Battery degradation diagnosis method determination device, battery degradation diagnosis system, battery degradation diagnosis method determination method, and battery degradation diagnosis method

Abstract

In order to determine an appropriate battery degradation diagnosis method, the battery degradation diagnosis method determination device according to the present disclosure prepares, in advance and in a multi-dimensional vector space constituted by at least two indices having a data sampling rate of a battery measuring device and an S / N ratio (C-rate) of a power supply load apparatus, information about the multi-dimensional vector space in which a battery degradation diagnosis method is assigned to each among a plurality of regions defined by one or more threshold values set for each index. Furthermore, the battery degradation diagnosis method determination device according to the present invention executes: a process for calculating at least two index values on the basis of battery charging / discharging characteristic data that has been measured; a process for identifying, in the multi-dimensional vector space, one region corresponding to the at least two index values that have been calculated; and a process for determining, as the battery degradation diagnosis method for the battery, a battery degradation diagnosis method corresponding to the identified region.

Description

[Technical Field]

[0001] This disclosure relates to a battery degradation diagnosis method determination device, a battery degradation diagnosis system, a method for determining battery degradation diagnosis method, and a battery degradation diagnosis method. [Previous Technology]

[0002] In recent years, used batteries have been used in various machines (battery-mounted machines) in electric vehicles and various factories. Typically, batteries deteriorate in quality (capacity or output reduction) with use. Therefore, it is necessary to periodically diagnose the degradation status of batteries. In particular, the market demand for diagnostics of used batteries has increased, with a desire for rapid degradation diagnosis without removing the battery from the battery-mounted machine. To achieve this, it is necessary to apply appropriate diagnostic methods to each combination of "power supply load device and battery testing device".

[0003] For example, generally speaking, in DC interruption methods that diagnose battery degradation at high speeds, degradation diagnosis within the range of high-speed charge / discharge rates (C-rate) and data sampling rates (data sampling rate) has become mainstream. Furthermore, for example, Patent Document 1 addresses the issue of accurately diagnosing whether the degradation of a secondary battery is caused by electrode degradation or electrolyte degradation, and discloses a method of "comparing the rate of change or the amount of change between the measured voltage characteristics after current interruption and the initial value for diagnosis." [Prior Art Documents] [Patent Documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2021-140991 [Summary of the Invention]

[0005] [The problem that the invention aims to solve]

[0006] In the general DC interruption method described above, battery degradation diagnosis in low-speed regions is not implemented. Therefore, there is no solution to the problems of "insufficient sensitivity, noise, and irregular sampling cycles, especially in low-speed regions, caused by the combination of the power supply load machine and the battery testing device." Furthermore, in order to diagnose degradation conditions at high speed without removing the battery from the battery mounting machine, the on / off control of the charging and discharging circuit and current diagnosis after the output of the power load has been stabilized are necessary conditions.

[0007] Furthermore, in the technology disclosed in Patent Document 1, if the C-rate is set to a low speed, the recovery voltage after current interruption is very low, the sensitivity deteriorates, and noise becomes significant. Therefore, even if the technology of Patent Document 1 is directly applied, it cannot accurately diagnose battery degradation. That is, although the technology of Patent Document 1 can address situations where both the C-rate and sampling rate are in the high-speed range, it cannot address situations where these systems are in the low-speed range.

[0008] However, in the market, there are charging and discharging machines with various charging and discharging rates (C-rate) and measuring devices with various data sampling rates. Therefore, in order to meet the above-mentioned demand for degradation diagnosis of existing batteries, it is necessary to determine the appropriate degradation diagnosis method corresponding to a wider range of C-rate and sampling rate combinations.

[0009] This disclosure provides a technique for determining an appropriate battery degradation diagnosis method that corresponds to a wider range of combinations of charge / discharge rates (C-rate) caused by the charging / discharging machine and the sampling rate of the measuring device, in view of this situation. [Means for solving the problem]

[0010] To solve the above-mentioned problems, this disclosure proposes a battery degradation diagnosis method determination device, which determines a battery degradation diagnosis method suitable for the battery being diagnosed, and includes: a memory device and a processor. The memory device stores information in a multi-dimensional vector space. This information is allocated to the battery degradation diagnosis method in a multi-dimensional vector space composed of at least two indices: the data sampling rate of the measuring device measuring the battery and the S / N ratio of the battery's power supply to the load machine. These indices correspond to multiple regions defined by threshold values ​​of 1 or more set at each indices. The processor performs the following processes: obtaining information from a multidimensional vector space from a memory device; calculating at least two index values ​​based on measured battery charge / discharge characteristic data; identifying a region in the multidimensional vector space corresponding to the calculated at least two index values; and determining a battery degradation diagnosis method corresponding to the identified region as the battery degradation diagnosis method.

[0011] Further features of this disclosure will become apparent from the description in this specification and the attached drawings. Furthermore, the form of this disclosure is achieved and realized through a combination of elements and a variety of elements, along with subsequent detailed description and the attached claims. The description in this specification is merely exemplary and does not limit the claims or applicability of this disclosure in any way. [Effects of the Invention]

[0012] According to the technology disclosed herein, in battery degradation diagnosis, it is possible to use an appropriate battery degradation diagnosis method that corresponds to a wider range of combinations of charge / discharge rates (C-rate) caused by the charge / discharge machine and the sampling rate of the measuring device. Therefore, it becomes possible to obtain accurate battery degradation diagnosis results.

Implementation Method

[0014] Hereinafter, embodiments of the present disclosure will be described with reference to the attached drawings. In the attached drawings, elements that are functionally identical may be indicated by the same component symbols. Furthermore, although the attached drawings show specific embodiments and examples in accordance with the principles of the present disclosure, these are only for understanding the present disclosure and are not intended to be a limiting interpretation of the present disclosure.

[0015] In this embodiment, a full and detailed description is provided to enable practitioners to implement this disclosure. However, other installations and configurations may also be adopted. It should be understood that changes in the structure or the substitution of various elements can be made without departing from the technical concept and spirit of this disclosure. Therefore, the following description should not be interpreted in a limiting manner.

[0016] <Summary> When battery degradation diagnosis is performed without removing the battery from the battery mounting machine (in the case of real-time diagnosis), the battery to be diagnosed is connected to a measuring device. This measuring device measures the voltage, current, and temperature (hereinafter also referred to as "V, I, T") of the battery and the power supply load machine such as a charging / discharging machine or air conditioner, and sends the measurement data to the battery degradation diagnosis device. The battery degradation diagnosis device then uses the measurement data received from the measuring device (also referred to as the battery degradation state measuring device) to diagnose the battery degradation state using a specific battery degradation diagnosis method (direct battery degradation state diagnosis method).

[0017] However, the direct diagnostic method is only suitable for a specific combination of charge and discharge speed (C-rate: power supply to the load machine) and data sampling speed (data acquisition interval at the measuring device) (e.g., C-rate in the high-speed region and data sampling speed in the high-speed region), and is not suitable for combinations of speed regions other than this.

[0018] Therefore, in this embodiment, the accuracy of the measurement data caused by each diagnostic method is predicted based on two indicators: the charge / discharge rate (C-rate) and the data sampling rate. The appropriate diagnostic method is determined and applied according to the combination of the power supply load machine and the measuring device. When diagnosing the degradation condition at high speed without removing the battery from the battery mounting machine, an appropriate diagnostic method can be applied, enabling high-accuracy battery degradation diagnosis. Furthermore, C-rate is an indicator that converts the proportion of current flowing relative to the battery's full capacity to a time-dependent ratio, and is synonymous with the S / N ratio of the measured current value.

[0019] Figure 1 is a diagram illustrating the outline of the battery degradation diagnosis method for this embodiment. As shown in Figure 1, in the direct diagnosis method, various measuring devices and power supply load machines are used. As measuring devices, machines that perform data sampling at high speed (e.g., sampling speed of 100S / s to 1000S / s) (e.g., analyzer, terminal, etc.), at medium speed (e.g., sampling speed of 10S / s to 100S / s) (e.g., BMS (Battery Management System), etc.), and at low speed (e.g., sampling speed of 1S / s to 10S / s) (e.g., OBDII (On Board Diagnosis II)), etc., are applicable. Furthermore, as a power supply load machine, it is applicable to rapid charging and discharging machines (C-rate of 0.5C~5C: voltage S / N ratio of 60dB~80dB), ordinary charging and discharging machines (C-rate of 0.2C~0.5C: voltage S / N ratio of 40dB~60dB), and low power load machines such as air conditioners (C-rate of 0.01C~0.2C: voltage S / N ratio of 20dB~40dB).

[0020] In this embodiment, the C-rate and sampling rate are calculated based on the charge-discharge characteristic data (voltage, current, temperature) obtained from the measuring device at the start of charge-discharge, and it is determined which region these belong to (e.g., which of the nine regions from "low-speed region and low-speed region" to "high-speed region and high-speed region": measurement data accuracy prediction forms I to IX). Based on the determination result, the optimal battery degradation diagnosis method for the combination of the measuring device and the power supply load machine used in battery degradation diagnosis is determined. Specifically, the diagnosis method that matches the combination of the measuring device and the power supply load machine that corresponds to each region shown in FIG1 (e.g., nine regions in FIG1: measurement data accuracy prediction forms I to IX) is pre-stored in the memory device of the battery degradation diagnosis method determination device (described later). For example, diagnostic method A is assigned when there is a high C-rate and a high sampling rate (Region I); diagnostic method B is assigned when there is a high C-rate and a low sampling rate (Region II); diagnostic method C is assigned when there is a low C-rate and a high sampling rate (Region III); and diagnostic method D is assigned when there is a low C-rate and a low sampling rate (Region IV). In this way, a battery degradation diagnostic method suitable for the combination of the measuring device and the power supply load machine used in battery degradation diagnosis is determined.

[0021] In addition, in this embodiment, as one example, although two index values ​​(charge-discharge rate (C-rate) and data sampling rate) are used to determine (judge) the battery degradation diagnosis method, it is also possible to consider other indexes, or even other indexes in addition to these, to determine the method.

