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 by the secondary battery 11 to be estimated. The optimization unit 25 makes the search range when acquiring a predetermined parameter from the plurality of parameters in the first time period different from the search range when acquiring a predetermined parameter in the second time period which is different from the first time period.

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 in the embodiment, 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 makes the search range when acquiring a predetermined parameter among the plurality of parameters in a first time different from the search range when acquiring the predetermined parameter in a second time different from the first time.

[0007] (2) In the secondary battery state estimation system described in (1) above, the degradation estimation unit may have different search ranges for obtaining a first parameter which is one of the plurality of parameters at a predetermined time and for obtaining a second parameter which is different from the first parameter at the plurality of parameters at a predetermined time.

[0008] (3) In the secondary battery state estimation system described in (2) above, the degradation estimation unit may set the search range for acquiring the first parameter in the first time to be larger than the search range for acquiring the second parameter in the first time, and the search range for acquiring the first parameter in the second time to be smaller than the search range for acquiring the second parameter in the second time.

[0009] (4) In the secondary battery state estimation system described in (1) above, the degradation estimation unit may have different search ranges from each other and set up a first group and a second group from which at least one parameter is classified from each other from the plurality of parameters, and may change the classification of the plurality of parameters into the first group and the second group at different time intervals.

[0010] (5) In the secondary battery state estimation system described in (1) above, the degradation estimation unit may classify the plurality of parameters into a first group having at least one parameter and a second group having at least one parameter, and may make the search range for acquiring the parameters of the first group at a predetermined time different from the search range for acquiring the parameters of the second group at a predetermined time.

[0011] (6) In the secondary battery state estimation system described in (5) above, the degradation estimation unit may set the search range for acquiring the parameters of the first group in the first time to be larger than the search range for acquiring the parameters of the second group in the first time, and set the search range for acquiring the parameters of the first group in the second time to be smaller than the search range for acquiring the parameters of the second group in the second time.

[0012] (7): A secondary battery state estimation system according to one aspect of the present invention (for example, a secondary battery state estimation device 10 in the embodiment) includes a degradation estimation unit (for example, an 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 in the embodiment, 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, a secondary battery 11 in the embodiment), wherein the degradation estimation unit distinguishes the search range when acquiring a first parameter among the plurality of parameters in a predetermined time from the search range when acquiring a second parameter that is different from the first parameter among the plurality of parameters in the predetermined time.

[0013] (8): 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, 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 (e.g., step S02 in the embodiment); and differentiating the search range when acquiring a predetermined parameter among the plurality of parameters in a first time and the search range when acquiring the predetermined parameter in a second time different from the first time (e.g., step S03 in the embodiment).

[0014] (9): 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, 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 (e.g., step S02 in the embodiment); and differentiating the search range when acquiring a first parameter among the plurality of parameters in a predetermined time and the search range when acquiring a second parameter among the plurality of parameters that is different from the first parameter in the predetermined time (e.g., step S03 in the embodiment).

[0015] (10): 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 secondary battery to be estimated (e.g., secondary battery 11 in the embodiment) to perform the following steps: (e.g., step S02 in the embodiment) to acquire 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, etc. in the embodiment) 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; and (e.g., step S02 in the embodiment) to make the search range for acquiring a predetermined parameter among the plurality of parameters in the first time different from the search range for acquiring the predetermined parameter in the second time which is different from the first time (e.g., step S03 in the embodiment).

[0016] (11): The program according to one aspect of the present invention causes a computer of an electronic device (for example, the system 1 in the embodiment) including a processing unit (for example, the optimization unit 25 in the embodiment) that estimates the degree of deterioration of a secondary battery to be estimated (for example, the secondary battery 11 in the embodiment), based on voltage data (for example, the closed-circuit voltage (CCV) or open-circuit voltage (OCV) in the embodiment) obtained by the secondary battery to be estimated or obtained by data obtained by the secondary battery, to obtain a plurality of parameters (for example, the positive electrode expansion / contraction rate a and the positive electrode shift amount b, the negative electrode expansion / contraction rate c and the negative electrode shift amount d, etc. in the embodiment) related to the state of the secondary battery to be estimated, and a search range when obtaining a first parameter among the plurality of parameters at a predetermined time, and a search range when obtaining a second parameter different from the first parameter among the plurality of parameters at the predetermined time are made different (for example, step S03 in the embodiment).

