Battery status estimation device

Abstract

The present invention aims to provide a battery state estimation device that accurately estimates the degradation state of an entire battery system by taking into account the state of health (SOH) distribution of battery cells. Referring to Figure 1 , the battery state estimation device of the present invention estimates the SOH of a battery cell using the relationship between the temporal differential of the output voltage of a battery cell during a pause period and the battery temperature. The SOH of multiple battery cells is then used to estimate the degradation state of the entire battery system.

Description

Technical Field

[0001] The present invention relates to a technology for estimating the state of a secondary battery cell. Background Art

[0002] In storage battery systems, electric vehicles, and other similar battery-related systems, it is important to estimate the state of secondary batteries (e.g., state of health (SOH)) during battery operation. Battery life prediction is also important to estimate the remaining life of the battery.

[0003] When measuring battery status, battery characteristics are strongly correlated with battery temperature. In a storage battery system, battery cell temperature fluctuates. Therefore, battery temperature measurement methods must be considered. As a specific example, there are methods that pre-record the temperature characteristics of battery impedance depending on battery temperature and use these temperature characteristics to estimate the battery status (Patent Documents 1 and 2).

[0004] The battery system has multiple battery cells, and the SOH of each battery cell has deviations due to the manufacturing process. This deviation is further amplified by the temperature distribution of the submodules within the battery cell. This temperature distribution becomes the main reason for the accelerated degradation of the battery cell. Battery cells with high temperatures deteriorate faster than battery cells with low temperatures. Since the battery cell with the lowest SOH has the highest resistance value, the temperature rise is further accelerated. As a result, the performance of the entire battery system is dragged down by the battery cell with the lowest SOH. If the SOH distribution is not measured but only the average SOH is measured, the battery system may be rapidly degraded.

[0005] Prior art literature

[0006] Patent Literature

[0007] Patent Document 1: Japanese Patent Application Publication No. 2018-091716

[0008] Patent Document 2: Japanese Patent Application Publication No. 2019-039764 Summary of the Invention

[0009] Problems to be solved by the invention

[0010] Patent Documents 1 and 2 assume that the battery temperature is uniform within the battery system. Furthermore, these documents require a sine wave or rectangular wave for impedance measurement, complicating the circuit structure. Furthermore, measuring the temperature characteristics of impedance requires processing the frequency response, complicating the analysis process.

[0011] When estimating SOH using temperature-dependent parameters, the battery temperature must be known at the time of measurement. Methods such as those described in Patent Documents 1 and 2 that use known functions to estimate the battery state are useful for estimating the state of individual battery cells with uniform temperatures. However, to estimate the SOH distribution within a battery system, the temperature distribution within the battery system must also be considered.

[0012] The present invention has been made in view of the above-mentioned problems, and an object of the present invention is to provide a battery state estimation device that can accurately estimate the degradation state of the entire battery system by taking into account the SOH distribution of battery cells.

[0013] Means for solving problems

[0014] The battery state estimation device of the present invention estimates the SOH of a battery cell using the correspondence between the time differential of the output voltage of the battery cell during a pause period and the battery temperature, and estimates the degradation state of the entire battery system using the SOH of multiple battery cells.

[0015] Effects of the Invention

[0016] The battery state estimation device of the present invention can reflect the temperature distribution of the battery cells in the SOH estimation result. This allows the degradation state of the entire battery system to be estimated taking into account the SOH distribution of the battery cells. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a configuration diagram of the battery state estimation device 100 according to the first embodiment.

[0018] Figure 2 This is a flowchart illustrating the procedure of the calculation unit 110 estimating the degradation state of the battery system 200 .

[0019] Figure 3 This is a graph illustrating the temporal change of battery voltage.

[0020] Figure 4 This is an example of the correspondence relationship data 121 .

[0021] Figure 5 It is a graph explaining the calculation procedure of the evaluation parameter Ea.

[0022] Figure 6 It is a graph showing the changes with time of Ea and charge-discharge cycles.

[0023] Figure 7 This is a flowchart illustrating the procedure for the calculation unit 110 to estimate the degradation state of the battery system 200 in the second embodiment.

[0024] Figure 8This is an example of the SOH distribution of each battery cell 210 in the battery system 200 .

