System for estimating the state of a secondary battery, method for estimating the state of a secondary battery, and program

Abstract

This invention provides a secondary battery state estimation system that can suppress the decrease in the accuracy of estimating the internal state of a secondary battery. [Solution] The system 1, which includes a secondary battery state estimation device 10, includes an optimization unit 25 that estimates the degree of deterioration of the secondary battery 11 to be estimated. The optimization unit 25 determines whether or not to estimate the degree of deterioration of a predetermined substance contained in the electrodes of the secondary battery 11 to be estimated. The optimization unit 25 determines whether or not the closed-circuit voltage (CCV) or open-circuit voltage (OCV) obtained from the secondary battery 11 to be estimated satisfies predetermined conditions in a voltage range or capacity range corresponding to the predetermined substance.

Description

[Technical Field]

[0001] This invention relates to a secondary battery state estimation system, a secondary battery state estimation method, and a program. [Background technology]

[0002] In recent years, research and development has been conducted on rechargeable batteries that contribute to energy efficiency, in order to ensure that more people have access to affordable, reliable, sustainable, and advanced energy. Conventionally, there are known devices that acquire an OCV curve showing the change in open circuit voltage (OCV) according to the discharge capacity, based on historical data of the battery's voltage and current, and an OCP curve showing the change in open circuit potential (OCP) according to the respective discharge capacities of the positive and negative electrodes (see, for example, Patent Documents 1 and 2). [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] International Publication No. 2023 / 054443 [Patent Document 2] Japanese Patent Publication No. 2023-48545 [Overview of the project] [Problems that the invention aims to solve]

[0004] Incidentally, in secondary battery technology, a challenge is to suppress the decrease in the accuracy of estimating the internal state, including OCV curves, even when there is a bias in the data acquired from the secondary battery. For example, in the conventional devices described above, if the data acquired corresponding to the region in which the capacity of the electrode active material manifests is insufficient, the accuracy of estimating the internal state may decrease. For example, if the manifestation of the capacity of the electrode active material is characteristic in the low SOC (State of Charge) region, but the frequency of the secondary battery state is biased towards the high SOC region, the accuracy of estimating the internal state based on data acquired in that state may decrease.

[0005] This invention aims to solve the above-mentioned problems by suppressing the decrease in the accuracy of estimating the internal state of a secondary battery. [Means for solving the problem]

[0006] In order to solve the above problems and achieve the above objectives, the present invention employs the following embodiments. (1) A secondary battery state estimation system according to one aspect of the present invention (for example, system 1 in the embodiment) comprises a region acquisition unit (for example, optimization unit 25 in the embodiment) that acquires a voltage region or capacity region corresponding to a predetermined substance contained in the electrodes of a secondary battery to be estimated (for example, secondary battery 11 in the embodiment), and a degradation estimation unit (for example, also the optimization unit 25 in the embodiment) that determines whether or not to estimate the degree of degradation of the predetermined substance based on the determination result of whether or not the voltage data (for example, closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) acquired by the secondary battery to be estimated satisfies predetermined conditions in the voltage region or capacity region acquired by the region acquisition unit.

[0007] (2) In the secondary battery state estimation system described in (1) above, the degradation estimation unit may determine whether or not to estimate the degree of degradation of the predetermined substance based on the determination result of whether or not the number of voltage data satisfies a predetermined number of data conditions in the voltage region or the capacity region acquired by the region acquisition unit.

[0008] (3) In the secondary battery state estimation system described in (1) above, the degradation estimation unit may determine whether or not to estimate the degree of degradation of the predetermined substance based on the determination result of whether or not the distribution of the voltage data satisfies predetermined data distribution conditions in the voltage region or the capacity region acquired by the region acquisition unit.

[0009] (4) In the secondary battery state estimation system described in any one of (1) to (3) above, the predetermined substance is silicon contained in the negative electrode of the secondary battery to be estimated, and the degradation estimation unit may determine whether or not to estimate the degree of degradation of the predetermined substance based on the determination result of whether or not the voltage data obtained by the region acquisition unit satisfies the predetermined conditions in the voltage region below a predetermined voltage or the capacity region below a predetermined charge capacity.

[0010] (5) In the secondary battery state estimation system described in any one of (1) to (3) above, the degradation estimation unit may estimate the degree of degradation of the predetermined substance based on the voltage data in the voltage region or capacity region acquired by the region acquisition unit and voltage information (e.g., open-circuit voltage (OCV) in the embodiment) or potential information (e.g., open-circuit potential (OCP) in the embodiment) acquired for the secondary battery to be estimated, if the voltage data satisfies the predetermined conditions.

