Estimation method, estimation device, and computer program

Abstract

This technology provides a method for estimating the capacity range of energy storage elements. [Solution] The estimation method involves a computer performing the following steps: estimating the capacity of an energy storage element according to multiple overall discharge characteristics based on the partial charge-discharge profiles of the energy storage element; estimating the capacity range of the energy storage element based on each estimated capacity; and outputting the estimated capacity range.

Description

Technical Field

[0001] The present invention relates to an estimation method, an estimation apparatus, and a computer program.

Background Art

[0002] For stabilizing and effectively utilizing the electric power generated by power generation facilities such as solar power generation facilities and wind power generation facilities, the use of energy storage elements is expanding. It is known that the energy storage element deteriorates as it repeatedly charges and discharges, and the capacity of the energy storage element gradually decreases.

[0003] Patent Document 1 discloses a technique that can estimate the overall discharge characteristics of an energy storage element at a predetermined time point after the start of operation and diagnose the full charge capacity of the energy storage element based on the overall discharge characteristics without stopping the operation of the system equipped with the energy storage element.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] The technique of Patent Document 1 does not estimate the capacity range of the energy storage element.

[0006] An object of the present disclosure is to provide a technique capable of estimating the capacity range of an energy storage element.

Means for Solving the Problems

[0007] An estimation method according to one aspect of the present disclosure estimates the capacity of the energy storage element according to a plurality of overall discharge characteristics based on a partial charge / discharge profile of the energy storage element, estimates the capacity range of the energy storage element based on each estimated capacity, and a computer executes a process of outputting the estimated capacity range. [Effects of the Invention]

[0008] According to this disclosure, the capacity range of the energy storage element can be estimated. [Brief explanation of the drawing]

[0009] [Figure 1] This diagram shows an overview of the remote monitoring system. [Figure 2] This figure shows an example of the configuration of a container for an energy storage system. [Figure 3] This figure shows an example of the electrical connection configuration of an energy storage system. [Figure 4] This block diagram shows an example configuration of a remote monitoring system. [Figure 5] This is a schematic diagram showing an example of a partial charge / discharge profile and overall discharge characteristics. [Figure 6] This figure shows an example of a confidence distribution diagram. [Figure 7] This figure shows two partial charge / discharge profiles with different widths of the charge-voltage domain, and an example of candidate overall discharge characteristics based on each partial charge / discharge profile. [Figure 8] This diagram illustrates the change in capacity range due to changes in the partial charge / discharge profile. [Figure 9] This flowchart shows an example of the processing steps performed by the estimation device. [Figure 10] This flowchart shows an example of the processing steps performed by the estimation device. [Figure 11] This is a schematic diagram showing an example of a results screen displaying estimation results. [Figure 12] This is a schematic diagram showing an example of how detailed information is displayed. [Figure 13] This flowchart shows an example of the processing procedure performed by the estimation device of the second embodiment. [Modes for carrying out the invention]

[0010] (1) An estimation method according to one aspect of the present disclosure involves a computer performing the following processes: estimating the capacity of an energy storage element according to a plurality of overall discharge characteristics based on partial charge-discharge profiles of the energy storage element; estimating the capacity range of the energy storage element based on each of the estimated capacities; and outputting the estimated capacity range.

[0011] In this specification, "energy storage element" refers to all elements that store energy. The smallest unit of an energy storage element is called an energy storage cell. A "partial charge / discharge profile" refers to, for example, a portion of the charge / discharge profile between the upper and lower voltage limits set for an energy storage element, or a portion of the charge / discharge profile between the upper and lower State of Charge (SOC) limits set for an energy storage element. The term "partial" is used to distinguish it from the overall discharge characteristics. "Overall discharge characteristics" refer to characteristics shown by a continuous discharge curve between the upper and lower voltage limits set for the energy storage element, or characteristics shown by a continuous discharge curve between the upper and lower SOC limits set for the energy storage element. "The capacity of an energy storage element" refers to the full charge capacity of the energy storage element, and is the total amount of electricity generated when the energy storage element is discharged from a fully charged state (SOC 100%) to a state of charge (SOC 0%) at a constant current value. The capacity of an energy storage element can be calculated by subtracting the capacity corresponding to the upper limit voltage from the capacity (amount of electricity) corresponding to the lower limit voltage of the overall discharge characteristics.

[0012] The applicant has proposed a technology (e.g., Japanese Patent Application No. 2021-114387) that estimates the overall discharge characteristics of an energy storage system equipped with energy storage elements based on operational data, and diagnoses the full charge capacity based on the estimated overall discharge characteristics. In the above technology, a partial charge / discharge profile of the energy storage elements is generated based on operational data, and a single overall discharge characteristic is estimated based on the generated partial charge / discharge profile. A single full charge capacity is calculated from the single estimated overall discharge characteristic.

[0013] The operation data collected from the power storage system may not be data suitable for capacity estimation depending on the quality of the data and the analysis method used for capacity estimation. For example, operation data such as when the charging or discharging of the power storage element has not been performed for a long period or when there is a bias in the voltage range or state of charge (SOC) range during charging or discharging is likely not suitable for capacity estimation. When using operation data not suitable for capacity estimation, the accuracy of the estimation result becomes worse than when using operation data suitable for capacity estimation.

[0014] In the estimation of the overall discharge characteristics in the above technology, at least one of the positive electrode single-pole characteristics and the negative electrode single-pole characteristics is adjusted so that the difference between the positive electrode single-pole characteristics and the negative electrode single-pole characteristics approaches (approximates) the partial charge-discharge profile. The optimal (plausible) overall discharge characteristics are estimated from the difference between the positive electrode single-pole characteristics and the negative electrode single-pole characteristics after the most approximate adjustment. That is, one overall discharge characteristic is finally determined from among a number of candidate overall discharge characteristics obtained by adjusting the positive electrode single-pole characteristics and the negative electrode single-pole characteristics. The state of the operation data and the partial charge-discharge profile based on the operation data affects the number of candidate overall discharge characteristics. The more candidate overall discharge characteristics there are, the more difficult it is to uniquely determine the solution, and the reliability of the estimation is reduced. When only the capacity value is shown as the estimation result, the user cannot grasp the reliability of the presented capacity value.