[0022] <System Configuration Example: Overview> Figure 2 is a diagram showing the overall configuration example of the battery degradation diagnosis system 1 including the battery degradation diagnosis method determination device 10 as described in this embodiment.

[0023] The battery degradation diagnosis system 1 includes: a battery degradation diagnosis method determination device 10 for determining a method for diagnosing the degradation state of a battery; a charging and discharging machine 30 for charging and discharging a battery (secondary battery) mounted at a battery mounting machine 20; a measuring device (battery degradation state measuring device) 40 for measuring the voltage, current, and temperature (V, I, T) of the battery; a group of devices A50 for performing various processing during measurement; a group of devices B60 for processing the measured data of voltage, current, and temperature; and a battery degradation diagnosis device 70 for diagnosing the degradation state of the target battery (battery mounted at a battery mounting machine 20) using the measured data (V, I, T) based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10.

[0024] Device group A50 includes a charge / discharge circuit switching control device 501, a power load monitoring device 502, a measurement current interruption signal control device 503, an output stability determination device 504, and a rest point determination device 505. Device group B60 includes a measurement data amplification device 601, a high-frequency component interruption adjustment device 602, a measurement data smoothing processing device 603, and a phase difference correction device 604. The choice of which device in device group A50 or device group B60 will be used is determined based on the combination of the measurement device and the power supply load machine, determined by the charge / discharge rate (C-rate) obtained from the charge / discharge characteristic data at the start of charge / discharge, the sampling rate, and the connection status of the measurement device.

[0025] Furthermore, the battery degradation diagnosis method determination device 10, each device in device group B60, and battery degradation diagnosis device 70 can be installed in the cloud (configured as a cloud server). Also, at least a portion (or all) of device group A50 can be installed in the aforementioned cloud server, or it can be installed outside the cloud server, similar to the measurement device 40. Hereinafter, we will briefly explain in what situations the devices in device group A50 and device group B60 are selected and configured into a system.

[0026] (i) When the charge / discharge rate (C-rate) is in the low-speed range, in order to improve the measurement sensitivity, the measurement data amplification device 601 is selected (applied) and the system is configured by connecting it to the battery degradation diagnosis method determination device 10 and the battery degradation diagnosis device 70.

[0027] (ii) When the charge / discharge rate (C-rate) is in the low-speed range and the S / N ratio is poor (regardless of whether the measurement data is amplified), the high-frequency component blocking adjustment device 602 is selected, and the system is configured such that only the measurement data of the low-frequency components required for the diagnosis of battery degradation are extracted.

[0028] (iii) When the data sampling rate is in the low-speed region, the measurement data smoothing processing device 603 is selected, and the system is configured by smoothing the measurement data so that the data-unacquired areas are interpolated.

[0029] (iv) When a measuring device 40 is installed between the battery mounting machine 20 and the charging / discharging machine 30 and connected to a direct charging / discharging port (when a measuring device 40 with high-speed data sampling speed (e.g., an analyzer, a terminal) is used), a charging / discharging circuit opening / closing control device 501 is selected, and the system is configured to control the opening and closing of the charging / discharging circuit (within the measuring device) of the battery mounting machine 20. This enables battery measurement at the measuring device. This is because the battery mounting machine 20 is equipped with gates and the like for battery and circuit protection and safety measures; without communication compatible with various charging and communication standards, it is impossible to control the charging / discharging circuit and thus impossible to measure the battery degradation state.

[0030] (v) When the charging / discharging rate (C-rate) is in a low-speed range (when the power supply load device is a low-circuit load such as an air conditioner), a power load monitoring device 502 that monitors the power load status of the battery in the battery-equipped machine 20 and the start point (rest point) of the current cutoff for measurement is selected. The system is configured such that when preset conditions (voltage value, time, etc.) are met, the current cutoff process required for battery degradation diagnosis is implemented. Furthermore, when the power load monitoring device 502 detects that a current cutoff has been implemented at the power supply load device, the measurement data (V, I, T) that was originally sent directly from the measurement device 40 to the battery degradation diagnosis method determination device 10 is switched so that it is sent to the battery degradation diagnosis method determination device 10 via the power load monitoring device 502.

[0031] (vi) When the charging / discharging rate (C-rate) is in a low-speed range (when the power supply load device is a low-circuit load such as an air conditioner), the system is configured such that, together with or in place of the power load monitoring device 502, a measurement current interruption signal control device 503 is selected, and the system is configured such that when preset conditions (voltage value, time, etc.) are met, the current interruption (current from the power source) required for battery degradation diagnosis is implemented. The current interruption command from the power source is given to the measurement device 40 from the battery degradation diagnosis method determination device 10. If the current is interrupted, the measurement data (V, I, T) that were originally sent directly from the measurement device 40 to the battery degradation diagnosis method determination device 10 are switched so that they are sent to the battery degradation diagnosis method determination device 10 via the measurement current interruption signal control device 503.

[0032] (vii) When the charging / discharging rate (C-rate) is in a low-speed range (when the power supply load device is a low-circuit load such as an air conditioner), the output stability determination device 504 is selected, either together with or in place of the aforementioned power load monitoring device 502 and / or the current interruption signal control device 503, to monitor the power before current interruption and determine whether a stable range has been reached. The output stability determination device 504 monitors the measurement data (battery output data: values ​​of V, I, and T) generated by the measuring device 40. If the power of the monitored object reaches a stable range, it notifies the battery degradation diagnosis method determination device 10 of the result that the battery output data has reached a stable range. In this way, the battery degradation diagnosis method determination device 10 sends a current interruption command to the measuring device. If the current is interrupted, the measurement data (V, I, T) that were originally sent directly from the measuring device 40 to the battery degradation diagnosis method determination device 10 can also be configured to be sent to the battery degradation diagnosis method determination device 10 via the output stability determination device 504 or the measuring current interruption signal control device 503.

[0033] (viii) When the data sampling rate is in a low-speed range (e.g., 1S / s to 10S / s) or a medium-speed range (e.g., 10S / s to 100S / s) (e.g., when OBDII or BMS is used as the measuring device 40), the system is configured such that the rest point determination device 505 is selected, and the current interruption (stopping of power supply to the charging / discharging device 30) point is determined by detecting the decrease in the V and I values ​​of the battery-mounted device 20. If the rest point determination device 505 determines the rest point (current interruption), the measurement data (V, I, T) that was originally sent directly from the measuring device 40 to the battery degradation diagnosis method determination device 10 is switched so that it is sent to the battery degradation diagnosis method determination device 10 via the measuring current interruption signal control device 503.

[0034] The determination of the rest point serves as a trigger for sending subsequent V, I, and T measurement data to the battery degradation diagnosis method determination device 10 for battery degradation diagnosis. That is, if the rest point is not determined, battery degradation diagnosis caused by V, I, and T cannot be performed. Furthermore, the rest point determination device 505 differs from the power load monitoring device 502; it does not monitor the battery's output power and provides functions unrelated to stable power supply.

[0035] Furthermore, in the measurement of battery state caused by a measurement device 40 (e.g., OBDII or BMS) with a data sampling rate (sampling rate) in the low-speed or medium-speed range, there may be a phase difference (sampling interval shift) caused by irregular sampling rates. Irregular sampling rates may result from a shift in the timing of the data acquired at the measurement device 40. In order to correct this sampling rate shift (but this does not mean that there is an error in the measured data value), a phase difference correction device 604 can be selected at device group B60 along with the rest point specifying device 505. At this time, the rest point (current interruption start point) can be used as the reference point for performing phase difference correction. That is, phase difference correction is a process of forcibly correcting the sampling rate of the measured data after the rest point to the determined sampling rate.

[0036] <Complete Processing at Battery Deterioration Diagnosis System 1: From the Start of Diagnosis to the End> Figure 3 is a flowchart illustrating the processing (complete processing) at Battery Deterioration Diagnosis System 1 from the start of battery deterioration diagnosis to the end of diagnosis. Furthermore, the processing shown in the flowchart of Figure 3 is only one example. For instance, the Battery Deterioration Diagnosis System 1 constructed after step 307 can also be other configurations (e.g., only the measurement data smoothing processing device 603 is applied, but the rest point specifying device 505 and the phase difference correction device 604 are not applied, etc.).

[0037] (i) Step 301 determines whether the measuring device (battery degradation state measuring device) 40 is directly connected to the charging / discharging connection port of the charging / discharging machine 30. Regarding whether a direct connection has been made, for example, the determination can be made by enabling the measuring device 40 to exchange signals with the charging / discharging machine 30, and the determination result can be sent to the battery degradation diagnosis method determination device 10. Alternatively, the connection status confirmed by the user can be used as a signal to be sent from the measuring device 40 to the battery degradation diagnosis method determination device 10.

[0038] When it is determined that the measuring device 40 is directly connected to the charging / discharging port of the charging / discharging machine 30 (when step 301 is YES), the measuring device 40 being used is identified as a high-speed data sampling rate device such as an analyzer or terminal, and the process moves to step 302. When it is determined that the measuring device 40 is not directly connected to the charging / discharging port of the charging / discharging machine 30 (when step 301 is NO: the measuring device 40 is directly connected to the battery mounting machine 20), the measuring device 40 being used is identified as a low- or medium-speed data sampling rate device such as OBDII or BMS, and the process moves to step 303.

[0039] (ii) Step 302 The calculation unit of the battery degradation diagnosis method determination device 10 (hereinafter referred to as the "calculation unit" in the description of FIG3) sends a signal command to the charging and discharging circuit opening and closing control device 501 in a manner that implements the opening and closing control of the charging and discharging circuit of the battery mounting machine 20. If the charging and discharging circuit opening and closing control device 501 receives the signal command, it controls the charging and discharging circuit of the battery mounting machine 20 to a closed state (if the charging and discharging circuit is open, it is closed; if it is already closed, it is maintained). This is because, as described above, the battery mounting machine 20 is equipped with a gate or the like for battery, circuit protection and safety measures. Therefore, when a measuring device (battery degradation state measuring device) 40 is installed between the charging / discharging machine 30 and the battery mounting machine 20 and directly connected to the charging / discharging connection port, if communication compatible with various charging and communication specifications is not implemented, the charging / discharging circuit of the battery mounting machine 20 cannot be controlled, and the battery degradation state cannot be measured.

