Secondary battery state estimation system, secondary battery state estimation method, and recording medium

Abstract

The present invention provides a secondary battery state estimation system, a secondary battery state estimation method, and a recording medium that suppress a reduction in the estimation accuracy of an internal state even when the number of parameters of the internal state of a secondary battery to be diagnosed is increased. A system (1) provided with a secondary battery state estimation device (10) is provided with an optimization unit (25) that estimates the degree of deterioration of a secondary battery (11) to be estimated. The optimization unit acquires a plurality of parameters relating to the state of the secondary battery to be estimated on the basis of voltage data (closed-circuit voltage (CCV) or open-circuit voltage (OCV)) acquired in the secondary battery to be estimated. The optimization unit performs a predetermined optimization process on at least one parameter selected exclusively from the plurality of parameters in sequence for each of the plurality of voltage regions or the plurality of capacity regions on the basis of a predetermined correspondence relationship between the plurality of voltage regions or the plurality of capacity regions and the plurality of parameters.

Description

Technical Field

[0001] This invention relates to a state estimation system for secondary batteries, a state estimation method for secondary batteries, and a recording medium. Background Technology

[0002] In recent years, research and development have been conducted on secondary batteries that contribute to energy efficiency in order to ensure that more people can afford, rely on, and achieve sustainable and advanced energy pathways.

[0003] Previously, for example, there was a known device that, based on historical data of the voltage and current of a battery and an OCP curve representing the change of the open circuit potential (OCP) corresponding to the discharge capacity of the positive and negative terminals, obtained an OCV curve representing the change of the open circuit voltage (OCV) corresponding to the discharge capacity (for example, see International Publication No. 2023 / 054443 and Japanese Patent Application Publication No. 2023-48545). Summary of the Invention

[0004] In technologies related to secondary batteries, it is a challenge to suppress the decrease in the accuracy of estimating internal states, including OCV curves, even when the number of parameters of the internal states to be diagnosed increases. For example, in the devices of the aforementioned prior art, when uniform diagnostic processing is performed on the parameters of the multiple internal states to be diagnosed, there is a possibility that the fluctuation (uncertainty) of the diagnostic results increases with the increase in the number of parameters.

[0005] The objective of this invention is to suppress the decrease in the accuracy of internal state estimation even when the number of parameters of the internal state of a secondary battery that is the subject of diagnosis increases.

[0006] The first aspect of the present invention relates to a state estimation system for a secondary battery, comprising a degradation estimation unit that obtains multiple parameters related to the state of the secondary battery under estimation based on voltage data obtained in the secondary battery under estimation, or voltage data obtained by means of data obtained in the secondary battery, thereby estimating the degree of degradation of the secondary battery under estimation. The degradation estimation unit obtains at least one parameter sequentially and exclusively selected from the multiple parameters according to each of the multiple voltage regions or multiple capacity regions, based on a predetermined correspondence between multiple voltage regions or multiple capacity regions and the multiple parameters.

[0007] The second approach, based on the state estimation system of the secondary battery described in the first approach, may also involve the degradation estimation unit establishing a correspondence between each of the plurality of parameters and the voltage region or capacity region in which the capacity characteristics are manifested by each parameter in the plurality of voltage regions or the plurality of capacity regions, as a predetermined correspondence.

[0008] The third approach, based on the state estimation system of the secondary battery described in the first or second approach, may also involve the following: when the number of parameters that establish a correspondence with the first voltage region according to the prescribed correspondence is less than the number of parameters that establish a correspondence with the second voltage region, the degradation estimation unit first obtains the parameters that establish a correspondence with the first voltage region in the first voltage region compared to the second voltage region.

[0009] The fourth scheme, based on the state estimation system of the secondary battery described in the first or second scheme, may also involve the following: when the number of parameters that establish a correspondence with the first capacity region according to the prescribed correspondence is less than the number of parameters that establish a correspondence with the second capacity region, the degradation estimation unit first obtains the parameters that establish a correspondence with the first capacity region in the first capacity region compared to the second capacity region.

[0010] The fifth approach, based on the secondary battery state estimation system described in the first approach, may also include parameters that are related to the degree of degradation of silicon contained in the negative electrode of the secondary battery being estimated.

[0011] The sixth aspect of the present invention relates to a method for estimating the state of a secondary battery, which is a method for estimating the state of a secondary battery executed by an electronic device. The electronic device includes a processing unit for estimating the degree of degradation of a secondary battery to be estimated. The method for estimating the state of a secondary battery includes the following processing: obtaining multiple parameters related to the state of the secondary battery to be estimated based on voltage data obtained in the secondary battery to be estimated, or voltage data obtained by means of data obtained in the secondary battery; and obtaining at least one parameter selected sequentially from the multiple parameters according to each of the multiple voltage regions or multiple capacity regions, based on a predetermined correspondence between multiple voltage regions or multiple capacity regions and the multiple parameters.