[0015] According to the estimation method in (1) above, since the estimation result of the capacity range of the power storage element can be provided, it leads to grasping the reliability of the output of the estimation result and can improve the reliability of the output result. The capacity range depends on the number of candidates for the overall discharge characteristics based on the charge-discharge profile. The more candidate overall discharge characteristics there are, the more capacities calculated from the overall discharge characteristics there are, and the wider the capacity range becomes. As described above, the more candidate overall discharge characteristics there are, the lower the reliability of the estimation. The reliability of the output of the estimation result can be recognized from the capacity range.

[0016] Since the accuracy of the capacity estimation can be recognized, users can decide whether or not to accept the estimation results obtained by this method based on their accuracy, thus improving convenience. For example, if the capacity range is wide, the user can instead use the capacity obtained from other measurement results or other calculation methods, and if the capacity range is narrow, the results of this method can be given more weight, making it easier to link with other capacity information.

[0017] (2) In the estimation method described in (1) above, confidence information indicating the reliability of the estimation of each capacity may be derived and the derived confidence information may be output.

[0018] According to the estimation method described in (2) above, the reliability of the capacity estimation can be quantified, thus providing a clearer indication of the accuracy of the estimation result output.

[0019] (3) In the estimation method described in (2) above, the reliability may be derived based on the difference between the charge-discharge profile and the overall discharge characteristics fitted to the charge-discharge profile.

[0020] According to the estimation method described in (3) above, reliability can be calculated efficiently and accurately based on the shape error between the charge / discharge profile and the overall discharge characteristics.

[0021] (4) In the estimation method described in (2) above, the reliability may be derived based on the probability of occurrence of the internal parameters of the mathematical model that calculates the overall discharge characteristics in the charge-discharge profile based on measured values ​​including the current and voltage of the energy storage element.

[0022] According to the estimation method described in (4) above, the confidence level can be calculated with high accuracy using the probability of occurrence of the internal parameters of the mathematical model, for example, by using methods such as the MCMC (Markov Chain Monte Carlo) method.

[0023] (5) In any one of the estimation methods described in (2) to (4) above, the confidence information may include the distribution of the confidence or a graph showing the relationship between the capacity and the confidence.

[0024] According to the estimation method described in (5) above, the distribution of confidence levels and the confidence levels for each capacity can be visually and clearly recognized, thus improving user convenience.

[0025] (6) In any one of the estimation methods described in (2) to (5) above, the estimation method may be such that the reliability satisfies a predetermined requirement among the capacity ranges corresponding to each capacity estimated from the plurality of overall discharge characteristics based on the charge-discharge profile.

[0026] According to the estimation method described in (6) above, the amount of data output can be reduced, making it easier to understand the estimation results. By considering the confidence level, a more likely capacity range can be appropriately estimated.

[0027] (7) In any one of the estimation methods described in (1) to (6) above, the optimal capacity corresponding to the optimal overall discharge characteristic among the plurality of overall discharge characteristics may be obtained, and the obtained optimal capacity may be associated with the capacity range and output.

[0028] According to the estimation method described in (7) above, the optimal capacity and capacity range based on the charge-discharge profile can be provided in an integrated manner, allowing for a more accurate determination of the capacity of the energy storage element.

[0029] (8) In any one of the estimation methods described in (1) to (7) above, a screen displaying the capacity range may be output using a rod-shaped object whose length changes according to the width of the capacity range.

[0030] According to the estimation method described in (8) above, the estimated capacity range can be easily grasped at a glance, improving user convenience.

[0031] (9) In the estimation method of (8) above, the optimal capacity corresponding to the optimal overall discharge characteristic among the plurality of overall discharge characteristics may be obtained, and the object showing the optimal capacity may be superimposed on the rod-shaped object.

[0032] According to the estimation method described in (9) above, the optimal capacity and capacity range based on the charge-discharge profile can be presented together in a visually clear manner, thereby improving the visibility of the estimation results.

[0033] (10) In the estimation method of (8) or (9) above, a screen may be output that displays a plurality of rod-shaped objects corresponding to each of the capacity ranges based on the charge-discharge profiles over a plurality of periods.

[0034] According to the estimation method described in (10) above, the time-series changes in the capacity range can be recognized at a glance, and it becomes easy to compare the capacity range for each period.

[0035] (11) In any one of the estimation methods (1) to (10) above, if the optimal capacity corresponding to the optimal overall discharge characteristic among the plurality of overall discharge characteristics is less than a predetermined value and the width of the capacity range is less than a predetermined value, warning information may be output.

[0036] According to the estimation method described in (11) above, if an estimation result indicating a decrease in the capacity of the energy storage element is obtained and the estimation result is reasonably accurate, warning information can be output. Since warning information can be output considering the accuracy of the estimation result, the reliability of the warning information can be improved.

[0037] (12) An estimation device according to one aspect of the present disclosure includes a processing unit that estimates the capacity of an energy storage element according to a plurality of overall discharge characteristics based on a partial charge-discharge profile of the energy storage element, estimates the capacity range of the energy storage element based on each of the estimated capacities, and outputs the estimated capacity range.

[0038] (13) A computer program according to one aspect of the present disclosure causes the computer to perform the following processes: estimate the capacity of the energy storage element according to a plurality of overall discharge characteristics based on the partial charge-discharge profile of the energy storage element, estimate the capacity range of the energy storage element based on each of the estimated capacities, and output the estimated capacity range.

[0039] This disclosure will be described in detail with reference to drawings illustrating embodiments thereof.

[0040] (First Embodiment) Figure 1 shows an overview of the remote monitoring system 100. The remote monitoring system 100 enables remote access to information regarding the energy storage elements 51 included in the energy storage system 5. The remote monitoring system 100 includes an estimation device 1 and a user device 3. The estimation device 1 and the user device 3 are communicated together via a network N. The number of energy storage systems 5 may be one or more. The estimation device 1 may be provided within any of the energy storage systems 5.

[0041] Estimation device 1 is an information processing device capable of various information processing and information transmission / reception, such as a server computer, personal computer, or quantum computer. Estimation device 1 acquires measurement data regarding the energy storage elements 51 transmitted from each energy storage system 5 that is subject to remote monitoring. Based on the measurement data, estimation device 1 performs various analysis processes, including the estimation of the energy storage capacity of the energy storage elements 51, and provides the analysis results to the user via user device 3.

