Deterioration state estimation device for power storage system, deterioration state estimation method, and power storage system operation optimization method

Abstract

This deterioration state estimation device which estimates a deterioration state of a power storage system including a secondary battery and a DC-AC converter comprises an estimation calculation processing unit (27) that: estimates an internal resistance (R) and an open circuit voltage (Voc) with respect to the charge rate of the secondary battery, on the basis of a charge / discharge voltage (Vc,d) and a charge / discharge current (I) of the secondary battery and an input / output power of the DC-AC converter; estimates an efficiency (ηpcs) of the DC-AC converter and an auxiliary machine power consumption (Eau,l) of the DC-AC converter; and estimates the round trip efficiency (RTE) with respect to the rated input / output rate on the AC side of the DC-AC converter and the charge rate of the secondary battery, on the basis of the estimated open circuit voltage, the internal resistance, the efficiency of the DC-AC converter, and the auxiliary-machine power consumption of the DC-AC converter.

Description

Apparatus for estimating degradation state of power storage system, method for estimating degradation state, and method for optimizing operation of power storage system

[0001] The present invention relates to a degradation state estimation device and degradation state estimation method for a power storage system that estimates the degradation state of a power storage system including a secondary battery and an orthogonal converter, and a method for optimizing operation of the power storage system.

[0002] In recent years, battery energy storage systems (BESS) have been expected to serve as temporary power storage devices for countering output fluctuations and time shifting of variable renewable energy (VRE). As storage batteries, i.e., secondary batteries, lithium-ion batteries (LIBs), which have particularly excellent energy density and power density, have been attracting attention.

[0003] LIBs have issues in terms of cost and resources (cobalt, nickel, lithium, copper, etc.), and to solve these issues, efforts are being made to reuse LIBs that have reached the end of their lifespan as stationary BESSs, which are operated in a relatively gentle manner, after being used in vehicles where high performance is required.However, LIBs are subject to a phenomenon known as degradation, in which their performance decreases over time, and reuse requires classification based on the intended use and remaining performance at the start of the reuse, as well as degradation diagnosis to understand their condition during operation.

[0004] Japanese Patent Application Publication No. 2016-145795

[0005] For example, Patent Literature 1 discloses a power storage system that includes a lithium ion battery (LIB) and a power conditioning system (PCS) with an orthogonal conversion function and a step-up / step-down function. Regarding the LIB, a method for estimating the degradation state by estimating the energy efficiency is proposed.

[0006] However, not only Patent Document 1 but also other conventional technologies do not provide a method for estimating the degradation state of the entire power storage system including an orthogonal converter such as a PCS. Therefore, conventional technologies cannot perform the above-described degradation diagnosis with sufficient accuracy for practical use.

[0007] Therefore, an object of the present invention is to provide a degradation state estimation device for a power storage system, a degradation state estimation method, and a storage system operation optimization method that make it possible to estimate the degradation state of the entire power storage system including a secondary battery and an orthogonal converter.

[0008] In order to achieve the above object, a degradation state estimation device according to an embodiment of the present invention is a degradation state estimation device that estimates the degradation state of a power storage system including a secondary battery and an AC-to-AC converter, and is equipped with an estimation calculation processing unit that estimates the open-circuit voltage and internal resistance of the secondary battery relative to its state of charge, based on the charge / discharge voltage and charge / discharge current of the secondary battery and the input / output power of the AC-to-AC converter, and estimates the efficiency of the AC-to-AC converter and the power consumption of auxiliary equipment of the AC-to-AC converter, based on the estimated open-circuit voltage, internal resistance, efficiency of the AC-to-AC converter, and power consumption of the auxiliary equipment of the AC-to-AC converter.

[0009] Furthermore, a degradation state estimation method according to an embodiment of the present invention is a degradation state estimation method for a power storage system including a secondary battery and an AC-to-AC converter, and includes performing an estimation calculation process to estimate the degradation state of the power storage system by estimating the open circuit voltage versus charging rate of the secondary battery, estimating the internal resistance, estimating the efficiency of the AC-to-AC converter, estimating the auxiliary power consumption of the AC-to-AC converter, and estimating the charge / discharge energy efficiency versus the charging rate of the secondary battery and the rated input / output ratio of the AC-to-AC converter based on the estimated open circuit voltage curve, internal resistance curve, efficiency curve of the AC-to-AC converter, and a history of the auxiliary power consumption of the AC-to-AC converter.

[0010] According to the present invention, it is possible to estimate the degradation state of the entire power storage system including the secondary battery and the AC-to-AC converter.

[0011] FIG. 1 is a diagram illustrating an example of the configuration of a power storage system according to an embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a degradation state estimation device 6. FIG. 3 is a diagram illustrating an example of the specifications of the LIB module 3 in FIG. 2. FIG. 4 is a conceptual diagram illustrating an approximate curve for correcting the open-circuit voltage. FIG. 5 is a conceptual diagram illustrating an equivalent circuit of a lithium-ion battery. FIG. 6 is a diagram illustrating the estimation error of the analysis results obtained using a Kalman filter. FIG. 7 is a diagram illustrating an example of a Gaussian function. FIG. 8 is a conceptual diagram illustrating an example of a model used for estimating the efficiency curve of a PCS. FIG. 9 is a graph illustrating a simulated operation pattern of power input and output to the PCS AC side of the BESS used in the verification. FIG. 10 is a graph illustrating a histogram of power input and output to the PCS AC side. FIG. 11 is a graph illustrating the results of the profile of open-circuit voltage and internal resistance learned and estimated by BT-MGFFD over a predetermined elapsed time. FIG. 12 is a graph illustrating the evolution of the estimation error of charge / discharge voltage over time. Fig. 13 is a graph showing the results of determining the transition of the PCS efficiency curve and the estimated power consumption of PCS auxiliaries, which are learned and estimated by PCS-MGFFD from the operation time-series data of the LIB module and PCS. Fig. 14 is a graph showing the results of estimating the charge / discharge power on the DC side of the LIB module from the input / output power on the AC side of the PCS. Fig. 15 is a graph showing the estimated RTE of the entire BESS with respect to the charging rate of the LIB and the rated input / output ratio on the AC side of the PCS. Fig. 16 is a flowchart showing an example of the operation of the degradation state estimation device 6 of the embodiment.