Advantages of the Invention

[0017] According to the above (1), by providing a deterioration estimation unit that changes the search range for each different timing of obtaining a predetermined parameter, even when the number of parameters of the internal state to be diagnosed of the secondary battery increases, a decrease in the estimation accuracy of the internal state can be suppressed. For example, compared with the case where the search range is not changed, an increase in the variation (uncertainty) of the estimation result accompanying an increase in the number of parameters can be suppressed.

[0018] In the case of the above (2), by providing a deterioration estimation unit that changes the search ranges of different parameters at the same timing, even when the number of parameters of the internal state to be diagnosed of the secondary battery increases, a decrease in the estimation accuracy of the internal state can be suppressed. For example, compared with the case where the search ranges of different parameters are not changed, an increase in the variation (uncertainty) of the estimation result accompanying an increase in the number of parameters can be suppressed.

[0019] In the case of (3) above, by providing a deterioration estimation unit that changes the relative magnitudes of the search ranges of different parameters at different timings, even when the number of parameters increases, it is possible to suppress a decrease in the estimation accuracy of the internal state.

[0020] In the case of (4) above, by providing a deterioration estimation unit that changes the classification of a plurality of parameters for groups with different search ranges at different timings, even when the number of parameters increases, it is possible to suppress a decrease in the estimation accuracy of the internal state.

[0021] In the case of (5) above, by providing a deterioration estimation unit that changes the relative search ranges of parameters in different groups at the same timing, even when the number of parameters increases, it is possible to suppress a decrease in the estimation accuracy of the internal state.

[0022] In the case of (6) above, by providing a deterioration estimation unit that changes the relative magnitudes of the search ranges of parameters in different groups at different timings, even when the number of parameters increases, it is possible to suppress a decrease in the estimation accuracy of the internal state.

[0023] According to (7) above, by providing a deterioration estimation unit that changes the search ranges of a plurality of parameters at the same timing, even when the number of parameters of the internal state to be diagnosed in the secondary battery increases, it is possible to suppress a decrease in the estimation accuracy of the internal state. For example, it is possible to suppress an increase in the variation (uncertainty) of the estimation results accompanying an increase in the number of parameters compared to the case where the search range of each parameter is not changed.

[0024] According to (8) or (10) above, by changing the search range for each different timing at which a predetermined parameter is acquired, even when the number of parameters of the internal state to be diagnosed in the secondary battery increases, it is possible to suppress a decrease in the estimation accuracy of the internal state. For example, it is possible to suppress an increase in the variation (uncertainty) of the estimation results accompanying an increase in the number of parameters compared to the case where the search range is not changed.

[0025] According to (9) or (11) above, by changing the search range of multiple parameters at the same time, it is possible to suppress a decrease in the accuracy of estimating the internal state, even when the number of parameters of the internal state to be diagnosed in a secondary battery increases. For example, compared to when the search range of each parameter is not changed, it is possible to suppress the increase in variability (uncertainty) of the estimation results that comes with an increase in the number of parameters. [Brief explanation of the drawing]

[0026] [Figure 1] A block diagram showing the functional configuration of a system equipped with a secondary battery state estimation device according to an embodiment of the present invention. [Figure 2] This figure shows an example of an OCV curve obtained based on the OCP curves of the positive electrode and the negative electrode, respectively, by the optimization unit of the secondary battery state estimation device in an embodiment of the present invention. [Figure 3] This figure shows an example of the information flow in the parameter optimization process performed by the optimization unit of the secondary battery state estimation device in an embodiment of the present invention. [Figure 4] A figure showing examples of parameters for the positive electrode OCP curve and the negative electrode OCP curve, respectively, set by the secondary battery state estimation device in an embodiment of the present invention. [Figure 5] A flowchart showing the process performed by the secondary battery state estimation device in an embodiment of the present invention. [Figure 6] A figure showing examples of the variability (uncertainty) of parameter values ​​obtained in each embodiment and comparative example of the present invention. [Modes for carrying out the invention]

[0027] 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, 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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 ).