[0011] (6): A method for estimating the state of a secondary battery according to one aspect of the present invention is a method executed by an electronic device (e.g., system 1 in the embodiment) equipped with a processing unit (e.g., optimization unit 25 in the embodiment) that estimates the degree of deterioration of a predetermined substance contained in the electrodes of a secondary battery to be estimated (e.g., secondary battery 11 in the embodiment), and includes a region acquisition step (e.g., steps S01 and S02 in the embodiment) for acquiring a voltage region or capacity region corresponding to the predetermined substance, and a deterioration estimation step (e.g., step S04 in the embodiment) for determining whether or not to estimate the degree of deterioration of the predetermined substance based on the determination result of whether or not voltage data (e.g., closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) acquired by data acquired by or to be acquired by the secondary battery to be estimated satisfies predetermined conditions in the voltage region or capacity region acquired by the region acquisition step.

[0012] (7): A program according to one aspect of the present invention causes a computer in an electronic device (e.g., system 1 in the embodiment) equipped with a processing unit (e.g., optimization unit 25 in the embodiment) that estimates the degree of deterioration of a predetermined substance contained in the electrodes of a secondary battery to be estimated (e.g., secondary battery 11 in the embodiment), to execute a region acquisition step (e.g., steps S01 and S02 in the embodiment) that acquires a voltage region or capacity region corresponding to the predetermined substance, and a deterioration estimation step (e.g., step S04 in the embodiment) that determines whether or not to estimate the degree of deterioration of the predetermined substance based on the determination result of whether or not the voltage data (e.g., closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) acquired by the data acquired by or to be acquired by the secondary battery to be estimated satisfies predetermined conditions in the voltage region or capacity region acquired by the region acquisition step. [Effects of the Invention]

[0013] According to (1) above, if the voltage data obtained from the secondary battery does not meet the predetermined conditions, the estimation of the degree of degradation of the predetermined substance will not be performed, thereby suppressing a decrease in estimation accuracy.

[0014] In the case of the above (2) or (3), according to the number or distribution of voltage data obtained from the secondary battery, it is possible to appropriately determine whether it is necessary to estimate the degree of deterioration of a predetermined substance.

[0015] In the case of the above (4), it is possible to improve the estimation accuracy of the degree of deterioration with respect to silicon in which the capacity characteristics appear at the end of discharge (or at the end of deterioration).

[0016] In the case of the above (5), based on the voltage data and the voltage information or potential information, it is possible to accurately estimate the degree of deterioration of a predetermined substance.

[0017] According to the above (6) or (7), when the voltage data obtained from the secondary battery does not satisfy the predetermined conditions, it is possible to suppress a decrease in the estimation accuracy by not performing the estimation of the degree of deterioration of a predetermined substance.

Brief Description of the Drawings

[0018] [Figure 1] A block diagram showing the functional configuration of a system including a state estimation device for a secondary battery in an embodiment of the present invention. [Figure 2] A diagram showing an example of an OCV curve obtained based on the OCP curves of each of the positive electrode and the negative electrode by an optimization unit of a state estimation device for a secondary battery in an embodiment of the present invention. [Figure 3] A diagram showing an example of the flow of information in parameter optimization processing by an optimization unit of a state estimation device for a secondary battery in an embodiment of the present invention. [Figure 4] A diagram showing an example of parameters for each of the positive electrode OCP curve and the negative electrode OCP curve set by a state estimation device for a secondary battery in an embodiment of the present invention. [Figure 5] A diagram showing an example of the correspondence relationship between the history data obtained by a state estimation device for a secondary battery in an embodiment of the present invention and the negative electrode OCP curve and the OCV curve. [Figure 6] A flowchart showing the processing executed by a state estimation device for a secondary battery in an embodiment of the present invention. [Modes for carrying out the invention]

[0019] Hereinafter, a secondary battery state estimation system, a secondary battery state estimation method, and a program according to embodiments of the present invention will be described with reference to the attached drawings. The secondary battery according to this embodiment is, for example, attached to or permanently installed in various electrical devices. Various electrical devices include, for example, electric vehicles, electric mobile devices, electric machinery, and power supply devices. Electric vehicles include, for example, electric automobiles, saddle-type vehicles, and kick scooters equipped with a rotating electric machine powered by the secondary battery, hybrid vehicles combining a rotating electric machine and an internal combustion engine, and fuel cell vehicles combining a secondary battery and a fuel cell. Electric mobile devices include, for example, robots, mobile work machines, aircraft, and mobile devices on and underwater. Electric machinery includes, for example, construction machinery equipped with a rotating electric machine as a power source. Power supply devices include, for example, stationary or mobile power supply devices that discharge and charge secondary batteries, or exchange devices that provide and receive secondary batteries to users in a so-called battery sharing service.

[0020] Furthermore, various electrical devices may be equipped with an external charging function, such as in PHV (Plug-in Hybrid Vehicle) or PHEV (Plug-in Hybrid Electric Vehicle), which allows them to be charged by an external power source (external DC power source and external AC power source). Various electrical devices may also be equipped with a function to supply power to an external source using the power of a secondary battery. In addition, a rotating electric machine mounted on an electric vehicle may exchange power with a secondary battery, for example, through regenerative operation using rotational power input from the wheels, or through power generation using power input from an internal combustion engine, in addition to the traction operation.

