Battery Diagnostic System

Abstract

A battery diagnostic system (100) for estimating the SOH which indicates the degree of deterioration of a secondary battery (110) comprises a model unit (172), an SOH calculation unit (173), and an SOH estimation unit (174). The model unit acquires usage history data which indicates the usage state of the secondary battery, and calculates the SOH on the basis of the usage history data. The SOH calculation unit acquires, as sensing data, a physical quantity which changes according to the degree of deterioration of the secondary battery, and calculates the SOH on the basis of the sensing data. On the basis of the SOH which has been calculated by the model unit and the SOH which has been calculated by the SOH calculation unit, the SOH estimation unit estimates an optimized SOH by synthesizing both of the calculation results.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on Japanese Patent Application No. 2021-214442 filed on December 28, 2021, the contents of which are incorporated herein by reference. [Technical field]

[0002] The present disclosure relates to a battery diagnostic system. [Background technology]

[0003] Conventionally, a remaining life assessment method for a secondary battery module has been proposed in, for example, Patent Document 1. Specifically, a remaining life assessment device acquires charging information of the secondary battery module from a charger, and calculates the deterioration degree of the secondary battery module as an actual measurement value based on the charging information. Here, the deterioration degree is the current full charge capacity relative to the battery capacity of a new battery. The deterioration degree is SOH (State of Health). In addition, the remaining life assessment device acquires output information of the secondary battery module, and calculates a predicted value of the deterioration degree by a prediction formula using the output information.

[0004] The remaining life assessment device then compares the actual measurement value with the predicted value, and calculates the remaining life if the difference between the actual measurement value and the predicted value is equal to or less than a predetermined value. If the difference between the actual measurement value and the predicted value exceeds the predetermined value, the remaining life assessment device corrects the prediction formula based on the actual measurement value. The remaining life assessment device re-calculates the predicted value using the corrected prediction formula, and calculates the remaining life if the difference between the actual measurement value and the predicted value is equal to or less than the predetermined value. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2020-119658 A Summary of the Invention

[0006] However, in the above-mentioned conventional technology, the actual value of the deterioration degree of the secondary battery module calculated by the remaining life assessment device is obtained based on the section capacity measurement by current integration, and therefore the actual value includes sensing error in the charger and logic error in the calculation process, making it difficult to predict the remaining life with high accuracy.

[0007] In view of the above, an object of the present disclosure is to provide a battery diagnostic system capable of improving the accuracy of estimating the SOH of a secondary battery.

[0008] According to the first and second aspects of the present disclosure, a battery diagnostic system estimates a SOH that indicates a degree of deterioration of a secondary battery.

[0009] In a first aspect, a battery diagnostic system includes a model unit, a SOH calculation unit, and a SOH estimation unit.

[0010] The model unit acquires usage history data indicating the usage state of the secondary battery and calculates the SOH based on the usage history data. The SOH calculation unit acquires a physical quantity that changes according to the degree of deterioration of the secondary battery as sensing data and calculates the SOH based on the sensing data. The SOH estimation unit combines the results of the SOH calculated by the model unit and the SOH calculated by the SOH calculation unit to estimate an optimal SOH. The sensing data is the impedance data of the secondary battery obtained by electrochemical impedance spectroscopy. The SOH calculation unit calculates the SOH by Gaussian process regression using the impedance data as input. The battery diagnostic system further includes a memory unit (150) in which information on a specific frequency within the frequency range used in electrochemical impedance spectroscopy is stored in advance. The SOH calculation unit calculates the SOH using the imaginary component of the impedance corresponding to the specific frequency stored in the memory unit, among the impedance data.

[0011] According to this, both the error caused by the cell variation of the secondary battery generated in the model unit and the sensing error generated in the SOH calculation unit are optimized in the SOH estimation unit. Therefore, it is possible to reduce the influence of the sensing error of the SOH calculated in the SOH calculation unit. Therefore, it is possible to improve the estimation accuracy of the SOH of the secondary battery.

[0012] In a second aspect, a battery diagnostic system includes a data acquisition unit, a data processing unit, and a calculation unit.

[0013] The data acquisition unit acquires time-series data indicating a usage state of the secondary battery. The data processing unit acquires the time-series data from the data acquisition unit and processes the time-series data as histogram data.

[0014] The calculation unit calculates the SOH as an estimated value based on a preset calculation model using either the time series data acquired by the data acquisition unit or the histogram data acquired by the data processing unit. The histogram data includes parameters of the secondary battery, such as the SOC, temperature, current, and ΔDOD. The calculation unit calculates the SOH using each parameter and a product of two or more of the parameters.

