State determination device for secondary battery, charge / discharge control device, and secondary battery system

Abstract

To provide a device for determining a state of a secondary battery capable of properly acquiring the state of the secondary battery.SOLUTION: Disclosed is a device 100 for determining a state of a secondary battery 210 which includes: a candidate value extracting part 120 for obtaining a plurality of candidate values for the parameters of the secondary battery at each of a plurality of times based on the state quantity which is a result of measuring the state of the secondary battery 210 at the plurality of times; and a calculation part 140 for obtaining an estimated value of the parameter and the secular change of the estimated value based on the plurality of candidate values and a predetermined function.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a state determination device for a secondary battery, a charge and discharge control device, and a secondary battery system.

Background Art

[0002] As background art in this technical field, the summary of Patent Document 1 below states, "Accurately determine the deterioration state of a secondary battery for practical current values. A state determination device for a secondary battery having a positive electrode and a negative electrode, which determines a capacity reduction parameter group A based on the charge and discharge characteristics per reference amount of the positive electrode and the negative electrode and the current value A, and determines a resistance increase parameter group B based on the charge and discharge characteristics per reference amount of the positive electrode and the negative electrode, the capacity reduction parameter group A, and a current value B larger than the current value A. A state determination device for a secondary battery, a state determination method for a secondary battery, a secondary battery system, and a charge and discharge control device having the state determination device." It is possible to use the secondary battery and the state determination method for the secondary battery known in Patent Document 1, and in this regard, the content of Patent Document 1 is incorporated in this specification.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, in the above-described technology, there is a desire to more appropriately acquire the state of the secondary battery. This invention has been made in view of the above circumstances, and an object thereof is to provide a state determination device for a secondary battery, a charge and discharge control device, and a secondary battery system that can appropriately acquire the state of the secondary battery.

Means for Solving the Problems

[0005] To solve the above problems, a state determination device for a secondary battery according to the present invention includes a candidate value extraction unit that obtains a plurality of candidate values for parameters of the secondary battery at each of a plurality of times based on state quantities that are results of measuring the state of the secondary battery at a plurality of times, and a calculation unit that obtains an estimated value of the parameter and a change over time of the estimated value based on the plurality of candidate values and a predetermined function. The state quantity includes measurement results of voltage and current in the secondary battery, the parameter includes the effective weight of the positive electrode active material, the effective weight of the negative electrode active material, or the capacitance deviation between the positive electrode and the negative electrode in the secondary battery, the candidate value extraction unit calculates a plurality of the candidate values by an optimization method using the discharge curves of the positive electrode alone and the negative electrode alone stored in advance and the state quantity, the function describes the deterioration behavior of the parameter, and the estimated value is the candidate value belonging to the candidate value set having the smallest difference from the function among candidate value sets that are combinations of a plurality of the candidate values at each time It is characterized by this.

Advantages of the Invention

[0006] According to the present invention, the state of the secondary battery can be appropriately acquired.

Brief Description of the Drawings

[0007]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Modes for Carrying Out the Invention

[0008] [Overview of Embodiment] In recent years, efforts have been made to efficiently utilize energy by using secondary batteries such as lithium-ion batteries as power sources for vehicles and energy storage power sources for smart houses. However, it is known that secondary batteries deteriorate in characteristics due to charge and discharge and storage. In particular, since the power sources for the above applications are assumed to have a long usage period, it is important to appropriately determine the deterioration state of the secondary battery in order to continuously use the product equipped with the secondary battery stably.

[0009] The characteristic deterioration of the secondary battery is due to the deterioration of the active material contained in the electrode of the secondary battery. The active material is one of the materials constituting the electrode of the secondary battery and plays a role in advancing the charge and discharge reaction of the secondary battery by storing and releasing ions. For example, in the case of a lithium-ion secondary battery, when the secondary battery is charged, lithium ions are released from the positive electrode active material and stored in the negative electrode active material, thereby advancing the charge reaction. Also, the discharge reaction follows the reverse process of this.

[0010] To determine the deterioration state of the secondary battery, it is desirable to accurately estimate the deterioration state of the above-described active material. To grasp the deterioration state of the active material, in addition to measuring the charge and discharge capacity of the secondary battery, a method of measuring current-voltage responses such as resistance values is used. For example, by applying the technique of Patent Document 1, it is considered that the deterioration state of the active material of the positive electrode and negative electrode in the secondary battery can be detected using the charge and discharge curve of the secondary battery.

[0011] Also, by applying the technique of Patent Document 1, the charge and discharge curve of the secondary battery can be computationally reproduced based on the pre-stored discharge curves of the positive electrode and negative electrode alone, and in the process, the effective weight of the positive electrode active material, the effective weight of the negative electrode active material, and the value of the parameter corresponding to the capacity deviation between the positive electrode and negative electrode can be obtained. All of these parameters are strongly correlated with the deterioration state of the active material, and hereinafter, the parameters correlated with the deterioration state of the active material are collectively referred to as "deterioration parameters".

