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 a decrease in the accuracy of internal state estimation even when the number of parameters of the internal state to be diagnosed for the secondary battery increases. [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 acquires a plurality of parameters related to the state of the secondary battery 11 to be estimated based on voltage data (closed circuit voltage (CCV) or open circuit voltage (OCV)) acquired from the secondary battery 11 to be estimated. Based on a predetermined correspondence between a plurality of voltage regions or a plurality of capacity regions and a plurality of parameters, the optimization unit 25 sequentially performs a predetermined optimization process on at least one parameter that is exclusively selected from the plurality of parameters for each of the plurality of voltage regions or a plurality of capacity regions.

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 the number of parameters of the internal state to be diagnosed increases. For example, in the conventional devices described above, if a uniform diagnostic process is performed for each of the multiple internal state parameters to be diagnosed, the variability (uncertainty) of the diagnostic results may increase as the number of parameters increases.

[0005] To solve the above-mentioned problems, this invention aims to suppress a decrease in the accuracy of estimating the internal state of a secondary battery, even when the number of parameters of the internal state to be diagnosed increases. [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) includes a degradation estimation unit (for example, optimization unit 25 in the embodiment) that estimates the degree of degradation of a secondary battery to be estimated by acquiring a plurality of parameters relating to the state of the secondary battery to be estimated (for example, positive electrode expansion / contraction ratio a and positive electrode shift amount b, negative electrode expansion / contraction ratio c and negative electrode shift amount d, active material capacity ratio e, etc.) based on voltage data (for example, closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) acquired by or obtained from data acquired by a secondary battery (for example, secondary battery 11 in the embodiment), wherein the degradation estimation unit sequentially acquires at least one parameter exclusively selected from the plurality of parameters for each of the plurality of voltage regions or plurality of capacity regions based on a predetermined correspondence between the plurality of voltage regions or plurality of capacity regions and the plurality of parameters.

[0007] (2) In the secondary battery state estimation system described in (1) above, the degradation estimation unit may associate each of the plurality of parameters with the voltage range or capacitance range among the plurality of voltage ranges or the plurality of capacitance ranges in which each parameter exhibits capacitance characteristics.

[0008] (3) In the secondary battery state estimation system described in (1) or (2) above, if the number of parameters associated with the first voltage region by the predetermined correspondence is less than the number of parameters associated with a second voltage region different from the first voltage region, the degradation estimation unit may acquire the parameters associated with the first voltage region in the first voltage region before the second voltage region.

[0009] (4) In the secondary battery state estimation system described in (1) or (2) above, if the number of parameters associated with the first capacity region by the predetermined correspondence is less than the number of parameters associated with a second capacity region different from the first capacity region, the degradation estimation unit may acquire the parameters associated with the first capacity region in the first capacity region before the second capacity region.

[0010] (5) In the secondary battery state estimation system described in (1) above, the plurality of parameters may include parameters relating to the degree of degradation of silicon contained in the negative electrode of the secondary battery to be estimated.

[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 secondary battery to be estimated (e.g., secondary battery 11 in the embodiment), and includes the steps of: acquiring a plurality of parameters relating to the state of the secondary battery to be estimated (e.g., positive electrode expansion / contraction ratio a and positive electrode shift amount b, negative electrode expansion / contraction ratio c and negative electrode shift amount d, active material capacity ratio e, etc.) based on voltage data (e.g., closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) acquired by or acquired by the secondary battery to be estimated (e.g., step S02 in the embodiment); and acquiring at least one parameter that is exclusively selected from the plurality of parameters sequentially for each of the plurality of voltage regions or the plurality of capacity regions, based on a predetermined correspondence between the plurality of voltage regions or the plurality of capacity regions and the plurality of parameters (e.g., steps S04, S05, and S06 in the embodiment).