[0015] According to this method, the SOH is estimated using either the time series data or the histogram data of the secondary battery. Therefore, it is possible to reduce the inclusion of errors such as sensing errors compared to the current integration method. Therefore, it is possible to improve the estimation accuracy of the SOH of the secondary battery. [Brief description of the drawings]

[0016] The above and other objects, features and advantages of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: [Figure 1] FIG. 1 is a diagram showing a configuration of a battery diagnosis system according to a first embodiment; [Diagram 2] FIG. 2 is a diagram showing a pre-processing for acquiring a specific frequency in advance and a process for calculating SOH using the specific frequency; [Diagram 3] FIG. 3 is a diagram showing the relationship between the imaginary component of impedance Zimage and SOH, and the correlation with frequency. [Figure 4] FIG. 4 is a diagram showing specific frequencies versus the number of dimensions. [Diagram 5] FIG. 5 shows the errors of the training data, cross-validation data, and validation data for each number of dimensions. [Figure 6] FIG. 6 is a graph showing the real component Zreal and the imaginary component Zimage of the impedance measured by the impedance generator plotted for each frequency. [Figure 7]FIG. 7 is a diagram showing the estimated SOH value of the SOH calculation unit and the actual measured SOH value when the secondary battery is charged and discharged at a temperature of 45° C. and an SOC between 30% and 90%. [Figure 8] FIG. 8 is a diagram showing an estimated SOH value of the SOH calculation unit and an actual measured SOH value when the secondary battery is charged and discharged at a temperature of 10° C. and an SOC between 10% and 90%. [Figure 9] FIG. 9 is a diagram showing the errors of the calculation results of the SOH estimation unit, the SOH calculation unit, and the model unit with respect to the actual SOH measurement value for degradation conditions A, B, and C. [Figure 10] FIG. 10 is a diagram showing the calculation results of the SOH estimation unit, the SOH calculation unit, and the model unit and the actual measured value of SOH for deterioration condition A. [Figure 11] FIG. 11 is a diagram showing the calculation results of the SOH estimation unit, the SOH calculation unit, and the model unit and the actual measurement value of SOH for degradation condition B. [Figure 12] FIG. 12 is a diagram showing the calculation results of the SOH estimation unit, the SOH calculation unit, and the model unit and the actual measurement value of SOH for degradation condition C. [Figure 13] FIG. 13 is a diagram showing a flow of pre-processing and calculation of sensing data according to the second embodiment; [Figure 14] FIG. 14 is a diagram showing a flow of pre-processing and calculation of sensing data according to the third embodiment; [Figure 15] FIG. 15 is a diagram showing a configuration of a battery diagnosis system according to a fourth embodiment; [Figure 16] FIG. 16 is a diagram showing a flow of calculating the SOH according to the fourth embodiment. [Figure 17] FIG. 17 is a diagram showing the accuracy of the calculation result when the product of parameters is included in the calculation of SOH. [Figure 18] FIG. 18 is a diagram showing the accuracy of the calculation result when the product of parameters is not included in the calculation of the SOH. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Hereinafter, a number of embodiments for carrying out the present disclosure will be described with reference to the drawings. In each embodiment, the same reference numerals may be used to designate parts corresponding to matters described in the preceding embodiment, and duplicated descriptions may be omitted. In each embodiment, when only a part of the configuration is described, other embodiments described previously may be applied to other parts of the configuration. In addition to combinations of parts that are specifically indicated as being possible in each embodiment, it is also possible to partially combine embodiments even if not indicated, as long as there is no particular problem with the combination.

[0018] (First embodiment) DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS A first embodiment will now be described with reference to the drawings. A battery diagnostic system according to this embodiment is a system for estimating the SOH, which indicates the degree of deterioration of a secondary battery.

[0019] 1, the battery diagnostic system 100 includes a secondary battery 110, a temperature sensor 120, a current sensor 121, a voltage sensor 122, and a data acquisition unit 130. The battery diagnostic system 100 also includes an impedance generator 140, a storage unit 150, a specific frequency calculation unit 160, and an arithmetic unit 170.

[0020] The secondary battery 110 constitutes a battery module in which a number of battery cells are connected in series. Each battery cell is, for example, a lithium-ion secondary battery. The secondary battery 110 constitutes a power source for an electrically powered vehicle such as an electric car or a hybrid car. The battery module also includes a configuration in which the battery cells are connected in parallel.

[0021] The temperature sensor 120 measures the temperature of the secondary battery 110. The temperature sensor 120 is installed in the secondary battery 110. The current sensor 121 measures a current value of the secondary battery 110. The current sensor 121 is connected to the secondary battery 110. The voltage sensor 122 measures a voltage value of the secondary battery 110. The voltage sensor 122 is connected to the secondary battery 110. Each of the sensors 120 to 122 outputs a detection signal to the data acquisition unit 130 at any time.

[0022] The data acquiring unit 130 periodically acquires data on the temperature, current value, and voltage value of the secondary battery 110. To this end, the data acquiring unit 130 includes a temperature acquiring unit 131, a current value acquiring unit 132, and a voltage value acquiring unit 133.

[0023] The temperature acquisition unit 131 periodically acquires information on the temperature T of the secondary battery 110 measured by the temperature sensor 120. For example, the temperature acquisition unit 131 calculates the temperature T from the distribution of the temperatures of the secondary battery 110 acquired over a certain period of time. For example, the temperature T can be an average value calculated from the frequency distribution of the temperatures of the secondary battery 110 acquired over a certain period of time. The temperature acquisition unit 131 outputs the information on the temperature T of the secondary battery 110 to the calculation unit 170.