[0012] Moreover, when applying the technology of Patent Document 1, it is considered that the value of the degradation parameter can be estimated by comparing the discharge curve calculated by the above procedure with the measured value of the discharge curve of the secondary battery. However, such a method assumes that there is no measurement error in the measured value of the discharge curve of the secondary battery. Generally, the measured value contains a certain measurement error due to the influence of the device and the measurement environment. Also, even if measured with the same type of secondary battery, due to differences in manufacturing conditions such as lot numbers, the measured value contains minute errors (hereinafter referred to as sample dependence).

[0013] Thus, the measured value of the discharge curve of the secondary battery contains certain measurement errors and sample dependence. These cause a decrease in the estimation accuracy when obtaining the estimated value of the degradation parameter of the secondary battery. Therefore, it is difficult to obtain the estimated value with high precision simply by the technology applying Patent Document 1. This means that the degradation state of the secondary battery cannot be accurately predicted using the estimated value of the degradation parameter. Therefore, the embodiment described later is configured in consideration of the above points and enables accurate determination of the degradation state of the secondary battery.

[0014] More specifically, in the embodiment described later, the state of the secondary battery is measured at various times. Among these, two or more candidate values that become the estimated value of a predetermined degradation parameter are obtained from the measurement data at several times, and the change over time of each candidate value is obtained based on the obtained candidate values and a predetermined function. Next, among these candidate values, the "most likely candidate value" that is the most probable (estimated to be close to the true value) is selected. Next, this most likely candidate value is adopted as the estimated value of the degradation parameter, and the degradation state of the secondary battery is predicted using this estimated value.

[0015] FIG. 1 is a diagram showing the procedure for obtaining the estimated value of the degradation parameter A in the comparative example and the candidate values of the degradation parameter A in the first embodiment. Here, the comparative example is the technology applying Patent Document 1 described above. In the method of the comparative example, when the state of the secondary battery is measured at time t1, an estimated value AE(t1) of the degradation parameter A at time t1 is obtained based on the measurement result. Thereafter, even at times t2 and t3, estimated values AE(t2) and AE(t3) of the degradation parameter A at each time are similarly obtained. On the other hand, in the first embodiment, when measuring the state of the secondary battery, for a single measurement time t, a plurality of candidate values AC1(t), AC2(t),... that are candidates for the estimated value are obtained.

[0016] In the example shown in FIG. 1, in each embodiment, a plurality of candidate values AC1(t1), AC2(t1),... for the degradation parameter A are obtained at time t1. Thereafter, even at times t2 and t3, candidate values AC1(t2), AC2(t2),... and candidate values AC1(t3), AC2(t3),... at each time are similarly obtained. And in the first embodiment, the degradation state of the secondary battery is predicted by selecting the maximum likelihood candidate value from among these plurality of candidate values. Thereby, according to the first embodiment, it is possible to accurately determine the degradation state of the secondary battery with respect to a practical current value.

[0017] [First Embodiment] Hereinafter, various embodiments will be described in detail with reference to the drawings and the like. In the following description, in all the figures, those having the same function are denoted by the same reference numerals, and the repeated description thereof may be omitted. Also, in the following description, the secondary battery (storage battery) may be simply abbreviated as "battery".

[0018] <Overall Configuration of the First Embodiment> FIG. 2 is a block diagram of a state determination device 100 according to the first embodiment. In FIG. 2, the secondary battery module 200 includes a plurality of secondary batteries 210 connected in series or in parallel. The state determination device 100 is a device that determines the state of the secondary battery module 200 or individual secondary batteries 210. In the following description, an example in which a lithium-ion secondary battery is applied as the secondary battery 210 will be described, but the secondary battery is not limited to a lithium-ion secondary battery.

[0019] Further, in the following description, as an operation of the state determination device 100, an operation of determining the state of each secondary battery 210 will be described. However, the operation of the secondary battery module 200 can also be determined in the same manner. In FIG. 2, the state determination device 100 includes a measurement unit 110, a degradation parameter extraction unit 120 (candidate value extraction unit, candidate value extraction means), a memory 130, a calculation unit 140 (calculation means), a degradation prediction unit 150, and an output unit 160.

[0020] FIG. 3 is a block diagram of the computer 900. The state determination device 100 shown in FIG. 2 includes one or more computers 900 shown in FIG. 3. In FIG. 3, the computer 900 includes a CPU 901, a RAM 902, a ROM 903, an HDD 904, a communication I / F 905, an input / output I / F 906, and a media I / F 907. The communication I / F 905 is connected to a communication circuit 915. The input / output I / F 906 is connected to an input / output device 916. The media I / F 907 reads and writes data from / to a recording medium 917.

[0021] The ROM 903 stores a control program executed by the CPU, various data, etc. The CPU 901 realizes various functions by executing an application program read into the RAM 902. The inside of the state determination device 100 shown in FIG. 2 above is shown with functions realized by an application program or the like as blocks.

[0022] Returning to FIG. 2, the measurement unit 110 measures the state quantity of the secondary battery 210 and stores it in the memory 130. Here, the state quantity of the secondary battery 210 represents a quantity related to the charge / discharge characteristics of the secondary battery 210, such as the capacity, resistance value, temperature, voltage, current, etc. of the secondary battery 210 at the measurement time point.

