation.

4. A simulation apparatus according to claim 1, wherein the correction processing unit determines the correction amount based on the moving average of the deviations between the value obtained by the reference simulation for at least one state index value and the measured values ​​of each of the plurality of module batteries at multiple times within a predetermined period in the past.

5. A simulation apparatus according to claim 1, wherein the correction processing unit, when the operating state of the storage battery assumed at a certain time during the target period of the operation plan is similar to the operating state at a certain time in the past, sets the correction amount to the correction amount adopted at that certain time in the past.

6. A simulation apparatus according to any one of claims 1 to 5, wherein the correction processing unit sets the upper limit of the predetermined allowable range as the correction amount if the value to be set as the correction amount is greater than the predetermined allowable range, and sets the lower limit of the predetermined allowable range as the correction amount if the value to be set as the correction amount is less than the predetermined allowable range.

7. A simulation apparatus according to any one of claims 1 to 5, wherein the correction processing unit sets the correction amount to 0 if the value to be set as the correction amount falls outside the predetermined allowable range.

8. A simulation apparatus according to any one of claims 1 to 5, characterized in that the at least one state index value includes the temperature of the module battery.

9. A simulation apparatus according to claim 8, wherein the correction processing unit obtains the individual simulation results for each of the multiple module batteries based on the relationship that the sum of the difference between the i-1th and ith calculation target time (i is a natural number of 2 or more) calculated by the reference simulation and the correction amount for the ith calculation target time for each of the multiple module batteries is equal to the temperature change between the i-1th and ith calculation target time in each of the multiple module batteries.

10. A simulation apparatus according to any one of claims 1 to 5, characterized in that the at least one state index value includes the internal resistance of the module battery.

11. A simulation apparatus according to claim 10, wherein the correction processing unit obtains the individual simulation results for the internal resistance of each of the plurality of module batteries based on the relationship that the sum of the internal resistance at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for the internal resistance of each of the plurality of module batteries is equal to the internal resistance of each of the plurality of module batteries at the i-th calculation target time.

12. A simulation device according to claim 10, characterized in that, in any of the plurality of module batteries, if the ratio or difference between the individually simulated value of the internal resistance at a certain time and the value of the internal resistance at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in the module battery.

13. A simulation apparatus according to any one of claims 1 to 5, characterized in that the at least one state index value includes at least one of the discharge end-stop depth, which is the discharge depth when the module battery reaches the discharge stop voltage, and the charge end-stop depth, which is the discharge depth when the module battery reaches the charge stop voltage.

14. A simulation apparatus according to claim 13, wherein the at least one state index value includes the discharge end-side stop depth, and the correction processing unit obtains the individual simulation results for the discharge end-side stop depth of each of the plurality of module batteries based on the relationship that the sum of the discharge end-side stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for each of the plurality of module batteries is equal to the discharge end-side stop depth at the i-th calculation target time for each of the plurality of module batteries.

15. A simulation device according to claim 13, characterized in that, in any of the plurality of module batteries, if the ratio or difference between the value of the individual simulation for the discharge end-stop depth at a certain time and the value of the discharge end-stop depth at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in the module battery.

16. A simulation apparatus according to claim 13, wherein the at least one state index value includes the charging end-stop depth, and the correction processing unit obtains the individual simulation results for the charging end-stop depth of each of the plurality of module batteries based on the relationship that the sum of the charging end-stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for each of the plurality of module batteries is equal to the charging end-stop depth at the i-th calculation target time for each of the plurality of module batteries.

17. An operation management device for managing the operation of a battery, each comprising a plurality of module batteries, each composed of a plurality of single cells, comprising: an operation feasibility determination unit that determines whether or not to operate the battery according to the operation plan based on the results of a simulation in a simulation device according to any one of claims 1 to 5; and a charge / discharge management unit that sends the operation plan, which has been determined to be operational by the operation feasibility determination unit to be operational, to a control device of the battery, and causes the control device to operate the battery according to the operation plan.

18. An operation management device according to claim 17, characterized in that, when the at least one state index value includes the internal resistance of the plurality of module batteries, an operation management device is estimated to have occurred in any of the plurality of module batteries if the ratio or difference between the individually simulated value for the internal resistance at a certain time and the value of the internal resistance at that time is greater than a predetermined threshold.

19. An operation management device according to claim 17, characterized in that, when the at least one state index value includes a discharge end-stop depth which is the discharge depth when the plurality of module batteries reach a discharge stop voltage, an operation management device is determined to have occurred in any of the plurality of module batteries if the ratio or difference between the individually simulated value of the discharge end-stop depth at a certain time and the value of the discharge end-stop depth at that time is greater than a predetermined threshold.

20. A method for simulating the operation of a storage battery comprising a plurality of module batteries, each composed of a plurality of single cells, comprising: a reference simulation step of performing a reference simulation targeting at least one state index value, assuming that when a predetermined operation plan for the storage battery is executed, at least one state index value changes in the same way in each of the plurality of module batteries; and a correction processing step of obtaining individual simulation results for each of the plurality of module batteries by correcting the simulation results for the at least one state index value obtained from the reference simulation with a correction amount within a predetermined allowable range set according to each of the plurality of module batteries.

21. A simulation method according to claim 20, further comprising: a decision tree creation step of creating a decision tree in advance in which the value of the correction amount is determined according to the conditions of the feature amount by learning the relationship between a feature quantity that characterizes the operating state of the storage battery during a predetermined learning target period determined for the past operating period of the storage battery, and the deviation between the value obtained by the reference simulation for at least one state index value and the measured value in each of the plurality of module batteries, wherein in the correction processing step, the correction amount for each of the plurality of module batteries is determined based on the decision tree.