[0012] The seventh aspect of the present invention relates to a recording medium having a recording program, wherein an electronic device includes a processing unit for estimating the degree of degradation of a presumed secondary battery, wherein the program causes a computer of the electronic device to perform the following processing: obtaining multiple parameters related to the state of the presumed secondary battery based on voltage data obtained in the presumed secondary battery, or voltage data obtained by means of data obtained in the secondary battery; and obtaining at least one parameter sequentially and exclusively selected from the multiple parameters, according to each of the multiple voltage regions or multiple capacity regions, based on a predetermined correspondence between the multiple voltage regions or multiple capacity regions and the multiple parameters.

[0013] According to the first scheme described above, a degradation estimation unit is provided that obtains parameters that are selected sequentially and exclusively from multiple parameters. Therefore, even when the number of parameters relating to the internal state of the secondary battery that is the subject of diagnosis increases, the decrease in the estimation accuracy of the internal state can be suppressed. For example, compared to obtaining all parameters from multiple parameters in a comprehensive manner rather than in stages, each parameter can be obtained uniquely, thus stabilizing each parameter and improving the estimation accuracy.

[0014] In the second scheme described above, each parameter is obtained according to each region of the voltage region or capacity region that shows the capacity characteristics of each parameter, thus improving the estimation accuracy of each parameter.

[0015] In the case of the third or fourth scheme mentioned above, the region to be processed is selected exclusively from multiple voltage regions or capacity regions that correspond to multiple parameters, in the order of the least repetition of different parameters. Therefore, it is possible to stabilize each parameter and improve the estimation accuracy.

[0016] In the case of the fifth scheme described above, the estimation accuracy of the degree of degradation can be improved for silicon that exhibits capacity characteristics at the end of the discharge period (or the end of the degradation period).

[0017] According to the sixth or seventh scheme described above, by obtaining parameters that are selected sequentially and exclusively from multiple parameters, even when the number of parameters of the internal state of the secondary battery that is the subject of diagnosis increases, the decrease in the estimation accuracy of the internal state can be suppressed. For example, compared with obtaining all of the multiple parameters in a comprehensive manner rather than in stages, obtaining each parameter uniquely can stabilize each parameter and improve the estimation accuracy. Attached Figure Description

[0018] Figure 1 This is a block diagram illustrating the functional structure of a system equipped with the state estimation device for a secondary battery according to an embodiment of the present invention.

[0019] Figure 2This is a diagram showing an example of an OCV curve obtained by the optimization unit of the secondary battery state estimation device in an embodiment of the present invention based on the OCP curves of the positive and negative electrodes respectively.

[0020] Figure 3 This diagram illustrates an example of the information flow during parameter optimization processing performed by the optimization unit of the secondary battery state estimation device in an embodiment of the present invention.

[0021] Figure 4 This is a diagram showing examples of the parameters of the positive electrode OCP curve and the negative electrode OCP curve set by the state estimation device of the secondary battery in an embodiment of the present invention.

[0022] Figure 5 This is a flowchart illustrating the process performed by the secondary battery state estimation device in an embodiment of the present invention.

[0023] Figure 6 This is a diagram illustrating an example of a voltage region among the various parameters related to the state of a secondary battery, as described in an embodiment of the present invention, that contributes to the shape change of the OCV curve. Detailed Implementation

[0024] Hereinafter, the secondary battery state estimation system, secondary battery state estimation method and recording medium involved in the embodiments of the present invention will be described with reference to the accompanying drawings.

[0025] The secondary batteries involved in the implementation are, for example, detachable or fixedly configured in various electrical devices.

[0026] Various electrical devices include electric vehicles, electric mobile bodies, electric machinery, and power supply units. Electric vehicles include, for example, electric motor vehicles powered by a rotary electric motor driven by a secondary battery, motorcycles and scooters, hybrid vehicles based on a combination of a rotary electric motor and an internal combustion engine, and fuel cell vehicles based on a combination of a secondary battery and a fuel cell. Electric mobile bodies include, for example, robots, mobile work machines, flying vehicles, and water-based or underwater mobile bodies. Electric machinery includes, for example, construction machinery powered by a rotary electric motor. Power supply units include, for example, fixed or mobile power supply units for discharging and charging secondary batteries, or exchange devices for supplying and receiving secondary batteries to users through so-called battery sharing services.