[0023] The degradation parameter extraction unit 120 calculates candidate values of the degradation parameter A (parameter) using the state quantities of the secondary battery acquired by the measurement unit 110 at each measurement time t. At this time, the degradation parameter extraction unit 120 calculates two or more candidate values AC1(t), AC2(t),... (see FIG. 1) of the degradation parameter based on the state quantity at at least one of the times.

[0024] Also, the degradation parameter extraction unit 120 may obtain candidate values of a plurality of types of degradation parameters (for example, degradation parameters A, B, C) based on the state quantities at each time. The degradation parameter extraction unit 120 may obtain two or more candidate values for at least one type of the degradation parameters A, B, C. The degradation parameter extraction unit 120 associates and stores the candidate values extracted as described above, the state quantities of the secondary battery, and the parameter names in the memory 130. Further, the memory 130 stores at least one type of function used by the calculation unit 140.

[0025] The calculation unit 140 compares the state quantities of the secondary battery stored in the memory 130, a plurality of candidate values of the degradation parameter, and a predetermined function. Thereby, the calculation unit 140 identifies the maximum likelihood candidate value estimated to be closest to the true value among the plurality of candidate values. Details of the operation behavior of the calculation unit 140 will be described later.

[0026] The degradation prediction unit 150 calculates the degradation state (such as the internal resistance and the capacity reduction rate of the secondary battery 210) and the remaining life of the secondary battery 210 using the maximum likelihood candidate value identified by the calculation unit 140 as the estimated value of the degradation parameter. Note that the life of the secondary battery 210 may be considered to be, for example, when the capacity of the secondary battery 210 falls below a predetermined lower limit value or when the internal resistance of the secondary battery 210 exceeds a predetermined upper limit value, and the life of the secondary battery is regarded as having reached this condition.

[0027] In the calculation of the deterioration state and remaining life, the time-dependent change of the estimated value of the deterioration parameter is predicted using a predetermined function such as an exponential function or a power function described later, and based on the result, the deterioration state of the secondary battery 210 at a future time point is predicted. Note that the prediction method of the life and the like in the deterioration prediction unit 150 is not limited to the above-described procedure. For example, by applying machine learning or the like to the estimated value obtained by the calculation unit 140, the time-dependent change of the estimated value may be predicted to predict the deterioration state of the secondary battery 210.

[0028] The output unit 160 outputs the predicted values of the deterioration state and remaining life of the secondary battery 210 determined by the deterioration prediction unit 150 to a display device for the user, a battery life management device (not shown), and the like. Further, when it is determined that the secondary battery has already reached the end of its life at the time of outputting the predicted value, a warning is displayed.

[0029] <Details of the Deterioration Parameter Extraction Unit 120> Next, the detailed operation of the deterioration parameter extraction unit 120 will be described. First, the reason why the estimated value (or candidate value) of the deterioration parameter is obtained in the deterioration parameter extraction unit 120 will be explained. When applying the technology described in Patent Document 1, it is considered that the charge and discharge curve of the secondary battery can be reproduced by calculation using the previously stored charge and discharge curves of the positive and negative electrodes alone and the values of the deterioration parameters. According to this method, the value of the deterioration parameter can be optimized so that the charge and discharge curve of the secondary battery reproduced by calculation coincides with the charge and discharge curve of the actually measured secondary battery. The optimized value of the deterioration parameter can be used as the estimated value (or candidate value) of the deterioration parameter.

[0030] Such a procedure is called parameter optimization or numerical optimization. It is also considered that when there is no measurement error in the measured value, an estimated value close to the true value of the deterioration parameter can be obtained by the above optimization method. However, generally, due to the influence of measurement error, a plurality of solutions are obtained as candidates for the estimated value. The details will be described with reference to FIG. 4.

[0031] FIG. 4 is a diagram showing an overview of parameter optimization. The horizontal axis of FIG. 4 represents values within the range that can be assumed as the parameter value Ax of the degradation parameter A. For all the parameter values Ax on the horizontal axis of FIG. 4, the calculated value of a certain state quantity (e.g., battery voltage) of the secondary battery 210 can be obtained. Also, as described above, the measured value of the state quantity (e.g., battery voltage) of the secondary battery 210 at a certain measurement time t is stored in the memory 130. Then, for all the parameter values Ax of the degradation parameter A, the difference between the calculated value and the measured value can be obtained. Let this difference be δ.

[0032] The vertical axis in FIG. 4 is the absolute value of the difference δ, which is the absolute difference value |δ|. In the illustrated example, the absolute difference value |δ| has minimum values appearing at three locations where the parameter values Ax = α, β, γ. Let these minimum values be δ(α), δ(β), δ(γ) respectively. The values α, β, γ at which these minimum values appear are candidate values for the estimated value AE(t).

[0033] Here, assume that the measured value of the state quantity includes a measurement error with a magnitude of δn. In the illustrated example, the measurement error δn is larger than any of the minimum values δ(α), δ(β), δ(γ), and all the minimum values δ(α), δ(β), δ(γ) are included within the range of the measurement error δn. Therefore, based only on the information shown in FIG. 4, it becomes difficult to determine which of the candidate values α, β, γ should be adopted as the estimated value AE(t).