[0024] In order to reduce the calculation load, it is also possible to use, as the temperature T, an average value of the temperatures of the secondary battery 110 acquired over a certain period of time, or the like.

[0025] The current value acquiring unit 132 periodically acquires information on the current I of the secondary battery 110 measured by the current sensor 121. For example, the current value acquiring unit 132 calculates the current I from a distribution of the current of the secondary battery 110 acquired over a certain period of time. For example, the current I can be an average value calculated from a frequency distribution of the current of the secondary battery 110 acquired over a certain period of time. The current value acquiring unit 132 outputs the information on the current I of the secondary battery 110 to the calculation unit 170.

[0026] In order to reduce the calculation load, the current I may be an average value of the current of the secondary battery 110 acquired over a certain period of time.

[0027] The voltage value acquisition unit 133 periodically acquires information on the voltage V of the secondary battery 110 measured by the voltage sensor 122. For example, the voltage V can be an average value calculated from a frequency distribution of the voltage values ​​of the secondary battery 110 acquired over a certain period of time. The voltage value acquisition unit 133 outputs the information on the voltage V of the secondary battery 110 to the calculation unit 170.

[0028] In order to reduce the calculation load, the voltage V may be an average value of the voltage of the secondary battery 110 acquired over a certain period of time.

[0029] In addition, the data acquisition unit 130 stores information on temperature T acquired by the temperature acquisition unit 131, information on current I acquired by the current value acquisition unit 132, and information on voltage V acquired by the voltage value acquisition unit 133 in the memory unit 150 as usage history data indicating the usage state of the secondary battery 110.

[0030] The usage history data includes time series data and histogram data. The time series data includes data on the temperature T, SOC, voltage V, and current I of the secondary battery 110. The histogram data is data obtained by processing the time series data into a histogram. The SOC is acquired by the calculation unit 170, which will be described later.

[0031] The impedance generating device 140 is a device that acquires the impedance of the secondary battery 110 by electrochemical impedance spectroscopy (EIS). The impedance is a physical quantity that changes according to the degree of deterioration of the secondary battery 110. The impedance data EIS is sensing data measured by the impedance generating device 140. The impedance generating device 140 has a superimposed current applying unit 141 and an impedance measuring unit 142.

[0032] The superimposed current application unit 141 applies a superimposed current in which a plurality of frequency components are superimposed to the secondary battery 110. By using the superimposed current, it is possible to collectively acquire the battery voltage when currents of a plurality of frequencies are applied to the secondary battery 110.

[0033] For example, a multiple sine wave can be used as the superimposed current. A square wave, a sawtooth wave, or a triangular wave can also be used as the superimposed current. Here, the current value of the harmonics of the fundamental frequency as the superimposed frequency is significantly reduced as the order increases, whereas the multiple sine wave does not reduce. For this reason, by using a multiple sine wave as the superimposed current, high measurement accuracy can be maintained. In the multiple sine wave, the frequency to be superimposed is not particularly limited and can be set arbitrarily.

[0034] The impedance measuring unit 142 acquires the current value of the superimposed current applied to the secondary battery 110 by the superimposed current application unit 141. The impedance measuring unit 142 also acquires a response voltage when the superimposed current is applied to the secondary battery 110. Therefore, the impedance is a value calculated by measuring a response voltage corresponding to the AC current applied to the secondary battery 110, and then dividing the response voltage as a complex number having absolute value and phase information by the AC current. In other words, the impedance includes a real component Zreal and an imaginary component Zimage.

[0035] Specifically, the impedance measuring unit 142 calculates the impedance of the secondary battery 110 for each of a plurality of frequency components by using a discrete Fourier transform. The current value and voltage value when the superimposed current is applied can be the detection values ​​of the current sensor 121 and the voltage sensor 122. As the discrete Fourier transform, a fast discrete Fourier transform (FFT) can be adopted.

[0036] The impedance generating device 140 outputs the calculated impedance for each of the multiple frequency components to the calculation unit 170. The impedance generating device 140 may store impedance data in the storage unit 150.

[0037] The impedance generating device 140 can be configured, for example, by utilizing a power conversion device that constitutes an in-vehicle power control unit. This eliminates the need to separately provide the impedance generating device 140 including a superimposed current generating unit. In addition, a large superimposed current can be generated. This allows the device to be configured as suitable for on-board diagnosis of the in-vehicle secondary battery 110. Alternatively, the device can be configured such that the superimposed current generating unit is disposed in an in-vehicle charging device (not shown) or an external charging device.

[0038] The specific frequency calculation unit 160 is a device for acquiring in advance, by electrochemical impedance spectroscopy, information on a specific frequency required for calculating an optimal SOH of the secondary battery 110. The specific frequency calculation unit 160 may or may not be mounted on the vehicle.

[0039] That is, the specific frequency is a frequency determined by machine learning using the impedance data EIS previously acquired of the secondary battery 110. In addition, the specific frequency is a frequency that has a large effect on the SOH of the secondary battery 110.

[0040] The optimal SOH is the SOH finally estimated by the calculation unit 170. The degree of influence of the secondary battery 110 on the SOH corresponds to the strength of correlation between the imaginary component Zimage of the impedance and the SOH. The specific frequency is, for example, a specific frequency in a frequency range greater than 1 Hz, preferably a frequency range greater than 10 Hz.