[0042] User device 3 is an information processing terminal device capable of various information processing and information transmission / reception, such as a personal computer, smartphone, or tablet terminal. User device 3 is used by an operator, such as the administrator or maintenance personnel of the energy storage system 5, which is an example of a user. User device 3 can display the analysis results provided by the estimation device 1.

[0043] The energy storage system 5 is, for example, an ESS (Energy Storage System) and is used in power generation systems such as solar power generation systems, wind power generation systems, hydroelectric power generation systems, biomass power generation systems, geothermal power generation systems, and thermal power generation systems. In addition to a number of energy storage elements 51, the energy storage system 5 may include power-related devices 52 such as a power conditioner. The energy storage system 5 stores the electricity supplied from the power generation system and supplies the stored electricity to other power-consuming equipment (loads) or the power grid.

[0044] The energy storage system 5 may also be used for other purposes, such as being connected to a power grid (grid connection) to suppress voltage and frequency fluctuations in the power grid, being installed on the premises of power consumers such as factories and used for BCP (Business Continuity Plan) measures and energy management such as peak shifting, or being used for electricity trading in the electricity market. The energy storage system 5 may also be used in a backup power supply system (emergency power supply system) together with power converters such as converters and inverters. The energy storage system 5 is not limited to industrial use, but may also be for household use.

[0045] Each energy storage system 5 is equipped with a communication device 6 for transmitting measurement data related to the energy storage element 51 to the estimation device 1, or is connected to a communication device 6. The communication device 6 transmits data to the estimation device 1 via the network N.

[0046] Network N is a wired or wireless network, including, for example, the Internet, a carrier network that implements wireless communication according to a predetermined mobile communication standard, or a general optical fiber line. Network N may also include a local network for the manufacturer or maintenance provider of the energy storage element 51.

[0047] Figure 2 shows an example of the configuration of container C of the energy storage system 5. Container C of the energy storage system 5 contains multiple (e.g., nine) energy storage panels. Although not shown, the energy storage system 5 may be configured by omitting container C and installing multiple energy storage panels outdoors. Each energy storage panel may contain, for example, three banks. Each bank is configured by electrically connecting multiple energy storage modules in series. Each energy storage module has multiple energy storage cells connected in series. The three banks are connected in parallel to each other. A configuration in which multiple banks are connected in parallel is also called a domain. When the required output voltage is low (when fewer series connections of energy storage modules are needed), the energy storage panel may contain, for example, six banks. The number of banks that the energy storage panel has, and the number of energy storage modules that make up each bank, can be arbitrarily selected. The energy storage panel in Figure 2 has one domain, but it may have multiple domains.

[0048] A battery storage module is constructed by connecting multiple battery cells in series. In one example, the battery cells are lithium-ion secondary batteries. Alternatively, the battery cells may be solid-state batteries, lead-acid batteries, redox flow batteries, zinc-air batteries, alkaline manganese batteries, lithium-sulfur batteries, sodium-sulfur batteries, silver-zinc oxide batteries, nickel-metal hydride batteries, molten salt thermal batteries, etc., or they may be capacitors. The number of battery cells constituting the battery storage module can be arbitrarily selected.

[0049] Figure 3 shows an example of the electrical connection configuration of the energy storage system 5. Figure 3 shows the electrical connection configuration in one of the energy storage panels included in the energy storage system 5. The energy storage system 5 comprises multiple banks, a domain management device M, and communication equipment 6. The domain management device M and communication equipment 6 may be housed in a control panel separate from the energy storage panel, which is built into the container C. In the example in Figure 3, the energy storage panel comprises three banks: bank #1, bank #2, and bank #3.

[0050] Bank #1, Bank #2, and Bank #3 are all equipped with a bank management device 53 and control boards 54 provided in each energy storage module.

[0051] Each bank is connected to the outside (power source, power recipient, etc.) via a power circuit. For example, the power source for a bank is a power generation facility, and the power recipient of the bank is a load. A power converter (not shown) may be provided between the bank and the power source or power recipient. The bank stores (charges) the power supplied from the power source and supplies (discharges) the stored power to the external power recipient.

[0052] The control board 54 acquires measurement data for each energy cell of the energy storage module through various sensors (not shown). Sensors provided in the bank include, for example, a current sensor for measuring the current flowing through the energy cell, a voltage sensor for measuring the voltage of the energy cell, and a temperature sensor for measuring the temperature of the energy cell. The measurement data can be repeatedly acquired at appropriate intervals, such as 0.1 seconds, 0.5 seconds, or 1 second. The control board 54 has a communication function compliant with a predetermined communication protocol and outputs the acquired measurement data, including the current, voltage, and temperature of the energy cell, to the bank management device 53.

[0053] The bank management device 53 is a device for monitoring the status of the bank. The bank management device 53 acquires measurement data including the current, voltage, and temperature of the energy storage cells, and monitors the status of the bank at each time point by calculating the State of Charge (SOC) of the bank based on the acquired measurement data.

[0054] The domain management device M is a device for monitoring the status of the domain (the entire bank). The domain management device M is connected to the bank management devices 53 of each bank in a communicative manner. The domain management device M aggregates measurement data from the bank management devices 53 of each bank belonging to the domain. Existing communication standards such as CAN (Controller Area Network) are used for communication between the domain management device M and each bank management device 53. Alternatively, communication standards such as LIN (Local Interconnect Network), ECHONET®, and ECHONETLight® may be used.

[0055] Communication device 6 securely transmits data to estimation device 1 wirelessly or via wired connection. Communication device 6 may be, for example, a network interface card. Domain management device M transmits measurement data of the energy storage elements 51 acquired from each bank management device 53 to estimation device 1 via communication device 6. Domain management device M or communication device 6 may retain measurement data for a predetermined time and transmit the measurement data to estimation device 1 at predetermined intervals.

[0056] Estimation device 1 collects and stores measurement data received from each energy storage system 5. The measurement data transmitted to estimation device 1 may be associated with identification information of the energy storage element 51, connection configuration data, etc. Estimation device 1 can identify which energy storage system 5 the data transmitted from the communication device 6 pertains to, and store the data in correspondence with the relevant information.

[0057] Figure 4 is a block diagram showing an example configuration of the remote monitoring system 100. The estimation device 1 comprises a processing unit 11, a storage unit 12, and a communication unit 13. The estimation device 1 may be a single computer, or it may be a computer system composed of multiple computers and peripheral devices. The estimation device 1 may be a virtualized virtual machine, or it may be a cloud.