[0034] As described above, when the measured value of the state quantity includes a measurement error, multiple candidate values for the estimated value AE(t) of the degradation parameter A are obtained, and it may become difficult to uniquely determine the estimated value AE(t) of the degradation parameter A. This embodiment determines which of such candidate values α, β, γ, ··· should be adopted as the estimated value AE(t). Therefore, this embodiment is not limited to the secondary battery system described in Patent Document 1 and can be widely applied to parameter estimation for various other physical systems.

[0035] <Details of the calculation unit 140> Next, the details of the calculation unit 140 will be described. Suppose that the above-described deterioration parameter extraction unit 120 calculates a plurality of candidate values AC1(t), AC2(t), AC3(t),... as candidates for the estimated value AE(t) of the deterioration parameter A at a certain measurement time t. Here, it may be considered that the candidate values AC1(t), AC2(t), AC3(t),... have the same meaning as the candidate values α, β, γ shown in FIG. 4. Therefore, the deterioration parameter extraction unit 120 similarly calculates candidate values AC1(tk), AC2(tk), AC3(tk),... at a plurality of measurement times tk (where k = 0, 1, 2, 3,...). The estimated value AE(t) will be one of these candidate values AC1(tk), AC2(tk), AC3(tk),.... When these candidate values are arranged in the order of the measurement time t, it becomes as follows in Equation (1).

[0036] (AC1(t0), AC2(t0), AC3(t0),...) at t = t0 (AC1(t1), AC2(t1), AC3(t1),...) at t = t1 (AC1(t2), AC2(t2), AC3(t2),...) at t = t2 :... Equation (1)

[0037] Here, when each candidate value in Equation (1) is rearranged, it becomes as follows in Equation (2).

[0038] (AC1(t0), AC1(t1), AC1(t2),...) (AC2(t0), AC2(t1), AC2(t2),...) (AC3(t0), AC3(t1), AC3(t2),...) :... Equation (2)

[0039] Each row of Equation (2) contains one candidate value of the degradation parameter A measured at each time. That is, each row of Equation (1) shows the change over time of the degradation parameter. Further, Equation (2) comprehensively lists all possible combinations of the candidate values of the degradation parameter A measured at each time. Therefore, the change over time of the maximum likelihood candidate value, i.e., the estimated value AE(t), of the degradation parameter is included in one of the rows shown in Equation (2). Thus, each row in Equation (2) will be referred to as a "candidate value set".

[0040] Next, a method for identifying the candidate value set representing the change over time of the maximum likelihood candidate value from among the candidate value sets shown in Equation (2) will be described. Since the degradation parameter of the secondary battery is a quantity indicating the degree of degradation of the secondary battery, its value is considered to change monotonically over time. Therefore, a set showing non-monotonic time dependence can be excluded from the candidates for the maximum likelihood candidate value as a non-physical candidate value set.

[0041] Figure 5 is a diagram showing examples of the time changes of a plurality of candidate values. In Figure 5, graph G1 shows the time change of the candidate value AC1(t), and graph G2 shows the time change of the candidate value AC2(t). In graph G1, the candidate value AC1(t) decreases monotonically with time. If the degradation parameter A decreases monotonically with time, the candidate value AC1(t) is valid in that regard. On the other hand, in graph G2, the candidate value AC2(t) shows non-monotonic time dependence. Therefore, the candidate value AC2(t) can be excluded from the candidates for the maximum likelihood candidate value as an inappropriate candidate value for the degradation parameter A. This process is called the "inappropriate candidate value exclusion process".

[0042] In general, it is known that the characteristic degradation of a secondary battery progresses qualitatively according to a specific functional form. For example, when graphite is applied to the negative electrode material of a secondary battery, it is known that its characteristic degradation (degradation of capacity and resistance) progresses in proportion to the square root of the usage period of the secondary battery (square root rule). Therefore, the calculation unit 140 of the present embodiment utilizes such knowledge and empirical rules regarding battery degradation to narrow down the set of degradation parameters shown in Equation (2). The specific procedure is shown below. In the memory 130, based on empirical rules and the like, a function that describes the degradation behavior of the secondary battery is stored for each type of degradation parameter. These functions are referred to as behavior description functions. As an example, a behavior description function F(t) shown in the following Equation (3) can be applied. In Equation (3) below, G, H, and J are constants. The behavior description function F(t) of Equation (3) assumes that the degradation characteristics of the secondary battery progress exponentially with respect to the measurement time t.

[0043] F(t)=G-H×exp(-J×t) … Equation (3)

[0044] Also, as another example, for instance, a power function shown in the following Equation (4) can be applied as the behavior description function F(t). The aforementioned square root rule and the like are also included in this. In Equation (4) below, G, H, and J are constants.

[0045] F(t)=G+H×t J … Equation (4)

[0046] Note that the behavior description function F(t) stored in the memory 130 is not limited to the above, and various functions may be applied according to the type of degradation parameter.