[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 degradation of a secondary battery to be estimated (e.g., secondary battery 11 in the embodiment) to execute the following steps: (e.g., step S02 in the embodiment) which is used to obtain voltage data (e.g., closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) obtained from or acquired by the secondary battery to be estimated, which relates to a plurality of parameters relating to the state of the secondary battery to be estimated (e.g., positive electrode expansion / contraction ratio a and positive electrode shift amount b, negative electrode expansion / contraction ratio c and negative electrode shift amount d, active material capacity ratio e, etc. in the embodiment); and (e.g., step S04, step S05, and step S06 in the embodiment) which is used to obtain a plurality of parameters, based on voltage data (e.g., closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) which is obtained from or acquired by the secondary battery to be estimated. [Effect of the Invention]

[0013] According to (1) above, by providing a deterioration estimation unit that sequentially and exclusively obtains parameters to be selected from a plurality of parameters, even when the number of parameters of the internal state to be diagnosed of the secondary battery increases, it is possible to suppress a decrease in the estimation accuracy of the internal state. For example, compared with the case where all of a plurality of parameters are obtained collectively instead of step by step, each parameter can be obtained uniquely, each parameter can be stabilized, and the estimation accuracy can be improved.

[0014] In the case of (2) above, since each parameter is obtained for each voltage range or capacity range in which the capacity characteristics of each parameter are exhibited, the estimation accuracy of each parameter can be improved.

[0015] In the case of (3) or (4) above, since the processing target region is exclusively selected in ascending order of the number of overlapping different parameters from among a plurality of voltage ranges or capacity ranges associated with the plurality of parameters, each parameter can be stabilized and the estimation accuracy can be improved.

[0016] In the case of (5) above, it is possible to improve the estimation accuracy of the degree of deterioration with respect to silicon in which capacity characteristics are exhibited at the end of discharge (or at the end of deterioration).

[0017] According to (6) or (7) above, by sequentially and exclusively obtaining parameters to be selected from a plurality of parameters, even when the number of parameters of the internal state to be diagnosed of the secondary battery increases, it is possible to suppress a decrease in the estimation accuracy of the internal state. For example, compared with the case where all of a plurality of parameters are obtained collectively instead of step by step, each parameter can be obtained uniquely, each parameter can be stabilized, and the estimation accuracy can be improved. [Brief Description of the Drawings]

[0018] [Figure 1]Block diagram showing the functional configuration of a system including a secondary battery state estimation device according to an embodiment of the present invention. [Figure 2] Diagram showing an example of an OCV curve obtained based on the OCP curves of each of the positive and negative electrodes by an optimization unit of a secondary battery state estimation device according to an embodiment of the present invention. [Figure 3] Diagram showing an example of the flow of information in parameter optimization processing by an optimization unit of a secondary battery state estimation device according to an embodiment of the present invention. [Figure 4] Diagram showing examples of parameters for each of the positive electrode OCP curve and the negative electrode OCP curve set by a secondary battery state estimation device according to an embodiment of the present invention. [Figure 5] Flowchart showing the processing executed by a secondary battery state estimation device according to an embodiment of the present invention. [Figure 6] Diagram showing an example of a voltage region contributing to the shape change of an OCV curve in each of a plurality of parameters related to the state of a secondary battery according to an embodiment of the present invention.

Mode 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 accompanying drawings. The secondary battery according to this embodiment is, for example, attached to, detachable or fixedly positioned 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, flying vehicles, 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 (carbon) 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 detection values such as voltage, current, and temperature regarding 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 active material capacity ratio e is a ratio set by the capacity of each active material for electrodes (each of the positive and negative electrodes) that are composed of a mixture of multiple active materials, such as a mixed material. The parameter optimization process performed by the optimization unit 25 includes, for example, the generation of an OCV curve based on the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)), and the optimization (resetting) of 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 estimated 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 performs a predetermined optimization process stepwise on multiple parameters based on a predetermined correspondence between multiple parameters and multiple voltage regions or multiple capacitance regions. The predetermined correspondence is, for example, a correspondence between each of the multiple parameters and the voltage region or capacitance region (expression region) in which each parameter exhibits capacitance characteristics. The expression region for each parameter is, for example, the voltage or capacitance region in which the shape of the estimated OCV curve changes when each parameter corresponding to various degradation states of the secondary battery 11 is changed independently. For example, if the expression region is set based on whether or not the shape of the OCV curve changes with respect to the change in each parameter, the presence or absence of a shape change may be determined based on an appropriate threshold set for the degree of change in the shape of the OCV curve. The expression region for each parameter may be acquired, for example, based on an OCP curve (reference OCP curve) stored in the storage unit 21 in advance, after each execution of a series of optimization processes, or it may be acquired in advance and stored in the storage unit 21.