[0040] (iii) Step 303 The power supply to the load machine (charger / discharger 30 or air conditioner, etc.) begins to operate. If it is a charge / discharger 30, it begins to charge / discharge the battery-equipped machine 20. If it is a load machine such as an air conditioner, it begins to operate or continues to operate.

[0041] (iv) Step 304 The battery degradation diagnosis method determination device 10 communicates with the measuring device 40 and directly obtains the measurement data (initial charge and discharge characteristic data: voltage V, current I, temperature T) from the measuring device 40 under the condition that the measuring current is not interrupted.

[0042] (v) Step 305 The calculation unit calculates the charge / discharge rate (C-rate) based on the current value (measured value) included in the acquired measurement data and the capacity information of the battery being diagnosed (stored in the memory unit 102 described later). To explain more specifically, the battery degradation diagnosis method determination device 10 stores in the memory unit (described later) information on the battery capacity corresponding to a plurality of types of batteries (identified by battery ID) (e.g., 40Ah). Then, the battery degradation diagnosis method determination device 10 obtains the battery capacity information corresponding to the "battery identification information (battery ID) obtained from the battery mounting machine 20 via the measuring device 40" from the memory unit, and can calculate the actual C-rate based on this information and the measured current value. Alternatively, the calculation of the C-rate can also be performed by the measuring device 40. In this case, the battery degradation diagnosis method determination device 10 obtains C-rate information from the measuring device 40. Alternatively, it can be configured to input charge / discharge rate (C-rate) information from the input unit (not shown) of the battery degradation diagnosis method determination device 10. The obtained C-rate information is temporarily stored in a memory (not shown).

[0043] (vi) Step 306 The calculation unit calculates the sampling rate (sampling speed) of the measuring device 40 based on the acquisition interval (time) of the measurement data caused by the measuring device 40. The calculated (acquired) sampling speed information is temporarily stored in a memory not shown.

[0044] (vii) Step 307 The calculation unit determines whether the calculated sampling rate is smaller than 100 S / s (the sampling rate threshold). If the sampling rate of the measuring device 40 is smaller than 100 S / s (when step 307 is YES), the processing moves to step 308. On the other hand, if the sampling rate of the measuring device 40 is 100 S / s or more (when step 307 is NO), the processing moves to step 311.

[0045] (viii) Steps 308 to 310 Steps 308 to 310 are performed when the sampling speed of the measuring device 40 is in the low-speed or medium-speed range.

[0046] The calculation unit is configured by selecting (applying) the measurement data smoothing processing device 603 and the phase difference correction device 604 from the device group B60. That is, the measurement data provided by the battery degradation diagnosis method determination device 10 is smoothed (interpolated) by the measurement data smoothing processing device 603, and the sampling speed (sampling interval) and value are corrected (corrected to a value that matches the sampling speed) by the phase difference correction device 604, and then delivered to the battery degradation diagnosis device 70. Furthermore, the calculation unit is configured by selecting (applying) the rest point specifying device 505 from the device group A50. In addition, it is not necessary to apply all of the measurement data smoothing processing device 603, the rest point specifying device 505, and the phase difference correction device 604; it is also possible to use only one of them.

[0047] (ix) Step 311 The calculation unit determines whether the calculated C-rate is smaller than 0.5C (the first C-rate threshold). If the C-rate of the power supply load machine (charging / discharging machine 30 or load) is smaller than 0.5C (if step 311 is YES), the processing unit moves to step 312. On the other hand, if the C-rate of the power supply load machine is 0.5C or higher (if step 311 is NO), the processing unit moves to step 314.

[0048] (x) Steps 312 and 313 Steps 312 and 313 are performed when the C-rate of the power supply to the load machine (charging / discharging machine 30 or load) is below the medium speed range.

[0049] The calculation unit is configured by selecting (applying) the measurement data amplification device 601 and the high-frequency component blocking adjustment device 602 from the device group B60. That is, the measurement data provided by the battery degradation diagnosis method determination device 10 is amplified by the measurement data amplification device 601, and the noise components contained in the amplified measurement data are blocked by the high-frequency component blocking adjustment device 602 before being delivered to the battery degradation diagnosis device 70. Alternatively, it is not necessary to apply both the high-frequency component blocking adjustment device 602 and the measurement data amplification device 601, and it is also possible to set up a device that applies only one of them.

[0050] (xi) Step 314: The calculation unit determines whether the calculated C-rate is smaller than 0.2C (the second C-rate threshold). If the C-rate of the power supply load machine (charging / discharging machine 30 or load) is smaller than 0.2C (if step 314 is YES), the processing unit moves to step 315. On the other hand, if the C-rate of the power supply load machine is 0.2C or higher (if step 314 is NO), the processing unit moves to step 320.

[0051] (xii) Steps 315 to 317 and 319 are performed when the C-rate of the power supply to the load machine (charging / discharging machine 30 or load) is low. Steps 315 to 317 are the same as those described above, and are used for system configuration.

[0052] The calculation unit is configured by selecting (applicable) a power load monitoring device 502, a measurement current interruption signal control device 503, and an output stability judgment device 504 from the device group A50. That is, the power load status of the battery being diagnosed and the start point (stop point) of the measurement current interruption are monitored by the power load monitoring device 502. Furthermore, the measurement current interruption signal control device 503 controls the current interruption required for battery degradation diagnosis when the preset conditions (the measured voltage value, the time from the start of measurement, etc.) are met at the measurement device 40. Furthermore, the output stability determination device 504 monitors the battery output data caused by the power load and determines whether the power has reached a stable range (in addition, the stop point (current interruption point) is not detected, but the monitoring is performed on whether the power has become stable before the stop). In addition, it is not necessary to use all of the power load monitoring device 502, the measuring current interruption signal control device 503, and the output stability determination device 504, and it is also possible to use only one of them.

[0053] (xiii) Step 318 The calculation unit obtains the output (current) value of the battery of the diagnostic object from the measuring device 40.

[0054] (xiv) Step 319: The calculation unit calculates the standard deviation of the obtained battery output value and determines whether it converges to within 1%. This allows the subsequent processing (the interruption of charging / discharging current caused by step 320) to be performed once the battery output has stabilized. When the standard deviation converges to within 1% (when step 319 is YES), the processing proceeds to step 320. On the other hand, when the standard deviation is greater than 1% (when step 319 is NO), the processing proceeds to step 318, and the calculation unit repeatedly performs steps 318 and 319 until the standard deviation of the battery output value converges to within 1%.

[0055] (xv) Step 320 The calculation unit sends the command to cut off the charging and discharging current through the measuring device 40 to the power supply load machine (charging and discharging machine 30 or load (air conditioner) etc.), thereby cutting off the current supply to the battery-mounted machine 20 is implemented.

[0056] Furthermore, by performing steps 301 to 319, the calculation unit (battery degradation diagnosis method determination device 10) is able to identify which region of the combination of the power supply load machine (charging / discharging machine 30 or load, etc.) and the measuring device 40 corresponds to as shown in Figure 1, and is able to determine the battery degradation diagnosis method corresponding to each region (suitable for that combination). The information of the determined battery degradation diagnosis method is sent to the battery degradation diagnosis device 70.

[0057] (xvi) Step 321 The calculation unit obtains the charge and discharge characteristic data (V, I, T values) from the measuring device 40 (when the system configuration does not include the device included in the device group A50), or through the desired device included in the device group A50 (which varies depending on the system configuration).

[0058] (xvii) Step 322: The calculation unit transmits the obtained charge / discharge characteristic data (V, I, T values) after the charge / discharge current is interrupted to the battery degradation diagnosis device 70 via one of the device groups B60 as needed. Then, the battery degradation diagnosis device 70 diagnoses the battery based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the measured data (charge / discharge characteristic data (V, I, T values)). For example, battery degradation diagnosis methods include those using SOH (State of Health), IR (Internal Resistance), and abnormal degradation degree.

[0059] <Information on the battery degradation diagnosis method and applicable device that corresponds (optimal) to the accuracy prediction form of the measurement data used in the battery degradation diagnosis method determination> Figure 4 is a diagram showing the "information on the battery degradation diagnosis method and applicable device that corresponds (suitable) to the accuracy prediction form of the measurement data (hereinafter also referred to as "accuracy prediction form of measurement data applicable information")" 400 held by the battery degradation diagnosis method determination device 10.

[0060] Measurement data accuracy prediction mode application information 400 is a component item, which includes "measurement data accuracy prediction mode 4001 composed of each region I to IX shown in Figure 1 (regarding the division number, it is only one example)," "calculated charge and discharge rate (C-rate) 4002", "calculated data sampling rate 4003", "battery degradation diagnosis method 4004 corresponding to each measurement data accuracy prediction mode 4001 (optimal)", and "device 4005 to 4013 selected corresponding to each measurement data accuracy prediction mode 4001".

[0061] According to the measurement data accuracy prediction mode application information 400, when the C-rate 4002 after current interruption is 0.5~5C and the data sampling rate 4003 is 100~1000S / s, the measurement data accuracy prediction mode 4001 is determined to be Region I. It can be seen that in this case, the battery degradation diagnosis method 4004 is set as diagnosis method A, and the charge / discharge circuit opening and closing control device 4008 is applied. Similarly, when it is determined to be Region II~IV, one of the battery degradation diagnosis methods 4004 and devices 4005~4013 corresponding to each of these is applied.

[0062] Although not shown in the measurement data accuracy prediction mode application information 400, when the C-rate 4002 after current interruption is 0.2~0.5C and the data sampling rate 4003 is 100~1000S / s, the measurement data accuracy prediction mode 4001 is determined to be region V (see Figure 1). In this case, the battery degradation diagnosis method 4004 is, for example, set as diagnosis method E, and the charge / discharge circuit opening and closing control device 4008 (not shown in Figure 4) is applied. This is because, in the case of region V, as can be seen from Figure 1, for example, an analyzer and a terminal (high-speed data sampling rate) are used as the measurement device 40.