[0047] Next, the calculation unit 140 selects one set from the set of candidate value sets shown in Equation (2), and optimizes the constants G, H, and J in the behavior description function F(t) so that the difference δf (not shown) between the selected candidate value set and the behavior description function F(t) such as Equation (3) or (4) becomes minimum. Next, the calculation unit 140 obtains the difference δg (not shown) between the behavior description function F(t) calculated using the optimized constants G, H, and J and the selected candidate value set.

[0048] The calculation unit 140 executes the above-described operations for all candidate value sets shown in Equation (2) or for candidate value sets that have not been excluded by the above-described inappropriate candidate value exclusion process. As a result, for each candidate value set, the optimal values of the constants G, H, and J are obtained, and a difference δg (not shown) between the function F(t) calculated using the optimal values of the constants G, H, and J and the selected candidate value set is obtained.

[0049] Here, since the behavior description function F(t) shown in Equations (3) and (4) is a function that (empirically) describes the degradation behavior of the secondary battery 210, it is considered that the true values of the degradation parameters also change over time according to the behavior description function F(t). Therefore, the calculation unit 140 can determine that the candidate value set for which the difference δg between the candidate value set and the behavior description function F(t) is minimized is the set of maximum likelihood candidate values. The content of this process will be described again with reference to FIG. 5.

[0050] In the graph G3 in FIG. 5, the time change of the candidate value AC3(t) is shown, and in the graph G4, the time change of the candidate value AC4(t) is shown. In addition, examples of the above-described behavior description function F(t) are also shown in the graphs G3 and G4. In the illustrated example, since the candidate value AC3(t) almost coincides with the behavior description function F(t), the difference δg between the candidate value set and the behavior description function F(t) becomes a small value. On the other hand, since the candidate value AC4(t) deviates from the behavior description function F(t), the difference δg (not shown) between the two becomes large. Therefore, if the candidates for the maximum likelihood candidate values are only the candidate value AC3(t) and the candidate value AC4(t), the calculation unit 140 selects the candidate value AC3(t) as the maximum likelihood candidate value.

[0051] Next, the processing procedure in the calculation unit 140 when a plurality of candidate value sets with approximately the same difference δg between the candidate value set and the behavior description function F(t) occur will be described. For the sake of simplicity of discussion, it is assumed that there are two candidates for the maximum likelihood candidate values, the candidate values AC5(t) and AC6(t) (not shown), and that the state quantity of the secondary battery 210 is measured at the measurement times t0, t1, t2, t3,..., tn of n + 1 times.

[0052] First, the calculation unit 140 calculates candidate values AC5(t) and AC6(t) at n + 1 measurement times t0, t1, t2, t3, …, tn. Then, for the candidate values AC5(t) and AC6(t), the calculation unit 140 obtains the difference δg between each candidate value pair and the function F(t), assuming that the two are substantially the same.

[0053] Here, based on the candidate value AC5(t), let the results of optimizing the constants G, H, and J in the behavior description function F(t) such as equations (3) and (4) be constants Gx, Hx, and Jx. Similarly, based on the candidate value AC6(t), let the results of optimizing the constants G, H, and J in the behavior description function F(t) be constants Gy, Hy, and Jy.

[0054] The calculation unit 140 calculates the predicted value ACE5(tm) of the candidate value AC5(t) at a future time tm (where m > n) based on the constants Gx, Hx, and Jx. Similarly, the calculation unit 140 calculates the predicted value ACE6(tm) of the candidate value AC6(t) at the future time tm based on the constants Gy, Hy, and Jy.

[0055] Next, when the current time actually reaches time tm, the measurement unit 110 measures the state quantity of the secondary battery 210 and stores it in the memory 130. Next, the degradation parameter extraction unit 120 calculates the candidate values AC5(tm) and AC6(tm) at time tm based on the state quantity at time tm. Next, the calculation unit 140 obtains the differences δ5 = |AC5(tm) - ACE5(tm)| and δ6 = |AC6(tm) - ACE6(tm)|. Then, for the differences δ5 and δ6, if the relationship "δ5 ≦ δ6" holds, the calculation unit 140 selects the candidate value AC5(t) as the maximum likelihood candidate value, and in other cases, selects the candidate value AC6(t) as the maximum likelihood candidate value. Through the above procedure, the calculation unit 140 identifies the set of maximum likelihood candidate values from the sets of candidate values in equation (2), and outputs the identified set of maximum likelihood candidate values as the estimated value AE(t) of the degradation parameter A.

[0056] <Example of Life Prediction> Next, examples and comparative examples of life prediction will be described. First, in the examples and comparative examples, a secondary battery 210 in which the negative electrode active material is graphite and the positive electrode active material is LiNiCoMnO2 was applied. In the test, the charge rate of the secondary battery 210 was adjusted to 80%, and it was left standing in a thermostatic chamber controlled at a constant temperature. The standing secondary battery was taken out of the thermostatic chamber every 30 days, and the capacity of the secondary battery and the charge rate dependence (SOC-OCV curve) of the open circuit voltage were measured under an environment of 25°C.