[0012] Hereinafter, embodiments will be described with reference to the drawings.

[0013] First, before describing the embodiment in detail, the significance of estimating the degradation state of the entire power storage system including the secondary battery and the orthogonal converter will be described.

[0014] Lithium-ion batteries (LIBs), a typical secondary battery, have a tendency to decrease their charge / discharge energy efficiency due to degradation. This is mainly caused by an increase in Joule heat due to an increase in internal resistance (electrical resistance and polarization resistance), which increases charge / discharge energy loss. Here, charge / discharge energy loss is defined as E lib,lossLet the charge / discharge current of the LIB be I and the internal resistance be R. Then, lib,loss can be expressed by the following equation (1).

[0015]

[0016] R is a parameter of the LIB that changes with deterioration and cannot be intentionally changed in operation, but I is a parameter related to charge / discharge operation control, so it can be adjusted. Therefore, by keeping I low, E lib,loss This reduces the energy consumption, resulting in improved charge / discharge energy efficiency.

[0017] On the other hand, the BESS includes not only the LIB but also an AC-DC converter, such as a power conditioning system (PCS), through which the DC LIB is connected to the AC transmission and distribution grid (power grid). Therefore, the final round-trip efficiency (RTE) of the BESS for temporary storage of VRE mentioned at the beginning is the product of the efficiencies of the LIB and the PCS (hereinafter, the parts of the BESS that perform AC-DC conversion and step-up / step-down functions are collectively referred to as PCS). While the energy efficiency of the BESS is often treated as a single fixed value in simulations, in reality, the energy efficiency varies depending on the ratio of input / output power to the rated value (hereinafter referred to as the "rated input / output ratio"). Furthermore, the RTE of the BESS is generally treated as a single value. However, as mentioned above, the energy efficiency of both the LIB and the PCS varies depending on the input / output power. Therefore, it is easy to imagine that the RTE of the entire BESS will fluctuate during operation. Above all, the sensitivity of LIB and PCS to energy efficiency relative to the rated input / output ratio is positive and negative in the low input / output range, i.e., there is a trade-off relationship, so understanding the energy efficiency of each will lead to improvements in the RTE and operational economy of the BESS.

[0018] Therefore, the inventors estimated the energy efficiency characteristics of the LIB and the PCS during the operation of the BESS and verified a method for comprehensively diagnosing the RTE of the BESS. In this embodiment, the method for comprehensively diagnosing the RTE of the BESS is shown together with the verification results.

[0019] <Configuration of Power Storage System (BESS)> FIG. 1 is a diagram showing an example of the configuration of a power storage system according to an embodiment.

[0020] The power storage system 1 shown in Fig. 1 corresponds to the above-mentioned BESS. This power storage system 1 mainly includes a power conditioning system 2, a LIB module 3, a battery management unit (BMU: Battery Management Unit) 4, an energy management unit (EMU: Energy Management Unit or EMS: Energy Management system) 5, a degradation state estimation device 6, and a battery temperature measurement unit 7.

[0021] The LIB module 3 corresponds to the module having the LIB described above. The power conditioner 2 corresponds to the PCS described above. The degradation state estimation device 6 realizes a function of estimating the degradation state of the entire BESS including the LIB and PCS, and further a function of estimating the charge / discharge energy efficiency and operational economy of the BESS based on the results of the degradation state estimation.

[0022] The functions of the degradation state estimation device 6 can be realized as a program executed by one or more computers. Furthermore, the degradation state estimation device 6 does not necessarily have to be provided within the power storage system 1. The degradation state estimation device 6 may be provided outside the power storage system 1. When the degradation state estimation device 6 is provided outside the power storage system 1, the degradation state estimation device 6 may be provided at any location on a network (cloud) that is capable of communicating with the power storage system 1. In this case, the power storage system 1 is provided with a function of transmitting various pieces of information required to estimate the degradation state of the BESS to the degradation state estimation device 6. Furthermore, the degradation state estimation device 6 is provided with a function of receiving the various pieces of information transmitted from the power storage system 1. In this embodiment, a case where the degradation state estimation device 6 is provided within the power storage system 1 will be described.

[0023] The deterioration state estimating device 6 will be described in detail later.

[0024] The power conditioner 2 is an AC-AC converter that converts power between DC and AC. The power conditioner 2 converts power supplied from, for example, an electric power grid 9 (such as an external electric power company), power supplied from a solar power generation system, or power supplied from the LIB module 3 so that it can be used by electrically driven devices including the specific load 8. Furthermore, the power conditioner 2 may also function as a charger for charging a storage battery. Furthermore, if the specific load 8 is an electrical device driven by AC power, the power conditioner 2 converts the DC power supplied from the LIB module 3 into AC power. Depending on the electrical device of the specific load 8, the power conditioner 2 may also boost the voltage of the power. Furthermore, the power conditioner 2 not only supplies power to the specific load 8, but can also reduce the consumption of power supplied from the power grid 9 by releasing the energy stored in the LIB module 3 when consumption of power supplied from the power grid 9 is at its maximum. In this case, when the demand for electricity decreases, such as late at night, the discharged LIB module 3 can be charged to full charge through the power conditioner 2.

[0025] The specific loads 8 to which the power storage system 1 supplies power are intended to be devices to which power should be supplied when the power supply from the power grid 9 is stopped (e.g., during a power outage), and include, for example, electronic devices such as computers and communication devices, to which power is supplied for power backup purposes.