[0021] Figure 1 is a block diagram showing the functional configuration of a system 1 equipped with a secondary battery state estimation device 10 according to an embodiment. As shown in Figure 1, the system 1 of the embodiment comprises, for example, a vehicle 2 and a server 3. The vehicle 2 and the server 3 are connected, for example, via a wired or wireless communication network 4. The network 4 is, for example, the Internet, a mobile communication network, a LAN (Local Area Network), and a WAN (Wide Area Network). For example, the LAN is a wired LAN (Local Area Network) of a predetermined standard such as Ethernet, or a wireless LAN of various standards such as Wi-Fi and Bluetooth (registered trademark). The secondary battery state estimation device 10 of this embodiment is configured, for example, by a server 3.

[0022] Vehicle 2 includes, for example, a secondary battery 11, a battery sensor 12, a battery control unit 13, a power control unit 14, a rotating electric machine 15, a drive mechanism 16, and an overall processing unit 17. The secondary battery 11 is one of various types of batteries that undergo repeated charging and discharging, such as lithium-ion batteries, sodium-ion batteries, or nickel-metal hydride batteries. The electrolyte of the secondary battery 11 is a non-aqueous electrolyte such as a liquid, solid, or polymer.

[0023] The positive electrode active material that constitutes the positive electrode of the secondary battery 11 is, for example, a metal oxide containing lithium ions in the case of a lithium-ion battery. Metal oxides containing lithium ions include, for example, single or mixed composite oxides of lithium and metals such as nickel, cobalt, manganese, and aluminum. These composite oxides are classified, for example, from the viewpoint of their crystal structure into layered rock salt type, spinel type, and olivine type. Examples of layered rock salt type composite oxides include lithium cobalt oxide (LCO: LiCoO2) and nickel-cobalt-manganese oxide (NCM: Li(Ni x Co y Mn z )O2), Nickel-cobalt-aluminum oxide (NCA:LiNi x Co y Al xsuch as O2). Spinel-type composite oxides include, for example, lithium manganate (LMO: LiMn2O4) and lithium nickel manganate (LNMO: LiNi x Mn y O4), etc. Olivine-type composite oxides include, for example, lithium iron phosphate (LFP: LiFePO4) and lithium manganese iron phosphate (LMFP: LiMn x Fe (1-x) PO4), etc.

[0024] The negative electrode active material constituting the negative electrode of the secondary battery 11 is formed from, for example, a carbon material, an oxide-based material, or a mixed material in the case of a lithium-ion battery. The carbon material includes, for example, graphite (black lead) and hard carbon (non-graphitizable carbon), etc. The oxide-based material includes, for example, lithium titanate (LTO: Li4Ti5O 12 ), etc. The mixed material includes, for example, a mixed material of a metal material such as Si and Sn and a carbon material, such as a mixed material of graphite and silicon oxide (SiO x ).

[0025] The battery sensor 12 includes, for example, various sensors for detecting the state of the secondary battery 11. The battery sensor 12 includes, for example, a voltage sensor, a current sensor, and a temperature sensor, etc. The battery sensor 12 outputs signals of various detected values such as voltage, current, and temperature related to the state of the secondary battery 11.

[0026] The battery control unit 13 is, for example, a so-called BMU (Battery Management Unit) that monitors and controls the state of the secondary battery 11. The battery control unit 13 is a software function unit that functions when a predetermined program is executed by a processor such as a CPU (Central Processing Unit). The software function unit is an ECU (Electronic Control Unit) that includes an ECU (Electronic Control Unit) equipped with a processor such as a CPU, a ROM (Read Only Memory) for storing programs, a RAM (Random Access Memory) for temporarily storing data, and electronic circuits such as a timer. At least a part of the battery control unit 13 may be an integrated circuit such as an LSI (Large Scale Integration).

[0027] The battery control unit 13 stores, for example, information relating to the secondary battery 11 and a predetermined program. The information relating to the secondary battery 11 includes, for example, identification information such as an ID (IDentifier) ​​exclusively assigned to the secondary battery 11, the date and time of manufacture, the initial capacity, and information relating to the state of the secondary battery 11 based on the output of the battery sensor 12. The information relating to the state of the secondary battery 11 includes, for example, the charge status such as the charge rate, remaining capacity (SOC: State Of Charge) or energy amount, the charge and discharge history such as the number of charge cycles, information relating to the current state such as voltage and temperature, information relating to the current degradation state such as the degree of degradation, and information relating to the presence or absence of abnormalities.