[0057] After the capacity measurement, the charge rate of the secondary battery was adjusted to 80% and left standing in the thermostatic chamber again. After another 30 days, it was taken out and the capacity measurement was performed. This operation was repeated for about 900 days (storage test). In this study, two conditions of 60°C and 25°C were examined as the temperature of the thermostatic chamber during the storage test.

[0058] FIG. 6 is a diagram showing the predicted curve and measurement results of the capacity retention rate of the secondary battery 210. In the graph G10 in FIG. 6, it is a graph when the temperature of the thermostatic chamber during the storage test is 60°C, and the graph G20 is a graph when the temperature of the thermostatic chamber during the storage test is 25°C. In the graphs G10 and G20, the measured values 14 and 24 indicated by the white-outlined circles and the measured values 16 and 26 indicated by the black-filled circles are the measured values of the capacity retention rate.

[0059] Among these, the measured values 14 and 24 are data for about 200 days out of the test data for 900 days, and were used as teacher data for prediction. The remaining measured values 16 and 26 were used as verification data for the prediction results. Also, the solid predicted curves L11 and L21 are predicted curves predicted based on the measured values 14 and 24 according to the examples of the first embodiment. Also, the dashed predicted curves L12 and L22 are predicted curves predicted according to the comparative example based on the measured values 14 and 24. Here, the comparative example was obtained by the method shown in Patent Document 1.

[0060] The following describes the procedure for creating the prediction curves L12 and L22 according to the comparative example. In the method of the comparative example, for the charge-discharge curve of the secondary battery, the estimated value of the degradation parameter of the secondary battery can be obtained by fitting the potential curves of the positive electrode and the negative electrode respectively by the least square method or the like. In order to obtain the prediction curves L12 and L22 in the comparative example, first, for each charge-discharge curve acquired every 30 days, the fitting operation by the least square method was repeated to obtain the time-dependent change of the degradation parameter. Further, the time-dependent change of the degradation parameter was extrapolated to obtain the change of the degradation parameter over time, and based on the result, the battery capacity at a future time point was calculated to obtain the prediction curves L12 and L22.

[0061] FIG. 7 is a diagram showing the result of predicting the time-dependent change of the effective mass of the positive electrode active material in the comparative example. That is, the upper table in FIG. 7 shows the time-dependent change of the estimated value of the effective mass of the positive electrode active material extracted from the test results at 60°C. That is, in the comparative example, the degradation parameter is the "effective mass of the positive electrode active material", and the estimated value of the degradation parameter was obtained approximately every 30 days. Also, the lower graph in FIG. 7 shows the estimated value and its extrapolated value. The exponential function behavior description function F(t) shown in Equation (3) was applied to the curve used for extrapolation (extrapolation curve).

[0062] Next, the procedure for creating the prediction curves L11 and L21 according to this example will be described. In this example, when obtaining the degradation parameter by the least square method, a plurality of candidate values were calculated. Specifically, when fitting the charge-discharge curve of the secondary battery and the potential curves of the positive and negative electrodes, a plurality of initial values of the parameters used for fitting were prepared. This is because it is known that the candidate values of the degradation parameter obtained as a result of fitting depend on the initial value. In fact, in the fitting using the charge-discharge curve 180 days after the start of the test, as a result of examining about 100 types of initial values of the parameters used for fitting, a plurality of different values were obtained as candidate values of the degradation parameter. The result is shown in FIG. 8.

[0063] FIG. 8 is a diagram showing the result of predicting the change over time in the effective mass of the positive electrode active material in this example. That is, the upper table in FIG. 8 shows a plurality of candidate values of the effective mass of the positive electrode active material extracted from the test results at 60° C., and the change over time of the estimated values selected from these candidate values. That is, also in this example, the degradation parameter is the "effective mass of the positive electrode active material". Further, the lower graph in FIG. 8 shows the estimated value and its extrapolated value. As shown in the upper table of FIG. 8, it can be seen that the candidate values of the degradation parameter are not always uniquely determined, and there are a plurality of candidate values due to the influence of measurement errors and the like.

[0064] In order to obtain the change over time of the maximum likelihood candidate value of the degradation parameter, it is necessary to determine which candidate value is physically reasonable. Therefore, assuming that the change over time of the degradation parameter follows the behavior description function F(t) of the exponential function shown in Equation (3), a set showing an exponential change over time behavior was selected as the estimated value from among the combinations of candidate values shown in FIG. 8. The selected estimated value is shown at the right end of the upper table in FIG. 8. In this example, it is considered that the estimated value thus obtained correctly describes the change over time of the degradation parameter. Thereafter, extrapolated values of the capacity reduction parameter were obtained in the same manner as in the comparative example or the method of Patent Document 1, and prediction curves L11 and L21 were obtained by calculating the battery capacity at a future time using these values.

[0065] FIG. 9 is a diagram showing the error between the measured values and the prediction curves in the above-described example and comparative example. That is, FIG. 9 shows the results of calculating the errors between the measured values 16 and 26 (see FIG. 26), which are verification data, and the prediction curves L11, L12, L21, and L22. The root mean square error (RMSE) was adopted as the definition of the error. The smaller the RMSE, the more accurately the life can be predicted. As is clear from FIG. 9, it can be understood that the prediction curves L11 and L21 according to this example have smaller RMSE values than the prediction curves L12 and L22 according to the comparative example, and the life could be predicted with high accuracy.