[0026] The LIB module 3 includes a secondary battery (storage battery) 11 that outputs DC current and voltage, a cell monitor unit (CMU: Cell Monitor Unit) 12, and a protection unit 13. The number of LIB modules 3 is appropriately set according to the design of the power supply amount for a specific load, etc. To form a high-capacity secondary battery, multiple LIB modules 3 may be electrically connected to form a single assembly, i.e., a single battery pack. While this embodiment uses a lithium-ion battery as an example of a secondary battery, this is not limiting. Similar to lithium-ion batteries, the LIB module 3 can be easily applied to batteries with different structures as long as they have a small memory effect and good self-discharge characteristics. For example, it can be applied to nanowire batteries, which are improved versions of lithium-ion batteries.

[0027] The secondary battery 11 of this embodiment is not limited to the battery internal materials (electrode materials, etc.) or cell structure, and the type of exterior material may be a cylindrical can type, a rectangular can type, a laminate type, etc. The connection type of the secondary batteries 11 that make up the LIB module 3 can be any known connection type such as a single cell, a series battery pack, or a parallel battery pack.

[0028] The battery temperature measurement unit 7 measures the temperature using a temperature sensor (not shown) located in contact with each secondary battery 11. The usable ambient temperature range for the lithium-ion batteries within the device is approximately 5 to 40°C. However, depending on the installation environment (cold or tropical climate), a battery temperature control mechanism can be installed within the device if necessary. This battery temperature control mechanism is comprised of a fan and heater that adjusts the temperature within the usable range of the secondary batteries 11 (approximately 5 to 40°C) based on the temperature measured by the battery temperature measurement unit 7, so as not to degrade battery performance if the temperature exceeds the upper or lower limit of a preset temperature range. Of course, if the usable temperature range for the secondary batteries 11 expands with future battery improvements, the device can accommodate all of those temperature ranges.

[0029] The cell monitoring unit 12 has a function of continuously measuring the output voltage, current, and temperature of each secondary battery 11, which is a single battery (or single cell), and transmitting the measurement results to the battery management unit 4. This cell monitoring unit 12 measures the charge / discharge voltage at regular intervals during the charge / discharge process for estimating the charge / discharge curve, under the control of the calculation control unit 14, which will be described later.

[0030] Furthermore, the cell monitoring unit 12 has a function of transmitting the output voltage, current, and temperature acquired from the secondary battery 11 to the battery management unit 4 as monitor information.

[0031] The battery management unit 4 has a function of determining the occurrence of an abnormality such as overcharging, over-discharging, or temperature rise based on the received monitoring information, and controlling the protection unit 13 to stop charging or output (discharging) to the secondary battery 11 to prevent overcharging and over-discharging. If an emergency abnormality occurs due to a failure of the secondary battery 11, the protection unit 13 stops charging or output (discharging) to the secondary battery 11 by electrically cutting off the power. The protection unit 13 may also have a function of avoiding danger by notifying the battery management unit 4 of the abnormality. The abnormality determination function may be installed in either the cell monitoring unit 12 on the LIB module 3 side or the battery management unit 4 on the energy storage system 1 side, but it may also be installed in each to enhance safety by double determination. In the double determination, the order of determination is predetermined; for example, the cell monitoring unit 12 first determines whether an abnormality has occurred, and then the battery management unit 4 makes a second determination whether an abnormality has occurred. As a judgment procedure at this time, normally, if either of the two judgment units judges that an abnormality has occurred, a protective action is executed by the protection unit 13. Note that, depending on the design concept, it is also possible to configure the protection unit 13 to execute a protective action only when both units judge that an abnormality has occurred, and to issue a warning when only one unit judges that an abnormality has occurred.

[0032] Furthermore, the battery management unit 4 has a function of centrally aggregating the monitor information transmitted from the cell monitoring units 12 of each LIB module 3 and transmitting it to the higher-level energy management unit 5. Based on this monitor information, the energy management unit 5 instructs the power conditioner 2 to charge and discharge the LIB module 3. The power conditioner 2 controls the charging and discharging of the LIB module 3 in accordance with the instructions.

[0033] The energy management unit 5 is composed of an arithmetic and control unit 14 , a display unit 15 , a server 16 , and an interface unit 17 .

[0034] The calculation control unit 14 has functions equivalent to those of a computer's calculation processing unit, and instructs the battery management unit 4 or the power conditioner 2 to charge and discharge the LIB module 3. In addition, an upper limit charging voltage value and a lower limit discharging voltage value are set in advance for each LIB module 3, and based on the monitor information transmitted from the battery management unit 4, the calculation control unit 14 instructs the battery management unit 4 or the power conditioner 2 to stop charging or discharging.

[0035] The display unit 15 is configured by, for example, a liquid crystal display unit, and displays the operating status of the power storage system 1, the remaining capacity of the LIB module 3 (secondary battery 11), and warnings under the control of the calculation control unit 14. The display unit 15 may also be configured by employing a touch panel or the like and used as an input device.

[0036] The server 16 stores the latest information, such as the operating status of the power storage system 1, monitor information on the LIB module 3, etc., and information on charge / discharge curves, transmitted to the energy management unit 5, as needed. The interface unit 17 communicates with a centralized management system composed of an externally installed server, etc., via a network 18 such as the Internet.

[0037] <Configuration of Deterioration State Estimation Device 6> Fig. 2 is a diagram showing an example of the configuration of the deterioration state estimation device 6. Fig. 3 is a diagram showing an example of the specifications of the LIB module 3 in Fig. 2. However, the configuration of the deterioration state estimation device 6 and the specifications of the LIB module 3 are not limited to the examples shown in Fig. 2 and Fig. 3.

[0038] The degradation state estimation device 6 (particularly the estimation calculation processing unit 27) shown in Fig. 2 applies the method for comprehensively diagnosing the RTE of the BESS mentioned at the beginning, the details of which will be described later.