[0025] Figure 9 This is an example of setting a lower threshold and an upper threshold for the SOH distribution.

[0026] Figure 10A The process of SOH deviating from the threshold is schematically shown.

[0027] Figure 10B The change in SOH distribution caused by SOH deviation from the threshold is schematically shown.

[0028] Figure 10C This is a schematic diagram for explaining an example of simply calculating the failure rate.

[0029] Figure 11 is an example of a failure rate curve. DETAILED DESCRIPTION

[0030] <Basic Concept of the Invention>

[0031] Typically, stationary storage battery systems use a balancing controller to even out the capacity of the entire battery system. This balancing controller can conceal the true degradation state of degraded battery cells. Furthermore, the balancing controller maintains the maximum output voltage (V_max), minimum output voltage (V_min), and average output voltage (V_ave) of each battery cell at approximately constant levels.

[0032] The inventors of the present invention have discovered that the differences between V_max, V_min, and V_ave become significant during a pause after a battery cell is discharged. Therefore, the present invention proposes estimating the degradation state of the battery system using the relationship between the battery voltage and battery temperature during the pause.

[0033] The inventors of the present invention have discovered that a battery cell's SOH is strongly correlated with its voltage conversion characteristics during a pause and the battery temperature. This voltage conversion characteristic is temperature-dependent. By using the relationship between the voltage conversion characteristics during a pause and the battery temperature, the battery cell's SOH can be estimated. The voltage conversion characteristic can be expressed as the time differential of voltage (ΔV / Δt). This can be calculated as the voltage difference between two points in time during a pause.

[0034] For example, by monitoring the SOH of the battery cell with the highest temperature and the SOH of the battery cell with the lowest temperature over time, an evaluation parameter similar to the activation energy (Ea) of the battery cell can be calculated as an evaluation indicator of the degradation state of the entire battery system. Ea can be used to classify the degradation state of the entire battery system. Details are described in Embodiment 1.

[0035] Furthermore, the SOH distribution of the battery cells can be estimated using at least two of V_max, V_min, and V_ave. The proportion of the SOH distribution that deviates from a threshold can be used to estimate the failure rate of the battery system. By determining where the estimated failure rate falls within the degradation curve, the future state of the battery system can be predicted. Details are described in Embodiment 2.

[0036] <Implementation Method 1>

[0037] Figure 1 This is a structural diagram of a battery state estimation device 100 according to Embodiment 1 of the present invention. Battery state estimation device 100 estimates the degradation state of a battery system 200. Battery system 200 is, for example, a stationary storage battery system. Battery state estimation device 100 and battery system 200 are connected via a communication line. The communication line can be either wired or wireless. For example, an appropriate communication line or a communication network such as the Internet can be used.

[0038] The battery system 200 includes battery cells 210 and a battery management unit 220. Each battery cell 210 includes a measurement circuit. The measurement circuit measures the output voltage, battery temperature, and battery current of the battery cell 210 and transmits the information to the battery management unit 220. The battery management unit 220 obtains the output voltage, battery temperature, and battery current from each battery cell 210.

[0039] The battery management unit 220 obtains the maximum voltage (V_max) of the output voltages of each battery cell 210, the minimum voltage (V_min) of the output voltages of each battery cell, and the average output voltage (V_ave) of each battery cell. The battery management unit 220 also obtains the maximum battery temperature (T_max) of each battery cell 210, the minimum battery temperature (T_min) of each battery cell 210, and the average battery temperature (T_ave) of each battery cell 210. The battery management unit 220 also obtains the total current (I_tot) of the battery system 200. I_tot can be calculated as the sum of the battery currents of each battery cell 210. The battery management unit 220 outputs measurement data 230 that describes these seven values.

[0040] The battery state estimation device 100 includes a calculation unit 110, a storage unit 120, and an output unit 130. The calculation unit 110 obtains measurement data 230 via a communication line. The storage unit 120 is a storage device that stores correspondence data 121, which will be described later. The calculation unit 110 uses the measurement data 230 and the correspondence data 121 in accordance with the procedure described later to estimate the degradation state of the battery system 200. The output unit 130 outputs the estimated result.