[0063] When the C-rate 4002 after current interruption is 0.5~5C and the data sampling rate 4003 is 10~100S / s, the measurement data accuracy prediction mode 4001 is determined to be region VI (see Figure 1). In this case, the battery degradation diagnosis method 4004 is, for example, set as diagnosis method F, and the rest point identification device 4012 and the phase difference correction device 4013 (not shown in Figure 4) can be applied. This is because, in the case of region VI, as can be seen from Figure 1, for example, a BMS (medium-speed data sampling rate) is used as the measurement device 40.

[0064] When the C-rate 4002 after current interruption is 0.2~0.5C and the data sampling rate 4003 is 10~100S / s, the measurement data accuracy prediction mode 4001 is determined to be region VII (see Figure 1). In this case, the battery degradation diagnosis method 4004 is, for example, set as diagnosis method G, and the rest point identification device 4012 and the phase difference correction device 4013 (not shown in Figure 4) can be applied. This is because, in the case of region VII, as can be seen from Figure 1, for example, a BMS (medium-speed data sampling rate) is used as the measurement device 40. In addition, for region VII, the same diagnosis method F as for region VI can also be applied.

[0065] When the C-rate 4002 after current interruption is 0.01~0.2C and the data sampling rate 4003 is 10~100S / s, the measurement data accuracy prediction mode 4001 is determined to be region VIII (see Figure 1). In this case, the battery degradation diagnosis method 4004 is, for example, set as diagnosis method H, and can be applied to the measurement data amplification device 4005, the power load monitoring device 4009, the measurement current interruption signal control device 4010, the rest point determination device 4012, and the phase difference correction device 4013 (not shown in Figure 4). This is because, in the case of region VIII, as can be seen from Figure 1, for example, a low power load (such as an air conditioner) is used as the power supply load machine, and a BMS (medium speed data sampling rate) is used as the measurement device 40. In addition, for region VIII, the same diagnostic method F as for region VI, or the same diagnostic method G as for region VII, can be applied.

[0066] When the C-rate 4002 after current interruption is 0.2~0.5C and the data sampling rate 4003 is 1~10S / s, the measurement data accuracy prediction mode 4001 is determined to be region IX (see Figure 1). In this case, the battery degradation diagnosis method 4004 is, for example, set as diagnosis method I, and the rest point identification device 4012 and the phase difference correction device 4013 (not shown in Figure 4) can be applied. This is because, in the case of region IX, as can be seen from Figure 1, for example, OBDII (low-speed data sampling rate) is used as the measurement device 40. In addition, for region VIII, the same diagnosis method B as region II or the same diagnosis method D as region IV can also be applied.

[0067] <Diagnosis at the battery degradation diagnosis device> Figure 5 is a diagram showing the management information 500 held by the battery degradation diagnosis device 70 and containing the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the indicators used in the diagnosis.

[0068] The battery degradation diagnosis device 70 diagnoses the degradation of a target battery based on measurement data (V, I, T) by using a battery degradation diagnosis method that determines the prediction pattern (e.g., regions I to IX) corresponding to multiple measurement data accuracy. At the battery degradation diagnosis device 70, since it corresponds to various battery degradation diagnosis methods (diagnosis methods A to D...), multiple indicators (SOH, IR, abnormal degradation degree, etc.) are calculated.

[0069] The management information 500 shown in Figure 5 is a component item, which includes the measurement data accuracy prediction form 5001, the charge-discharge rate (C-rate) 5002 that determines the measurement data accuracy prediction form 5001, the data sampling rate 5003, the battery degradation diagnosis method 5004 that is attached to the measurement data accuracy prediction form 5001, and the SOH 5005, the IR 5006, and the abnormal degradation degree 5007, which are calculated based on the measurement data as the first indicator.

[0070] In each battery degradation diagnosis method 5004, although not all of SOH 5005, IR 5006, and abnormal degradation degree 5007 are used, it is possible to calculate all the index values. For example, it can be set as follows: "In battery degradation diagnosis method A, SOH is used for diagnosis; in diagnosis method B, IR is used for diagnosis; in diagnosis method C, abnormal degradation degree is used for diagnosis; in diagnosis method D, SOH, IR, and abnormal degradation degree are used for diagnosis; in diagnosis method E, SOH and IR are used for diagnosis, ...".

[0071] <Basic Configuration Example and Operation of Battery Deterioration Diagnosis System> (i) Basic Configuration Example Figure 6A is a diagram showing a basic configuration example of battery deterioration diagnosis system 1. This basic configuration example includes all the components shown in Figure 2 except for device group A50 and device group B60. That is, the battery deterioration diagnosis system 1 shown in the basic configuration example includes a battery deterioration diagnosis method determination device 10, a battery mounting machine 20, a charging and discharging machine 30, a measuring device (battery deterioration state measuring device) 40, a battery deterioration diagnosis device 70, and a memory device 710, etc. In addition, in Figure 6A, the battery deterioration diagnosis method determination device 10, the battery deterioration diagnosis device 70, and the memory device 710 can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing mode without going through a cloud server.

[0072] The measuring device 40, as an internal component, includes a detection unit 401 and a communication unit 402. The detection unit 401 detects the voltage value V, current value I, and temperature T of the battery-mounted device 20. The communication unit 402 is configured to communicate with the battery degradation diagnosis method determination device 10 and the charging / discharging device 30, and transmits the voltage value V, current value I, and temperature T detected by the detection unit 401 to the battery degradation diagnosis method determination device 10.

[0073] The battery degradation diagnosis method determination device 10, as an internal component, includes a calculation unit 101 composed of a processor (CPU, etc.), a memory unit (memory device) 102 that stores calculation results, various data, and parameters, and a communication unit 103. The communication unit 103 receives measurement data (V, I, T) from the measuring device 40 and transmits it to the calculation unit 101. The calculation unit 101, as described above, calculates the charge / discharge rate (C-rate) stored in the memory unit 102 and the sampling rate of the measuring device 40. Furthermore, if the calculation unit determines a prediction pattern of the measurement data accuracy (e.g., region I to IX) based on the C-rate and the sampling rate, it determines the battery degradation diagnosis method corresponding to that pattern, notifies the battery degradation diagnosis device 70, and sends a charge / discharge current cutoff command to the measuring device 40. In addition, information on battery degradation diagnosis methods corresponding to the accuracy prediction of measurement data (see Figure 4) is stored in memory unit 102. However, in the basic system configuration example of Figure 6A, since device group A50 and device group B60 are not present, the selection device information (4004~4013) is not stored in memory unit 102.

[0074] The communication unit 402 of the measuring device 40, upon receiving a charge / discharge current interruption command from the battery degradation diagnosis method determination device 10, transmits it to the charge / discharge machine 30. The charge / discharge machine 30 then responds to the command by stopping the power supply to the battery-mounted device 20 (current interruption). Alternatively, the current interruption can be implemented by an electrical load such as an air conditioner or vehicle power supply, instead of the charge / discharge machine 30 (this is also the case in the various system configurations described later). Furthermore, the system can also be configured to stop the power supply at the user's instigation.

[0075] If the power supply to the battery-equipped device 20 is interrupted, the measuring device 40 acquires charge-discharge characteristic data (measurement data: V, I, T) after the current is cut off. This charge-discharge characteristic data is submitted to the battery degradation diagnosis device 70 via the battery degradation diagnosis method determination device 10, and battery degradation diagnosis is performed using the aforementioned battery degradation diagnosis method. The diagnosis results (including SOH, IR, and abnormal degradation degree) are stored in the memory (memory device) 710. The diagnosis results can be provided to the user or made available for viewing upon request.

[0076] (ii) The processing content of the battery degradation diagnosis system 1 is shown in Figure 6B, which is a flowchart for explaining the processing content of the battery degradation diagnosis system 1 caused by the basic configuration example.

[0077] (ii-1) Step 601 The power supply to the load machine (charger / discharger 30 or air conditioner, etc.) begins to operate. If it is the charge / discharger 30, it begins to charge / discharge the battery-equipped machine 20. If it is the load machine such as an air conditioner, it begins to operate or continues to operate.

[0078] (ii-2) Step 602 The battery degradation diagnosis method determination device 10 communicates with the measuring device 40 and directly obtains the measurement data (initial charge and discharge characteristic data: voltage V, current I, temperature T) from the measuring device 40 under the condition that the measuring current is not interrupted.

[0079] (ii-3) Step 603: The calculation unit 101 acquires the charging / discharging rate (C-rate) of the power supplied to the load machine, which has been input from the input device (not shown) of the battery degradation diagnosis method determination device 10. Alternatively, the calculation unit 101 may also be configured, as described above (refer to step 305 in Figure 3), to calculate the charging / discharging rate (C-rate) based on the current value (measured value) included in the acquired measurement data and the capacity information of the battery of the diagnostic target at the time of manufacture (which is stored in the memory unit 102 described later). The C-rate information is stored in the memory unit 102.

[0080] (ii-4) Step 604 The calculation unit calculates the data sampling rate (data sampling rate) of the measuring device 40 based on the acquisition interval (time) of the measurement data caused by the measuring device 40. The calculated data sampling rate information is stored in the memory unit 102.

[0081] (ii-5) Step 605 The calculation unit 101 determines which of the multiple regions shown in Figure 1 the combination of the obtained charge-discharge rate (C-rate) and sampling rate belongs to, and determines the battery degradation diagnosis method corresponding to the specified region.

[0082] (ii-6) Step 606 The calculation unit 101 sends the command to cut off the charging and discharging current through the measuring device 40 to the power supply load machine (charging and discharging machine 30 or load (air conditioner) etc.), thereby cutting off the current supply to the battery-mounted machine 20 is implemented.

[0083] (ii-7) Step 607 The calculation unit 101 obtains charge and discharge characteristic data (V, I, T values) from the measuring device 40.

[0084] (ii-8) Step 608: The calculation unit 101 sends the obtained charge / discharge characteristic data (V, I, T values) after the charge / discharge current is interrupted to the battery degradation diagnosis device 70. The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the measurement data (charge / discharge characteristic data (V, I, T values)). As a battery degradation diagnosis method, for example, there are methods using SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0085] <Structure Example of a Battery Deterioration Diagnosis System Without Measuring Device 40> (i) Structural Example Figure 7A is a diagram illustrating the configuration example of a battery deterioration diagnosis system 1 that does not have a measuring device (battery deterioration state measuring device) 40.