[0039] The degradation state estimation device 6 shown in FIG. 2 includes a measurement unit 21, a charging power supply unit 22, a discharge unit 23, a discharge load unit 24, a measurement unit 25, a time measurement unit 26, an estimation calculation processing unit 27, an SOC calculation unit 28, a function derivation unit 29, and a state estimation unit 30.

[0040] The measuring unit 21 measures the voltage and current on the direct current (DC) side and the voltage and current on the alternating current (AC) side of the power conditioner 2. The measurement results are transmitted to the estimation calculation processing unit 27.

[0041] The charging power supply unit 22 detects the charging state of the secondary battery 11 and outputs a DC current voltage that matches the rating to the secondary battery 11, within a range up to the upper charging voltage limit of the secondary battery 11, to charge the secondary battery 11. This charging power supply unit 22 is provided as a power supply dedicated to estimating the charge / discharge curve of the secondary battery 11, but a battery charging power supply unit provided in the power storage system or the power conditioner 2 may be used instead. In this embodiment, the measurement unit 25 and the charging power supply unit 22 form a charging unit.

[0042] The discharge unit 23 includes a discharge load unit 24, and is electrically connected to the secondary battery 11 by operating a switch (not shown), causing a predetermined amount of power (assumed to be a constant current or a constant voltage) to be discharged from the secondary battery 11. This discharge load unit 24 may be a resistor or an electronic load, but it may also be possible to simulate a load and regenerate power to the power grid without providing such a dedicated load.

[0043] The measurement unit 25 measures the DC voltage and DC current output by the LIB module 3 (secondary battery 11). The measurement is performed periodically at predetermined time intervals, measuring the DC voltage and DC current output from the LIB module 3. Note that, instead of actually measuring the voltage and current, it is also possible to use the voltage and current values ​​contained in the latest monitor information sent from the battery management unit 4 and stored in the server 16 of the energy management unit 5.

[0044] The time measurement unit 26 is a timer for measuring the time during which power is charged and discharged from the LIB module 3, and measures the measurement timing.

[0045] The SOC calculation unit 28 calculates the state of charge (SOC) at the timing when the measurement unit 25 measures the voltage. The SOC value is a value obtained by dividing the amount of charging current Qc [Ah] by the full charge capacity FCC [Ah] at that time. The SOC value of the state of charge in this embodiment can be calculated using a Kalman filter, which will be described later. The full charge capacity FCC value can be calculated using an appropriate method, such as a charge / discharge voltage difference method, an AC impedance method, a discharge curve differentiation method, or an optimization filter. For example, it may be calculated by dividing the difference between the accumulated charge amounts at two points in time by the difference between the SOC estimated by the Kalman filter.

[0046] The function derivation unit 29 derives approximate curves of the open-circuit voltage function and impedance function to be corrected and sets them as pre-estimated functions. Various curve fitting techniques can be applied to derive the approximate curves, and pre-determined approximate curves can be stored and set as pre-estimated functions. An initial open-circuit voltage function and an initial pre-estimated function can also be derived.

[0047] The estimation calculation processing unit 27 is a calculation processing unit (CPU or the like) that stores a calculation algorithm using a relational expression described below, and estimates a charge / discharge curve based on machine learning and estimated state parameters of the secondary battery 11, an efficiency curve of the secondary battery 11, and an efficiency curve of the power conditioner 2. This estimation calculation processing unit 27 does not have to be provided exclusively within the degradation state estimation device 6, and the calculation control unit 14 of the energy management unit 5 can substitute for the processing function.

[0048] In particular, the estimation calculation processing unit 27 has the function of estimating the open circuit voltage and internal resistance relative to the state of charge of the secondary battery 11 based on the charge / discharge voltage and charge / discharge current of the secondary battery 11 in each LIB module 3 and the input / output power of the DC-AC converter, estimating the efficiency of the power conditioner 2 and the power consumption of the auxiliary devices of the power conditioner 2, and estimating the charge / discharge energy efficiency (RTE) relative to the state of charge of the secondary battery 11 and the rated input / output ratio on the AC side of the power conditioner 2 based on the estimated open circuit voltage, internal resistance, efficiency of the DC-AC converter, and power consumption of the auxiliary devices of the DC-AC converter, thereby estimating the degradation state of the BESS. Furthermore, the estimation calculation processing unit 27 has the function of estimating the charge / discharge energy efficiency and operational economy of the BESS based on the results of the degradation state estimation performed.

[0049] The state estimation unit 30 can estimate state parameters including the state of charge, open circuit voltage, and internal resistance (internal impedance) of the secondary battery 11 using a Kalman filter based on an electrochemical model, which in this embodiment is an extended Kalman filter that supports nonlinearity.

[0050] The technology described in this embodiment can be applied to online estimation using network communication between an external device 19 and a system server 20 that manages multiple other power storage systems 1 via a network communication network 18 such as the Internet. Note that, in Fig. 1, one power storage system 1 is shown as a representative for ease of understanding the explanation, but the number of power storage systems 1 is not limited to one.

[0051] (Outline of Estimation Using a Kalman Filter) The inventors have studied the estimation of charge / discharge curves used for diagnosing deterioration in charge / discharge cycles of a lithium-ion battery using a Kalman filter and a Gaussian function. Fig. 4 is a conceptual diagram showing an approximate curve for correcting the open circuit voltage. Fig. 5 is a conceptual diagram showing an equivalent circuit of a lithium-ion battery. Fig. 7 is a diagram showing an example of a Gaussian function.