[0027] It should be noted that various electrical devices, such as those in PHVs (Plug-in Hybrid Vehicles) or PHEVs (Plug-in Hybrid Electric Vehicles), can also have external charging capabilities, allowing them to be charged by an external power source (external DC power and external AC power). These electrical devices can also supply power to external sources using electricity from a secondary battery. Furthermore, the rotary motor mounted in an electric vehicle can, in addition to traction, exchange power with the secondary battery through regenerative braking based on rotational power input from the wheel side or through power generation based on power input from the internal combustion engine.

[0028] Figure 1 This is a block diagram showing the functional structure of system 1, which includes a secondary battery state estimation device 10 with an implementation method.

[0029] like Figure 1 As shown, System 1 (secondary battery state estimation system, electronic device) of the embodiment includes, for example, a vehicle 2 and a server 3. Vehicle 2 and server 3 are connected, for example, via a wired or wireless communication network (network) 4. 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 specified standard such as Ethernet, or a wireless LAN of various standards such as Wi-Fi and Bluetooth (registered trademark).

[0030] The secondary battery state estimation device 10 in the embodiment is, for example, composed of a server 3.

[0031] Vehicle 2 includes, for example, a secondary battery 11, a battery sensor 12, a battery control unit 13, an electric control unit 14, a rotary motor 15, a drive mechanism 16, and an overall processing unit 17.

[0032] The secondary battery 11 is, for example, a type of battery that is repeatedly charged and discharged, such as a lithium-ion battery, a sodium-ion battery, or a nickel-metal hydride battery. The electrolyte of the secondary battery 11 is, for example, a non-aqueous electrolyte such as a liquid, a solid, or a polymer.

[0033] The positive electrode active material constituting 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.

[0034] Metal oxides containing lithium ions include, for example, monomers or mixtures of multiple different composite oxides formed from lithium with metals such as nickel, cobalt, manganese, and aluminum. Composite oxides are classified, for example, from a crystal structure perspective, 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)O2). x Co y Mn z O2), nickel-cobalt-aluminum oxide (NCA:LiNi) x Co y Al x O2), etc. Spinel-type composite oxides include lithium manganese oxide (LMO:LiMn2O4) and lithium nickel manganese oxide (LNMO:LiNi). x Mn y Olivine-type composite oxides include lithium iron phosphate (LFP: LiFePO4) and lithium manganese iron phosphate (LMFP: LiMn). x Fe （1-x） PO4, etc.

[0035] In the case of a lithium-ion battery, the negative electrode active material constituting the negative electrode of the secondary battery 11 is formed from carbon materials, oxide-based materials, or a mixture of materials. Examples of carbon materials include graphite and hard carbon (difficult-to-graphitize carbon). Examples of oxide-based materials include lithium titanate (LTO: Li4Ti5O). 12 ), etc. Hybrid materials include, for example, graphite and silicon oxide (SiO₂). x Mixed materials, such as those containing metals like Si and Sn mixed with carbon materials.

[0036] The battery sensor 12 may include various sensors for detecting the state of the secondary battery 11. The battery sensor 12 may include, for example, a voltage sensor, a current sensor, and a temperature sensor. The battery sensor 12 may output signals containing various detected values ​​such as voltage, current, and temperature related to the state of the secondary battery 11.

[0037] The battery control unit 13, for example, is a so-called BMU (Battery Management Unit) that monitors and controls the state of the secondary battery 11. The battery control unit 13 is, for example, a software function unit that performs its functions by executing a predetermined program by a processor such as a CPU (Central Processing Unit). This software function unit is a type of ECU (Electronic Control Unit), which includes a processor such as a CPU, ROM (Read Only Memory) for storing the program, RAM (Random Access Memory) for temporarily storing data, and electronic circuits such as timers. It should be noted that at least a portion of the battery control unit 13 may also be an integrated circuit such as an LSI (Large Scale Integration).

[0038] The battery control unit 13 stores, for example, information related to the secondary battery 11 and prescribed procedures. The information related to the secondary battery 11 includes, for example, identification information such as an ID (IDentifier) ​​exclusively assigned to the secondary battery 11, manufacturing date, initial capacity, and information related to the state of the secondary battery 11 obtained based on the output of the battery sensor 12. The information related to the state of the secondary battery 11 includes, for example, charging status such as charge rate, remaining capacity (SOC) or power, charging and discharging history such as number of charging cycles, voltage and temperature, and other information related to the current state; information related to the current degradation state such as the degree of degradation; and information related to the presence or absence of abnormalities.

[0039] The power control unit 14 is connected to the secondary battery 11 and the rotary motor 15. The power control unit 14 includes, for example, a transformer such as a DC-DC converter that converts voltage under direct current; and a power converter such as a DC-AC converter that converts power between direct current and alternating current. The power control unit 14 controls the power transfer between the secondary battery 11 and the rotary motor 15, for example, based on control signals obtained from the overall processing unit 17.