[0041] Figure 2 This is a flowchart illustrating the steps of the calculation unit 110 in estimating the degradation state of the battery system 200. The calculation unit 110 can execute this flowchart, for example, at each predetermined cycle. Figure 2 The following describes each step.

[0042] ( Figure 2 : Step S201)

[0043] The calculation unit 110 obtains the measurement data 230. The calculation unit 110 can determine whether the battery system 200 is in a pause period after a discharge period based on the sign of I_tot. That is, if I_tot is positive, it is a discharge period; if it is negative, it is a charge period. If I_tot is 0±α (α is an appropriate determination threshold), it is a pause period. The calculation unit 110 can use this information to determine whether it is a pause period after a discharge period. If V_ave is less than the determination threshold V_thres, the calculation unit 110 proceeds to step S202; otherwise, this flowchart ends.

[0044] ( Figure 2 : Step S202)

[0045] The calculation unit 110 sets the current time t0 to the variable time.

[0046] ( Figure 2 : Step S203)

[0047] The calculation unit 110 obtains V_max at time t0 and V_max at time (t0+t). The calculation unit 110 calculates the time differential of V_max (dV_max / dt) by dividing the difference between them by time t. The calculation unit 110 also calculates the time differentials (dV_min / dt) and (dV_ave / dt) for V_min and V_ave, respectively. Figure 3 The relationship between the time differentials is shown in FIG.

[0048] ( Figure 2 : Step S204)

[0049] The calculation unit 110 uses the time differentials obtained in S203 to refer to the correspondence data 121, thereby calculating the SOH of the corresponding battery cell 210. The battery cell 210 corresponding to V_max has SOH_min, the battery cell 210 corresponding to V_min has SOH_max, and the battery cell 210 corresponding to V_ave has SOH_ave. Figure 4 An example of the correspondence data 121 is described in FIG.

[0050] ( Figure 2 : Step S205)

[0051] The calculation unit 110 uses at least two of the three SOHs (SOH_max, SOH_min, SOH_ave) obtained in S204 to calculate the evaluation parameter Ea representing the degradation state of the battery system 200. For example, Ea can be calculated using the same consideration method as the activation energy of the battery cell 210. Figure 5 A specific example of the calculation steps is described in .

[0052] ( Figure 2 : Step S206)

[0053] The calculation unit 110 estimates the SOH region indicating the degradation state of the battery system 200 according to the evaluation parameter Ea calculated in S205. Figure 6 An example of the SOH region is illustrated in FIG.

[0054] Figure 3 This graph illustrates the temporal changes in battery voltage. The time differential of the battery voltage changes significantly during the pause period following the discharge period. Furthermore, the three time differentials dV_max / dt, dV_min / dt, and dV_ave / dt at times t to t+t0 have different values.

[0055] Figure 4 is an example of the correspondence data 121. The relationship between the time differential dV / dt of the battery voltage and the SOH can be expressed by a linear function ( Figure 4 However, the slope of the function is different for each battery temperature. Therefore, the correspondence data 121 describes the function for each battery temperature. Figure 4 The calculation unit 110 first determines the slope of the function using the battery temperature, thereby determining Figure 4 The calculation unit 110 substitutes the time differential obtained in S203 into the determined function to obtain the corresponding SOH.

[0056] The calculation unit 110 assumes that the battery cell 210 outputting V_max degrades at T_min. Therefore, the calculation unit 110 calculates SOH_max by substituting dV_max / dt into the function determined using T_min. The calculation unit 110 assumes that the battery cell 210 outputting V_min degrades at T_max. Therefore, the calculation unit 110 calculates SOH_min by substituting dV_min / dt into the function determined using T_max. The calculation unit 110 assumes that the battery cell 210 outputting V_ave degrades at T_ave. Therefore, the calculation unit 110 calculates SOH_ave by substituting dV_ave / dt into the function determined using T_ave.

[0057] Figure 5 This is a graph explaining the calculation procedure of the evaluation parameter Ea. Here, an example using a battery cell 210 having SOH_max and a battery cell 210 having SOH_min will be described.