[0041] The configuration of the secondary battery 110 differs depending on the electric vehicle in which it is mounted. Therefore, the characteristics of the secondary battery 110 differ depending on, for example, the vehicle model. Therefore, the specific frequency differs depending on the configuration of the secondary battery 110. The specific frequency calculation unit 160 is used to acquire a specific frequency corresponding to the secondary battery 110 mounted on the electric vehicle. A method of acquiring the specific frequency will be described later.

[0042] The storage unit 150 is, for example, a rewritable non-volatile memory. The storage unit 150 stores programs for controlling the data acquisition unit 130, the impedance generator 140, and the calculation unit 170. The storage unit 150 also stores usage history data input from the data acquisition unit 130 and the calculation unit 170 as needed.

[0043] Furthermore, the storage unit 150 stores information on a plurality of specific frequencies within the frequency range used in the measurement of electrochemical impedance spectroscopy in the impedance generating device 140. The information on the plurality of specific frequencies is input in advance from the specific frequency calculation unit 160.

[0044] The calculation unit 170 estimates an optimal SOH of the secondary battery 110. The calculation unit 170 is configured by a device such as a processor. The calculation unit 170 has a SOC calculation unit 171, a model unit 172, a SOH calculation unit 173, and a SOH estimation unit 174.

[0045] The SOC calculation unit 171 calculates a charging rate indicating the remaining battery capacity of the secondary battery 110. The charging rate of the secondary battery 110 is expressed as a percentage, which is the ratio of the remaining capacity to the full charge capacity of the secondary battery 110. The charging rate of the secondary battery 110 is SOC (State Of Charge).

[0046] For example, the SOC calculation unit 171 calculates an integrated value of the current value of the secondary battery 110 acquired by the current value acquisition unit 132, and calculates the charging rate of the secondary battery 110 based on the integrated value. Information on the SOC calculated by the SOC calculation unit 171 is stored in the storage unit 150 and is output to the SOH calculation unit 173.

[0047] Model unit 172 acquires usage history data of secondary battery 110 from storage unit 150. Model unit 172 also calculates the SOH by applying the usage history data to a theoretical formula, which is a preset calculation model. Model unit 172 outputs the calculated SOH to SOH estimation unit 174.

[0048] The SOH calculation unit 173 acquires impedance data EIS as sensing data from the impedance generator 140. The SOH calculation unit 173 converts the impedance data EIS into data at a predetermined temperature and a predetermined SOC using a temperature conversion model and an SOC conversion model. The predetermined temperature is, for example, 25° C. The predetermined SOC is, for example, 50%. This makes it possible to calculate an SOH that is independent of the environment in which the secondary battery 110 is placed and the state of the secondary battery 110.

[0049] The SOH calculation unit 173 does not use all the impedance data EIS corresponding to the measurement frequency, but uses the impedance data EIS corresponding to a plurality of specific frequencies stored in the storage unit 150. That is, the SOH calculation unit 173 calculates the SOH based on machine learning using the imaginary component Zimage of the impedance corresponding to a plurality of specific frequencies among the impedance data EIS as input. This makes it possible to reduce the number of input data used by the SOH calculation unit 173. Therefore, the calculation load of the SOH calculation unit 173 can be reduced.

[0050] Specifically, the SOH calculation unit 173 calculates the SOH by Gaussian Process Regression (GPR) using the impedance data EIS as an input as a machine learning method. GPR is one of the models that estimates a predicted value using current and past states as input values. By not using the real component Zreal of the impedance, which has a large measurement error, the estimation accuracy of the SOH calculated by the SOH calculation unit 173 is improved. In addition, since the machine learning method is used, the estimation accuracy of the SOH is improved compared to the current integration method. The SOH calculation unit 173 outputs the calculated SOH to the SOH estimation unit 174.

[0051] Based on the SOH calculated by model unit 172 and the SOH calculated by SOH calculation unit 173, SOH estimation unit 174 combines the results of both calculations to estimate an optimal SOH. Specifically, SOH estimation unit 174 corrects the SOH calculated by model unit 172 with the SOH calculated by SOH calculation unit 173. SOH estimation unit 174 calculates the degree of correction based on the variance of the SOH calculated by model unit 172 and the variance of noise in SOH calculation unit 173, and estimates the final SOH.

[0052] The SOH estimation unit 174 obtains an optimal SOH estimation result, for example, several times a day or once a day. Of course, the optimal SOH estimation frequency is not limited to these, and a necessary frequency is appropriately set.

[0053] Specifically, the SOH estimation unit 174 estimates the optimal SOH using a non-linear Kalman filter. The non-linear Kalman filter is preferably an extended Kalman filter. The above is the overall configuration of the battery diagnosis system 100 according to this embodiment.

[0054] Next, a description will be given of the operation of calculation unit 170. First, model unit 172 calculates the SOH based on the usage history data stored in storage unit 150, and outputs the calculated SOH to SOH estimation unit 174.