[0040] The rotary motor 15 is, for example, a three-phase AC brushless DC motor. The rotary motor 15 generates rotational power by using electricity supplied from the power control unit 14 to perform a traction operation. For example, when connected to the wheels of the vehicle 2, the rotary motor 15 generates driving force by using electricity supplied from the power control unit 14 to perform a traction operation. The rotary motor 15 can also generate electricity by using rotational power input from the wheel side of the vehicle 2 for regenerative operation. When connected to the internal combustion engine of the vehicle 2, the rotary motor 15 can also generate electricity using the power of the internal combustion engine.

[0041] The drive mechanism 16 is a power transmission mechanism connected to the rotor of the rotary motor 15. The drive mechanism 16 may include, for example, components such as gears, belts, and chains. The drive mechanism 16 transmits power between the rotary motor 15 and the wheels of the vehicle 2. The drive mechanism 16 may also include limiting mechanisms that restrict power transmission, such as electric parking brakes and parking locking mechanisms that stop the rotation of the wheels or drive shaft.

[0042] The overall processing unit 17 comprehensively controls the operation of the vehicle 2. The overall processing unit 17 may include, for example, a software function unit. At least a portion of the overall processing unit 17 may also include an integrated circuit.

[0043] The overall processing unit 17 includes, for example, an input / output unit and a communication unit.

[0044] The input / output section includes, for example, various operating devices such as keyboards, touch panels, mice, and buttons; display devices such as liquid crystal displays or organic EL (electro-luminescence) displays; and various input / output devices such as microphones for sound input and speakers for sound output. The input / output section accepts operations performed by users or other operators, or input operations as sound input, and outputs signals corresponding to the input operations.

[0045] The communication unit and server 3 exchange various information via network 4. For example, the communication unit sends information to server 3 by combining information such as date and time, identification information of vehicle 2 or secondary battery 11, and information related to secondary battery 11 obtained from battery control unit 13.

[0046] Server 3 may include a software function unit. At least a portion of the overall processing unit 17 may also include an integrated circuit. Server 3 may include, for example, a storage unit 21, an acquisition unit 22, a preprocessing unit 23, an OCV estimation unit 24, an optimization unit 25 (degradation estimation unit, processing unit), and a diagnostic unit 26.

[0047] Storage unit 21 stores, for example, various information related to secondary battery 11 obtained in advance by server 3 or obtained by server 3 from vehicle 2 at appropriate times, as well as information generated by server 3, and prescribed procedures.

[0048] The acquisition unit 22 acquires, for example, time-series data of the 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 accumulating the time-series data of the current.

[0049] The preprocessing unit 23 performs processes such as cleaning and filtering of the time series data acquired by the acquisition unit 22. For example, the preprocessing unit 23 excludes data that has been missing or is abnormal from the time series data.

[0050] The OCV estimation unit 24 estimates the open-circuit voltage (OCV, voltage data) of the secondary battery 11 as follows, and obtains the history data of the open-circuit voltage (OCV) in the secondary battery 11.

[0051] For example, the OCV estimation unit 24 extracts data from the time series data processed by the preprocessing unit 23 that shows changes below a predetermined threshold caused by the charging and discharging of the secondary battery 11. The OCV estimation unit 24 sets the data representing changes below the predetermined threshold as data at a time when the closed-circuit voltage (CCV, voltage data) can be regarded as the open-circuit voltage (OCV).

[0052] The OCV estimation unit 24 may, for example, use an appropriate machine learning model to estimate the open-circuit voltage (OCV) of the secondary battery 11 to be estimated, based on the time series data processed by the preprocessing unit 23. The OCV estimation unit 24 may, for example, use data obtained from experiments conducted on the secondary battery 11 with known degradation conditions or simulations of a predetermined model of the secondary battery 11 to construct a machine learning model that outputs the open-circuit voltage (OCV) or voltage data associated with the open-circuit voltage (OCV). The OCV estimation unit 24 inputs the current and closed-circuit voltage (CCV) data detected at appropriate times in the secondary battery 11 to the machine learning model, thereby obtaining the open-circuit voltage (OCV) of the secondary battery 11 at any given time.

[0053] The OCV estimation unit 24 can, for example, use an appropriate equivalent circuit model to estimate the open-circuit voltage (OCV) of the secondary battery 11 to be estimated.

[0054] The OCV estimation unit 24 stores the open-circuit voltage (OCV) at any time (date, time, etc.) as open-circuit voltage (OCV) history data in the storage unit 21.

[0055] It should be noted that resume data refers to data obtained during an appropriate period, and is not limited to a series of data such as time series data.