[0043] The optimization unit 25 acquires, for example, regions (overlapping regions) in the voltage region or capacitance region where the overlap state of the expression regions of multiple parameters differs. The overlap state is, for example, the number of overlapping parameters. For each of the multiple overlapping regions, the optimization unit 25 sequentially performs a predetermined optimization process on at least one parameter exclusively selected from the multiple parameters. For example, the optimization unit 25 exclusively selects the overlapping regions to be processed from among the multiple overlapping regions in order of the smallest number of overlapping different parameters. For example, the optimization unit 25 sequentially performs a predetermined optimization process on each of the overlapping regions with the smallest number of overlapping parameters, which are exclusively selected from among the multiple overlapping regions. For example, if the number of parameters in the first overlapping region among the multiple overlapping regions is less than the number of parameters in a second overlapping region that is different from the first overlapping region, the optimization unit 25 will perform a predetermined optimization process in the first overlapping region before the second overlapping region.

[0044] 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.

[0045] (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 5 is a flowchart showing the processes performed by the secondary battery state estimation device 10 in the embodiment. Note that the series of processes from step S01 to step S07 shown in Figure 5 are repeatedly executed at appropriate intervals.

[0046] As shown in Figure 5, 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).

[0047] Next, the optimization unit 25 obtains, for example, the expression range (OCV change range) for each of the multiple parameters, that is, the voltage or capacitance range in which the shape of the OCV curve changes with each parameter (step S02). Next, the optimization unit 25 obtains, for example, overlapping regions where the overlapping states of the expression regions of multiple parameters are different, i.e., overlapping regions of the OCV change range (step S03). Next, the optimization unit 25 selects, for example, the overlapping region with the fewest overlapping parameters from among several overlapping regions. The optimization unit 25 then selects, for example, the parameters included in the selected overlapping region from among several parameters (step S04).

[0048] Next, the optimization unit 25 searches for and optimizes each of the selected parameters in the selected overlapping region by a predetermined optimization process based on the estimated OCV curve and the history data of the secondary battery 11 (step S05). Next, the optimization unit 25 determines, for example, whether the optimization for all of the multiple parameters has been completed (step S06). If the result of this determination is "YES", the optimization unit 25 proceeds to step S07. On the other hand, if the result of this determination is "NO", the optimization unit 25 returns to step S04.

[0049] Next, the diagnostic unit 26 obtains diagnostic values ​​related to the degradation state of the secondary battery 11 based on the OCV curve estimated after the optimization of multiple parameters by the optimization unit 25 (step S07). Then, the diagnostic unit 26 proceeds to the end of the process.

[0050] Figure 6 shows examples of voltage regions that contribute to the change in the shape of the OCV curve for each of several parameters relating to the state of the secondary battery 11 according to the embodiment. For example, the positive electrode active material of the secondary battery 11 shown in Figure 6 is nickel-cobalt-aluminum oxide (NCA:LiNi x Co y Al x 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 of ). As shown in Figure 6, for example, in the first voltage region from zero to the first voltage V1, which includes the end of discharge (or end of degradation), five parameters out of several parameters, namely the positive electrode expansion / contraction ratio a, positive electrode shift amount b, negative electrode expansion / contraction ratio c, negative electrode shift amount d, and active material capacity ratio e, exhibit capacity characteristics. The active material capacity ratio e is, for example, the ratio of graphite (Gr) to silicon oxide (SiO) at the negative electrode. x ) and the resulting capacity ratio (=Gr / SiO x ). The number of overlapping parameters in the first voltage region is 5. For example, in the second voltage region from the first voltage V1 to the second voltage V2, four parameters out of several parameters—positive electrode expansion / contraction ratio a, positive electrode shift amount b, negative electrode expansion / contraction ratio c, and negative electrode shift amount d—express the capacitance characteristics. The number of overlapping parameters in the first voltage region is 4. In this case, the optimization unit 25 first performs a predetermined optimization process on the positive electrode scaling ratio a, positive electrode shift amount b, negative electrode scaling ratio c, and negative electrode shift amount d based on the history data in the second voltage region. Next, the optimization unit 25 performs a predetermined optimization process on the active material capacity ratio e based on the history data in the first voltage region.