[0058] The processing unit 11 comprises one or more processors such as a CPU (Central Processing Unit), an MPU (Micro-Processing Unit), or a GPU (Graphics Processing Unit). The processing unit 11 includes memory, which is a temporary storage medium such as SRAM (Static Random Access Memory) or DRAM (Dynamic Random Access Memory). The processing unit 11 may also include functions such as a timer for measuring the elapsed time from the time a measurement start instruction is given to the time a measurement end instruction is given, a counter for counting numbers, and a clock for outputting date and time information. The CPU and other components of the processing unit 11 control each part of the hardware by reading and executing various computer programs stored in the storage unit 12, thereby making the entire device function as an estimation device in this disclosure. The processing unit 11 may be implemented in software, or part or all of it may be implemented in hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0059] The storage unit 12 includes, for example, a non-volatile storage device such as a hard disk or flash memory. The storage unit 12 is separate from the estimation device 1 and may be one or more externally connected external storage devices. The storage unit 12 stores various computer programs and data that the processing unit 11 refers to. In this embodiment, the storage unit 12 stores a program 121 that causes the computer to execute processing related to the estimation of the capacity range of the energy storage element 51, and an energy storage element DB (Data Base) 122 that stores various information related to the energy storage element 51. The program 121 may also include a program that implements a web server function. The processing unit 11 can perform a web server function by program 121 to present various information to the user device 3 via a web page and to accept information from the user.

[0060] The computer program (program product) including program 121 may be provided on a non-temporary recording medium 1A on which the computer program is recorded in a readable format. The recording medium 1A is a portable memory such as a CD-ROM, USB memory, or SD (Secure Digital) card. The processing unit 11 reads the desired computer program from the recording medium 1A using a reading device (not shown) and stores the read computer program in the storage unit 12. Alternatively, the computer program may be provided by communication. Program 121 may be a single computer program or may consist of multiple computer programs. Program 121 may also be executed on a single computer or executed collaboratively by multiple computers.

[0061] The energy storage element DB122 stores various information related to the capacity estimation process. For example, the energy storage element DB122 stores measurement data received from the energy storage system 5, formulas used for the capacity estimation process, table data, and estimation results. Whenever the processing unit 11 receives measurement data, including identification information of the energy storage element 51, current, voltage, and temperature, transmitted from the energy storage system 5, it stores the received measurement data in the energy storage element DB122 in chronological order. Each time the processing unit 11 performs the capacity estimation process, it stores the obtained estimation result in the energy storage element DB122.

[0062] The communication unit 13 includes a communication device that enables communication via the network N. The processing unit 11 sends and receives data to and from the user device 3 through the communication unit 13.

[0063] The configuration of the estimation device 1 is not limited to the example described above, and may include, for example, a display unit for displaying images, an operation unit for receiving user input, and so on.

[0064] The user device 3 comprises a processing unit 31, a storage unit 32, a communication unit 33, a display unit 34, and an operation unit 35.

[0065] The processing unit 31 comprises one or more processors such as CPUs and GPUs. The storage unit 32 comprises a non-volatile storage device such as a hard disk or flash memory. The storage unit 32 stores various computer programs and data referenced by the processing unit 31. The computer programs stored in the storage unit 32 include program 321. Program 321 may include a web browser function. By executing program 321, the processing unit 31 accesses the estimation device 1 via the web browser and performs processing related to the output of various information, including the capacity estimation result of the energy storage element 51.

[0066] The communication unit 33 includes a communication device that enables communication via the network N. The processing unit 31 sends and receives data to and from the estimation device 1 via the communication unit 33.

[0067] The display unit 34 includes a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 34 displays various information, including the estimated capacity range, in accordance with instructions from the processing unit 31.

[0068] The operation unit 35 is an interface that receives user input. The operation unit 35 includes, for example, a keyboard, mouse, touch panel device with a built-in display, speaker, and microphone. The operation unit 35 receives user input and sends control signals to the processing unit 31 according to the content of the operation.

[0069] The following describes the method for estimating the capacity range of the energy storage element 51, which is performed by the estimation device 1.

[0070] The estimation device 1 generates a partial charge-discharge profile based on time-series data of measurement data including the current and voltage of the energy storage element 51, and estimates the optimal (most likely) overall discharge characteristics of the energy storage element 51 (hereinafter also referred to as the optimal overall discharge characteristics) corresponding to the generated partial charge-discharge profile. By calculating the capacity corresponding to the optimal overall discharge characteristics, the optimal (most likely) capacity (hereinafter also referred to as the optimal capacity) for the energy storage element 51 can be determined. The generation of the partial charge-discharge profile and the estimation of the overall discharge characteristics can be performed using known techniques (for example, the techniques described in Japanese Patent Application No. 2021-114387).

[0071] Figure 5 is a schematic diagram showing an example of a partial charge-discharge profile and overall discharge characteristics. In Figure 5, the horizontal axis represents electric charge (Ah), and the vertical axis represents voltage or potential (V). In Figure 5, S represents the partial charge-discharge profile (electric charge-voltage plot), and curve Qa represents the overall discharge characteristics (electric charge (capacity)-voltage characteristics). Alternatively, the partial charge-discharge profile may be a SOC-voltage plot, and the overall discharge characteristics may be a SOC-voltage characteristics. Figure 5 also shows the curve Px representing the positive electrode unipolar characteristics and the curve Nx representing the negative electrode unipolar characteristics used to estimate the overall discharge characteristics.

[0072] While a detailed explanation is omitted, the overall discharge characteristics are determined by calculating the difference between the potential of the positive electrode unipolar characteristics and the potential of the negative electrode unipolar characteristics of the energy storage element 51. The positive electrode unipolar characteristics and negative electrode unipolar characteristics depend on internal state parameter values ​​such as positive electrode efficiency (utilization rate), charge reserve amount, discharge reserve amount, and positive electrode discharge start position. By optimizing the internal state parameter values ​​so that the overall discharge characteristics approach the partial charge / discharge profile, the optimal overall discharge characteristics corresponding to the optimized internal state parameter values ​​can be obtained.