[0028] The power control unit 14 is connected to the secondary battery 11 and the rotating electric machine 15. The power control unit 14 includes, for example, a voltage converter such as a DC-DC converter that converts DC voltage and a power converter such as a DC-AC converter that converts power between DC and AC. The power control unit 14 controls the power transfer between the secondary battery 11 and the rotating electric machine 15 based on control signals received from, for example, the control unit 17.

[0029] The rotating electric machine 15 is, for example, a three-phase AC brushless DC motor. The rotating electric machine 15 generates rotational power by performing a motoring operation using power supplied from the power control unit 14. When the rotating electric machine 15 is connected to the wheels of a vehicle 2, for example, it generates driving force by performing a motoring operation using power supplied from the power control unit 14. The rotating electric machine 15 may also generate power by performing a regenerative operation using rotational power input from the wheels of the vehicle 2. When the rotating electric machine 15 is connected to the internal combustion engine of the vehicle 2, it may also generate power using the power of the internal combustion engine.

[0030] The drive mechanism 16 is a power transmission mechanism connected to the rotor of the rotating electric machine 15. The drive mechanism 16 includes, for example, gears, belts, and chains. The drive mechanism 16 transmits power between, for example, the rotating electric machine 15 and the wheels of the vehicle 2. The drive mechanism 16 may also include a regulating mechanism to restrict power transmission, such as an electric parking brake and parking lock mechanism that stops the rotation of the wheels or drive shafts.

[0031] The integrated processing unit 17 comprehensively controls the operation of the vehicle 2. The integrated processing unit 17 includes, for example, a software function unit. At least a part of the integrated processing unit 17 may include an integrated circuit. The integrated processing unit 17 includes, for example, an input / output unit and a communication unit. The input / output unit includes, for example, various operating devices such as a keyboard, touch panel, mouse, and buttons; a display device such as a liquid crystal display or an organic EL (Electro-Luminescence) display; and various input / output devices such as a microphone for voice input and a speaker for sound output. The input / output unit receives input operations, such as operations by an operator such as a user or voice input, and outputs a signal corresponding to the input operation. The communications unit transmits and receives various types of information to and from the server 3 via the network 4. For example, the communications unit transmits information to the server 3 that is a combination of information such as the date and time, identification information of the vehicle 2 or secondary battery 11, and information about the secondary battery 11 received from the battery control unit 13.

[0032] Server 3 includes, for example, a software function unit. At least a portion of the integrated processing unit 17 may include an integrated circuit. Server 3 includes, for example, a storage unit 21, an acquisition unit 22, a pre-processing unit 23, an OCV estimation unit 24, an optimization unit 25, and a diagnostic unit 26. The memory unit 21 stores various types of information, such as information about the secondary battery 11 that the server 3 acquires in advance or receives from the vehicle 2 at an appropriate time, and information generated by the server 3, as well as a predetermined program.

[0033] The acquisition unit 22 acquires, for example, time-series data such as voltage, current, and temperature of the secondary battery 11 from the vehicle 2. The voltage of the secondary battery 11 is, for example, the closed-circuit voltage (CCV). The acquisition unit 22 acquires the discharge capacity (discharge amount) by, for example, integrating the time-series data of the current.

[0034] The preprocessing unit 23 performs processing such as cleansing and filtering of the time-series data acquired by the acquisition unit 22. For example, the preprocessing unit 23 excludes data that is missing or abnormal from the time-series data.

[0035] The OCV estimation unit 24 estimates the open-circuit voltage (OCV) of the secondary battery 11 to be estimated, as follows, and acquires historical data of the open-circuit voltage (OCV) in the secondary battery 11. The OCV estimation unit 24 extracts, for example, data from the time-series data processed by the preprocessing unit 23 in which changes caused by charging and discharging of the secondary battery 11 are below a predetermined threshold. The OCV estimation unit 24 considers, for example, data showing changes below a predetermined threshold to be data at a timing where the closed-circuit voltage (CCV) can be considered to be the open-circuit voltage (OCV). The OCV estimation unit 24 may, for example, estimate the open-circuit voltage (OCV) of the secondary battery 11 to be estimated using an appropriate machine learning model from time-series data processed by the preprocessing unit 23. The OCV estimation unit 24 constructs a machine learning model that outputs the open-circuit voltage (OCV) or voltage data related to the open-circuit voltage (OCV) using data obtained based on tests performed on a secondary battery 11 whose degradation state is known or simulations performed on a predetermined model of the secondary battery 11. The OCV estimation unit 24 obtains the open-circuit voltage (OCV) of the secondary battery 11 to be estimated at any given time by inputting current and closed-circuit voltage (CCV) data detected at appropriate timings in the secondary battery 11 to be estimated into the machine learning model. The OCV estimation unit 24 may, for example, estimate the open-circuit voltage (OCV) of the secondary battery 11 to be estimated using an appropriate equivalent circuit model. The OCV estimation unit 24 stores the open-circuit voltage (OCV) obtained at any arbitrary timing (date and time, etc.) in the storage unit 21 as history data of the open-circuit voltage (OCV). Historical data refers to data acquired over appropriate periods, and is not limited to a series of data such as time-series data.