[0051] As described above, the system 1 equipped with the secondary battery state estimation device 10 of the embodiment includes an optimization unit 25 that performs a predetermined optimization process on parameters that are sequentially and exclusively selected from a plurality of parameters. This makes it possible to suppress a decrease in the estimation accuracy of the internal state of the secondary battery 11 even when the number of parameters increases. For example, compared to the case where a predetermined optimization process is performed on all of the multiple parameters at once rather than stepwise, each parameter can be uniquely acquired, each parameter can be stabilized, and the estimation accuracy can be improved.

[0052] The optimization unit 25 performs a predetermined optimization process for each voltage region or capacitance region in which the capacitance characteristics of each parameter manifest. Therefore, compared to, for example, performing a predetermined optimization process in a region in which the capacitance characteristics of each parameter do not manifest, the estimation accuracy of each parameter can be improved. From among multiple voltage or capacitance regions that can be associated with multiple parameters, the regions to be processed are exclusively selected in order of the smallest overlap of different parameters. This stabilizes each parameter and improves estimation accuracy. 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, the accuracy of estimating the degree of degradation of the negative electrode can be improved.

[0053] (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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] 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]

[0059] 1...System (secondary battery state estimation system), 2...Vehicle, 3...Server, 4...Network, 10...Secondary battery state estimation device (electronic 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 (degradation estimation unit, processing unit), 26...Diagnostic unit.

Claims

1. The system includes a degradation estimation unit that estimates the degree of degradation of a secondary battery by acquiring a plurality of parameters relating to the state of the secondary battery based on voltage data acquired from or obtained by the secondary battery, The aforementioned deterioration estimation unit, Based on a predetermined correspondence between multiple voltage regions or multiple capacitance regions and the multiple parameters, at least one parameter is sequentially selected exclusively from the multiple parameters for each of the multiple voltage regions or multiple capacitance regions. A system for estimating the state of a secondary battery.

2. The aforementioned deterioration estimation unit, As the aforementioned predetermined correspondence, Each of the aforementioned multiple parameters is associated with a voltage region or capacitance region among the aforementioned multiple voltage regions or capacitance regions in which each parameter exhibits capacitance characteristics. The secondary battery state estimation system according to claim 1.

3. The aforementioned deterioration estimation unit, If the number of parameters associated with the first voltage region by the predetermined association is less than the number of parameters associated with a second voltage region different from the first voltage region, the parameters associated with the first voltage region are acquired in the first voltage region before those associated with the second voltage region. A secondary battery state estimation system according to claim 1 or claim 2.

4. The aforementioned deterioration estimation unit, If the number of parameters associated with the first capacity region by the predetermined mapping is less than the number of parameters associated with a second capacity region different from the first capacity region, the parameters associated with the first capacity region are acquired in the first capacity region before those associated with the second capacity region. A secondary battery state estimation system according to claim 1 or claim 2.

5. The aforementioned plurality of parameters include parameters relating to the degree of degradation of silicon contained in the negative electrode of the secondary battery being estimated. The secondary battery state estimation system according to claim 1.

6. A method for estimating the state of a secondary battery, which is performed by an electronic device equipped with a processing unit for estimating the degree of degradation of a secondary battery to be estimated, The steps include: obtaining a plurality of parameters relating to the state of the secondary battery to be estimated based on voltage data obtained from or acquired by the secondary battery; A step of sequentially obtaining at least one parameter exclusively selected from the plurality of parameters for each of the plurality of voltage regions or plurality of capacitance regions, based on a predetermined correspondence between the plurality of voltage regions or plurality of capacitance regions and the plurality of parameters. 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 secondary battery under consideration, The steps include: obtaining a plurality of parameters relating to the state of the secondary battery to be estimated based on voltage data obtained from or acquired by the secondary battery; A step of sequentially obtaining at least one parameter exclusively selected from the plurality of parameters for each of the plurality of voltage regions or plurality of capacitance regions, based on a predetermined correspondence between the plurality of voltage regions or plurality of capacitance regions and the plurality of parameters. A program that executes the command.

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