[0058] Let ΔN_max be the number of charge-discharge cycles required for battery cell 210 with SOH_max to degrade from SOH1 to SOH2, and let ΔN_min be the number of charge-discharge cycles required for battery cell 210 with SOH_min to degrade from SOH1 to SOH2. If the activation energy of battery cell 210 is Ea, then the following equation (1) holds true based on Arrhenius's equation. k is the Boltzmann constant. T_max_ave is the average value of T_max from SOH1 to SOH2. T_min_ave is the average value of T_min from SOH1 to SOH2. Further calculating Ea from Equation 1 yields the following equation (2).

[0059] [Formula 1]

[0060]

[0061] [Formula 2]

[0062]

[0063] Equation 2 is calculated based on SOH_max and SOH_min, so it can be assumed to statistically represent the state of the entire battery system 200. Therefore, Ea in Equation 2 can be considered to be an evaluation parameter similar to activation energy, which is virtually possessed by the entire battery system 200. Therefore, in S205, the calculation unit 110 calculates Ea as a parameter for evaluating the degradation state of the battery system 200.

[0064] Figure 6 It is a graph showing the change of Ea and charge-discharge cycle over time. It is known that the activation energy of the battery cell 210 changes as the charge-discharge cycle is repeated. Figure 6 It is assumed that Ea, which indicates the degradation state of the entire battery system 200, also changes over time. Therefore, in S206, the calculation unit 110 can estimate the degradation state of the battery system 200 according to the calculation result of Ea. For example, Figure 6 As shown, the degradation state can be divided into three regions, and it can be estimated which region the battery system 200 is currently in.

[0065] <Implementation 1: Summary>

[0066] The battery state estimation device 100 of the first embodiment obtains the battery voltages and battery temperatures of two or more battery cells 210 from the measurement data 230 and uses these data to refer to the correspondence data 121 to determine the state of health (SOH) of these battery cells 210. The battery state estimation device 100 uses this SOH to estimate the degradation state of the entire battery system 200. This allows the degradation state to be estimated by taking into account the temperature characteristics of dV / dt. Furthermore, by using the SOH of two or more battery cells 210, the degradation state of the entire battery system 200 can be estimated.

[0067] The battery state estimation device 100 of the first embodiment calculates the evaluation parameter Ea based on SOH_max and SOH_min. Ea is calculated using the same method as the activation energy of the battery cell 210 and therefore represents the degradation state. Furthermore, since Ea is calculated based on SOH_max and SOH_min, it represents the state of the entire battery system 200. This allows the degradation state of the entire battery system 200 to be estimated by taking into account the temperature characteristics of the battery system 200.

[0068] <Implementation Method 2>

[0069] In the first embodiment, the degradation state of the entire battery system 200 was estimated using an evaluation parameter Ea, which is similar to activation energy. In the second embodiment of the present invention, an example is described in which, instead of using Ea, the SOH distribution of each battery cell 210 within the battery system 200 is estimated, and the failure rate of the battery system 200 is estimated based on this distribution. The configurations of the battery state estimation device 100 and the battery system 200 are the same as those in the first embodiment.

[0070] Figure 7 This is a flowchart explaining the steps of estimating the degradation state of the battery system 200 by the calculation unit 110 in the second embodiment. Figure 2 The calculation unit 110 executes S701 to S702 instead of S205 to S206.

[0071] ( Figure 7 : Step S701)

[0072] The calculation unit 110 estimates the SOH distribution of each battery cell 210 in the battery system 200 using at least two of the three SOHs (SOH_max, SOH_min, SOH_ave) obtained in S204. Figure 8 is described in .

[0073] ( Figure 7 : Step S702)

[0074] The calculation unit 110 estimates the failure rate of the battery system 200 according to the SOH distribution calculated in S701. Figures 9 to 11 is described in .

[0075] Figure 8 This is an example of the SOH distribution of each battery cell 210 in the battery system 200. The calculation unit 110 can estimate the frequency distribution (or probability distribution) of the SOH of each battery cell 210 by using at least two of the three SOHs (SOH_max, SOH_min, SOH_ave). Figure 8 , the results of estimating the distribution using three SOHs according to the following definition are shown.