[0036] Figure 2 shows an example of an OCV curve obtained by the optimization unit 25 of the secondary battery state estimation device 10 in the embodiment, based on the OCP curves of the positive electrode and the negative electrode, respectively. As shown in Figure 2, the optimization unit 25 obtains an OCP curve (OCP curve) that shows the change in open-circuit potential (OCP) according to the respective discharge capacity x (Ah) of the positive and negative electrodes of the secondary battery 11, based on, for example, several parameters relating to the state of the secondary battery 11. The optimization unit 25 obtains a positive electrode OCP curve (=fca(x)) and a negative electrode OCP curve (=fan(x)) by, for example, applying several parameters to an OCP curve (reference OCP curve) that has been previously stored in the memory unit 21.

[0037] The reference OCP curves stored in the memory unit 21 are obtained, for example, through pre-conducted tests or simulations using appropriate models. The reference OCP curves are, for example, the individual OCP curves for each active material constituting the positive electrode and negative electrode of the secondary battery 11. The optimization unit 25 estimates an OCV curve (=fca(x)-fan(x)) that shows the change in open-circuit voltage (OCV) according to the discharge capacity x (Ah), based, for example, the difference between the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)).

[0038] Figure 3 shows an example of the information flow during the parameter optimization process performed by the optimization unit 25 of the secondary battery state estimation device 10 in the embodiment. As shown in Figure 3, the multiple parameters relating to the state of the secondary battery 11 include, for example, positive electrode capacity a and positive electrode position b, negative electrode capacity c and negative electrode position d, and active material capacity ratio e. The parameter optimization process performed by the optimization unit 25 includes, for example, generating an OCV curve based on the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)), and optimizing (resetting) the multiple parameters. The optimization unit 25 optimizes the multiple parameters relating to the state of the secondary battery 11 based, for example, the OCV curve estimated based on the OCP curve and the history data of the secondary battery 11.

[0039] The optimization unit 25 performs a predetermined optimization process based on an error function that shows the error between the OCV curve obtained based on the respective OCP curves of the positive and negative electrodes and the historical data of the open-circuit voltage (OCV) of the secondary battery 11. The error function is, for example, the weighted mean squared error (Weighted RMSE) or the weighted mean absolute error (Weighted MAE). The predetermined optimization process is, for example, a local optimization algorithm such as the BFGS method, the conjugate gradient method and the COBYLA method, or a global optimization algorithm such as a genetic algorithm, the differential evolution method, the SHGO method and the simulated annealing method. In the parameter optimization process, for example, the optimization unit 25 repeatedly resets multiple parameters, obtains the positive electrode OCP curve and the negative electrode OCP curve, and estimates the OCV curve so that the value of the error function is less than or equal to a predetermined value.

[0040] Figure 4 shows examples of parameters for the positive electrode OCP curve and the negative electrode OCP curve, respectively, set by the secondary battery state estimation device 10 in the embodiment. The parameters shown in Figure 4, by acting on a predetermined mathematical model of a reference positive electrode OCP curve (=gca(y)) and a reference negative electrode OCP curve (=gan(y)) with a dimensionless variable y, generate a mathematical model of a positive electrode OCP curve (=fca(x)) and a negative electrode OCP curve (=fan(x)) with discharge capacity x (Ah) as the variable. In the case of an electrode composed of a mixture of multiple active materials, such as a mixed material, each of the reference positive electrode OCP curve and reference negative electrode OCP curve shown in Figure 4 is a composite OCP curve obtained from the individual OCP curves of each active material.

[0041] The parameters shown in Figure 4 are, for example, the positive electrode capacitance a and positive electrode position b, which convert the reference positive electrode OCP curve to a positive electrode OCP curve, and the negative electrode capacitance c and negative electrode position d, which convert the reference negative electrode OCP curve to a negative electrode OCP curve. The positive electrode capacity a and positive electrode position b are, for example, the positive electrode expansion / contraction ratio a with respect to the magnitude of the discharge capacity width and the positive electrode shift amount b with respect to the position in the discharge capacity direction, and the dimensionless variable y is converted into discharge capacity x (= a × y + b). The negative electrode capacity c and negative electrode position d are, for example, the negative electrode expansion / contraction ratio c with respect to the magnitude of the discharge capacity width and the negative electrode shift amount d with respect to the position in the discharge capacity direction, and the dimensionless variable y is converted into discharge capacity x (= c × y + d).