[0033] 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 related to the state of the secondary battery 11.

[0034] 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).

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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)).

[0046] 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, and negative electrode capacity c and negative electrode position d. 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.

[0047] 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.

[0048] 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.

[0049] 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).

[0050] When the optimization unit 25 performs a predetermined optimization process, it classifies multiple parameters into multiple groups with different search conditions. These multiple groups may include, for example, a first group with no search range limitations and a second group with a limited search range. For example, the search range set for the parameters of the first group is the entire target area, encompassing everything within the range set by predetermined lower and upper limits. For example, the search range set for the parameters of the second group is a limited area within the entire target area, encompassing everything within the range set by a reference value based on past estimates and predetermined limit values. The reference value based on past estimates may be, for example, the mean, median, most recent estimate, or a predicted value predicted by a regression model from past estimates. The predetermined limit values ​​may be, for example, a fixed value or a value that changes depending on past estimates. For example, if a parameter p is given a predetermined lower limit pmin and upper limit pmax, and a reference value p0 and a predetermined limit value pr based on past estimates, the search range for when the parameter p is classified into the first group is pmin ≤ p ≤ pmax. The search range for when the parameter p is classified into the second group is, for example, p0 - pr ≤ p ≤ p0 + pr.

[0051] When the optimization unit 25 classifies multiple parameters into multiple groups with different search conditions, it sets the grouping according to, for example, the type of degradation mechanism or degradation state of the secondary battery 11 to which each parameter relates. The degradation mechanism or type of degradation state of the secondary battery 11 is, for example, degradation of the positive electrode capacity, degradation of the negative electrode capacity, and the amount of ion loss responsible for electrical conduction. For example, the positive electrode expansion / contraction ratio a is related to the degradation of the positive electrode capacity, the negative electrode expansion / contraction ratio c is related to the degradation of the negative electrode capacity, and the positive electrode shift amount b and the negative electrode shift amount d are related to the amount of ion loss.

[0052] Table 1 below shows an example in which, for example, in each of the predetermined optimization processes performed multiple times, the positive electrode scaling ratio a, positive electrode shift amount b, negative electrode scaling ratio c, and negative electrode shift amount d are classified into a first group of parameters and a second group of parameters. As shown in Table 1 below, the optimization unit 25 classifies, for example, positive electrode scaling ratio a and negative electrode scaling ratio c, which are related to different types of degradation states, into groups independently of each other. The optimization unit 25 also classifies, for example, positive electrode shift amount b and negative electrode shift amount d, which are related to the same type of degradation state, into the same group by combining them.

[0053] [Table 1]

[0054] As shown in Table 1 above, the optimization unit 25 changes the classification (grouping) of multiple parameters for each predetermined optimization process that is repeatedly executed. For example, by changing the classification, the optimization unit 25 makes the search range for obtaining a predetermined parameter from among multiple parameters at an appropriate first time point different from the search range for obtaining a predetermined parameter at an appropriate second time point that is different from the first time point. In the example in Table 1 above, the optimization unit 25 changes the classification of multiple parameters by a predetermined cyclical setting.