[0052] As an example, we will show a case where multiple LIB modules connected in series are charged and discharged and time-series data is acquired. While cycle tests using small lithium-ion battery cells are generally used for degradation diagnosis and state estimation, here we considered a LIB module consisting of large 50 Ah cells as a single battery pack (assembly). During the charge / discharge cycle test, we used constant power mode to simulate the charging and discharging conditions of the power conditioner 2. Room temperature is desirable for the ambient temperature during the cycle test, but due to the inevitable resistance heat that occurs during charging and discharging, a module temperature range of approximately 20°C to 30°C was set.

[0053] In this study, we applied the modified Gaussian free-form deformation (MGFFD) method to estimate the open-circuit voltage and internal impedance. MGFFD is known as an effective method for estimating the open-circuit voltage curve versus the state of charge of the LIB. In MGFFD, a Gaussian function is directly added to the open-circuit voltage curve versus the state of charge of the LIB, gradually deforming the open-circuit voltage curve in a direction perpendicular to the state of charge axis and gradually reducing the error.

[0054] In Figure 4, D prior estimate represents the prior estimate of the open circuit voltage by graph learning. The deformation amount M represents a Gaussian function. D posterior estimate represents the posterior estimate of the open circuit voltage estimated by the Kalman filter. An accurate estimate is calculated based on the charging rate S of the LIB at the time of correction. oc,0 represents the open circuit voltage estimated from the time series data at

[0055] As shown in Figure 4, the open circuit voltage and internal impedance of a lithium-ion battery are transformed by adding a height-adjusted Gaussian function. That is, the error (estimation error) between the pre-estimated value (scalar) at the current state of charge SOC and the posterior estimated value (scalar) currently estimated using a Kalman filter is multiplied by the learning rate L to obtain the height of the Gaussian function, which is then added to the pre-estimated value (vector) of graph learning. The graph function transformed in this way is the posterior estimated value (vector) of graph learning.

[0056] Next, the error index I maer Define as the mean absolute error rate.

[0057]

[0058] Here, the combination of the learning rate L and the rough range of the correction width σ affects the accuracy of the charge / discharge energy estimation, so we analyzed the relationship between these conditions and the estimation error index. In this analysis, the actual initial V oc (S oc ) into the C of the Kalman filter k The results are shown in Figure 6. Figure 6 shows the combination of the learning rate L and the rough correction width σ and the error index I maer Each dot represents the best accuracy point over the entire learning rate L.

[0059] From the results shown in Fig. 6, the estimation process is performed when the learning rate L is greater than 10 -2 In most cases, it diverges, and when the correction width σ is reduced, it approaches convergence. This is because a large value of the learning rate L and the correction width σ leads to excessive graph deformation. ～ In addition, the tendency of the optimal correction width σ is different when the learning rate L is L=10 -3.8 and L=10 -3.2 It was found that there was a boundary change.

[0060] <Method for Comprehensively Diagnosing the RTE of a BESS> As mentioned at the beginning, the inventors estimated the energy efficiency characteristics of the LIB and the PCS during operation of the BESS and verified a method for comprehensively diagnosing the RTE of the BESS. The specific method and the verification results are described below. The method described here is applied to the function of the estimation calculation processing unit 27 in the degradation state estimation device 6.

[0061] For this verification, a BESS was constructed using an actual PCS and LIB module and used for the experiment. The ambient temperature of the BESS installation area was maintained at 23±5°C, and the voltage, current, and charge rate of the LIB module (DC side) were measured using the battery management system (BMS) installed in the module. The voltage and current on the AC side of the PCS were measured using a clamp-type power logger.

[0062] (Estimation of LIB efficiency curve) Estimation of the charge / discharge energy efficiency of a LIB module requires estimation of the open circuit voltage, internal resistance, and full charge capacity. oc , internal resistance is R, full charge capacity is C ｆｃ Among these, V oc The learning estimation of and R was performed using the MGFFD method mentioned above. Specifically, V oc and the charging rate S of R oc The estimation was performed by learning the characteristic profile for . To distinguish it from the MGFFD for PCS described later, the MGFFD for LIB module is referred to as BT-MGFFD here.

[0063] In BT-MGFFD, the estimated open circuit voltage V^ is calculated using the following equation (3): oc The estimated charge / discharge voltage V^ is calculated from the estimated internal resistance R^, and the actual measured charge / discharge voltage V^ is calculated. ｃ,ｄ Estimation error V ～ ｃ,ｄ =V ｃ,ｄ -V^ ｃ,ｄ This estimation error is then used to calculate the estimation error V ～ oc , R ～ V～ ｃ,ｄ From V ～ oc and R ～ The charge / discharge current I is defined as positive during charging and negative during discharging.

[0064]

[0065] V oc and R is the discrete value vector of charging rate S oc = [0.01, 0.02,…, 0.99] as vectors corresponding to the vectors D̂ － , the posterior estimation is D^, and the correction amount at that time is M = [M １ , M ２ ,… M ９９ M is the charging rate S of the LIB to be estimated at the time of correction. oc,0 The prior estimation error V ～ oc － (S oc,0 ) or R ～－ (S oc,0 ) with learning rate L lib Variance σ with height multiplied by lib ２ In other words, the target estimation error is defined as D ～－ (S oc,0 ), it can be expressed as the following equation (4).

[0066]

[0067] This correction, i.e., V oc In this case, the learning of BT-MGFFD progresses by sequentially and continuously performing the processing described in FIG. 4 using the charge / discharge time-series data.

[0068] Similarly, for R, V ～ c,d Based on the relationship of Equation (3), BT-MGFFD was performed to learn the profile. Here, the learning parameters of BT-MGFFD are set to L lib = σ lib = 0.01. Also, at the start of learning, the discrete value vector V ocSince the initial values ​​of R and V must be given, the correct V obtained in advance through charge / discharge tests must be oc and R as references, and V oc An offset is added to R, and R is set to a discrete value vector that is linear and deviates from the correct value, resulting in an initial value that includes error.