22. A simulation method according to claim 21, characterized in that, in the decision tree creation step, the actual temperature, actual voltage, and actual current of each of the plurality of module batteries are obtained in advance for at least the predetermined learning target period, the feature quantities are calculated and stored at predetermined time increments based on the actual temperature, the actual voltage, and the actual current, and in the correction processing step, the correction amount for each of the plurality of module batteries at a certain time within the target period of the operation plan is set to a value that corresponds to the feature quantity at that time, which is assumed in the decision tree from the operation plan and the results of the reference simulation.

23. A simulation method according to claim 20, characterized in that, in the correction processing step, the correction amount is determined based on the moving average of the deviations between the value obtained by the reference simulation for at least one state index value at multiple times within a predetermined period in the past and the measured values ​​of each of the multiple module batteries.

24. A simulation method according to claim 20, characterized in that, in the correction processing step, if the operating state of the storage battery assumed at a certain time during the target period of the operation plan is similar to the operating state at a certain time in the past, the correction amount is set to the correction amount adopted at that certain time in the past.

25. A simulation method according to any one of claims 20 to 24, characterized in that, in the correction processing step, if the value to be set as the correction amount is greater than the predetermined allowable range, the upper limit of the predetermined allowable range is set as the correction amount, and if the value to be set as the correction amount is less than the predetermined allowable range, the lower limit of the predetermined allowable range is set as the correction amount.

26. A simulation method according to any one of claims 20 to 24, characterized in that, in the correction processing step, if the value to be set as the correction amount falls outside the predetermined allowable range, the correction amount is set to 0.

27. A simulation method according to any one of claims 20 to 24, characterized in that the at least one state index value includes the temperature of the module battery.

28. A simulation method according to claim 27, characterized in that, in the correction processing step, the individual simulation results for each of the multiple module batteries are obtained based on the relationship that the sum of the difference between the i-1th and ith calculation target time (i is a natural number of 2 or more) calculated by the reference simulation and the correction amount for the ith calculation target time for each of the multiple module batteries is equal to the temperature change between the i-1th and ith calculation target time in each of the multiple module batteries.

29. A simulation method according to any one of claims 20 to 24, characterized in that the at least one state index value includes the internal resistance of the module battery.

30. A simulation method according to claim 29, characterized in that, in the correction processing step, the individual simulation results for the internal resistance of each of the plurality of module batteries are obtained based on the relationship that the sum of the internal resistance at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for the internal resistance of each of the plurality of module batteries is equal to the internal resistance of each of the plurality of module batteries at the i-th calculation target time.

31. A simulation method according to claim 29, characterized in that, in any of the plurality of module batteries, if the ratio or difference between the individually simulated value of the internal resistance at a certain time and the value of the internal resistance at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in the module battery.

32. A simulation method according to any one of claims 20 to 24, characterized in that the at least one state index value includes at least one of the discharge depth at the end of discharge, which is the discharge depth when the module battery reaches the discharge stop voltage, and the charge depth at the end of charge, which is the discharge depth when the module battery reaches the charge stop voltage.

33. A simulation method according to claim 32, wherein the at least one state index value includes the discharge end-side stop depth, and in the correction processing step, the individual simulation results for the discharge end-side stop depth of each of the plurality of module batteries are obtained based on the relationship that the sum of the discharge end-side stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for each of the plurality of module batteries is equal to the discharge end-side stop depth at the i-th calculation target time for each of the plurality of module batteries.

34. A simulation method according to claim 32, characterized in that, in any of the plurality of module batteries, if the ratio or difference between the value of the individual simulation for the discharge end-stop depth at a certain time and the value of the discharge end-stop depth at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in the module battery.

35. A simulation method according to claim 32, wherein the at least one state index value includes the charging end-stop depth, and in the correction processing step, the individual simulation results for the charging end-stop depth of each of the plurality of module batteries are obtained based on the relationship that the sum of the charging end-stop depth at the i-th calculation target time (i is a natural number) calculated by the reference simulation and the correction amount for each of the plurality of module batteries is equal to the charging end-stop depth at the i-th calculation target time for each of the plurality of module batteries.

36. A method for managing the operation of a battery storage system comprising a plurality of module batteries, each composed of a plurality of single cells, comprising: an operation feasibility determination step of determining whether or not to operate the battery storage system according to the operation plan based on the results of a simulation in the simulation method described in any one of claims 20 to 24; and a charge / discharge management step of sending the operation plan, which was determined to be operational in the operation feasibility determination step, to a control device of the battery storage system, and causing the control device to operate the battery storage system according to the operation plan.

37. An operation management method according to claim 36, characterized in that, when the at least one state index value includes the internal resistance of the plurality of module batteries, if the ratio or difference between the individually simulated value of the internal resistance at a certain time and the value of the internal resistance at that time is greater than a predetermined threshold, an operation management method is determined to be in which a failure has occurred in the module battery.

38. An operation management method according to claim 36, characterized in that, when the at least one state index value includes a discharge end-stop depth which is the discharge depth when the plurality of module batteries reach a discharge stop voltage, an operation management method is characterized in that, in any of the plurality of module batteries, if the ratio or difference between the value of the individual simulation for the discharge end-stop depth at a certain time and the value of the discharge end-stop depth at that time is greater than a predetermined threshold, it is estimated that a failure has occurred in the module battery.

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