[0086] The battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery degradation diagnosis device 70, a memory device 710 (such as a memory), and a measurement data input device 80. The internal structure of the battery degradation diagnosis method determination device 10 and the battery degradation diagnosis device 70 is the same as in the basic configuration example. Furthermore, in Figure 7A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, and the memory device 710 are the same as in Figure 6, and can be configured as a cloud server, or the entire system or a part thereof can be configured as a locally deployed or edge computing system without going through a cloud server.

[0087] The battery degradation diagnosis system 1 shown in Figure 7A differs from the basic configuration example (Figure 6A) by replacing the battery mounting machine 20, the charge / discharge machine 30, and the measuring device 40 with a measurement data input device 80. The measurement data input device 80 can be configured using a conventional computer, and in addition to a calculation unit (not shown), includes at least an input unit 801 and a communication unit 802. The user obtains information from another data server regarding the charge / discharge rate (C-rate) of the power supply load machine (charge / discharge machine 30, etc.) of the battery mounting machine 20 under diagnosis, the data sampling rate of the measuring device 40, and the charge / discharge characteristic data (V, I, T) measured by the measuring device 40, and inputs this information into the measurement data input device 80 using the input unit 801. The input unit 801 transmits the user-inputted data to the battery degradation diagnosis method determination device 10 via the communication unit 802. In addition, the charge and discharge characteristic data input here is data measured after the current is cut off.

[0088] The battery degradation diagnosis method determination device 10 determines the optimal battery degradation diagnosis method based on the C-rate and data sampling speed information received from the measurement data input device 80, in the same manner as described above, and notifies the battery degradation diagnosis device 70 along with the charge / discharge characteristic data (measurement data: V, I, T). The battery degradation diagnosis device 70 diagnoses the degradation state of the battery based on the notified battery degradation diagnosis method and stores the diagnosis results (including SOH, IR, and abnormal degradation degree) in the memory (memory device) 710. The diagnosis results can be provided to the user or made available for viewing upon request.

[0089] (ii) The operation diagram 7B of the battery degradation diagnosis system 1 is a flowchart used to explain the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG7A.

[0090] (ii-1) Step 701 If the user inputs the measurement data (charge and discharge characteristic data: voltage V, current I, temperature T values) of the battery to be diagnosed from the input section 801 of the measurement data input device 80, then the communication section 802 will send the charge and discharge characteristic data input into the battery degradation diagnosis method determination device 10.

[0091] (ii-2) Step 702: The calculation unit 101 of the battery degradation diagnosis method determination device 10 acquires the charging and discharging rate (C-rate) of the power supply to the load machine, which has been input from the input device (not shown) of the battery degradation diagnosis method determination device 10. Alternatively, the calculation unit 101 may also be configured, as described above (refer to step 305 in Figure 3), to calculate the charging and discharging rate (C-rate) based on the current value (measured value) included in the acquired measurement data and the capacity information of the battery of the diagnostic target at the time of manufacture (which is stored in the memory unit 102). The C-rate information is stored in the memory unit 102.

[0092] (ii-3) Step 703: The calculation unit 101 acquires information on the data sampling rate of the measuring device input to the input device (not shown) of the battery degradation diagnosis method determination device 10. Alternatively, the calculation unit 101 may calculate the data sampling rate based on the acquisition interval (time) of the measurement data (charge and discharge characteristic data) caused by the measuring device. The acquired data sampling rate information is stored in the memory unit 102.

[0093] (ii-4) Step 704 The calculation unit 101 determines which of the multiple regions shown in Figure 1 the combination of the obtained charge-discharge rate (C-rate) and data sampling rate belongs to, and determines the battery degradation diagnosis method corresponding to the specified region.

[0094] (ii-5) Step 705: The calculation unit 101 sends the obtained charge / discharge characteristic data (V, I, T values) after the charge / discharge current is interrupted to the battery degradation diagnosis device 70. The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the measurement data (charge / discharge characteristic data (V, I, T values)). As a battery degradation diagnosis method, for example, there are methods using SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0095] <Example of the configuration of a battery degradation diagnostic system 1 with the function of improving measurement sensitivity when the C-rate system is in the low-speed region>

[0096] (i) The system configuration example diagram 8A is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 that has the function of improving the measurement sensitivity when the charging and discharging speed is in a low speed region (e.g., region III, IV and VIII: see Figure 1).

[0097] The battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a charge / discharge machine 30, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a measurement data amplification device 601, and a memory device 710 such as a memory. The configuration shown in Figure 8A is the system configuration that will be implemented when "the battery degradation diagnosis method determination device 10 specifies the measurement data accuracy prediction mode (e.g., region I to IX) and selects (applies) the measurement data amplification device 601 from the device group B60" (see Figure 2).

[0098] The internal structure and function of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 are the same as those in the basic configuration example (Fig. 6A). Furthermore, in Fig. 8A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, the measurement data amplification device 601, and the memory device 710 can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server.

[0099] The measurement data amplification device 601 is an internal component that includes, for example, a memory unit 6012 for storing a transfer function for data amplification, and a calculation unit (processor such as a CPU) 6011 that reads the transfer function from the memory unit 6012, performs amplification calculations on the measurement data (charge and discharge characteristic data V, I, T) obtained by the battery degradation diagnosis method determination device 10, stores the calculation results in the memory unit 6012, and simultaneously provides them to the battery degradation diagnosis device 70.

[0100] The system configuration shown in Figure 8A can be used, for example, when the S / N ratio is good. When the S / N ratio is not good, as explained in Figure 9A, the high-frequency component blocking adjustment device 602 can be further selected from device group B60. In this case, the high-frequency component blocking adjustment device 602 can be arranged before or after the measurement data amplification device 601.

[0101] In addition, there is a lower limit value for noise that can be reduced by a high-frequency component blocking adjustment device (e.g., a low-pass filter) 602. Therefore, the signal amplification caused by the measurement data amplification device 601 can be derived from the viewpoint of noise reduction.

[0102] (ii) The operation diagram 8B of the battery degradation diagnosis system 1 is a flowchart used to explain the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG8A.

[0103] (ii-1) Step 801 The power supply to the load machine (charger / discharger 30 or air conditioner, etc.) begins to operate. If it is the charge / discharger 30, it begins to charge / discharge the battery-equipped machine 20. If it is the load machine such as an air conditioner, it begins to operate or continues to operate.

[0104] (ii-2) Step 802 The calculation unit 101 of the battery degradation diagnosis method determination device 10 communicates with the measurement device 40 using the communication unit 103, and directly obtains the measurement data (initial charge and discharge characteristic data: voltage V, current I, temperature T) from the measurement device 40 under the condition that the measurement current is not interrupted.

[0105] (ii-3) Step 803: The calculation unit 101 acquires the charging / discharging rate (C-rate) of the power supplied to the load machine, which has been input from the input device (not shown) of the battery degradation diagnosis method determination device 10. Alternatively, the calculation unit 101 may also be configured, as described above (refer to step 305 in Figure 3), to calculate the charging / discharging rate (C-rate) based on the current value (measured value) included in the acquired measurement data and the capacity information of the battery of the diagnostic target at the time of manufacture (which is stored in the memory unit 102). The C-rate information is stored in the memory unit 102.

[0106] (ii-4) Step 804 The calculation unit calculates the data sampling rate (data sampling rate) of the measuring device 40 based on the acquisition interval (time) of the measurement data caused by the measuring device 40. The calculated data sampling rate information is stored in the memory unit 102.

[0107] (ii-5) Step 805 The calculation unit 101 determines which of the multiple regions shown in Figure 1 the combination of the obtained charge-discharge rate (C-rate) and data sampling rate belongs to, and determines the battery degradation diagnosis method corresponding to the specified region.

[0108] (ii-6) Step 806 The calculation unit 101 determines whether the calculated charge / discharge rate (C-rate) is smaller than 0.5C (the first C-rate threshold). By this process, it determines whether the C-rate of the power supply load machine (charge / discharge machine 30 or load) is below the medium speed range.

[0109] When the C-rate of the power supply load machine (charging / discharging machine 30 or load) is less than 0.5C (when step 806 is YES), the processing system moves to step 807. On the other hand, when the C-rate of the power supply load machine is 0.5C or more (when step 806 is NO), the processing system moves to step 812.

[0110] (ii-7) Step 807 Calculation unit 101 is a data amplification device 601 applicable to measurement.

[0111] (ii-8) Step 808 The calculation unit 101 sends the command to cut off the charging and discharging current through the measuring device 40 to the power supply load machine (charging and discharging machine 30 or load (air conditioner) etc.), thereby cutting off the current supply to the battery-mounted machine 20 is implemented.

[0112] (ii-9) Step 809 The calculation unit 101 obtains charge and discharge characteristic data (V, I, T values) from the measuring device 40.

[0113] (ii-10) Step 810 The calculation unit 101 transmits the obtained charge-discharge characteristic data (V, I, T values) after the charge-discharge current is cut off to the measurement data amplification device 601. The calculation unit 701 of the measurement data amplification device 601 amplifies the charge-discharge characteristic data using a transfer function and sends the signal to the battery degradation diagnosis device 70.

[0114] (ii-11) Step 811 The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method and measurement data (charge and discharge characteristic data (V, I, T values)) determined by the battery degradation diagnosis method determination device 10. For example, battery degradation diagnosis methods may use SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0115] (ii-12) Step 812 The calculation unit 101 sends the command to cut off the charging and discharging current through the measuring device 40 to the power supply load machine (charging and discharging machine 30 or load (air conditioner) etc.), thereby cutting off the current supply to the battery-mounted machine 20 is implemented.

[0116] (ii-13) Step 813 The calculation unit 101 obtains charge and discharge characteristic data (V, I, T values) from the measuring device 40 and sends the information to the battery degradation diagnosis device 70.