[0056] Figure 2 This is a diagram showing 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 and negative electrodes respectively.

[0057] like Figure 2As shown, the optimization unit 25 obtains, for example, an OCP curve representing the change in open circuit potential (OCP) corresponding to the discharge capacity x (Ah) of the positive and negative electrodes of the secondary battery 11 based on multiple parameters related to the state of the secondary battery 11. The optimization unit 25 applies multiple parameters to the OCP curve (reference OCP curve) pre-stored in the storage unit 21, thereby obtaining the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)).

[0058] The reference OCP curve stored in the storage unit 21 is obtained in advance, for example, through experiments or simulations based on appropriate models. The reference OCP curve is, for example, the OCP curve of each active material cell constituting the positive and negative electrodes of the secondary battery 11. The optimization unit 25 estimates, for example, the OCV curve (=fca(x)-fan(x)) representing the change in open-circuit voltage (OCV) corresponding to the discharge capacity x (Ah) based on the difference between the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)).

[0059] Figure 3 This diagram illustrates an example of the flow of information during parameter optimization processing performed by the optimization unit 25 of the secondary battery state estimation device 10 in the embodiment.

[0060] like Figure 3 As shown, several parameters related 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, for example, a ratio set according to the capacity of each active material for an electrode (positive and negative electrodes) formed by mixing multiple active materials, such as a mixed material. The parameter optimization process performed by the optimization unit 25 includes, for example, generating OCV curves based on the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)), and optimizing (resetting) multiple parameters. The optimization unit 25 optimizes multiple parameters related to the state of the secondary battery 11, for example, based on the OCV curve estimated from the OCP curve and the historical data of the secondary battery 11.

[0061] The optimization unit 25 performs a prescribed optimization process, for example, based on an error function representing the error between the OCV curve estimated from the OCP curves of the positive and negative electrodes and historical data of the open-circuit voltage (OCV) of the secondary battery 11. The error function may be, for example, a weighted root mean square error (RMSE) or a weighted average absolute error (MAE). The prescribed optimization process may be, 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, differential evolution method, SHGO method, and annealing method. In the parameter optimization process, for example, the resetting of multiple parameters based on the optimization unit 25, the acquisition of the positive and negative OCP curves, and the estimation of the OCV curve are repeatedly performed to make the value of the error function below a predetermined value.

[0062] Figure 4 This is a diagram showing examples of the parameters of the positive electrode OCP curve and the negative electrode OCP curve set by the state estimation device 10 of the secondary battery in the embodiment.

[0063] Figure 4 The parameters shown, for example, act on the reference positive OCP curve (=gca(y)) and the reference negative OCP curve (=gan(y)) of the prescribed mathematical model based on the dimensionless variable y, thereby generating the positive OCP curve (=fca(x)) and the negative OCP curve (=fan(x)) of the mathematical model with the discharge capacity x (Ah) as the variable. Figure 4 The reference positive electrode OCP curve and the reference negative electrode OCP curve shown are composite OCP curves obtained by means of the individual OCP curves of each active material, for example, in the case of an electrode composed of a mixture of multiple active materials, such as a mixed material.

[0064] Figure 4 The parameters shown are, for example, the positive electrode capacity a and positive electrode position b when the reference positive electrode OCP curve is transformed into the positive electrode OCP curve, and the negative electrode capacity c and negative electrode position d when the reference negative electrode OCP curve is transformed into the negative electrode OCP curve.

[0065] The positive electrode capacity *a* and positive electrode position *b* are, for example, the positive electrode expansion / contraction rate *a* related to the width of the discharge capacity and the positive electrode offset *b* related to the position in the direction of the discharge capacity. These transform the dimensionless variable *y* into the discharge capacity *x* (= *a* × *y* + *b*). Similarly, the negative electrode capacity *c* and negative electrode position *d* are, for example, the negative electrode expansion / contraction rate *c* related to the width of the discharge capacity and the negative electrode offset *d* related to the position in the direction of the discharge capacity. These transform the dimensionless variable *y* into the discharge capacity *x* (= *c* × *y* + *d*).

[0066] The optimization unit 25 performs prescribed optimization processes on multiple parameters in stages, for example, based on a prescribed correspondence between multiple parameters and multiple voltage regions or multiple capacity regions. The prescribed correspondence is, for example, the correspondence between each parameter and the voltage region or capacity region (manifestation region) where the capacity characteristic is manifested by each parameter. The manifestation region of each parameter is, for example, the voltage or capacity region where the shape of the estimated OCV curve changes when each parameter is independently changed, corresponding to various degradation states of the secondary battery 11. For example, when the manifestation region is set based on the presence or absence of shape changes in the OCV curve resulting from changes in each parameter, the presence or absence of shape changes can also be determined based on an appropriate threshold set for the degree of shape change in the OCV curve. The manifestation region of each parameter can be obtained, for example, by performing a series of optimization processes based on an OCP curve (reference OCP curve) pre-saved in the storage unit 21, or it can be pre-saved and stored in the storage unit 21.