[0073] In optimizing internal state variable parameters, the parameters are adjusted to minimize the difference (hereinafter also referred to as the fitting error) between the charge / discharge profile and the overall discharge characteristics fitted to the charge / discharge profile. The fitting error may be expressed, for example, as the sum of the squares of the voltage differences between the voltage of the partial charge / discharge profile corresponding to a specific amount of electricity and the voltage of the overall discharge characteristics, or a linear sum thereof.

[0074] The estimation device 1 of this embodiment stores the entire history of the iterative calculations performed in the optimization of the internal state variable parameters in the energy storage element DB122. The information stored in the energy storage element DB122 includes, for example, all candidate overall discharge characteristics estimated by the iterative calculations and the fitting error corresponding to each candidate overall discharge characteristic. A candidate overall discharge characteristic (hereinafter also referred to as a candidate overall discharge characteristic) means an overall discharge characteristic that can become the optimal overall discharge characteristic based on the partial charge-discharge profile.

[0075] The estimation device 1 derives the capacity (Ah) corresponding to each of the multiple candidate overall discharge characteristics and the confidence level of the estimation for each of the multiple candidate overall discharge characteristics, based on the multiple candidate overall discharge characteristics stored and the fitting error corresponding to each candidate overall discharge characteristic.

[0076] The confidence level of the estimation indicates the accuracy of the capacity estimation. The accuracy of the capacity estimation is synonymous with the accuracy of the estimation of the overall discharge characteristics of the candidate for capacity calculation. The confidence level of the estimation is calculated, for example, based on the fitting error. The smaller the fitting error, the higher the confidence level of the estimation. The confidence level of the estimation may also be calculated, for example, by the following formula.

[0077]

number

[0078] In the formula, l is the confidence level of the estimation, and d is the fitting error.

[0079] As a method for calculating reliability, the likelihood function L can be defined as shown in the following equation, and the probability distribution of the full charge / discharge capacity (FCC) of the energy storage element can be determined using Bayes' theorem and the MCMC (Markov Chain Monte Carlo) method. In this method, the error between the partial charge / discharge profile (OCV,Ah) obtained from observation results and the estimated partial charge / discharge profile is converted into a reliability value called the likelihood function L or probability distribution p(FCC|Ah,OCV) expressed in the following equation, and the reliability can be determined together with the estimated FCC result. Note that the estimated charge / discharge profile here refers to a charge / discharge profile extracted from the overall discharge characteristics that corresponds to the same capacity range as the partial charge / discharge profile obtained from observation results, for comparison with the observation results. L(θ|Ah,OCV)∝p(OCV|Ah,θ) p(θ|Ah,OCV)∝L(θ|Ah,OCV)p(θ)=p(OCV|Ah,θ)p(θ) FCC = f(θ) In the equation, L is the likelihood function, Ah is the capacitance of the energy storage element, FCC is the full charge / discharge capacity of the energy storage element, OCV is the open-circuit voltage of the energy storage element, and θ is the internal state variable of the energy storage element.

[0080] The estimation device 1 generates a confidence distribution diagram representing the distribution of the confidence levels in the energy storage element 51, based on the capacity corresponding to the overall discharge characteristics of each candidate and the confidence level of the estimation. The confidence distribution diagram is an example of confidence information that shows the confidence level of the estimation.

[0081] Figure 6 shows an example of a confidence distribution diagram. The confidence distribution diagram can be generated by plotting the capacity and estimated confidence level corresponding to the overall discharge characteristics of each candidate on a two-dimensional coordinate system with capacity (Ah) on the horizontal axis and estimated confidence level on the vertical axis. In Figure 6, the confidence level plots are represented by cross marks (×).

[0082] The estimation device 1 may derive a confidence curve (approximation line) or a confidence function (approximation formula) that represents the confidence curve, based on the generated confidence distribution map. In Figure 6, curve M represents the confidence curve and the confidence function. The confidence curve and the confidence function are another example of confidence information that indicates the confidence of the estimation.

[0083] Estimation device 1, for example, divides the generated confidence distribution map into divided regions obtained by dividing the capacity into predetermined capacity widths, and identifies the maximum confidence value within each divided region. Estimation device 1 derives an approximation line for plotting the maximum confidence values ​​of each identified divided region, and an approximation function that represents the approximation line. The approximation method is not particularly limited and may be approximated by an appropriate function. The confidence curve and confidence function represent the likelihood of the estimated capacity occurring. The capacity at which the confidence function shows a peak value corresponds to the optimal capacity.

[0084] The estimation device 1 derives an estimated capacity range for the energy storage element 51 based on the capacities corresponding to the obtained overall discharge characteristics of each candidate. The capacity range can be defined as a capacity band where the maximum value among the capacities corresponding to each overall discharge characteristic of each candidate is the upper limit and the minimum value is the lower limit.

[0085] The estimated capacity range for the energy storage element 51 may be a portion of the total capacity range defined by the maximum and minimum estimated capacities, and may correspond to a range that satisfies a predetermined confidence requirement. For example, the X% confidence interval is defined as a capacity range where the ratio of the area corresponding to a specific capacity range to the area corresponding to the total capacity range (area of ​​the specific capacity range / area of ​​the total capacity range) exceeds X%, within the region enclosed by the confidence curve shown in Figure 6 and the x-axis (capacity axis). In Figure 6, the hatched area represents the area corresponding to the specific capacity range. The estimation device 1 can derive the X% confidence interval from the total capacity range as the final estimation result. The confidence interval to be extracted can be set in advance according to the estimation purpose. The estimation device 1 may be configured to accept user settings or changes to the confidence interval when performing the estimation process.

[0086] The estimation device 1 performs the above estimation process for each energy storage element 51 and stores the obtained estimation results in the energy storage element DB 122. The energy storage element DB 122 stores information such as the identification information of the energy storage element 51, the estimation period, the generated partial charge / discharge profile, the optimal overall discharge characteristics, the optimal capacity, the capacity range, the capacity corresponding to each candidate overall discharge characteristic, the reliability corresponding to each candidate overall discharge characteristic, the reliability distribution diagram, the reliability curve, and the reliability function, all associated with the estimation ID.

[0087] The estimation device 1 outputs the optimal capacity of the energy storage element 51 along with its capacity range as an estimation result. The output estimation result may include reliability information, partial charge / discharge profile, optimal overall discharge characteristics, reliability, etc.

[0088] The capacity range and estimation reliability of the energy storage element 51 are expected to change in accordance with the state of the partial charge / discharge profile. Specific examples are provided below.