[0117] (ii-14) Step 814 The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method and measurement data (charge and discharge characteristic data (V, I, T values)) determined by the battery degradation diagnosis method determination device 10. For example, battery degradation diagnosis methods may use SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0118] <Example of the configuration of a battery degradation diagnosis system 1 that amplifies the measurement data and extracts only the low-frequency components when the C-rate system is in a low-speed region>

[0119] (i) The system configuration example diagram 9A is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 that has the function of amplifying the measurement data when the charging and discharging speed is in a low-speed region (e.g., region III, IV and VIII: see Figure 1) and extracting only the low-frequency components of the amplified data (the components required in battery degradation diagnosis).

[0120] This battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a charge / discharge machine 30, a measurement device (battery degradation state measurement device) 40, a battery degradation diagnosis device 70, a measurement data amplification device 601, a high-frequency component blocking adjustment device 602, and a memory device 710 such as a memory. The configuration shown in Figure 9A is the system configuration that will be implemented when "the battery degradation diagnosis method determination device 10 specifies the measurement data accuracy prediction pattern (e.g., region I to IX) and selects the measurement data amplification device 601 and the high-frequency component blocking adjustment device 602 from device group B60" (see Figure 2). For example, the measurement data amplification device 601 is selected when the measurement sensitivity is not good, and the high-frequency component blocking adjustment device 602 is selected when the S / N ratio of the measurement data is not good.

[0121] The internal structure and function of the battery degradation diagnosis method determination device 10, the measuring device 40, the battery degradation diagnosis device 70, and the measurement data amplification device 601 are the same as those in the basic configuration example (Fig. 8A). Furthermore, in Fig. 9A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, the measurement data amplification device 601, the high-frequency component blocking adjustment device 602, and the memory device 710 can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server.

[0122] The high-frequency component blocking adjustment device 602 is an internal component that includes, for example, a memory unit 6022 that holds the filter coefficients (low-pass filter coefficients) and an calculation unit 6021 that obtains the filter coefficients from the memory unit 6022 and filters the measurement data obtained by the battery degradation diagnosis method determination device 10 to remove noise (high-frequency components) contained in the measurement data and extract low-frequency components. In addition, the optimal filter coefficients can be calculated, for example, by reverse calculation of the desired waveform obtained by filtering.

[0123] (ii) Operation diagram 9B of the battery degradation diagnosis system 1 is a flowchart used to explain the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG. 9A. In FIG. 9B, steps 901 to 903 are added to the process in FIG. 8B. Since the contents of steps 801 to 814 are the same as those in FIG. 8B, here, only the added steps 901 to 903 will be explained.

[0124] (ii-1) Step 901 When the C-rate of the power supply to the load machine (charging and discharging machine 30 or load) is smaller than 0.5C (when step 806 is YES), the calculation unit 101 applies the high-frequency component blocking adjustment device 602.

[0125] (ii-2) Step 902 The calculation unit 6021 of the high-frequency component blocking adjustment device 602 removes the high-frequency components (noise) from the amplified charge-discharge characteristic data (measurement data) and extracts only the low-frequency components required for battery degradation diagnosis. More specifically, at the high-frequency component blocking adjustment device 602, the ideal frequency components are calculated based on the reference data waveform obtained in advance using the measurement conditions of region I in FIG1, and the high-frequency components not included in the reference waveform are removed.

[0126] (ii-3) In step 903, the calculation unit 6021 determines whether the voltage S / N ratio = 20 * log 10 (VS / VN) is greater than 60 dB. If the voltage S / N ratio is greater than 60 dB (if step 903 is YES), the processing unit moves to step 811. On the other hand, if the voltage S / N ratio is less than 60 dB (if step 903 is NO), the processing unit moves to step 811. At this time, the high-frequency component blocking adjustment device 602 sends a notification to the measurement data amplification device 601 that "the voltage S / N ratio is insufficient".

[0127] If the measurement data amplification device 601 receives a notification from the high-frequency component blocking adjustment device 602 that "the voltage S / N ratio is not sufficient", it will change (increase) the amplification rate and amplify the value of the charge and discharge characteristic data again.

[0128] <Example of the configuration of a battery degradation diagnosis system 1 that has the function of smoothing the measurement data when the data sampling rate is in a low-speed region> (i) The system configuration example is shown in Figure 10A, which is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 that has the function of smoothing the measurement data and interpolating the data in the region where the data sampling rate is in a low-speed region (e.g., regions II, IV and IX: see Figure 1).

[0129] This battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a charge / discharge machine 30, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a measurement data smoothing processing device 603, and a memory device 710 such as a memory. The configuration shown in Figure 10A is a system configuration that will be implemented when "the battery degradation diagnosis method determination device 10 specifies the measurement data accuracy prediction pattern (e.g., region I to IX) and selects the measurement data smoothing processing device 603 from the device group B60" (see Figure 2). For example, the measurement data smoothing processing device 603 will be selected when the data acquisition interval of the measuring device 40 is large (the data sampling rate is low) and the charge / discharge characteristic data (measurement data) is not smooth (becomes step-like).

[0130] The internal structure and function of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 are the same as in the basic configuration example (Fig. 6A). Furthermore, in Fig. 10A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, the measurement data smoothing processing device 603, and the memory device 710 are the same as in Fig. 6A. They can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server.

[0131] The measurement data smoothing processing apparatus 603, as an internal component, includes a memory unit 6031 that holds smoothing filter coefficients and temporarily stores measurement data (charge-discharge characteristic data) acquired at specific intervals (e.g., 100ms intervals), and a calculation unit (processor such as a CPU) 6032 that performs processing (data smoothing processing) on ​​measurement data (obtaining a smooth regression curve) at time intervals (e.g., 10ms) that are less than the aforementioned specific intervals by reading smoothing filter coefficients from the memory unit 6031 and interpolating the measurement data for unacquired regions by applying a smoothing filter to the measurement data. Furthermore, the optimal filter coefficients can be calculated, for example, by inversely calculating the desired waveform obtained by filtering.

[0132] (ii) Operation diagram 10B of the battery degradation diagnosis system 1 is a flowchart for explaining the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG10A. In FIG10B, steps 1001 and 1002 are performed instead of steps 806 and 807 in FIG8B, and step 1003 is added. Since the contents of steps 801 to 805, 808, 809 and steps 812 to 814 are the same as those in FIG8B, only steps 1001 to 1004 will be explained here.

[0133] (ii-1) Step 1001 The calculation unit 101 of the battery degradation diagnosis method determination device 10 determines whether the data sampling rate calculated in step 804 is smaller than 100 S / s (sampling threshold value). When the data sampling rate is smaller than 100 S / s (when step 1001 is YES), the processing moves to step 1002. On the other hand, when the data sampling rate is 100 S / s or more (when step 1001 is NO), the processing moves to step 812.

[0134] (ii-2) Step 1002 Calculation unit 101 is a data smoothing processing device 603 applicable to measurement data.

[0135] (ii-3) Step 1003 The calculation unit 6032 of the data smoothing processing device 603 reads the smoothing filter coefficients (local linear regression coefficients or local polynomial regression coefficients) from the memory unit 6031, and applies the smoothing filter (local linear regression filter or local polynomial regression filter) to the charge and discharge characteristic data (battery characteristic data) to generate smoothed data, and then submits it to the battery degradation diagnosis device 70.

[0136] (ii-4) Step 1004 The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the smoothed data (smoothed charge and discharge characteristic data (V, I, T values)). As a battery degradation diagnosis method, for example, there are methods using SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0137] (iii) Example Figure 10C of charge-discharge characteristic data before and after smoothing processing is a diagram illustrating an example of charge-discharge characteristic data before and after smoothing processing. When the data sampling rate is in the low or medium speed range (regions II, IV, VI, VII, VIII, IX in Figure 1), the measurement data (charge-discharge characteristic data) is smoothed by the measurement data smoothing processing device 603 through local linear regression or local polynomial regression, and the regions where the data was not obtained are interpolated. This is because the obtained charge-discharge characteristic data exhibits different behavior before and after the rest point where the current is cut off.

[0138] <Example of the configuration of a battery degradation diagnosis system 1 with the function of obtaining battery characteristic data or manufacturing ID information of the battery to be diagnosed from the outside> (i) System configuration example Figure 11A is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 with the function of obtaining battery characteristic data or manufacturing ID information of the battery to be diagnosed from the outside.

[0139] The battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a charging and discharging machine 30, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a memory device 710, a battery data input device 90 that inputs the characteristic data of the battery to be diagnosed and the manufacturing ID of the battery, and a battery characteristic information collection device 110 that collects the characteristic data of each battery sent from the battery data input device 90 and submits it to the battery degradation diagnosis method determination device 10.

[0140] The internal structure and function of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 are the same as in the basic configuration example (Fig. 6A). Furthermore, in Fig. 11A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, the memory device 710, and the battery characteristic information collection device 110 are the same as in Fig. 6A. They can be configured as cloud servers, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server.

[0141] The battery data input device 90 can be configured using a conventional computer. As an internal component, in addition to having a calculation unit (not shown), it also includes at least an input unit 901 that allows the user to input battery characteristic data and manufacturing ID, and a communication unit 902 that sends the input information to the cloud (battery characteristic information collection device 110).

[0142] Similarly, the battery characteristic information collection device 110, as an internal component, can be constructed using a conventional computer. In addition to a calculation unit (not shown), it also includes at least a communication unit 1101 that receives battery characteristic data sent from the battery data input device 90 and stores it in the memory unit 102 of the battery degradation diagnosis method determination device 10. The battery characteristic data and manufacturing ID are obtained based on various battery types, and are managed by battery type in the memory unit 102. Furthermore, the functions of the battery characteristic information collection device 110 can also be implemented within the battery degradation diagnosis method determination device 10.

[0143] In system 1 as shown in Figure 11A, before performing a degradation diagnosis on the battery mounted on the battery mounting machine 20, the user uses the battery data input device 90 to send the battery's (the battery to be diagnosed) manufacturing characteristic data (capacity (Ah)) and manufacturing ID to the battery characteristic information collection device 110.

[0144] The calculation unit 101 of the battery degradation diagnosis method determination device 10 calculates the actual (real-time) charge and discharge rate (C-rate) based on the capacity information of the target battery at the time of manufacture obtained from the battery data input device 90 and the measured current value (I) contained in the measurement data obtained by the measuring device 40.