[0042] The optimization unit 25 determines, for example, whether to perform a predetermined optimization process for each predetermined substance contained in the electrodes of the secondary battery 11, based on the determination result of whether the history data of the secondary battery 11 satisfies predetermined conditions in a voltage range or capacity range corresponding to the predetermined substance. The predetermined substances contained in the electrodes of the secondary battery 11 are, for example, the positive electrode active material that constitutes the positive electrode and the negative electrode active material that constitutes the negative electrode. The voltage range or capacity range corresponding to the predetermined substance is, for example, the voltage range or capacity range (expression range) that brings out the capacity characteristics of each active material. The expression range of each active material may be acquired each time a series of determination processes are executed, for example, based on the individual OCP curve (reference OCP curve) and the latest multiple parameters of each active material that are stored in the storage unit 21 in advance, or it may be acquired in advance and stored in the storage unit 21.

[0043] The predetermined conditions in the voltage domain or capacitance domain (expression domain) are, for example, conditions relating to the number of historical data (data count) and distribution (data distribution). For example, a predetermined condition relating to the data count is that the number of historical data in the expression domain is greater than or equal to a predetermined threshold greater than zero. For example, a predetermined condition relating to the data distribution is that the historical data in the expression domain is distributed across multiple different domains that are divided within the expression domain, or that the historical data in the expression domain is distributed with intervals between each data being greater than or equal to a predetermined interval.

[0044] The optimization unit 25 diagnoses the state of each active material by performing a predetermined optimization process on the parameters to be diagnosed, for example, when the historical data satisfies predetermined conditions. The optimization unit 25 estimates the state of each active material based on the results of predetermined optimization processes performed in the past, for example, when the historical data does not satisfy predetermined conditions. The optimization unit 25 estimates the state of each active material based on the mean, median, most recent diagnostic value, or predicted value predicted by a regression model or the like from past diagnostic values, for example.

[0045] Figure 5 shows an example of the correspondence between the historical data acquired by the secondary battery state estimation device 10 in the embodiment and the negative electrode OCP curve and OCV curve. For example, the positive electrode active material of the secondary battery 11 shown in Figure 5 is nickel-cobalt-manganese oxide (NCM:Li(Ni x Co y Mn z The negative electrode active material is a silicon oxide (SiO2), and the negative electrode active material is graphite and silicon oxide (SiO2). x It is a mixed material with ). For example, silicon oxide (SiO2) used as the negative electrode active material. x In this case, the manifestation region is the voltage region EVs from zero to a predetermined voltage Vs and the capacity region ECs of a predetermined discharge capacity Cs or greater, including the end of discharge (or end of degradation), as shown in the negative electrode OCP curve and OCV curve.

[0046] For example, the voltage range EVA of the first historical data is a range from a first voltage Va1 that is smaller than a predetermined voltage Vs to a second voltage Va2 that is larger than a predetermined voltage Vs, and the silicon oxide (SiO x An overlap is observed with the voltage region EVs, which is the region where ) occurs. Furthermore, the capacitance region ECa of the first historical data ranges from zero to a first capacitance Ca that is greater than the predetermined discharge capacitance Cs, and is the region of silicon oxide (SiO x Overlap is observed with the capacity region ECs, which is the region where ) is expressed. Based on these, the optimization unit 25 determines that the first history data satisfies predetermined conditions and executes a predetermined optimization process to optimize silicon oxide (SiO x To diagnose the condition of ).

[0047] For example, the voltage range EVb of the second historical data is in the range from a third voltage Vb1 greater than a predetermined voltage Vs to a fourth voltage Vb2 greater than the third voltage Vb1, and the silicon oxide (SiO x No overlap is observed with the voltage region EVs, which is the region where ) occurs. Furthermore, the capacitance region ECb of the second historical data ranges from zero to a second capacitance Cb that is smaller than the predetermined discharge capacitance Cs, and the silicon oxide (SiO xNo overlap is observed with the capacity region ECs, which is the region where ) is expressed. As a result, the optimization unit 25 determines that the second history data does not satisfy the predetermined conditions and, based on the results of predetermined optimization processes performed in the past, determines the silicon oxide (SiO x To estimate the state of ).

[0048] The diagnostic unit 26 obtains diagnostic values ​​related to the degradation state of the secondary battery 11 based on the OCV curve estimated based on the OCP curve after the optimization of multiple parameters by the optimization unit 25. The diagnostic unit 26 takes the fully charged capacity of the secondary battery 11 in its initial state as 100%, and uses the percentage of the fully charged capacity at the time of degradation as the State of Health (SOH) diagnostic value. The fully charged capacity at the time of degradation is, for example, the difference between the discharge capacity at the fully charged voltage and the discharge capacity at the completely discharged voltage, which are obtained based on the OCV curve. The diagnostic unit 26 stores the history data of the SOH diagnostic values ​​in the storage unit 21 by associating the acquired SOH diagnostic values ​​with the date and time on which the OCV curve was obtained by the optimization unit 25.