[0089] Figure 7 shows two partial charge-discharge profiles with different widths of the charge-voltage domain, and an example of candidate overall discharge characteristics based on each partial charge-discharge profile. In Figure 7, the horizontal axis represents capacity (Ah) and the vertical axis represents voltage (V).

[0090] The upper part of Figure 7 shows a partial charge-discharge profile consisting of the charge-voltage plot for only region 1 of the charge-voltage regions of the energy storage element, which are divided into regions 1 to 4, and a candidate overall discharge characteristic based on that partial charge-discharge profile. The lower part of Figure 7 shows a partial charge-discharge profile consisting of the charge-voltage plot across regions 1 to 4, and a candidate overall discharge characteristic based on that partial charge-discharge profile. Regions 1 to 4 are divided by approximately the same charge width, with the charge value increasing from region 1 to region 4.

[0091] As conceptually shown in Figure 7, when the width of the charge-voltage domain of a partial charge-discharge profile is short and the information from the partial charge-discharge profile is insufficient, such as in the partial charge-discharge profile corresponding to region 1, there are a relatively large number of candidate overall discharge characteristics that can be estimated from the partial charge-discharge profile. It is difficult to uniquely determine the optimal overall discharge characteristic from among many candidates, and the estimation accuracy decreases. When the width of the charge-voltage domain of a partial charge-discharge profile is long and the information from the partial charge-discharge profile is sufficient, such as in the partial charge-discharge profiles corresponding to regions 1 to 4, the number of candidate overall discharge characteristics that can be estimated from the partial charge-discharge profile is relatively small. Therefore, it becomes easier to identify the optimal overall discharge characteristic, and the estimation accuracy improves.

[0092] Figure 8 illustrates the change in capacity range due to changes in the partial charge-discharge profile. For partial charge-discharge profiles with different charge-voltage domains shown in the upper part of Figure 8, the confidence curves obtained when estimating the overall discharge characteristics of each candidate based on the partial charge-discharge profile are shown in the middle and lower parts of Figure 8. In the middle and lower parts of Figure 8, the horizontal axis represents capacity (Ah), and the vertical axis represents the confidence of the estimation.

[0093] The central figure in Figure 8 represents the confidence curve for the partial charge-discharge profile, which is composed of the charge-voltage plots for each of the regions shown at the top of Figure 8: region 1, region 1-2, region 1-3, and region 1-4. The narrower the width of the charge-voltage region of the partial charge-discharge profile, the greater the variety of overall discharge characteristics that can be estimated from the partial charge-discharge profile, and the wider the capacity range derived from those overall discharge characteristics.

[0094] The lower part of Figure 8 shows the confidence curve for the partial charge-discharge profile, which is composed of the charge-voltage plots for region 1 and region 4, respectively. Even if the width of the charge-voltage region of the partial charge-discharge profile is the same, the candidate overall discharge characteristics based on the partial charge-discharge profile change depending on the charge-voltage band and the slope of the profile. Therefore, the distribution of confidence in the estimation of each candidate overall discharge characteristic changes, and the width of the capacity range widens or narrows.

[0095] Based on the above, the more candidate overall discharge characteristics based on the partial charge-discharge profile there are, and the lower the certainty of estimating the optimal overall discharge characteristics, the wider the capacity range becomes. The fewer candidate overall discharge characteristics based on the partial charge-discharge profile there are, and the higher the certainty of estimating the optimal overall discharge characteristics, the narrower the capacity range becomes. In other words, the capacity range serves as an indicator of the certainty of estimating the optimal overall discharge characteristics. In this embodiment, by providing the capacity range as the capacity estimation result, it becomes possible to grasp the certainty of the capacity estimation result.

[0096] Figures 9 and 10 are flowcharts illustrating an example of the processing procedure performed by the estimation device 1. The processing unit 11 of the estimation device 1 performs the following processing according to the program 121 stored in the storage unit 12. The processing unit 11 repeatedly performs the following processing, for example, at predetermined or appropriate time intervals. The processing unit 11 may start the following processing when it receives an instruction to perform estimation.

[0097] The processing unit 11 of the estimation device 1 acquires time-series data of the current and voltage of the energy storage element 51 during the estimation period (step S11). Based on the acquired time-series data of the current and voltage, the processing unit 11 generates a partial charge / discharge profile (step S12).

[0098] The processing unit 11 sets the internal state variable parameters (step S13). The processing unit 11 may first set a capacity value and then, based on the correspondence between the previously stored capacity value and the internal state variable parameters, set the value of the internal state variable parameter corresponding to the set capacity value.

[0099] The processing unit 11 estimates candidate overall discharge characteristics based on the positive and negative electrode unipolar characteristics corresponding to the set internal state parameter (step S14). The processing unit 11 calculates the fitting error between the estimated candidate overall discharge characteristics and the charge / discharge profile (step S15). The processing unit 11 stores the estimated candidate overall discharge characteristics and the calculated fitting error in association with each other in the energy storage element DB122 (step S16).

[0100] The processing unit 11 determines whether or not the optimal discharge characteristics have been estimated (step S17). For example, if it is determined that the optimal discharge characteristics have not been estimated because the estimated fitting error is not the minimum or is greater than or equal to a preset error threshold (S17: NO), the processing unit 11 returns to step S13. The processing unit 11 repeatedly performs the processes from step S13 onward to adjust the internal state variable parameters according to predetermined rules, estimate a new candidate overall discharge characteristic from the adjusted internal state variable parameters, and calculate the fitting error. Each time the calculation is performed, the processing unit 11 stores the newly estimated candidate overall discharge characteristic and fitting error in the energy storage element DB122.

[0101] For example, if the estimated fitting error is minimal or below the error threshold, and it is determined that the optimal discharge characteristics have been estimated (S17: YES), the processing unit 11 calculates the capacity corresponding to each candidate overall discharge characteristic (step S18). Through the processing in step S18, the optimal capacity corresponding to the optimal candidate overall discharge characteristic selected from among the candidate overall discharge characteristics is also calculated.

[0102] The processing unit 11 calculates the reliability of the estimation corresponding to each calculated capacity based on the fitting error of the overall discharge characteristics of each candidate stored in the energy storage element DB122 (step S19).