[0066] [Second Embodiment] Next, the second embodiment will be described. FIG. 10 is a block diagram of the secondary battery system 1 according to the second embodiment. The secondary battery system 1 includes a secondary battery module 200 and a charge / discharge control device 300. Here, the configuration of the secondary battery module 200 is the same as that of the first embodiment (see FIG. 2). The charge / discharge control device 300 includes a state determination device 100 and a charge / discharge control unit 310.

[0067] The charge / discharge control unit 310 controls the charge current and discharge current to the secondary battery 210 so that they are within a specified current range. In addition, the state determination device 100 in the present embodiment includes an operation condition determination unit 170 in addition to the same configuration as that of the first embodiment (see FIG. 2). As described above, the deterioration prediction unit 150 predicts the deterioration state (such as internal resistance and capacity reduction rate) of the secondary battery 210 at present and in the future based on the estimated value AE(t) of the deterioration parameter A.

[0068] Based on this deterioration state, the operation condition determination unit 170 sets the voltage allowable range (upper limit value and lower limit value) during the operation of the secondary battery 210, calculates the current range (upper limit value and lower limit value) of the charge / discharge current within which the battery voltage of the secondary battery 210 falls within this voltage allowable range, and commands this current range to the charge / discharge control unit 310. Thereby, the charge / discharge control unit 310 can control the charge / discharge current so as to be within a range corresponding to the estimated value AE(t) of the deterioration parameter A, and thereby the long life of the secondary battery 210 can be achieved.

[0069] [Effect of the Embodiment] According to the above-described embodiments as described above, the state determination device 100 of the secondary battery measures the state of the secondary battery 210 at a plurality of times (tk; k = 0, 1, 2, 3,...), and based on the state quantity which is the result of the measurement, a candidate value extraction unit (120) that obtains a plurality of candidate values (AC1(tk), AC2(tk), AC3(tk),...) for the parameter (A) of the secondary battery 210 at each of the plurality of times (tk; k = 0, 1, 2, 3,...), and a calculation unit 140 that obtains the change over time (AE(t)) of the estimated value of the parameter (A) based on the plurality of candidate values (AC1(tk), AC2(tk), AC3(tk),...) and a predetermined function (F(t)). Thereby, based on the plurality of candidate values (AC1(tk), AC2(tk), AC3(tk),...), the state of the secondary battery, such as the change over time (AE(t)) of the characteristics of the degradation parameter, can be appropriately obtained.

[0070] Further, the secondary battery 210 includes an electrode, the electrode contains an active material, and it is more preferable that the parameter (A) includes any one of the deactivation rate of the active material, the film thickness of the film on the surface of the active material, and the resistance value of the film on the surface of the active material. Thereby, any one of the deactivation rate of the active material, the film thickness of the film on the surface of the active material, and the resistance value of the film on the surface of the active material can be appropriately estimated.

[0071] Further, the state quantity includes the battery voltage of the secondary battery 210, the function (F(t)) is a function including an exponential function or a power function, the parameter (A) is a quantity representing the degradation state of the secondary battery 210, and it is more preferable that the calculation unit 140 outputs the estimated value (AE(t)) based on the function (F(t)) and the change over time of the battery voltage. Thereby, when the parameter (A) changes approximately according to an exponential function or a power function, a more appropriate estimated value (AE(t)) can be output.

[0072] Further, it is more preferable that the state determination device 100 of the secondary battery further includes an operation condition determination unit 170 that sets a voltage tolerance range during operation of the secondary battery 210 based on the estimated value (AE(t)) and determines the operation conditions of the secondary battery 210 so that the battery voltage of the secondary battery 210 falls within the voltage tolerance range. Thereby, appropriate operation conditions of the secondary battery 210 can be determined based on the estimated value (AE(t)).

[0073] [Modification Example] The present invention is not limited to the above-described embodiments, and various modifications are possible. The above-described embodiments are illustrated for easy understanding and explanation of the present invention, and are not necessarily limited to those having all the configurations described. Also, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Also, a part of the configuration of each embodiment can be deleted, or other configurations can be added or replaced. Also, the control lines and information lines shown in the figures indicate those considered necessary for explanation, and do not necessarily show all the control lines and information lines required in the product. In practice, it may be considered that almost all the configurations are interconnected. Possible modifications to the above embodiments are, for example, as follows.

[0074] (1) In the above embodiment, when there are p candidate values AC1(t) to ACp(t) of the degradation parameter at each of the n measurement times t0, t2, t3, …, t(n - 1), the set of the most likely candidate values is selected from among the p sets of candidate values. However, all combinations of candidate values that can be traced in time series may be used as the set of candidate values, and the most likely candidate value may be selected from among them. In the above example, the number of sets of candidate values is p n and becomes a very large number particularly when the number of measurements n is large. However, for example, by using the Viterbi algorithm or the like to search for the set of the most likely candidate values, the set of the most likely candidate values can be obtained with a relatively small amount of calculation.