[0069] Also, here, C fc The rated capacity of 40 Ah indicated by the LIB module manufacturer was used as a fixed value. fc If you need to estimate, for example, the cumulative charge / discharge electricity quantity ΔQ in two points of time series data c,d and the difference in charging rate ΔS oc From the following equation (5), C fc may be calculated.

[0070]

[0071] (Estimation of PCS Efficiency Curve) The estimation of the PCS efficiency curve was performed based on the model shown in FIG. 8, taking into consideration the power consumption of the PCS accessories.

[0072] FIG. 8 is a conceptual diagram showing an example of a model used for estimating the efficiency curve of a PCS.

[0073] FIG. 8 shows the relationship between the LIB module 3, the power conditioner 2, and the power grid 9. The LIB corresponds to the LIB module 3, and the PCS corresponds to the power conditioner 2. The amount of charging power of the LIB is E lib,c The discharge power of the LIB is expressed as E lib,d The input power (AC side) of the PCS is expressed as E pcs,in The output power of the PCS (on the AC side) is expressed as E pcs,out It is expressed as:

[0074] The PCS efficiency curve estimation is the efficiency of the PCS η pcs R is a discrete vector of rated input / output ratios typ = vector η corresponding to [0, 0.01,…, 1] pcs (R typ) and was performed in the same manner as in the BT-MGFFD method described above. Here, to distinguish it from the BT-MGFFD described above, the MGFFD for PCS is referred to as PCS-MGFFD.

[0075] In PCS-MG FDD, the power consumption of PCS auxiliary equipment is E au,l Considering this, the model is divided into charging and discharging, and the estimation error E ～ lib,c = E lib,c -E^ lib,c Or the estimated error E of the PCS output power ～ pcs,out = E pcs,out -E^ pcs,out is the estimation error η ～ pcs and E ～ au,l In other words, in the case of charging, the input power of the PCS is assumed to be E pcs,in , the amount of charging energy of LIB is E lib,c With attention focused on the amount of charging energy of the LIB, it is assumed that the relationship of the following equation (6) holds.

[0076]

[0077] In addition, in the case of discharge, the output power of the PCS is E pcs,out , the discharge energy of LIB is E lib,d With attention focused on the output power amount of the PCS, it is assumed that the relationship of the following equation (7) holds.

[0078]

[0079] Based on Equation (6) and Equation (7), the rated input / output ratio R of the AC side of the PCS to be estimated at the time of correction is calculated. typ,0 , that is, the rated power of the PCS is P pcs When this is set, R typ,0 =P pcs,in / P pcs , R during discharge typ,0 =P pcs,out / P pcs R pcsBy adding a Gaussian function centered on , learning by PCS-MGFFD proceeds in the same way as in the example of Equation (4) and Figure 2. The learning parameter of PCS-MGFFD is the learning rate L pcs and variance σ pcs ２ and L pcs = 0.01, σ pcs = 0.05, and L in equation (4) lib and σ lib It is treated the same as η pcs (R typ ) and E au,l For example, the initial value of [W] is set to a value different from the true value of the actual PCS characteristics as follows:

[0080]

[0081] (Simulated operation pattern of BESS) The simulated operation pattern (325,170 seconds in total) of the power input / output on the PCS AC side of the BESS used in this verification is shown in the graph in Figure 9. The horizontal axis of the graph indicates the elapsed time [x 10 s], and the vertical axis indicates the power value [W]. The power value on the vertical axis indicates charging as positive and discharging as negative, and sampling of the time series data was performed every 10 seconds. Furthermore, the operation of the BESS was performed within the range of "0.1 ≦ S oc The experiment was carried out in the range of "≤0.9". The histogram of the power input / output on the AC side of the PCS is shown in the graph in Figure 10. The horizontal axis of the graph shows the power value [W], and the vertical axis shows the number of occurrences.

[0082] <Results and Discussion> (Estimation of LIB open circuit voltage and internal resistance) Using the LIB operation time series data (voltage and current) based on the simulated operation pattern shown in Figure 9, the open circuit voltage V oc ・Regarding the profile of internal resistance R, the elapsed time (4×10 ４ The graphs in Figures 11(a) and (b) show the change in charge S every 10 seconds. oc The vertical axis indicates the open circuit voltage V oc The horizontal axis of the graph in FIG. oc The vertical axis shows the internal resistance R [Ω]. The different types of lines in each graph represent different elapsed times.

[0083] 11(a) and (b) show that as time passes, learning by MGFFD converges towards the end of the simulated operation, and the profile hardly changes at all.

[0084] (Estimation of the charge / discharge voltage of the LIB module) The current value of the charge / discharge time series data of the LIB module based on the simulated operation pattern shown in Figure 9 and the V oc Based on the profile of R, the charge / discharge voltage V of the LIB module is calculated based on the following equation (9): ｃ,ｄ was estimated sequentially.

[0085]

[0086] Charge / discharge voltage V ｃ,ｄ The graph in Figure 12 shows the change in the estimation error over time. The horizontal axis of the graph represents the elapsed time [×10 s], and the vertical axis represents the charge / discharge voltage V ｃ,ｄ The graph shows the estimated error [V] of the sampling data. The solid line in the graph shows the actual measured value, and the dashed line shows the moving average.

[0087] The moving average of the estimation error decreased over time, and at the end, it was shown that the error was limited to a maximum of about 3 V compared to the LIB module's rated voltage of 248.4 V.

[0088] (Estimation of PCS efficiency curve) Based on the simulated operation pattern of the BESS shown in Figure 9, the PCS efficiency curve learned and estimated by PCS-MGFFD from the operation time series data of the LIB module and PCS, and the results of determining the transition of the estimated power consumption of PCS auxiliary equipment are shown in the graphs of Figures 13(a) and (b), respectively. The horizontal axis of the graph in Figure 13(a) shows the rated input / output ratio on the AC side of the PCS, and the vertical axis shows the efficiency of the PCS. Different types of lines in the graph in Figure 13(a) represent different elapsed times. That is, the graph in Figure 13(a) shows the transition of the estimated power consumption of PCS auxiliary equipment over time (4 x 10 ４ 13(b) shows the change in PCS efficiency over time (×10 s) and the change in auxiliary power consumption (W).