[0076] Average value of SOH = SOH_ave

[0077] SOH median value = (SOH_max + SOH_min) / 2

[0078] Mode of SOH = 3 × median value - 2 × mean value (= SOH_mod)

[0079] In the above example, the SOH distribution is estimated using three SOHs, but the SOH distribution can be estimated as long as at least two SOHs are present. For example, assuming a normal SOH distribution, the SOH distribution can be estimated using SOH_max and SOH_min. Furthermore, by assuming that SOH_ave = (SOH_max + SOH_min) / 2, the SOH distribution can be estimated using either SOH_ave and SOH_max or SOH_ave and SOH_min.

[0080] Figure 9 This is an example of setting a lower and upper threshold for the SOH distribution. Calculation unit 110 sets the lower and upper thresholds for the SOH distribution, for example, according to the following equations. If the SOH distribution deviates from any of the thresholds, it can be estimated that the degradation state of the battery cell 210 corresponding to the deviation is abnormal. Cpk is a constant, for example, Cpk = 1.33.

[0081] Upper threshold = SOH_mod + (SOH_max - SOH_mod) × Cpk

[0082] Lower threshold = SOH_mod - (SOH_max - SOH_mod) × Cpk

[0083] Figure 10A The process of SOH deviating from the threshold is schematically shown. As time passes, there is a situation where any one of the three SOHs (SOH_max, SOH_min, SOH_ave) deviates from the threshold. Figure 10A The example of SOH_min deviating from the lower limit threshold is shown in FIG. Figures 8 and 9 The SOH distribution and upper and lower thresholds described in are used to determine whether the SOH deviates from the threshold for each sample.

[0084] Figure 10B Schematically shows the change of SOH distribution caused by SOH deviation from the threshold. Figure 10A As shown in FIG, when SOH_min deviates from the lower threshold, the entire SOH distribution moves toward the lower threshold, and a portion of the SOH distribution protrudes toward the region less than the lower threshold. Figure 10B The area ratio of the area (the shaded area) to the area of ​​the SOH distribution is regarded as the failure rate of the battery system 200. The calculation unit 110 calculates the failure rate of the battery system 200 by calculating this area ratio.

[0085] Figure 10C This is a diagram illustrating an example of how to simply calculate the failure rate. Figure 10B While an example of calculating the failure rate using the area of ​​the SOH distribution is shown, the failure rate calculation can be simplified by approximating the shape of the SOH distribution with a triangle. For example, the following triangle, with SOH_max to SOH_min as the base and SOH_mod as the vertex, can be considered an approximation of the SOH distribution. The calculation unit 110 can calculate the area ratio of the portion of this triangle that deviates from the threshold as the failure rate of the battery system 200.

[0086] Figure 11 This is an example of a failure rate curve. The calculation unit 110 uses the failure rate of the battery system 200 and the failure rate curve to determine the degradation state of the battery system 200 (e.g., whether it is in the wear-out failure stage). The calculation unit 110 outputs the failure rate of the battery system 200 and the degradation state determined using the failure rate from the output unit 130.

[0087] <Implementation 2: Summary>

[0088] The battery state estimation device 100 of the second embodiment estimates the frequency distribution of the SOH of each battery cell 210 using at least two of the three SOHs (SOH_max, SOH_min, and SOH_ave). The battery state estimation device 100 uses the estimated SOH distribution to estimate the degradation state of the battery system 200. When estimating the SOH distribution, the SOH is obtained by referring to the correspondence data 121. This allows the degradation state to be estimated by taking into account the temperature characteristics of dV / dt, similar to the first embodiment. Furthermore, by using the SOHs of two or more battery cells 210, the degradation state of the entire battery system 200 can be estimated.

[0089] The battery state estimation device 100 of the second embodiment calculates the failure rate of the battery system 200 by calculating the proportion of the portion of the SOH distribution that deviates from the threshold. This makes it possible to determine whether the entire battery system 200 is in the wear failure stage, etc., while taking into account the temperature characteristics of the battery system 200.

[0090] <Regarding Modifications of the Invention>

[0091] The present invention is not limited to the above-described embodiments and includes various variations. For example, the above-described embodiments are described in detail to facilitate understanding of the present invention and are not necessarily limited to having all the structures described. In addition, a portion of the structure of a certain embodiment can be replaced with the structure of another embodiment, and a structure of another embodiment can be added to the structure of a certain embodiment. In addition, for a portion of the structure of each embodiment, other structures can be added, deleted, or replaced.