[0055] The optimization unit 25 may change the classification of multiple parameters not only by predetermined cyclical changes as shown in Table 1 above, but also by predetermined regular settings based on, for example, the design characteristics, degradation mechanism, and degradation trend of the secondary battery 11. For example, if the positive electrode capacity of the secondary battery 11 is characterized by a tendency to selectively decrease, the optimization unit 25 may set the frequency of classifying the positive electrode expansion / contraction ratio a into the first group of parameters to be relatively high. For example, if the capacity degradation rate of the secondary battery 11 is relatively high in the early stages of degradation and relatively low in the late stages of degradation, the optimization unit 25 may change the frequency of classifying the positive electrode expansion / contraction ratio a and the negative electrode expansion / contraction ratio c into the first group of parameters to decrease as the degradation progresses from the early stages to the late stages.

[0056] 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.

[0057] (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 S05 shown in Figure 5 are repeatedly executed at appropriate intervals.

[0058] 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 several parameters, for example. 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)), for example. The optimization unit 25 obtains historical data of the secondary battery 11, for example (step S01).

[0059] Next, the optimization unit 25 classifies the multiple parameters into multiple groups with different search conditions. For example, the optimization unit 25 sets a different classification (grouping) for the multiple parameters than the previous processing (step S02). Next, the optimization unit 25 sets the search range for each parameter according to the search conditions set for each of the multiple groups (step S03). Next, the optimization unit 25 searches for and optimizes each of the multiple parameters according to the search range of each parameter by performing a predetermined optimization process based on the estimated OCV curve and the history data of the secondary battery 11 (step S04). Then, the optimization unit 25 proceeds to step S05.

[0060] 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 S05). Then, the diagnostic unit 26 proceeds to the end of the process.

[0061] Figure 6 shows examples of the variability (uncertainty) of parameter values ​​obtained in each embodiment and comparative example. The comparative example corresponds, for example, to the case in the embodiment described above where the optimization unit 25 does not classify multiple parameters into multiple groups, that is, the case where the search conditions for multiple parameters are set to be the same as each other. As shown in Figure 6, for example, with respect to the true value TV of an appropriate parameter which tends to decrease with increasing number of diagnoses, the parameter values ​​obtained in the embodiment show a greater reduction in variability (uncertainty) compared to the parameter values ​​obtained in the comparative example.

[0062] As described above, the system 1 equipped with the secondary battery state estimation device 10 of the embodiment includes an optimization unit 25 that classifies multiple parameters into multiple groups with different search conditions for each predetermined optimization process that is repeatedly performed, thereby suppressing a decrease in the accuracy of estimating the internal state of the secondary battery 11. By changing the classification of multiple parameters for each predetermined optimization process, the optimization unit 25 can suppress the increase in variability (uncertainty) of the estimation results that increases with an increase in the number of parameters, compared to, for example, when the search conditions are not changed. The optimization unit 25 classifies multiple parameters into a first group whose search range is not limited and a second group whose search range is limited. For example, compared to the case where the search range of all parameters is not limited, the optimization unit 25 can suppress the increase in variability (uncertainty) of the estimation results that occurs with an increase in the number of parameters. The optimization unit 25 can improve the estimation accuracy of each parameter by setting up groupings according to the type of degradation mechanism or degradation state of the secondary battery 11 to which each parameter is related.

[0063] (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.

[0064] In the embodiments described above, the multiple groups with different search conditions are set as a first group with no search range limit and a second group with a limited search range, but the invention is not limited to this. For example, the search condition may be the width of the search range. For example, the multiple groups may be set as a first group with a relatively large search range and a second group with a relatively small search range, and so on.

[0065] In the embodiments described above, the multiple parameters are classified into multiple groups, each with different search conditions set in advance, but the embodiments are not limited to this. For example, the multiple parameters may be classified into multiple different groups without any search conditions set in advance, and then different search conditions may be set for each of the multiple groups. The optimization unit 25 may, for example, set the search range width for acquiring the parameters of the first group at an appropriate first time to be larger than the search range width for acquiring the parameters of the second group at the first time. The optimization unit 25 may, for example, set the search range width for acquiring the parameters of the first group at an appropriate second time, different from the first time, to be smaller than the search range width for acquiring the parameters of the second group at the second time.