[0075] (2) Since the hardware of the state determination device 100 in the above embodiment can be realized by a general computer, a program or the like for executing various processes shown in FIGS. 2 and 10 may be stored in a storage medium or distributed via a transmission line.

[0076] (3) The processes shown in FIGS. 2 and 10 and other processes described above were described as software processes using a program in the above embodiment, but part or all of them may be replaced with hardware processes using an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0077] (4) The various processes executed in the above embodiment may be executed by a server computer via a network (not shown), and various data stored in the above embodiment may also be stored in the server computer.

Explanation of Reference Numerals

[0078] 1 Secondary battery system 100 State determination device 120 Degradation parameter extraction unit (candidate value extraction unit, candidate value extraction means) 140 Calculation unit (calculation means) 170 Operation condition determination unit 200 Secondary battery module 210 Secondary battery 300 Charge / discharge control device 310 Charge / discharge control unit A Degradation parameter (parameter)

Claims

1. A candidate value extraction unit that obtains a plurality of candidate values for parameters of the secondary battery at each of the plurality of times based on state quantities that are results of measuring the state of the secondary battery at a plurality of times; A calculation unit that obtains an estimated value of the parameter and a change over time of the estimated value based on the plurality of candidate values and a predetermined function; and The state quantity includes measurement results of voltage and current in the secondary battery; The parameter includes the effective weight of the positive electrode active material, the effective weight of the negative electrode active material, or the capacitance deviation between the positive electrode and the negative electrode in the secondary battery; The candidate value extraction unit calculates the plurality of candidate values by an optimization method using the discharge curves of the positive electrode alone and the negative electrode alone stored in advance and the state quantity; The function describes the degradation behavior of the parameter; The estimated value is a candidate value belonging to a candidate value set in which the difference from the function is minimized among candidate value sets that are combinations of the plurality of candidate values at each time. A state determination device for a secondary battery, characterized in that.

2. The calculation unit predicts the degradation state of the secondary battery at a future time point using the estimated value and the change over time of the estimated value. The state determination device for a secondary battery according to claim 1, characterized in that.

3. The secondary battery includes electrodes, and the electrodes include active materials. The parameter includes any one of the deactivation rate of the active material, the film thickness of the film on the surface of the active material, and the resistance value of the film on the surface of the active material. The state determination device for a secondary battery according to claim 1, characterized in that.

4. The state quantity includes the battery voltage of the secondary battery. The function is a function including an exponential function or a power function. The parameter is a quantity representing the degradation state of the secondary battery. The calculation unit outputs the estimated value based on the function and the change over time of the battery voltage. The state determination device for a secondary battery according to claim 1, characterized in that.

5. The apparatus further includes an operation condition determination unit that sets a voltage allowable range during operation of the secondary battery based on the estimated value and determines operation conditions of the secondary battery so that the battery voltage of the secondary battery falls within the voltage allowable range. The state determination device for a secondary battery according to any one of claims 1 to 4, characterized in that.

6. A candidate value extraction unit that obtains a plurality of candidate values for the parameters of the secondary battery at each of the plurality of times based on state quantities that are the results of measuring the state of the secondary battery at a plurality of times; A calculation unit that obtains an estimated value of the parameter and a change over time of the estimated value based on the plurality of candidate values and a predetermined function; A charge / discharge control unit that controls charge and discharge of the secondary battery, and The state quantity includes measurement results of voltage and current in the secondary battery, The parameter includes the effective weight of the positive electrode active material, the effective weight of the negative electrode active material, or the capacitance deviation between the positive electrode and the negative electrode in the secondary battery, The candidate value extraction unit calculates the plurality of candidate values by an optimization method using the pre-stored discharge curves of the positive electrode alone and the negative electrode alone and the state quantity, The function describes the degradation behavior of the parameter, The estimated value is the candidate value belonging to the candidate value set in which the difference from the function is the smallest among the candidate value sets that are combinations of the plurality of candidate values at each time. A charge / discharge control device characterized by the above.

7. A secondary battery module having a plurality of secondary batteries, A candidate value extraction unit that obtains a plurality of candidate values for the parameters of the secondary battery at each of the plurality of times based on state quantities that are the results of measuring the state of the secondary battery module at a plurality of times; A calculation unit that obtains an estimated value of the parameter and a change over time of the estimated value based on the plurality of candidate values and a predetermined function, and The state quantity includes measurement results of voltage and current in the secondary battery, The parameter includes the effective weight of the positive electrode active material, the effective weight of the negative electrode active material, or the capacitance deviation between the positive electrode and the negative electrode in the secondary battery, The candidate value extraction unit calculates the plurality of candidate values by an optimization method using the pre-stored discharge curves of the positive electrode alone and the negative electrode alone and the state quantity, The function describes the degradation behavior of the parameter, The estimated value is the candidate value belonging to the candidate value set in which the difference from the function is the smallest among the candidate value sets that are combinations of the plurality of candidate values at each time. A secondary battery system characterized by the above.

Images