[0055] Moreover, the SOH calculation unit 173 calculates the SOH based on the impedance input from the impedance generation device 140. Here, the SOH calculation unit 173 calculates the SOH using information on a plurality of specific frequencies stored in the storage unit 150. As shown in Fig. 2, the information on the plurality of specific frequencies is acquired in advance in preprocessing.

[0056] For example, the secondary battery 110 has a capacity of 50 Ah and is configured as NCM622 / Gr. The configuration of the secondary battery 110 when acquiring information on a specific frequency in advance in preprocessing is the same as the configuration of the secondary battery 110 employed in the battery diagnosis system 100.

[0057] In the first process, the correlation between the SOH of each specific frequency in N dimensions and the imaginary component Zimage of the impedance is calculated. For this purpose, the secondary battery 110 is degraded in advance under various conditions. The degradation conditions include, for example, storage at different temperatures and SOCs, and repeated charging and discharging at different temperatures, median SOCs, and ΔDODs. In addition, the transition of the SOH and the imaginary component Zimage of the impedance until the life of the secondary battery 110 is exhausted are acquired as data.

[0058] It should be noted that DOD (Depth Of Discharge) indicates the depth of discharge of the secondary battery 110. ΔDOD is calculated, for example, from the difference between the SOC at the start and end of charging and discharging.

[0059] This allows the correlation between the relationship between the imaginary component of impedance Zimage and SOH and a certain range of frequencies to be obtained, as shown in Figure 3. The horizontal axis in Figure 3 is a logarithmic scale. The larger the value showing the relationship between the imaginary component of impedance Zimage and SOH, the higher the importance.

[0060] Here, if all frequencies within a certain range are used to estimate the SOH, overfitting may occur, and the error in the extrapolation region will increase. Therefore, the larger the number of frequencies, i.e. the number of dimensions, the better. Therefore, using the data shown in Figure 3, SISSO, a type of machine learning, is used to calculate combinations of specific frequencies up to N dimensions. In other words, it is decided which frequencies within a certain range will be used to estimate the SOH. The frequencies determined in this way become the specific frequencies. By specifying only a few frequencies to be used to estimate the SOH, the versatility of the specific frequencies can be increased.

[0061] The above machine learning leads to a combination of the number of dimensions and the specific frequency as shown in FIG. 4. In the case of two dimensions, the specific frequencies are determined to be two, f21 and f22. The two frequencies correspond to two frequencies among the correlation lines shown in FIG. 3. Similarly, in the case of three dimensions, the specific frequencies are determined to be three, f31, f32, and f33, which correspond to three frequencies among the correlation lines shown in FIG. 3. In the cases of four dimensions and five dimensions, multiple frequencies are determined in the same manner.

[0062] Next, as shown in Figure 2, the accuracy of SOH is calculated when the imaginary component Zimage of the impedance corresponding to each specific frequency is input. The results are shown in Figure 5.

[0063] In Fig. 5, the training data is the data actually used for machine learning. The cross-validation data is data in which, for example, data for one type of degradation condition is removed from all data for multiple types of degradation conditions and used as training data, and the removed data is used as validation data, and machine learning is performed by sequentially changing all the multiple types of data into validation data. The validation data is unknown data that is not used for machine learning. The RMSE indicates the root mean square error (%) of each data against the measured SOH value.

[0064] The actual measured value of SOH is calculated as (current battery capacity / initial battery capacity) x 100(%) when the temperature of secondary battery 110 is 25°C, the SOC of secondary battery 110 is charged and discharged between 0%-100%, and the current of secondary battery 110 is C / 3.

[0065] Then, the number of dimensions that minimizes the error of each data with respect to the actual measurement value of SOH is calculated. In the example shown in FIG. 5, the error is minimized in the case of four dimensions. In this case, the number of specific frequencies is four. In this manner, the specific frequency calculation unit 160 determines the number of specific frequencies and the frequencies. After this, the specific frequency calculation unit 160 stores the number of specific frequencies and information on the frequencies in the storage unit 150. In this manner, the preprocessing is completed.

[0066] The SOH calculation unit 173 executes the calculation flow shown in Fig. 2 using the information of the four specific frequencies acquired in advance as described above. To this end, the SOH calculation unit 173 first acquires the impedance data EIS measured by the impedance generator 140. As shown in Fig. 6, the real component Zreal and the imaginary component Zimage of the impedance change for each frequency.

[0067] The SOH calculation unit 173 converts the impedance data EIS into data at, for example, a temperature of 25° C. and an SOC of 50% using a temperature conversion model and an SOC conversion model. This makes it possible to calculate the SOH at any temperature or SOC.

[0068] Next, SOH calculation unit 173 requests information on the four specific frequencies from storage unit 150 and acquires the information on the four specific frequencies from storage unit 150. In addition, SOH calculation unit 173 extracts, as input, imaginary components Zimage corresponding to the four specific frequencies from the impedance data group shown in FIG.

[0069] Thereafter, the SOH calculation unit 173 calculates the SOH by GPR using the four imaginary components Zimage of the impedance as input. The SOH calculation unit 173 outputs the calculated SOH to the SOH estimation unit 174.