[0049] (Operation of the secondary battery state estimation device) The operation of the secondary battery state estimation device 10 of this embodiment, particularly the processes performed by the optimization unit 25, will be described below. Figure 6 is a flowchart showing the processes performed by the secondary battery state estimation device 10 in the embodiment. The series of processes from step S01 to step S06 shown in Figure 6 are repeatedly performed at appropriate timings for each predetermined substance contained in the electrodes of the secondary battery 11.

[0050] As shown in Figure 6, first, the optimization unit 25 obtains individual OCP curves (reference OCP curves) for each active material constituting the positive electrode and negative electrode of the secondary battery 11, for example. The optimization unit 25 obtains the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)) based on the reference OCP curve and the latest multiple parameters. The optimization unit 25 estimates the OCV curve (=fca(x)-fan(x)) based on the difference between the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)). The optimization unit 25 obtains historical data of the secondary battery 11 (step S01).

[0051] Next, the optimization unit 25 obtains the expression region of the active material to be diagnosed, for example, based on the individual OCP curve (reference OCP curve) for each active material and the latest multiple parameters (step S02). Next, the optimization unit 25 acquires a target region, which is a voltage region or capacity region in which data exists, based on the historical data of the secondary battery 11 (step S03). The target region is, for example, the region between the minimum and maximum values ​​in the voltage region or capacity region of the historical data. Next, the optimization unit 25 determines whether the historical data satisfies predetermined conditions, such as whether there is an overlap between the expression region and the target region (step S04). If the result of this determination is "NO", the optimization unit 25 proceeds to step S05. On the other hand, if the result of this determination is "YES", the optimization unit 25 proceeds to step S06.

[0052] The optimization unit 25 then estimates the state of the active material to be diagnosed, for example, based on the results of a predetermined optimization process performed in the past (step S05). The optimization unit 25 then proceeds to the end of the process. Furthermore, the optimization unit 25 searches for and optimizes parameters by, for example, performing a predetermined optimization process on parameters related to the state of the active material to be diagnosed. The diagnostic unit 26 then obtains diagnostic values ​​related to the deterioration state of the active material to be diagnosed based on the OCV curve estimated after the optimization of the parameters by the optimization unit 25 (step S06). The diagnostic unit 26 then proceeds to the end of the process.

[0053] As described above, according to the system 1 equipped with the secondary battery state estimation device 10 of the embodiment, if the historical data acquired by the secondary battery 11 does not meet predetermined conditions, the estimation of the degree of deterioration of a predetermined substance contained in the electrodes of the secondary battery 11 is not performed, thereby suppressing a decrease in estimation accuracy. The optimization unit 25 can appropriately determine whether it is necessary to estimate the degree of degradation of a predetermined substance contained in the electrode, according to the number of data points or the data distribution in the region where the historical data acquired by the secondary battery 11 is generated. For example, even if silicon that exhibits capacity characteristics at the end of discharge (or the end of degradation) is included in the negative electrode of the secondary battery 11, it is possible to suppress a decrease in the accuracy of estimating the degree of degradation of the negative electrode.

[0054] (modified version) Modified examples of the embodiments are described below. Note that parts identical to those in the embodiments described above are denoted by the same reference numerals, and their descriptions are omitted or simplified. In the embodiment described above, the secondary battery state estimation device 10 is assumed to be composed of a server 3, but it is not limited to this. For example, at least one of the processes performed by the server 3 may be performed by the battery control unit 13 of the vehicle 2. In other words, the secondary battery state estimation device 10 may be composed of the server 3 and the battery control unit 13, or the battery control unit 13 alone.

[0055] In the embodiments described above, the multiple parameters were assumed to include positive electrode capacitance a and positive electrode position b, negative electrode capacitance c and negative electrode position d, and active material capacitance ratio e. However, the embodiments are not limited to these, and may include other parameters such as voltage correction parameters. For example, voltage correction parameters are parameters that correct the shape of the OCP curve or OCV curve. In the embodiment described above, the positive electrode position b or the negative electrode position d among the multiple parameters may be, for example, the relative position of the negative electrode OCP curve to the positive electrode OCP curve or the relative position of the positive electrode OCP curve to the negative electrode OCP curve.

[0056] In the embodiments described above, the OCP curve and OCV curve represent the change in open-circuit potential (OCP) or open-circuit voltage (OCV) according to the discharge capacity x (Ah), but the embodiments are not limited to this. For example, instead of discharge capacity (Ah), other capacity-related parameters such as charge capacity (Ah), remaining capacity (SOC: State Of Charge), or depth of discharge (DOD: Depth Of Discharge) may be used. Note that the trend of change in open-circuit potential (OCP) or open-circuit voltage (OCV) is inverse between discharge capacity (Ah) and charge capacity (Ah). For example, the capacity region above a predetermined discharge capacity corresponds to the capacity region below a predetermined charge capacity.