[0089] Figure 13(a) shows that the learning results of the PCS efficiency curve converged towards the end of the simulated operation, while Figure 13(b) shows that the estimated values ​​of auxiliary power consumption converged within a certain range.

[0090] (Estimation of LIB Module Input / Output from PCS Input / Output) Based on the learning results of BT-MGFFD and PCS-MGFFD shown in Figures 11 and 13, the DC charge / discharge power of the LIB module was estimated from the AC input / output power of the PCS. The graphs in Figures 14(a), 14(b), and 14(c) show the results. The horizontal axis of the graph in Figure 14(a) represents elapsed time [×10 s], and the vertical axis represents the DC charge / discharge power of the LIB module [W]. The solid line in graph 14(a) represents the actual measured value, and the dashed line represents its moving average. The horizontal axis of the graph in Figure 14(b) represents elapsed time [×10 s], and the vertical axis represents the estimation error [W] of the DC charge / discharge power of the LIB module. The solid line in graph 14(b) represents the actual measured value of the time-series data, and the dashed line represents its moving average. The graph in Figure 14(c) is an enlarged view of a portion of the graph in Figure 14(b). The vertical axis of each graph represents charging as positive and discharging as negative.

[0091] In Figure 14(b), a peak is seen that is thought to be due to the difference in response time between the AC side and DC side of the PCS, but as shown in Figure 14(c), the moving average converges to an error of within 30 W, which is considered to be at a level where there is no problem with the estimation accuracy for each certain time period.

[0092] (Development towards operational efficiency and economic optimization) Open circuit voltage V of LIB estimated by BT-MGFFD and PCS-MGFFD oc From the characteristic profile of the internal resistance R (Fig. 11), and the efficiency curve of the PCS and the transition of auxiliary power consumption (Fig. 13), the graph shown in Fig. 15 is obtained, that is, the charging rate S of the LIB. ocIt is possible to obtain information on the estimated RTE of the entire BESS relative to the rated input / output ratio on the AC side of the PCS. It is clear that this estimated RTE will vary depending on conditions such as the BESS model and the aging of the installed LIB and PCS. Therefore, when configuring a cloud battery management system that bundles and controls multiple BESSs using ICT, the MGFFD method described here is thought to be an effective means of improving overall operational efficiency and optimizing economic efficiency.

[0093] <Operation of Degradation State Estimation Device> Next, an example of the operation of the degradation state estimation device 6 of this embodiment will be described with reference to the flowchart shown in Fig. 16. First, an initial value / initial function derivation process is performed (step S1). Specifically, the initial open circuit voltage function V oc (S oc ) and the initial internal resistance function R(S oc ) to derive an approximate curve.

[0094] Next, the discharge unit 23 electrically connects the secondary battery 11 to the discharge load unit 24 to start discharging, and the timer unit 26 starts measuring time (step S2).

[0095] After the discharge starts, it is determined whether the voltage is at the lower limit voltage (step S3). If the voltage is at the lower limit voltage (YES) in the determination of step S3, charging is started (step S8). On the other hand, if the voltage is not at the lower limit voltage (NO), discharging is continued.

[0096] After the start of discharge, it is determined whether a predetermined time has elapsed (step S4). If the determination in step S4 is NO, discharge continues until the predetermined time has elapsed. If the predetermined time has elapsed (YES), the measurement unit 25 measures the discharge voltage and charge / discharge current of the secondary battery 11, and the SOC calculation unit 28 and other units calculate the DC voltage and current, SOC value, and AC voltage and current (step S5), and machine learning processing is performed (step S6). At this time, correction is repeatedly performed using the Kalman filter of the state estimation unit 30, and the accuracy of the post-estimated approximate curve is improved.

[0097] Next, it is determined whether the number of repeated corrections has reached a predetermined number of times (step S7). If the determination is negative (NO), the process returns to step S3 and continues discharging. If discharging is to continue, it is again determined whether the voltage is at the lower discharge limit (step S3). If the voltage is not at the lower discharge limit (NO), the processing routine from steps S3 to S7 is repeated. If the determination in step S7 is negative (YES), the LIB module charge / discharge curve, LIB efficiency curve, and PCS efficiency curve are estimated using approximate curves of the open circuit voltage function and the internal resistance function (step S14). The estimated LIB module charge / discharge curve and PCS efficiency curve are temporarily stored in the server 16 of the energy management unit 5.

[0098] If it is determined in step S3 that the lower limit discharge voltage has been reached (YES), charging is initiated (step S8). After charging is initiated, it is determined whether the voltage has reached the upper limit charge voltage (step S9). If it is determined in step S9 that the upper limit charge voltage has not been reached (NO), it is determined whether a predetermined time has elapsed (step S10). On the other hand, if it is determined that the upper limit charge voltage has been reached (YES), the process returns to step S2.

[0099] In the determination of step S10, after a predetermined time has elapsed (YES), the measurement unit 25 measures the discharge voltage V of the secondary battery 11. c,d The charge / discharge current I and the charge / discharge current I are measured, and the SOC calculation unit 28 and other units calculate the DC voltage and current, the SOC value, and the AC voltage and current (step S11), and machine learning processing is performed (step S12). At this time, the Kalman filter of the state estimation unit 30 is used to repeatedly perform corrections to improve the accuracy of the post-estimated approximate curve.

[0100] Next, it is determined whether the number of repeated corrections has reached a predetermined number of repetitions (step S13). If it is determined in step S13 that the number of repetitions has not been reached (NO), the process returns to step S9 and charging continues. If charging is to be continued, it is determined in step S9 whether the charging voltage is at the upper limit voltage. If the upper limit voltage has not been reached and it is determined in step S13 that the number of repetitions has not been reached, the processing routine from step S9 to step S12 is repeated.