[0103] The processing unit 11 generates a confidence distribution diagram representing the distribution of estimated confidence levels based on the calculated capacity and estimated confidence levels corresponding to the overall discharge characteristics of each candidate (step S20). Based on the generated confidence distribution diagram, the processing unit 11 derives a confidence curve and a confidence function showing the relationship between capacity and confidence level (step S21).

[0104] The processing unit 11 derives a capacity range defined by the maximum and minimum values ​​of each capacity, based on the capacity corresponding to the overall discharge characteristics of each candidate (step S22). The processing unit 11 then derives a capacity range from the entire derived capacity range that corresponds to a predetermined confidence interval (e.g., an 85% confidence interval) that satisfies a preset ratio (step S23). Step S23 may be omitted.

[0105] The processing unit 11 generates an estimation results screen that includes the optimal capacity, capacity range, reliability curve, partial charge / discharge profile, optimal overall discharge characteristics, and reliability (step S24). The processing unit 11 outputs the generated estimation results screen to the user device 3 (step S25) and terminates the series of processes.

[0106] Figure 11 is a schematic diagram showing an example of a results screen 340 that displays the estimation results. Based on the screen information transmitted from the estimation device 1, the processing unit 31 of the user device 3 displays the results screen 340 (Web screen) including the estimation results of the energy storage elements 51 of the energy storage system 5 that are to be displayed on the display unit 34.

[0107] The results screen 340 includes a capacity range graph 341 showing the change in the capacity range of the energy storage element 51. The horizontal axis of the capacity range graph 341 represents the number of years of operation, and the vertical axis represents the capacity (Ah) of the energy storage element 51. The number of years of operation is expressed in years, representing the time elapsed since the start of operation. In the capacity range graph 341, the capacity range is represented by a bar-shaped arrow object 342 whose length changes according to the width of the capacity range. The capacity value and capacity range of the energy storage element 51 can be recognized by the position and length of the bar-shaped arrow object 342 in the y-axis (capacity axis) direction. A circular object 343 (marked with a circle) representing the optimal capacity value is superimposed on the bar-shaped arrow object 342 representing the capacity range.

[0108] The estimation device 1 refers to the energy storage element DB 122 and reads the capacity range and optimal capacity value corresponding to the identification information of the energy storage element 51 to be displayed for a predetermined operating period. The estimation device 1 generates arrow objects 342 for each operating period, with lengths varying to correspond to the width of the capacity range defined by the difference between the maximum and minimum values ​​of the read capacity range. The estimation device 1 places the generated arrow objects 342 on the capacity range graph 341 at positions corresponding to the capacity values ​​of the capacity range and the operating period to be estimated. The estimation device 1 displays circular objects 343 superimposed on the arrow objects 342 at positions corresponding to the optimal capacity value for each operating period.

[0109] Figure 11 shows an example where the capacity range of the energy storage element 51 is represented by a bar-shaped arrow object 342. Alternatively, the capacity range of the energy storage element 51 may be represented by an appropriate display method that makes the capacity range recognizable. The capacity range of the energy storage element 51 may be represented, for example, by a numerical value or by a reliability curve.

[0110] At least one of the arrow object 342 and the circular object 343 included in the results screen 340 may be configured to function as a reception unit for receiving requests to display detailed information. The user can use the operation unit 35 to select a circular object 343 on the results screen 340 that corresponds to any operating period, thereby displaying the optimal overall discharge characteristics corresponding to the optimal capacity indicated by the selected circular object 343.

[0111] Figure 12 is a schematic diagram showing an example of detailed information display. Figure 12 is an example of the results screen 340 displayed when an arrow object 342 corresponding to any of the operating periods in Figure 11 is selected. When the estimation device 1 receives the selection of any of the circular objects 343 through the user device 3, it reads the confidence distribution diagram and the confidence curve or confidence function corresponding to the operating period of the selected arrow object 342 from the energy storage element DB 122. The estimation device 1 generates a results screen 340 that includes a confidence graph 344 showing the read confidence distribution diagram and the confidence curve or confidence function, and provides it to the user device 3. As shown in Figure 12, the results screen 340 may also display an arrow object 342 indicating the capacity range (confidence interval) and a circular object 343 indicating the optimal capacity, along with the confidence graph 344.

[0112] The detailed information may include, for example, information such as the optimal overall discharge characteristics, partial charge-discharge profiles, and the reliability of the estimation of the optimal overall discharge characteristics. If the estimation device 1 receives, for example, the selection of one of the circular objects 343 in Figure 11 through the user device 3, it may generate a results screen 340 that includes a discharge characteristics graph showing the optimal overall discharge characteristics and partial charge-discharge profiles corresponding to the operating period of the selected circular object 343.

[0113] Detailed information may be displayed as a separate screen from the results screen 340 in Figure 11, or it may be displayed as a pop-up on the results screen 340 in Figure 11. The configuration of the results screen 340 showing the estimation results is not limited to the examples shown in Figures 11 and 12. Detailed information may be displayed in response to a display request received using objects other than the arrow object 342 and the circular object 343, or it may be displayed initially without receiving a display request.

[0114] The estimation device 1 may output the result screen 340 at any time in response to receiving a request to output the result screen 340, for example, through the user device 3. The estimation device 1 may also switch between outputting and not outputting the result screen depending on whether the width of the estimated capacity range is greater than or equal to a predetermined value. For example, as an initial setting, the estimation device 1 may be configured not to output the result screen if the width of the estimated capacity range is greater than or equal to a predetermined value, as this is likely to indicate a decrease in estimation accuracy, and to output the result screen only if the width of the estimated capacity range is less than the predetermined value.

[0115] (Second Embodiment) In the second embodiment, warning information indicating that the capacity has deteriorated is output according to the estimated capacity range. Below, the differences from the first embodiment will be mainly explained, and components common to both embodiments will be denoted by the same reference numerals and their detailed descriptions will be omitted.

[0116] Figure 13 is a flowchart showing an example of the processing procedure performed by the estimation device 1 of the second embodiment. The estimation device 1 may, for example, start the following processing immediately after the completion of the processing shown in Figures 9 and 10.

[0117] The processing unit 11 of the estimation device 1 determines whether the optimal capacity for the current estimation period calculated in step S18 is less than a preset optimal capacity threshold (step S31). If it is determined that the current optimal capacity is equal to or greater than the optimal capacity threshold (S31: NO), the processing unit 11 terminates the process.