[0067] The optimization unit 25, for example, obtains regions (repeated regions) where the repetition states of multiple parameters differ from each other in a voltage or capacity region. The repetition state is, for example, the number of repeated parameters. The optimization unit 25, for example, performs a prescribed optimization process on at least one parameter, sequentially and exclusively selected from multiple parameters, for each of the multiple repeated regions. The optimization unit 25, for example, exclusively selects the repeated region to be processed from the multiple repeated regions, in order of the lowest number of repetitions of different parameters. The optimization unit 25, for example, performs a prescribed optimization process on the repeated region with the fewest repetitions, sequentially and exclusively selected from the multiple repeated regions.

[0068] For example, if the number of parameters in the first repeated region of multiple repeated regions is less than the number of parameters in the second repeated region which is different from the first repeated region, the optimization unit 25 performs the prescribed optimization process in the first repeated region first compared to the second repeated region.

[0069] The diagnostic unit 26 obtains a diagnostic value associated with the degradation state of the secondary battery 11, for example, based on an OCV curve estimated from the OCP curve after optimization of multiple parameters performed by the optimization unit 25. The diagnostic unit 26 sets the initial fully charged capacity of the secondary battery 11 to 100% and the percentage of the fully charged capacity at degradation to the SOH (State of Health) diagnostic value. The fully charged capacity at degradation is, for example, the difference between the discharge capacity at the fully charged voltage and the discharge capacity at the fully discharged voltage, obtained based on the OCV curve.

[0070] For example, the diagnostic unit 26 establishes a correspondence between the obtained SOH diagnostic value and the date and time of the OCV curve obtained by the optimization unit 25, thereby saving the history data of the SOH diagnostic value in the storage unit 21.

[0071] (The operation of the secondary battery state estimation device)

[0072] The operation of the secondary battery state estimation device 10 according to the embodiment, especially the processing performed by the optimization unit 25, will be described below.

[0073] Figure 5 This is a flowchart illustrating the processes performed by the secondary battery state estimation device 10 in the embodiment. It should be noted that... Figure 5 The series of processes shown in steps S01 to S07 are repeatedly executed at appropriate times.

[0074] like Figure 5 As shown, firstly, the optimization unit 25 obtains, for example, the OCP curves (reference OCP curves) of each active material monomer constituting the positive and negative electrodes of the secondary battery 11. The optimization unit 25 obtains, for example, the positive electrode OCP curve (=fca(x)) and the negative electrode OCP curve (=fan(x)) based on the reference OCP curves 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 the historical data of the secondary battery 11 (step S01).

[0075] Next, the optimization unit 25 obtains, for example, the display area (OCV variation range) of each of the multiple parameters, that is, the area of ​​voltage or capacity where the shape of the OCV curve changes with the change of each parameter (step S02).

[0076] Next, the optimization unit 25 obtains, for example, the repeating regions with different repeating states of the display regions of multiple parameters, that is, the repeating regions with different OCV change ranges (step S03).

[0077] Next, the optimization unit 25, for example, exclusively selects the repeating region with the fewest repetitions of a parameter from multiple repeating regions. The optimization unit 25, for example, exclusively selects the parameter contained in the selected repeating region from multiple parameters (step S04).

[0078] Next, the optimization unit 25 searches for and optimizes each of the selected parameters in the selected repeating region, for example, by performing a prescribed optimization process based on the estimated OCV curve and the history data of the secondary battery 11 (step S05).

[0079] Next, the optimization unit 25 determines, for example, whether the optimization performed on all parameters among the multiple parameters has ended (step S06). If the determination result is "yes", the optimization unit 25 proceeds the process to step S07. On the other hand, if the determination result is "no", the optimization unit 25 returns the process to step S04.

[0080] Next, the diagnostic unit 26 obtains a diagnostic value associated with the degradation state of the secondary battery 11, for example, based on the OCV curve estimated by the optimization unit 25 after optimizing multiple parameters (step S07). Then, the diagnostic unit 26 advances the process to the end.

[0081] Figure 6 This is a diagram illustrating an example of a voltage region among the various parameters related to the state of the secondary battery 11 involved in the embodiment that contribute to the shape change of the OCV curve.