[0070] The inventors calculated the SOH by the SOH calculation unit 173 when the temperature of the secondary battery 110 was set to 45° C. and a charge / discharge cycle was repeated multiple times between 30% and 90% SOC. The results are shown in Fig. 7. The horizontal axis in Fig. 7 represents the number of days. As shown in Fig. 7, the estimated SOH value by GPR was close to the actually measured SOH value.

[0071] The inventors also calculated the SOH by SOH calculation unit 173 when the temperature of secondary battery 110 was set to 10° C. and a charge / discharge cycle with an SOC between 10% and 90% was repeated multiple times. The results are shown in Fig. 8. The horizontal axis in Fig. 8 represents the number of days. As shown in Fig. 8, even when secondary battery 110 was placed in a cold environment, the estimated SOH value by GPR did not deviate significantly from the actual measured SOH value.

[0072] The SOH estimation unit 174 estimates an optimal SOH by an extended Kalman filter using the SOH calculated by the model unit 172 and the SOH calculated by the SOH calculation unit 173 as described above. Hereinafter, the optimal SOH will be referred to as an optimized SOH. The SOH estimation unit 174 outputs the optimized SOH to an external device. The external device uses the acquired optimized SOH for displaying to a user and for controlling charging and discharging the secondary battery 110, etc.

[0073] The inventors compared the calculation results of model unit 172, SOH calculation unit 173, and SOH estimation unit 174 under a plurality of degradation conditions with the actual measured SOH values. The results are shown in FIG.

[0074] Deterioration condition A is a case where the temperature of the secondary battery 110 is 45° C., and multiple cycles of charging and discharging the SOC between 0% and 100% are repeated. Deterioration condition B is a case where the temperature of the secondary battery 110 is 45° C., and multiple cycles of charging and discharging the SOC between 30% and 90% are repeated. Deterioration condition C is a case where the temperature of the secondary battery 110 is 10° C., and multiple cycles of charging and discharging the SOC between 10% and 90% are repeated. The charging current and discharging current for each of the deterioration conditions A, B, and C are 0.3 C and 1 C, respectively. The method of measuring the actual SOH value is the same as above.

[0075] 9 and 10, under degradation condition A, the error in the SOH calculation result by model unit 172 was 0.7%, with the maximum error being 2.6%. The error in the SOH calculation result by SOH calculation unit 173 was 1.2%, with the maximum error being 4.5%.

[0076] In contrast, the error in the calculation result of the optimized SOH by SOH estimation unit 174 was 0.3%, and the maximum error was 1.2%. Clearly, the optimized SOH by SOH estimation unit 174 was closer to the actual measured SOH value than the calculation results by model unit 172 and SOH calculation unit 173.

[0077] Under degradation condition B, as shown in FIGS. 9 and 11, the error in each calculation result was originally small, but the optimized SOH by SOH estimator 174 became even closer to the actual measured SOH value.

[0078] Under degradation condition C, as shown in FIGS. 9 and 12, the difference between the error of the optimized SOH of SOH estimation section 174 and the errors of the calculation results of model section 172 and SOH calculation section 173 became large.

[0079] It is considered that, under degradation condition C, abnormal degradation occurred due to lithium deposition occurring in the secondary battery 110 after 400 days had passed. For this reason, the SOH of the secondary battery 110 dropped sharply. The calculation results of the model unit 172 could not follow the sudden drop in SOH. However, since the SOH calculation unit 173 calculates the SOH using the imaginary component Zimage of the impedance, which is the sensing data, the calculation results of the SOH calculation unit 173 could follow the sudden drop in SOH. In other words, it can be said that the accuracy of the estimated value of SOH can be improved by the SOH calculation unit 173 alone.

[0080] As described above, in this embodiment, the calculation results of model unit 172 and SOH calculation unit 173 are combined and optimized in SOH estimation unit 174. Specifically, SOH estimation unit 174 uses the SOH calculated by model unit 172 as a base and corrects the SOH of model unit 172 with the SOH calculated by SOH calculation unit 173 as an actual measurement value, thereby estimating the final SOH.

[0081] As a result, both the error caused by cell variation of secondary battery 110, which occurs in model unit 172, and the sensing error caused in SOH calculation unit 173 can be optimized by SOH estimation unit 174. That is, it is possible to reduce the influence of the sensing error on the SOH calculated by SOH calculation unit 173. Therefore, it is possible to improve the estimation accuracy of the optimized SOH of secondary battery 110.

[0082] As a modified example, the battery diagnosis system 100 may employ only the calculation result of the SOH calculation unit 173 as the estimated value of the SOH. The SOH calculation unit 173 acquires a physical quantity that changes according to the degree of deterioration of the secondary battery 110 as sensing data, and estimates the SOH based on the sensing data. This makes it possible to reduce the presence of errors such as sensing errors compared to the current integration method. This makes it possible to improve the estimation accuracy of the SOH of the secondary battery 110.

[0083] Second embodiment In this embodiment, the description will be mainly focused on the differences from embodiment 1. In this embodiment, the SOH calculation unit 173 calculates the SOH using the voltage change during charging of the secondary battery 110 as sensing data.