[0092] While the above embodiments describe an example of calculating Ea using SOH_max and SOH_min, SOH_ave can also be used. Specifically, any combination of two of the three SOHs (SOH_max, SOH_min, and SOH_ave) can be used, or all three SOHs can be used. For example, a method is conceivable in which Ea is calculated for any combination of two of the three SOHs, Ea is calculated for the other combinations in the same manner, and the resulting Ea values ​​are averaged.

[0093] In the above embodiment, the correspondence data 121 may be pre-stored in the storage unit 120, or may be acquired from outside the battery state estimation device 100 and stored in the storage unit 120. The temporarily stored correspondence data 121 may also be updated.

[0094] In the above embodiments, the calculation unit 110 may be configured by hardware such as a circuit device having the function implemented therein, or may be configured by a calculation device such as a CPU (Central Processing Unit) executing software having the function implemented therein.

[0095] In the above embodiment, the output unit 130 can output the estimation result in any format. For example, it is possible to output data describing the estimation result and display it on a display device.

[0096] In the above embodiments, a stationary storage battery system is described as an example of battery system 200. However, the present invention is also applicable to other battery systems. For example, an on-vehicle battery system can be considered. Furthermore, secondary batteries are described as battery cells included in battery system 200. Examples of secondary batteries include lithium-ion batteries, lead-acid batteries, nickel-metal hydride batteries, and electric double-layer capacitors.

[0097] Description of Reference Signs

[0098] 100 Battery status estimation device

[0099] 110 Operation Unit

[0100] 120 Storage Department

[0101] 121 Correspondence data

[0102] 130 Output unit

[0103] 200 battery system

[0104] 210 battery cells

[0105] 220 Battery Management Department

[0106] 230 measurement data.

Claims

1. A battery state estimation device for estimating a state of a battery system having a plurality of battery cells, characterized in that: The battery state estimation device comprises: a storage unit storing correspondence data describing a correspondence between a time differential of an output voltage of the battery cell during a pause period after discharge, a temperature of the battery cell during the pause period, and a degradation state of the battery cell; as well as a calculation unit that calculates the degradation state of the battery cell using the correspondence data, The calculation unit obtains measurement data describing measurement results of the output voltages and battery temperatures of the plurality of battery cells. The calculation unit obtains a first output voltage of a first battery cell included in the battery system from the measurement result described in the measurement data, and obtains a first temperature of the first battery cell. The calculation unit obtains a second output voltage of a second battery cell included in the battery system from the measurement result described in the measurement data, and obtains a second temperature of the second battery cell. The calculation unit refers to the correspondence data using the time differential of the first output voltage and the first temperature to estimate a first degradation state of the first battery cell. The calculation unit refers to the correspondence data using the time differential of the second output voltage and the second temperature to estimate the second degradation state of the second battery cell. The calculation unit estimates the degradation state of the entire battery system by obtaining an evaluation parameter indicating the degradation state of the entire battery system using the first degradation state and the second degradation state.

2. The battery state estimation device according to claim 1, wherein The calculation unit obtains any one of the largest maximum voltage among the output voltages of the plurality of battery cells, the smallest minimum voltage among the output voltages of the plurality of battery cells, and the average voltage of the output voltages of the plurality of battery cells as the first output voltage, and obtains any one of the remaining two voltages as the second output voltage. The calculation unit uses the time differential of the first output voltage and the time differential of the second output voltage to refer to the corresponding relationship data, thereby obtaining at least two of the least degraded state among the degradation states of the multiple battery cells, the most degraded state among the degradation states of the multiple battery cells, and the average of the degradation states of the multiple battery cells, and uses these at least two degradation states to estimate the overall degradation state of the battery system.

3. The battery state estimation device according to claim 1, wherein The calculation unit obtains as the first temperature any one of the highest maximum temperature among the temperatures of the plurality of battery cells, the lowest minimum temperature among the temperatures of the plurality of battery cells, and an average temperature of the temperatures of the plurality of battery cells, and obtains as the second temperature any one of the remaining two temperatures. The calculation unit uses the first temperature and the second temperature to refer to the correspondence data, thereby obtaining at least two of the least degraded state among the degradation states of the multiple battery cells, the most degraded state among the degradation states of the multiple battery cells, and the average of the degradation states of the multiple battery cells, and uses these at least two degradation states to estimate the overall degradation state of the battery system.