[0145] The calculation unit 701 of the battery degradation diagnosis device 70 feeds back the calculated battery diagnostic index value (e.g., SOH) to the battery degradation diagnosis method determination device 10. The calculation unit 101 of the battery degradation diagnosis method determination device 10 is capable of correcting the capacity information of the target battery as needed based on the obtained battery diagnostic index value (current value), and updating the charge / discharge rate (C-rate) based on the corrected capacity information. Thus, the battery degradation diagnosis method determination device 10 becomes capable of determining a battery degradation diagnosis method that reflects the degradation status of the target battery at the current point in time.

[0146] (ii) The operation diagram 11B of the battery degradation diagnosis system 1 is a flowchart used to explain the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG11A.

[0147] (ii-1) Step 1101: The user uses the battery data input device 90 to input battery characteristic data (capacity (Ah), etc.) and manufacturing ID, etc., and sends the data to the battery characteristic information collection device 110. The battery characteristic information collection device 110 then sends the acquired battery characteristic data (capacity (Ah), etc.) and manufacturing ID, etc., to the battery degradation diagnosis method determination device 10. The battery degradation diagnosis method determination device 10 stores the received battery characteristic data (capacity (Ah), etc.) and manufacturing ID, etc., in the memory unit 102.

[0148] (ii-2) Step 1102 The power supply to the load machine (charger / discharger 30 or air conditioner, etc.) begins to operate. If it is the charge / discharger 30, it begins to charge / discharge the battery-equipped machine 20. If it is the load machine such as an air conditioner, it begins to operate or continues to operate.

[0149] (ii-3) Step 1103 The battery degradation diagnosis method determination device 10 communicates with the measuring device 40 and directly obtains the measurement data (initial charge and discharge characteristic data: voltage V, current I, temperature T) from the measuring device 40 under the condition that the measuring current is not interrupted.

[0150] (ii-4) Step 1104 The calculation unit 101 of the battery degradation diagnosis method determination device 10 calculates the charge-discharge rate (C-rate) based on the current value (measured value) included in the acquired measurement data and the capacity information of the battery of the diagnosis target at the time of manufacture (which is stored in the memory unit 102 described later). To explain more specifically, the calculation unit 101 obtains information on the battery capacity corresponding to the "battery identification information (battery ID) obtained from the battery mounting machine 20 via the measuring device 40" from the memory unit 102, and calculates the actual C-rate based on this information and the measured current value (charge-discharge characteristic data). The calculated C-rate information is temporarily stored in a memory not shown.

[0151] (ii-5) Step 1105 The calculation unit 101 calculates the sampling rate (sampling rate) of the measuring device 40 based on the acquisition interval (time) of the measurement data (charge and discharge characteristic data) obtained by the measuring device 40. The calculated sampling rate information is temporarily stored in a memory not shown.

[0152] (ii-6) Step 1106 The calculation unit 101 determines which of the multiple regions shown in Figure 1 the combination of the obtained charge-discharge rate (C-rate) and data sampling rate belongs to, and determines the battery degradation diagnosis method corresponding to the specified region.

[0153] (ii-7) Step 1107 The calculation unit 101 sends the command to cut off the charging and discharging current through the measuring device 40 to the power supply load machine (charging and discharging machine 30 or load (air conditioner) etc.), thereby cutting off the current supply to the battery-mounted machine 20 is implemented.

[0154] (ii-8) Step 1108 The calculation unit 101 obtains charge and discharge characteristic data (V, I, T values) from the measuring device 40 and sends the information to the battery degradation diagnosis device 70.

[0155] (ii-9) Step 1109 The battery degradation diagnosis device 70 diagnoses the battery based on the battery degradation diagnosis method and measurement data (charge and discharge characteristic data (V, I, T values)) determined by the battery degradation diagnosis method determination device 10. For example, battery degradation diagnosis methods may use SOH (State of Health), IR (Internal Resistance), or abnormal degradation degree. Furthermore, the battery degradation diagnosis device 70 sends the calculated SOH to the battery degradation diagnosis method determination device 10.

[0156] (ii-10) Step 1110 The calculation unit 101 of the battery degradation diagnosis method determination device 10 calculates the charge-discharge rate (C-rate) again based on the SOH obtained from the battery degradation diagnosis device 70.

[0157] (ii-11) Step 1111 The calculation unit 101 determines whether the absolute value of the difference between the "current C-rate (the C-rate calculated in step 1110)" and the "previous C-rate" is less than 0.05C (that is, whether the difference converges within a specific range). When the absolute value of the C-rate difference converges within the specific value (when step 1111 is YES), the process ends. On the other hand, when the absolute value of the C-rate difference converges within the specific value (when step 1111 is NO), the process moves to step 1106 and performs another determination on the battery degradation diagnosis method.

[0158] (iii) Figure 12 is a diagram showing the management information 1200 held by the battery degradation diagnosis device 70 and containing the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10, the indicators used in the diagnosis, and manufacturing information. The management information 1200 shown in Figure 12 is a component item that includes the manufacturing ID 1201, the battery model 1202, and information 5001 to 5007 of the management information 500 in Figure 5.

[0159] During manufacturing, ID1201 and battery model 1202 are information that is "input via battery data input device 90 and sent to battery degradation diagnosis device 70 via battery characteristic information collection device 110 and battery degradation diagnosis method determination device 10". In addition, the battery characteristic data that is also input is not included in management information 1200 because it is information used in battery degradation diagnosis method determination device 10 to calculate C-rate.

[0160] <Example of a configuration of a battery degradation diagnostic system 1 that can measure the state of battery degradation when the data sampling rate is in a high-speed range> (i) System configuration example Figure 13A is a diagram illustrating an example of a configuration of a battery degradation diagnostic system 1 that can measure the state of battery degradation when the data sampling rate is in a high-speed range (e.g., regions I, III, and V in Figure 1). Furthermore, since only a high-speed data sampling rate is required, this system configuration example can be applied even when the C-rate is in a low-speed range (e.g., when using an air conditioner or vehicle power supply as an electrical load).

[0161] This battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a charge / discharge machine 30, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a memory device 710 such as a memory, and a charge / discharge circuit opening / closing control device 501. The system configuration in Figure 13A differs from the aforementioned system configuration (refer to Figures 6A, 8A-11A), in that a measuring device 40 is installed between the battery mounting machine 20 and the charge / discharge machine 30. Since a gate is installed in the battery mounting machine 20 for battery and circuit protection and safety measures, when the measuring device 40 is directly connected to the charge / discharge port of the charge / discharge machine 30, if communication compatible with various charging and communication standards is not implemented, the charge / discharge circuit cannot be controlled, and the battery degradation state cannot be measured. Therefore, a charge / discharge circuit opening / closing control device 501 is provided, and the opening and closing of the charge / discharge circuit is controlled by the battery mounting device 20, so that measurements can be performed at the measuring device (battery degradation state measuring device) 40. In the system configuration example shown in FIG13A, this is implemented when "the battery degradation diagnosis method determination device 10 selects, for example, region I, III or V from the measurement data accuracy prediction mode (e.g., region I to IX), and selects the charge / discharge circuit opening / closing control device 501 from the device group A50" (see FIG2).

[0162] In this battery degradation diagnosis system 1, the internal structure and function of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 are the same as in the basic configuration example (Fig. 6A). Furthermore, in Fig. 13A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, and the memory device 710 are the same as in Fig. 6A, and can be configured as a cloud server, or the entire system or a part thereof can be configured as a locally deployed or edge computing configuration without going through a cloud server.

[0163] The measuring device 40 includes a detection unit 401 that detects the charge-discharge characteristic data (V, I, T) of the battery-mounted device 20, a communication unit 402 that sends the measured charge-discharge characteristic data (V, I, T) to the battery degradation diagnosis method determination device 10 or receives various commands (e.g., commands for opening and closing the charge-discharge circuit) from the battery degradation diagnosis method determination device 10 and sends them to the charge-discharge circuit opening and closing control device 501, and a charge-discharge circuit unit 403 that relays the charge-discharge actions (including start and stop) of the battery-mounted device 20 caused by the charge-discharge device 30. If the detection unit 401 detects a direct connection to the measuring device 40 at the charging port (charge-discharge connection port) of the battery-mounted device 20, the communication unit 402 notifies the battery degradation diagnosis method determination device 10 of this connection. Furthermore, if the detection unit 401 detects a charge / discharge start / stop signal from the charge / discharge machine 30, it will activate the charge / discharge circuit opening / closing control device 501 via the communication unit 402, thereby controlling the charge / discharge circuit of the battery-mounted machine 20 by opening / closing it.

[0164] The charge / discharge circuit opening / closing control device 501, as an internal component, includes a detection unit 5011 that detects commands for opening / closing the charge / discharge circuit from the measuring device 40, and a communication unit 5012. The communication unit 5012 sends the charge / discharge start / stop signal detected by the detection unit 5011 from the measuring device 40 to the battery-mounted device 20 and issues instructions for opening / closing the charge / discharge circuit, and notifies the battery degradation diagnosis method determination device 10 of the charge / discharge circuit opening / closing result received from the battery-mounted device 20.

[0165] (ii) The system configuration example in the case of an electric vehicle is shown in Figure 13B, which is a configuration example of a battery degradation diagnosis system 1 that is intended to be an electric vehicle 20' and has the function of measuring the battery degradation status when the data sampling speed is in a high-speed region (e.g., regions I, III and V in Figure 1).

[0166] As shown in FIG13B, the measuring device (battery degradation state measuring device) 40 and the charge / discharge circuit switching control device 501 are connected to the charging port of the electric vehicle 20' via the charging plug 21. If the measuring device 40 is connected to the charging port (charge / discharge connection port) of the electric vehicle 20', the detection unit 401 detects the connection and notifies the battery degradation diagnosis method determination device 10 via the communication unit 402 that "the measuring device 40 is directly connected to the charge / discharge connection port of the electric vehicle 20'". The other components in FIG13B are the same as those in FIG13A, so their description is omitted.

[0167] (iii) The operation diagram 13C of the battery degradation diagnosis system 1 is a flowchart used to explain the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in Figure 13A or Figure 13B.