[0084] 13, in preprocessing, the secondary battery 110 is degraded under various degradation conditions in advance to obtain the transition of the SOH up to the end of the life of the secondary battery 110. In addition, the voltage change during charging under the degradation conditions is obtained and stored in the storage unit 150 in advance.

[0085] The voltage change is, for example, a change in voltage value in the range from 3.6V to 3.7V. The voltage range of 3.6V-3.7V is a region in which the voltage value changes significantly when the secondary battery 110 deteriorates. After extensive research, the present inventors have discovered that the voltage change and the SOH are correlated in this voltage range. In other words, the voltage value is a physical quantity that changes according to the degree of deterioration of the secondary battery 110.

[0086] After the above pre-processing is completed, for example, the secondary battery 110 is charged at a charging station. The SOH calculation unit 173 acquires the voltage change of the secondary battery 110 acquired by the data acquisition unit 130 via the storage unit 150.

[0087] Furthermore, SOH calculation unit 173 extracts the voltage change of 3.6 V-3.7 V from the voltage changes acquired by data acquisition unit 130. Then, SOH calculation unit 173 calculates the SOH by GPR using the voltage change of 3.6 V-3.7 V as an input. In this manner, the SOH can also be calculated using the voltage change during charging of secondary battery 110 as sensing data.

[0088] As a modified example, the voltage change may be a change in the voltage value in the range from 4.0 V to 4.1 V. The accuracy of estimating the SOH can be improved in this voltage range as well. Of course, the voltage range is not limited to the range of 3.6 V-3.7 V and the range of 4.0 V-4.1 V, and other voltage ranges may be set.

[0089] Third embodiment In this embodiment, the differences from the first and second embodiments will be mainly described. In this embodiment, the SOH calculation unit 173 calculates the SOH using the amount of voltage change in the charging voltage relaxation as sensing data.

[0090] 14, in preprocessing, the secondary battery 110 is degraded under various degradation conditions in advance to obtain a transition in SOH up to the end of the life of the secondary battery 110. In addition, a voltage relaxation response after charging under the degradation conditions is obtained and stored in the storage unit 150 in advance.

[0091] The voltage relaxation response is, for example, the amount of voltage change over 10 minutes. After extensive research, the inventors have found that the voltage relaxation response after charging correlates with the SOH. In other words, the amount of voltage change in the voltage relaxation response is a physical quantity that changes according to the degree of deterioration of the secondary battery 110.

[0092] After the above pre-processing is completed, for example, the secondary battery 110 is charged at a charging station and is left stationary for 10 minutes or more. The SOH calculation unit 173 acquires the amount of voltage change of the secondary battery 110 acquired by the data acquisition unit 130 via the storage unit 150.

[0093] Furthermore, SOH calculation unit 173 extracts the amount of voltage change over 10 minutes from the voltage changes acquired by data acquisition unit 130. Then, SOH calculation unit 173 calculates the SOH by GPR using the amount of voltage change over 10 minutes as an input. In this manner, the SOH can also be calculated using the amount of voltage change during charging of secondary battery 110 as sensing data.

[0094] (Fourth embodiment) In this embodiment, differences from the above-mentioned embodiments will be mainly described. As shown in Fig. 15, a battery diagnosis system 100 according to this embodiment includes a secondary battery 110, a data acquisition unit 130, a data processing unit 180, a storage unit 150, and a calculation unit 170.

[0095] The data processing unit 180 acquires the time series data from the data acquiring unit 130, and processes the time series data as histogram data. Note that the histogram data may be processed by the data acquiring unit 130.

[0096] The data processing unit 180 includes an SOC calculation unit 181 and a parameter calculation unit 182. The SOC calculation unit 181 has the same function as the SOC calculation unit 171 shown in the first embodiment.

[0097] The parameter calculation unit 182 receives time series data of the secondary battery 110 and processes the time series data as histogram data. The histogram data includes the parameters of the SOC, temperature T, current I, and ΔDOD of the secondary battery 110.

[0098] Furthermore, the parameter calculation unit 182 calculates a product of preset parameters. The product of parameters includes at least one of SOC×T, ΔDOD×T, and I×ΔDOD. The parameter calculation unit 182 stores the product of parameters in the storage unit 150. When histogram data is used to estimate the SOH, it is not necessary to record time series data.

[0099] After extensive investigation, the inventors have found that, by including SOC×T, ΔDOD×T, and I×ΔDOD in the calculation model of the model unit 172, it is possible to predict SOH with high accuracy even when histogram data is used as input. Note that the C rate can also be used instead of the current I.

[0100] The calculation unit 170 has a model unit 172. The model unit 172 calculates the SOH as an estimated value using either the time series data or the histogram data based on a preset calculation model. When the calculation unit 170 uses the histogram data, it calculates the SOH using each parameter and a product of two or more parameters from among the each parameter. The above is the configuration of the battery diagnosis system 100 according to this embodiment.

[0101] Next, a flow of estimating SOH using histogram data will be described. As shown in Fig. 16, first, the data acquisition unit 130 acquires vehicle data such as time-series data. Next, the data processing unit 180 calculates the product of parameters in the parameter calculation unit 182. Thereafter, the parameter calculation unit 182 stores the calculated product of parameters in the storage unit 150.