4. The battery state estimation device according to claim 1, wherein The calculation unit estimates whether the battery system is in an initial degradation state, an accidental degradation state, or a wear-induced degradation state using the evaluation parameter.

5. The battery state estimation device according to claim 4, characterized in that The calculation unit obtains any one of the largest maximum voltage among the output voltages of the plurality of battery cells, the smallest minimum voltage among the output voltages of the plurality of battery cells, and the average voltage of the output voltages of the plurality of battery cells as the first output voltage, and obtains any one of the remaining two voltages as the second output voltage. The calculation unit obtains a first number of charge and discharge cycles required for the first battery cell to reach a fourth degradation state from a third degradation state, The calculation unit obtains a second number of charge and discharge cycles required for the second battery cell to reach the fourth degradation state from the third degradation state. The calculation unit calculates the evaluation parameter using the first charge and discharge cycle number and the second charge and discharge cycle number.

6. The battery state estimation device according to claim 4, characterized in that The calculation unit obtains as the first temperature any one of the highest maximum temperature among the temperatures of the plurality of battery cells, the lowest minimum temperature among the temperatures of the plurality of battery cells, and the average temperature of the temperatures of the plurality of battery cells, and obtains as the second temperature any one of the remaining two temperatures. The calculation unit calculates the evaluation parameter by assuming that the first battery cell has a time average value of the first temperature during a period in which the first battery cell reaches a fourth degradation state from a third degradation state. The calculation unit calculates the evaluation parameter assuming that the second battery cell has a time average value of the second temperature during a period in which the second battery cell reaches the fourth degradation state from the third degradation state.

7. The battery state estimation device according to claim 1, wherein The calculation unit estimates a distribution of the degradation states of the plurality of battery cells using the first degradation state and the second degradation state. The calculation unit estimates a failure rate of the battery system using the distribution.

8. The battery state estimation device according to claim 7, characterized in that The calculation unit obtains the largest maximum voltage among the output voltages of the plurality of battery cells as the first output voltage. The calculation unit obtains the smallest voltage among the output voltages of the plurality of battery cells as the second output voltage. The calculation unit obtains an average voltage of the output voltages of the plurality of battery cells as a third voltage. The calculation unit refers to the correspondence data using the time differential of the maximum voltage, thereby acquiring the most degraded state among the degraded states of the plurality of battery cells as the first degraded state. The calculation unit refers to the correspondence data using the time differential of the minimum voltage, thereby obtaining the least degraded state among the degraded states of the plurality of battery cells as the second degraded state. The calculation unit refers to the correspondence data using the time differential of the average voltage, thereby obtaining an average of the degradation states of the plurality of battery cells as a third degradation state. The calculation unit estimates the distribution using the first degradation state, the second degradation state, and the third degradation state.

9. The battery state estimation device according to claim 8, characterized in that The calculation unit estimates the distribution by calculating at least a mode of the distribution using the first degradation state, the second degradation state, and the third degradation state.

10. The battery state estimation device according to claim 7, wherein The calculation unit calculates an upper limit allowable value and a lower limit allowable value of the degradation state of the battery cell using the distribution. The calculation unit estimates a failure rate of the battery system according to a ratio of battery cells whose values ​​exceed the upper limit allowable value or battery cells whose values ​​fall below the lower limit allowable value among the plurality of battery cells.

11. The battery state estimation device according to claim 10, characterized in that The calculation unit calculates a first area of ​​a portion of the distribution exceeding the upper limit allowable value and a second area of ​​a portion of the distribution below the lower limit allowable value, The calculation unit calculates a ratio of battery cells exceeding the upper limit allowable value or battery cells below the lower limit allowable value among the plurality of battery cells using a ratio of the first area to the distributed area and a ratio of the second area to the distributed area.

12. The battery state estimation device according to claim 1, wherein The battery state estimation device further includes an output unit that outputs an estimation result of the degradation state of the battery system.

13. The battery state estimation device according to claim 1, wherein The battery system is a stationary battery system.

14. The battery state estimation device according to claim 1, wherein The computing unit acquires the measurement data from the battery system through communication.

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