[0057] Furthermore, a program for realizing all or part of the functions of System 1 equipped with the secondary battery state estimation device 10 in this invention may be recorded on a computer-readable recording medium, and all or part of the processing performed by System 1 may be performed by having the computer system read and execute the program recorded on this recording medium. Herein, "computer system" includes hardware such as the OS and peripheral devices. Furthermore, "computer system" also includes a WWW system equipped with a homepage provisioning environment (or display environment). Furthermore, "computer-readable recording medium" refers to portable media such as flexible disks, magneto-optical disks, ROMs and CD-ROMs, and storage devices such as hard disks built into a computer system. Furthermore, "computer-readable recording medium" also includes volatile memory (RAM) inside a computer system that acts as a server or client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line, which holds the program for a certain period of time.

[0058] Furthermore, the above program may be transmitted from a computer system that stores the program in a memory device or the like to another computer system via a transmission medium or by transmission waves within the transmission medium. Here, the "transmission medium" for transmitting the program refers to a medium that has the function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. Furthermore, the above program may be for the purpose of realizing a part of the functions described above. Moreover, it may be a so-called differential file (differential program) that can realize the functions described above in combination with a program already recorded in the computer system.

[0059] The embodiments of the present invention are presented as examples and are not intended to limit the scope of the invention. These embodiments can be carried out in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of Symbols]

[0060] 1...System (secondary battery state estimation system), 2...Vehicle, 3...Server, 4...Network, 10...Secondary battery state estimation device, 11...Secondary battery, 12...Battery sensor, 13...Battery control unit, 14...Power control unit, 15...Rotating electric machine, 16...Drive mechanism, 17...Integration processing unit, 21...Storage unit, 22...Acquisition unit, 23...Preprocessing unit, 24...OCV estimation unit, 25...Optimization unit, 26...Diagnostic unit.

Claims

1. A region acquisition unit that acquires a voltage range or capacitance range corresponding to a predetermined substance contained in the electrodes of the secondary battery to be estimated, A degradation estimation unit determines whether or not to estimate the degree of degradation of a predetermined substance based on the determination result of whether the voltage data obtained from the secondary battery to be estimated, or obtained from data obtained from the secondary battery, satisfies predetermined conditions in the voltage region or capacity region obtained by the region acquisition unit. Equipped with A system for estimating the state of a secondary battery.

2. The aforementioned deterioration estimation unit, The number of voltage data points is used to determine whether or not to estimate the degree of deterioration of the predetermined substance, based on the determination result of whether or not the predetermined number of data points in the voltage region or the capacitance region acquired by the region acquisition unit is met. The secondary battery state estimation system according to claim 1.

3. The aforementioned deterioration estimation unit, Based on the determination result of whether the distribution of the voltage data acquired by the region acquisition unit satisfies predetermined data distribution conditions in the voltage region or the capacitance region, a determination is made as to whether or not to estimate the degree of deterioration of the predetermined substance. The secondary battery state estimation system according to claim 1.

4. The aforementioned predetermined substance is silicon contained in the negative electrode of the secondary battery being estimated, The aforementioned deterioration estimation unit, Based on the voltage data, a determination is made whether or not the predetermined conditions are met in the voltage range below a predetermined voltage or the capacity range below a predetermined charge capacity, which are acquired by the region acquisition unit, to determine whether or not to estimate the degree of deterioration of the predetermined substance. A secondary battery state estimation system according to any one of claims 1 to 3.

5. The aforementioned deterioration estimation unit, If the voltage data satisfies the predetermined conditions, Based on the voltage data in the voltage region or capacity region acquired by the region acquisition unit and the voltage information or potential information acquired for the secondary battery to be estimated, the degree of deterioration of the predetermined substance is estimated. A secondary battery state estimation system according to any one of claims 1 to 3.

6. A method for estimating the state of a secondary battery, which is performed by an electronic device equipped with a processing unit that estimates the degree of degradation of a predetermined substance contained in the electrodes of the secondary battery to be estimated, A region acquisition step to acquire a voltage region or capacitance region corresponding to the predetermined substance, A degradation estimation step in which a determination is made whether or not to estimate the degree of degradation of a predetermined substance based on the determination result of whether or not the voltage data obtained from the secondary battery to be estimated, or obtained from data obtained from the secondary battery, satisfies predetermined conditions in the voltage region or capacity region obtained in the region acquisition step, and A method for estimating the state of a secondary battery, including the following.

7. A computer in an electronic device equipped with a processing unit for estimating the degree of degradation of a predetermined substance contained in the electrodes of a secondary battery to be estimated, A region acquisition step to acquire a voltage region or capacitance region corresponding to the predetermined substance, A degradation estimation step in which a determination is made whether or not to estimate the degree of degradation of a predetermined substance based on the determination result of whether or not the voltage data obtained from the secondary battery to be estimated, or obtained from data obtained from the secondary battery, satisfies predetermined conditions in the voltage region or capacity region obtained in the region acquisition step, and A program that executes the command.

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