[0102] Then, model unit 172 of calculation unit 170 calculates SOH using a preset model with the histogram data stored in storage unit 150 as input. That is, model unit 172 calculates SOH by using parameters and products of parameters, and calculating SOH=f{T, SOC, I, ΔDOD, SOC×T, ΔDOD×T, I×ΔDOD}, where f is a preset calculation formula.

[0103] The inventors compared the SOH calculation under various degradation conditions with the case where the product of parameters is included and the case where the product of parameters is not included, with the case where the SOH is calculated using time series data. The results are shown in Figures 17 and 18. The various degradation conditions are the same as those used in the first process in Figure 2, for example.

[0104] As shown in Figure 17, when the product of parameters was included in the SOH calculation, the error of the SOH calculation results using time series data and the SOH calculation results using histogram data was 0.8% compared to the actual SOH value. In other words, by including the product of parameters in the model, the accuracy of the SOH estimation improved regardless of whether time series data or histogram data was used.

[0105] In contrast, as shown in Figure 18, when the product of parameters is not included in the SOH calculation, the calculation results of SOH using time series data and the calculation results of SOH using histogram data have an error of 4.3% compared to the actual measured SOH value. This shows that including the product of parameters in the model can improve the accuracy of SOH estimation.

[0106] As described above, the SOH can be estimated using either the time series data or the histogram data of the secondary battery 110. In this case, since the time series data or the histogram data is used, it is possible to reduce the inclusion of errors such as sensing errors compared to the current integration method. Therefore, it is possible to improve the accuracy of estimating the SOH of the secondary battery 110.

[0107] The present disclosure is not limited to the above-described embodiment, and various modifications can be made as follows without departing from the spirit and scope of the present disclosure.

[0108] For example, the secondary battery 110 is not limited to being mounted on an electric vehicle, but may also be installed in a predetermined location. The SOH of the secondary battery 110 is not limited to the SOH of the entire secondary battery 110, but may be the SOH of a single battery cell or multiple SOH of the battery cells that make up the secondary battery 110.

[0109] Although the present disclosure has been described based on the embodiment, it is understood that the present disclosure is not limited to the embodiment or structure. The present disclosure also includes various modifications and modifications within the equivalent range. In addition, various combinations and forms, and other combinations and forms including only one element, more than one element, or less than one element, are also within the scope and concept of the present disclosure.

Claims

1. A battery diagnostic system for estimating a state of health (SOH) indicating a degree of deterioration of a secondary battery (110), comprising: a model unit (172) that acquires usage history data indicating a usage state of the secondary battery and calculates the SOH based on the usage history data; a SOH calculation unit (173) that acquires a physical quantity that changes according to a deterioration degree of the secondary battery as sensing data and calculates a SOH based on the sensing data; an SOH estimation unit (174) that estimates an optimal SOH by combining the SOH calculated by the model unit and the SOH calculated by the SOH calculation unit based on the SOH calculated by the model unit and the SOH calculation unit; Including, the sensing data is impedance data of the secondary battery obtained by electrochemical impedance spectroscopy; The SOH calculation unit calculates the SOH by Gaussian process regression using the impedance data as input, A storage unit (150) in which information on a specific frequency in the frequency range used in the electrochemical impedance spectroscopy is stored in advance, The SOH calculation unit calculates the SOH using an imaginary component of the impedance corresponding to the specific frequency stored in the storage unit, among the impedance data.

2. 2. The battery diagnostic system according to claim 1, wherein the impedance obtained by the electrochemical impedance spectroscopy is a value calculated by measuring a response voltage corresponding to an AC current applied to the secondary battery, and then dividing the response voltage as a complex number having information of an absolute value and a phase by the AC current.

3. 3. The battery diagnosis system according to claim 1, wherein the specific frequency is a frequency determined based on machine learning using impedance data of the secondary battery previously obtained by the electrochemical impedance spectroscopy, and is a frequency that has a large effect on the SOH of the secondary battery.

4. the sensing data is data on a voltage change during charging of the secondary battery, 2 . The battery diagnostic system according to claim 1 , wherein the SOH calculation unit calculates the SOH by Gaussian process regression using data on a voltage change during charging of the secondary battery as an input.

5. 3. The battery diagnostic system according to claim 1, wherein the SOH estimator estimates the SOH using a nonlinear Kalman filter.

6. A battery diagnostic system for estimating a state of health (SOH) indicating a degree of deterioration of a secondary battery (110), comprising: A data acquisition unit (130) that acquires time-series data indicating a usage state of the secondary battery; a data processing unit (180) that acquires the time series data from the data acquisition unit and processes the time series data as histogram data; a calculation unit (170) that calculates an SOH as an estimated value using either the time series data acquired by the data acquisition unit or the histogram data acquired by the data processing unit based on a preset calculation model; Including, The histogram data includes parameters of the SOC, temperature, current, and ΔDOD of the secondary battery, The calculation unit calculates an SOH using each of the parameters and a product of two or more of the each of the parameters.

7. The battery diagnostic system of claim 6 , wherein the product of parameters includes at least one of SOC×T, ΔDOD×T, and current×ΔDOD.

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