[0118] If it is determined that the current optimal capacity is less than the optimal capacity threshold (S31: YES), the processing unit 11 determines whether the width of the current capacity range derived in step S22 or step S23 is less than a preset capacity range threshold (step S32).

[0119] If the current capacity range is determined to be greater than or equal to the capacity range threshold (S32: NO), the processing unit 11 determines, based on the information stored in the energy storage element DB122, whether the estimation result for the previous estimation period satisfies specific requirements (step S33). The specific requirements are that the optimal capacity is less than the optimal capacity threshold, and the capacity range is greater than or equal to the capacity threshold. The previous estimation period is the estimation period immediately preceding the current estimation period.

[0120] If the processing unit 11 determines that the optimal capacity for the previous estimated period is less than the optimal capacity threshold and the width of the capacity range is not greater than or equal to the capacity threshold, and that the specific requirements are not met (S33: NO), the processing unit 11 terminates the process. If the processing unit 11 determines that the optimal capacity for the previous estimated period is less than the optimal capacity threshold and the width of the capacity range is greater than or equal to the capacity threshold, and that the specific requirements are met (S33: YES), the processing unit 11 proceeds to step S34.

[0121] If the processing unit 11 determines that the current capacity range is less than the capacity threshold (S32: YES), it proceeds to step S34.

[0122] If the previous estimation result is determined to meet specific requirements, and if the current capacity range is determined to be greater than or equal to the capacity threshold, the processing unit 11 generates warning information indicating that the capacity of the energy storage element 51 is degrading (step S34). The processing unit 11 outputs the generated warning information to the user device 3 (step S35). The processing unit 11 then terminates the series of processes.

[0123] Warning information may be output, for example, by displaying text or illustrations indicating that the capacity is degrading, or by outputting a predetermined alert sound, illuminating a lamp, or other similar means. The estimation device 1 may also display the warning information using the estimation result screen.

[0124] In the above configuration, a warning is output if, for two consecutive periods, the optimal capacity is below the optimal capacity threshold and the capacity range is greater than or equal to the capacity threshold. Alternatively, the estimation device 1 may not output a warning if the optimal capacity is below the optimal capacity threshold and the capacity range is greater than or equal to the capacity threshold. Instead of determining whether the optimal capacity is below the optimal capacity threshold, the estimation device 1 may determine whether the decrease in the optimal capacity from the previous to the current optimal capacity is greater than or equal to the threshold.

[0125] The embodiments disclosed herein should be considered in all respects to be illustrative and not restrictive. The technical features described in each embodiment can be combined with each other, and the scope of the present invention is intended to include all modifications within the claims and equivalents thereof. The sequences shown in each embodiment are not limiting, and within the bounds of consistency, the order of each processing step may be changed, and multiple processes may be executed in parallel. The processing entity for each process is not limiting, and within the bounds of consistency, the processing of each device may be executed by other devices.

[0126] The matters described in each embodiment can be combined with each other. Furthermore, the independent and dependent claims described in the claims can be combined with each other in any combination, regardless of the form of reference. In addition, the claims use a form in which claims referencing two or more other claims (multi-claim form), but are not limited to this. A form in which multi-claims referencing at least one multi-claim (multi-multi-claim) may also be used. [Explanation of symbols]

[0127] 100 Remote Monitoring Systems 1 Estimation device 11 Processing Section 12 Storage section 13 Communications Department 121 Programs 122 Energy Storage Element DB 1A Recording medium 3. User device 31 Processing Unit 32 Storage section 33 Communications Department 34 Display section 35 Control section 321 Programs 3A Recording media 5. Energy storage system 5 Energy storage element

Claims

1. The capacity of the energy storage element is estimated based on multiple overall discharge characteristics derived from the partial charge-discharge profiles of the energy storage element. Based on the estimated capacities, the capacity range of the energy storage element is estimated. Output the estimated capacity range. A method for estimating how much processing a computer will perform.

2. Confidence information indicating the reliability of the estimation of each capacity is derived, Output the derived confidence information. The estimation method according to claim 1.

3. The reliability is derived based on the difference between the charge / discharge profile and the overall discharge characteristics fitted to the charge / discharge profile. The estimation method according to claim 2.

4. The reliability is derived based on the probability of occurrence of the internal parameters of the mathematical model that calculates the overall discharge characteristics in the charge-discharge profile based on measured values ​​including the current and voltage of the energy storage element. The estimation method according to claim 2.

5. The confidence information includes a graph showing the distribution of the confidence levels or the relationship between the capacity and the confidence levels. The estimation method according to claim 2 or claim 3.

6. From the capacity ranges corresponding to each capacity estimated from the plurality of overall discharge characteristics based on the charge-discharge profiles, the capacity range whose reliability satisfies a predetermined requirement is estimated. The estimation method according to claim 2 or claim 3.

7. The optimal capacity is obtained according to the optimal overall discharge characteristic among the multiple overall discharge characteristics described above. The acquired optimal capacity and the capacity range are associated and output. The estimation method according to claim 1 or claim 2.

8. A screen displaying the capacity range is output using a rod-shaped object whose length changes according to the width of the capacity range. The estimation method according to claim 1 or claim 2.

9. The optimal capacity is obtained according to the optimal overall discharge characteristic among the multiple overall discharge characteristics described above. The object indicating the optimal capacity is superimposed and displayed on the aforementioned rod-shaped object. The estimation method according to claim 8.

10. Output a screen that displays multiple rod-shaped objects corresponding to each of the capacity ranges based on the charge / discharge profiles over multiple periods. The estimation method according to claim 8.

11. If the optimal capacity corresponding to the optimal overall discharge characteristic among the plurality of overall discharge characteristics is less than a predetermined value, and the width of the capacity range is less than a predetermined value, then warning information is output. The estimation method according to claim 1 or claim 2.

12. The capacity of the energy storage element is estimated based on multiple overall discharge characteristics derived from the partial charge-discharge profiles of the energy storage element. Based on the estimated capacities, the capacity range of the energy storage element is estimated. Output the estimated capacity range. It includes a processing unit that performs processing. Estimation device.

13. The capacity of the energy storage element is estimated based on multiple overall discharge characteristics derived from the partial charge-discharge profiles of the energy storage element. Based on the estimated capacities, the capacity range of the energy storage element is estimated. Output the estimated capacity range. A computer program that instructs a computer to perform a process.

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