[0082] For example, Figure 6 The positive electrode active material of the secondary battery 11 shown in one example is nickel-cobalt-aluminum oxide (NCA: LiNi). x Co y Al x O2), the negative electrode active material is graphite and silicon oxide (SiO2). x Mixed materials. For example... Figure 6 As shown, for example, in the first voltage region from zero to the first voltage V1, including the end of discharge (or the end of degradation), the capacity characteristics are manifested by five parameters among multiple parameters: positive electrode expansion / contraction rate a, positive electrode offset b, negative electrode expansion / contraction rate c, negative electrode offset d, and active material capacity ratio e. The active material capacity ratio e is, for example, the ratio of graphite (Gr) to silicon oxide (SiO) at the negative electrode. x The capacity ratio formed by (=Gr / SiO) x The parameter repetition count in the first voltage region is 5. For example, in the second voltage region from the first voltage V1 to the second voltage V2, the capacity characteristics are manifested by four parameters derived from the positive electrode expansion / contraction rate a, the positive electrode offset b, the negative electrode expansion / contraction rate c, and the negative electrode offset d. The parameter repetition count in the second voltage region is 4.

[0083] In this case, the optimization unit 25 first performs prescribed optimization processing on the positive electrode expansion / contraction rate a, positive electrode offset b, negative electrode expansion / contraction rate c, and negative electrode offset d based on the historical data in the second voltage region. Next, the optimization unit 25 performs prescribed optimization processing on the active material capacity ratio e based on the historical data in the first voltage region.

[0084] As described above, the system 1 of the secondary battery state estimation device 10 according to the embodiment includes an optimization unit 25 that performs a predetermined optimization process on parameters selected sequentially and exclusively from a plurality of parameters. This allows the reduction in the estimation accuracy of the internal state of the secondary battery 11 to be suppressed even when the number of parameters increases. For example, compared to the case where the predetermined optimization process is performed comprehensively on all parameters rather than in stages, each parameter can be uniquely obtained, allowing each parameter to be stabilized and improving the estimation accuracy.

[0085] The optimization unit 25 performs a prescribed optimization process for each region of the voltage region or capacity region in which the capacity characteristics of each parameter are manifested. Therefore, compared with the case where the prescribed optimization process is performed in the region where the capacity characteristics of each parameter are not manifested, the estimation accuracy of each parameter can be improved.

[0086] By selectively choosing regions to be processed from multiple voltage or capacity regions that correspond to multiple parameters, in the order of the least repetition of different parameters, the parameters can be stabilized and the estimation accuracy can be improved.

[0087] For example, even if the capacity characteristics of silicon are present at the end of the discharge period (or the end of the degradation period) in the negative electrode of the secondary cell 11, the estimation accuracy of the degradation degree of the negative electrode can be improved.

[0088] (Modified example)

[0089] The following describes variations of the embodiments. It should be noted that the same reference numerals are used for the parts that are the same as those in the embodiments described above, and the descriptions are omitted or simplified.

[0090] In the above embodiment, the secondary battery state estimation device 10 is configured with server 3, but is not limited thereto. For example, at least one of the processes performed by server 3 may also be performed by battery control unit 13 of vehicle 2. That is, the secondary battery state estimation device 10 may be configured with server 3 and battery control unit 13 or with only battery control unit 13.

[0091] In the above embodiments, the multiple parameters include positive electrode capacity a and positive electrode position b, negative electrode capacity c and negative electrode position d, and active material capacity ratio e, but are not limited to these. For example, the multiple parameters may also 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, etc.

[0092] In the above embodiments, the positive electrode position b or negative electrode position d among the multiple parameters can be, for example, the relative position of the negative electrode OCP curve with respect to the positive electrode OCP curve or the relative position of the positive electrode OCP curve with respect to the negative electrode OCP curve, etc.

[0093] In the above embodiments, the OCP and OCV curves are set as changes in open-circuit potential (OCP) or open-circuit voltage (OCV) corresponding to the discharge capacity x (Ah), but this is not a limitation. For example, capacity-related parameters such as charging capacity (Ah), remaining capacity (SOC: State Of Charge), or depth of discharge (DOD) can be used instead of discharge capacity (Ah). It should be noted that the tendency of changes in open-circuit potential (OCP) or open-circuit voltage (OCV) is reversed in discharge capacity (Ah) and charging capacity (Ah).

[0094] For example, the capacity range above the specified discharge capacity corresponds to the capacity range below the specified charging capacity.