[0066] In the embodiment described above, the optimization unit 25 changes the classification of multiple parameters by predetermined regular settings, but it is not limited to this, and for example, the classification of multiple parameters may be changed by random selection. For example, in the embodiment described above, the optimization unit 25 combines positive electrode shift amount b and negative electrode shift amount d, which are related to the same type of degradation state, and classifies them into the same group, but it is not limited to this, and they may be classified into groups independently of each other.

[0067] In the embodiments described above, the multiple parameters were assumed to include positive electrode capacitance a and positive electrode position b, and negative electrode capacitance c and negative electrode position d. However, the embodiments are not limited to these, and may include other parameters such as active material capacitance ratio and voltage correction parameters. For example, the active material capacitance ratio is a ratio set by the capacitance of each active material for an electrode composed of a mixture of multiple active materials, such as a mixed material. For example, the voltage correction parameter is a parameter that corrects the shape of the OCP curve or OCV curve, etc. 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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]

[0072] 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, The search range for acquiring a predetermined parameter from the plurality of parameters in the first time period is made different from the search range for acquiring the predetermined parameter in a second time period that is different from the first time period. A system for estimating the state of a secondary battery.

2. The aforementioned deterioration estimation unit, The search range when acquiring the first parameter, which is the predetermined parameter among the plurality of parameters, within a predetermined time, The search range for obtaining a second parameter, which is different from the first parameter, among the plurality of parameters within the predetermined time is made different. The secondary battery state estimation system according to claim 1.

3. The aforementioned deterioration estimation unit, The search range for obtaining the first parameter in the first time is set to be larger than the search range for obtaining the second parameter in the first time. The search range for obtaining the first parameter during the second time period is set to be smaller than the search range for obtaining the second parameter during the second time period. The secondary battery state estimation system according to claim 2.

4. The aforementioned deterioration estimation unit, A first group and a second group are established, each having a different search range, and each group is classified by at least one parameter from the plurality of parameters. At different time intervals, the classification of the multiple parameters into the first group and the second group is changed. The secondary battery state estimation system according to claim 1.

5. The aforementioned deterioration estimation unit, The aforementioned plurality of parameters are classified into a first group having at least one parameter and a second group having at least one parameter. The search range when acquiring the parameters of the first group at a predetermined time, The search range for obtaining the parameters of the second group at the predetermined time is to be different from the search range for obtaining the parameters of the second group at the predetermined time. The secondary battery state estimation system according to claim 1.

6. The aforementioned deterioration estimation unit, The search range for obtaining the parameters of the first group in the first time period is set to be larger than the search range for obtaining the parameters of the second group in the first time period. The search range for obtaining the parameters of the first group during the second time period is set to be smaller than the search range for obtaining the parameters of the second group during the second time period. The secondary battery state estimation system according to claim 5.

7. 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, The search range for acquiring the first parameter among the multiple parameters within a predetermined time is made different from the search range for acquiring a second parameter, which is different from the first parameter among the multiple parameters within the predetermined time. A system for estimating the state of a secondary battery.

8. 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 making the search range for acquiring a predetermined parameter from the plurality of parameters in the first time different from the search range for acquiring the predetermined parameter in a second time, which is different from the first time. A method for estimating the state of a secondary battery, including the following.

9. 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 making the search range for obtaining the first parameter among the plurality of parameters within a predetermined time different from the search range for obtaining a second parameter that is different from the first parameter among the plurality of parameters within the predetermined time. A method for estimating the state of a secondary battery, including the following.

10. 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 making the search range for acquiring a predetermined parameter from the plurality of parameters in the first time different from the search range for acquiring the predetermined parameter in a second time, which is different from the first time. A program that executes the command.

11. 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 making the search range for obtaining the first parameter among the plurality of parameters within a predetermined time different from the search range for obtaining a second parameter that is different from the first parameter among the plurality of parameters within the predetermined time. A program that executes the command.

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