[0101] If it is determined in step S13 that the number of repetitions has been reached (YES), the process proceeds to step S14.

[0102] Next, the estimation calculation processing unit 27 estimates the charge / discharge curve of the LIB module, the efficiency curve of the LIB, and the efficiency curve of the PCS using approximate curves of the open circuit voltage function and the internal resistance function, and stores them, for example, in the server 16 of the energy management unit 5 (step S14).

[0103] Finally, the estimation calculation processing unit 27 estimates the charge / discharge efficiency characteristics of the power storage system 1 based on the efficiency curve of the LIB and the efficiency curve of the PCS, and stores them, for example, in the server 16 of the energy management unit 5 (step S15).

[0104] The stored charge / discharge curves of the LIB module and the efficiency curves of the PCS can be read from the server 16 upon request and used for display on the display unit 15 as well as for other purposes.

[0105] <Summary> In this embodiment, the MGFFD method is applied not only to estimating the open-circuit voltage curve and internal resistance curve of the LIB module, but also to estimating the efficiency curve and auxiliary power consumption of the PCS. In other words, a combination of BT-MGFFD and PCS-MGFFD is employed. This makes it possible to accurately estimate the charge / discharge energy efficiency of the PCS based on the AC input / output terminals, taking into account the LIB charging rate and the rated input / output ratio of the PCS. This also makes it possible to estimate the RTE of the entire BESS, as well as the final utilization rate and operational economics of temporary renewable energy storage, enabling optimization of the operation of cloud battery management systems in microgrids and other applications.

[0106] The present invention is not limited to the above-described embodiments, and various modifications may be made without departing from the spirit of the present invention. Furthermore, various inventions that solve the above problems can be extracted by selecting or combining multiple disclosed constituent elements.

[0107] 1...energy storage system, 2...power conditioner (PCS), 3...LIB module, 4...battery management unit, 5...energy management unit, 6...deterioration state estimation device, 7...battery temperature measurement unit, 8...specific load, 9...power system, 11...secondary battery, 12...cell monitoring unit, 13...protection unit, 14...arithmetic and control unit, 15...display unit, 16...server, 17...interface unit, 18...network communication network, 19...external device, 20...system server, 21...measurement unit, 22...charging power supply unit, 23...discharging unit, 24...discharging load unit, 25...measurement unit, 26...time measurement unit, 27...estimation calculation processing unit, 28...SOC calculation unit, 29...function derivation unit, 30...state estimation unit.

Claims

1. A degradation state estimation device that estimates the degradation state of a power storage system including a secondary battery and an orthogonal converter, comprising an estimation calculation processing unit that estimates the open circuit voltage and internal resistance relative to the charging rate of the secondary battery based on the charging and discharging voltage and charging and discharging current of the secondary battery and the input and output power of an orthogonal converter, and estimates the efficiency of the orthogonal converter and the auxiliary power consumption of the orthogonal converter, and estimates the charging and discharging energy efficiency relative to the charging rate of the secondary battery and the rated input and output ratio of the AC side of the orthogonal converter based on the estimated open circuit voltage, internal resistance, efficiency of the orthogonal converter, and auxiliary power consumption of the orthogonal converter.

2. The degradation state estimation device according to claim 1, wherein the estimation calculation processing unit estimates the degradation state of a power storage system including the secondary battery and the orthogonal converter by estimating the charging rate of the secondary battery and the charge / discharge energy efficiency relative to a rated input / output ratio on the AC side of the orthogonal converter.

3. The degradation state estimation device according to claim 1, wherein the estimation calculation processing unit estimates an efficiency curve of the orthogonal transformer by defining the efficiency of the orthogonal transformer as a vector corresponding to a discrete value vector of a rated input / output ratio.

4. The degradation state estimation device according to claim 1, further comprising a state estimation unit that estimates state parameters including the charging rate, the open circuit voltage and the internal resistance of the secondary battery using a Kalman filter that supports nonlinearity.

5. The degradation state estimation device according to any one of claims 1 to 4, wherein the degradation state estimation device is provided outside the power storage system.

6. A method for estimating a degradation state of a power storage system including a secondary battery and an orthogonal converter, comprising: estimating an open circuit voltage for a charging rate of the secondary battery, estimating an internal resistance, estimating an efficiency of the orthogonal converter, estimating auxiliary power consumption of the orthogonal converter, and performing an estimation calculation process to estimate a degradation state of the power storage system by estimating a charge / discharge energy efficiency for the charging rate of the secondary battery and a rated input / output ratio of the orthogonal converter based on the estimated open circuit voltage curve, internal resistance curve, efficiency curve of the orthogonal converter and a history of auxiliary power consumption of the orthogonal converter.

7. The degradation state estimation method according to claim 6, wherein the estimation calculation process includes estimating the degradation state of a power storage system including the secondary battery and the orthogonal converter by estimating a charging / discharging energy efficiency relative to a charging rate of the secondary battery and a rated input / output ratio on the AC side of the orthogonal converter.

8. The degradation state estimating method according to claim 6, wherein the estimation calculation process includes estimating an efficiency curve of the orthogonal converter by defining the efficiency of the orthogonal converter as a vector corresponding to a discrete value vector of a rated input / output ratio.

9. The degradation state estimating method according to claim 6, further comprising estimating state parameters including the charging rate, the open circuit voltage and the internal resistance of the secondary battery using a Kalman filter corresponding to nonlinearity.

10. A method for optimizing the operation of a power storage system, which estimates the charge / discharge energy efficiency and operational economy of the power storage system based on the results of degradation state estimation performed by the degradation state estimation device according to claim 1 or the degradation state estimation method according to claim 6.

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