[0095] It should be noted that alternatively, a program for implementing all or part of the functions of the system 1, which includes the state estimation device 10 for a secondary battery according to the present invention, can be recorded on a computer-readable recording medium. The computer system then reads and executes the program recorded on the recording medium, thereby performing all or part of the processing performed by the system 1. It should be noted that the term "computer system" here includes hardware such as an operating system and peripheral devices. Furthermore, "computer system" also includes a WWW system with a homepage providing environment (or display environment). Additionally, "computer-readable recording medium" refers to removable media such as floppy disks, optical disks, ROMs, and CD-ROMs, as well as storage devices such as hard disks built into a computer system. Moreover, "computer-readable recording medium" also includes recording media that, when receiving program transmissions via a network such as the Internet or a communication line such as a telephone line, serve as volatile memory (RAM) within a computer system acting as a server or client, storing the program for a certain period of time.

[0096] Furthermore, the aforementioned program can also be transmitted from a computer system that has stored the program in a storage device or similar device to other computer systems via a transmission medium or by transmission waves within the transmission medium. Here, the "transmission medium" for transmitting the program refers to a medium capable of transmitting information, such as a network (communication network) like the Internet or a communication line (communication line) like a telephone line. Additionally, the aforementioned program can also be used to implement a portion of the aforementioned functions. Moreover, the aforementioned program can also be a program that can be implemented by combining it with a program that has already recorded the aforementioned functions in a computer system; this is known as a differential file (differential program).

[0097] The embodiments described herein are illustrative examples and are not intended to limit the scope of the invention. These embodiments can be implemented in a variety of other ways, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included within the scope and spirit of the invention as well as within the scope of the invention described herein and its equivalents.

Claims

1. A state estimation system of a secondary battery, wherein the state estimation system of the secondary battery includes a deterioration estimation section that acquires a plurality of parameters related to a state of a secondary battery that is an estimation target based on voltage data acquired in the secondary battery that is the estimation target or voltage data acquired by data acquired in the secondary battery, thereby estimating a degree of deterioration of the secondary battery that is the estimation target, the deterioration estimation section acquires at least one parameter that is sequentially and exclusively selected from the plurality of parameters for each of a plurality of voltage regions or a plurality of capacity regions based on a prescribed correspondence relationship between the plurality of voltage regions or the plurality of capacity regions and the plurality of parameters.

2. The state estimation system of a secondary battery according to claim 1, wherein the deterioration estimation section, as the prescribed correspondence relationship, establishes a correspondence relationship between each of the plurality of parameters and a voltage region or a capacity region in which the parameter exhibits a capacity characteristic in each of the plurality of voltage regions or the plurality of capacity regions.

3. The state estimation system of a secondary battery according to claim 1 or 2, wherein in a case where a number of parameters that establish a correspondence relationship with a first voltage region according to the prescribed correspondence relationship is smaller than a number of parameters that establish a correspondence relationship with a second voltage region different from the first voltage region, the deterioration estimation section acquires the parameters that establish a correspondence relationship with the first voltage region in the first voltage region first compared to the second voltage region.

4. The state estimation system of a secondary battery according to claim 1 or 2, wherein in a case where a number of parameters that establish a correspondence relationship with a first capacity region according to the prescribed correspondence relationship is smaller than a number of parameters that establish a correspondence relationship with a second capacity region different from the first capacity region, the deterioration estimation section acquires the parameters that establish a correspondence relationship with the first capacity region in the first capacity region first compared to the second capacity region.

5. The state estimation system of a secondary battery according to claim 1, wherein the plurality of parameters include a parameter related to a degree of deterioration of silicon included in a negative electrode of the secondary battery that is the estimation target.

6. A state estimation method of a secondary battery, which is a state estimation method of a secondary battery executed by an electronic device that includes a processing section that estimates a degree of deterioration of a secondary battery that is an estimation target, wherein the state estimation method of the secondary battery includes the following processes: acquiring a plurality of parameters related to a state of the secondary battery that is the estimation target based on voltage data acquired in the secondary battery that is the estimation target or voltage data acquired by data acquired in the secondary battery; and acquiring at least one parameter that is sequentially and exclusively selected from the plurality of parameters for each of a plurality of voltage regions or a plurality of capacity regions based on a prescribed correspondence relationship between the plurality of voltage regions or the plurality of capacity regions and the plurality of parameters.

7. A recording medium that records a program, wherein an electronic device includes a processing section that estimates a degree of deterioration of a secondary battery that is an estimation target, ​ The program causes a computer of the electronic device to execute the following processing: acquire a plurality of parameters related to a state of the secondary battery of the estimation target based on voltage data acquired in the secondary battery of the estimation target or voltage data acquired through data acquired in the secondary battery; and acquire at least one parameter that is sequentially and exclusively selected from the plurality of parameters for each of a plurality of voltage regions or a plurality of capacity regions based on a prescribed correspondence relationship between the plurality of voltage regions or the plurality of capacity regions and the plurality of parameters.

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