[0168] (iii-1) Step 1301 If the preparation for the battery degradation diagnosis of the battery-mounted machine 20 (or electric vehicle 20') is completed, the charging and discharging machine 30 sends a charging and discharging start signal to the measuring device (battery degradation state measuring device) 40.

[0169] (iii-2) Step 1302 The calculation unit 101 of the battery degradation diagnosis method determination device 10 determines whether charge and discharge characteristic data has been successfully obtained from the measuring device 40. If charge and discharge characteristic data cannot be obtained, it determines that the charge and discharge circuit of the battery-mounted device 20 is in an open state (cannot be powered).

[0170] When charge / discharge characteristic data is successfully obtained from the measuring device 40 (when step 1302 is YES), the processing system moves to step 1308. On the other hand, when charge / discharge characteristic data cannot be obtained from the measuring device 40 (when step 1302 is NO), the processing system moves to step 1303.

[0171] (iii-3) Step 1303 The calculation unit 101 of the battery degradation diagnosis method determination device 10 is adapted to the charging and discharging circuit opening and closing control device 501 (the application of the determination device 501), and issues a command by sending a charging start signal to the charging and discharging machine 30 through the measuring device 40.

[0172] (iii-4) Step 1304 If the charging / discharging machine 30 receives the above instruction, it sends a charging / discharging start signal to the measuring device 40.

[0173] (iii-5) Step 1305 If the measuring device 40 receives a charging / discharging start signal from the charging / discharging machine 30, it sends a charging / discharging start signal to the charging / discharging circuit opening / closing control device 501.

[0174] (iii-6) Step 1306 The charging and discharging circuit opening and closing control device 501, if it receives a charging and discharging start signal, sends a charging and discharging circuit closing request signal to the battery-mounted device 20.

[0175] (iii-7) Step 1307 If the charging and discharging circuit is closed, the charging and discharging circuit opening and closing control device 501 receives the charging and discharging opening and closing result (closed) from the battery-mounted machine 20 and sends it to the battery degradation diagnosis method determination device 10.

[0176] (iii-8) Step 1308 The calculation unit 101 of the battery degradation diagnosis method determination device 10 sends a charge / discharge start signal to the charge / discharge machine 30 via the communication unit 103 and the measuring device 40. If the charge / discharge machine 30 receives the charge / discharge start signal, it begins charging and discharging the battery-mounted device 20.

[0177] (iii-9) Step 1309 The calculation unit 101 of the battery degradation diagnosis method determination device 10 communicates with the measurement device 40 using the communication unit 103, and directly obtains the measurement data (initial charge and discharge characteristic data: voltage V, current I, temperature T) from the measurement device 40 under the condition that the measurement current is not interrupted.

[0178] (iii-10) Step 1310: The calculation unit 101 acquires the charging / discharging rate (C-rate) of the power supplied to the load machine, which has been input from the input device (not shown) of the battery degradation diagnosis method determination device 10. Alternatively, the calculation unit 101 may also be configured, as described above (refer to step 305 in Figure 3), to calculate the charging / discharging rate (C-rate) based on the current value (measured value) included in the acquired measurement data and the capacity information of the battery of the diagnostic target at the time of manufacture (which is stored in the memory unit 102). The C-rate information is stored in the memory unit 102.

[0179] (iii-11) Step 1311 The calculation unit 101 calculates the data sampling rate of the measuring device 40 based on the acquisition interval (time) of the measurement data caused by the measuring device 40. The calculated data sampling rate information is stored in the memory unit 102.

[0180] (iii-12) Step 1312 The calculation unit 101 determines which of the multiple regions shown in Figure 1 the combination of the obtained charge-discharge rate (C-rate) and data sampling rate belongs to, and determines the battery degradation diagnosis method corresponding to the specified region.

[0181] (iii-13) Step 1313 The calculation unit 101 sends the command to cut off the charging and discharging current to the charging and discharging machine 30 through the measuring device 40, thereby cutting off the current supply to the battery-mounted machine 20.

[0182] (iii-14) Step 1314 The calculation unit 101 obtains charge and discharge characteristic data (V, I, T values) from the measuring device 40 and sends the information to the battery degradation diagnosis device 70.

[0183] (iii-15) Step 1315 The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method and measurement data (charge and discharge characteristic data (V, I, T values)) determined by the battery degradation diagnosis method determination device 10. For example, battery degradation diagnosis methods may use SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0184] <Structure Example of Battery Degradation Diagnosis System 1 with Function to Monitor Power Load under Low Power Load> (i) System Configuration Example Figure 14A is a diagram showing a configuration example of a battery degradation diagnosis system 1 with the function to monitor the power load of a low power load when a power supply load machine such as an air conditioner is used in a low-speed C-rate region.

[0185] This battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a memory device 710 such as a memory, and a power load monitoring device 502. The system configuration in FIG14A differs from the aforementioned system configuration (refer to FIG6A, 8A-11A, and FIG13A) and does not include a charging / discharging machine 30. This is because the purpose is to perform degradation diagnosis of the mounted battery during operation under low power load. Furthermore, in this battery degradation diagnosis system 1, the internal configuration of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 is the same as in the basic configuration example (FIG6A).

[0186] Immediately after the start of operation, the measuring device 40 directly transmits the initial charge and discharge characteristic data (measurement data V, I, T) of the battery-mounted machine (air conditioner, etc.) 20 detected by the detection unit 401 to the battery degradation diagnosis method determination device 10. The battery degradation diagnosis method determination device 10 calculates the C-rate and data sampling rate (through calculations already explained) based on the charge and discharge characteristic data, and specifies the measurement data accuracy prediction pattern (see Figure 1: Areas I to IX). If the measurement data accuracy prediction pattern is specified, the communication unit 402 of the measuring device 40 receives the power load monitoring start command from the battery degradation diagnosis method determination device 10 and issues a power load monitoring start instruction to the power load monitoring device 502. After issuing the power load monitoring start instruction, the measuring device 40 provides the measurement data (V, I, T) to the power load monitoring device 502.

[0187] The power load monitoring device 502, as an internal component, includes a detection unit 5021 that detects (acquires) measurement data (V, I, T) provided by the measuring device 40, a calculation unit 5022, a memory unit 5023, and a communication unit 5024. The calculation unit 5022 calculates the power load value based on the measurement data acquired by the detection unit 5021, and sequentially or at specific time intervals sends the power load value (power load status) to the battery degradation diagnosis method determination device 10 via the communication unit 5024. Furthermore, the calculation unit 5022 compares the calculated power load value with a specific power load value (limit value) stored in the memory unit 5023, determines whether the measurement current interruption start point (rest point) has been reached, and sends the determination result (measurement current interruption start point (rest point) information) to the battery degradation diagnosis method determination device 10 via the communication unit 5024.

[0188] When the measurement current interruption start point (rest point) information received from the power load monitoring device 502 represents a "power stability" condition (when the calculated power load value reaches the threshold value), the battery degradation diagnosis method determination device 10 sends a measurement current interruption command to the measurement device 40. If a measurement current interruption command is received, the communication unit 402 of the measurement device 40 issues an instruction to the battery-mounted device 20 by implementing current interruption.

[0189] The detection unit 401 of the measuring device 40 detects the charge and discharge characteristic data (measurement data V, I, T) after the current of the battery-mounted device 20 is cut off. The measurement data is sent to the battery deterioration diagnosis device 70 via "communication unit 402 → power load monitoring device 502 → battery deterioration diagnosis method judgment device 10" to diagnose the deterioration state of the battery to be diagnosed.

[0190] Furthermore, in FIG14A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, and the memory device 710 are the same as those in FIG6A. They can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server. Also, it can be configured by integrating the functions of the power load monitoring device 502 into the measuring device (e.g., OBDII). Furthermore, by configuring the power load monitoring device 502 into the cloud server, even a battery-equipped machine (e.g., a vehicle) 20 that does not have a power load monitoring device 502 can perform degradation diagnosis of the target battery after the power is stabilized.

[0191] (ii) The operation diagram 14B of the battery degradation diagnosis system 1 is a flowchart used to explain the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG14A.

[0192] (ii-1) Steps 1401 and 1402 If the application of electrical load to the battery-mounted device 20 is started, the measuring device (battery degradation state measuring device) 40 uses the detection unit 401 to obtain charge and discharge characteristic data (voltage V, current I, temperature T) from the battery-mounted device 20 and sends the signal to the battery degradation diagnosis method determination device 10.

[0193] (ii-2) Step 1403 The calculation unit 101 of the battery degradation diagnosis method determination device 10 obtains the charging and discharging rate (C-rate) information of the power supply to the load machine, which is input from the input device (not shown). Alternatively, the calculation unit 101 may also be configured, as described above (refer to step 305 in Figure 3), to calculate the charging and discharging rate (C-rate) based on the current value (measured value) included in the obtained measurement data and the capacity information of the battery of the diagnostic target at the time of manufacture (which is stored in the memory unit 102). The C-rate information is stored in the memory unit 102.

[0194] (ii-3) Step 1404 The calculation unit 101 calculates the data sampling rate of the measuring device 40 based on the acquisition interval (time) of the measurement data caused by the measuring device 40. The calculated data sampling rate information is stored in the memory unit 102.

[0195] (ii-4) Step 1405 The calculation unit 101 determines which of the multiple regions shown in Figure 1 the combination of the obtained charge-discharge rate (C-rate) and data sampling rate belongs to, and determines the battery degradation diagnosis method corresponding to the specified region.

[0196] (ii-5) Step 1406 The calculation unit 101 determines whether the obtained C-rate (charge / discharge rate) is less than 0.2C (low-speed region: for example, regions III, IV, and VIII in Figure 1). When the C-rate (charge / discharge rate) is less than 0.2C (when step 1406 is YES), the processing unit moves to step 1407. When the C-rate (charge / discharge rate) is 0.2C or higher (when step 1406 is NO), the processing unit moves to step 1411.

[0197] (ii-6) Step 1407 Calculation unit 101, which is applicable (determined to be applicable) power load monitoring device 502.

[0198] (ii-7) Step 1408 The calculation unit 101 sends the instruction to cut off the charging and discharging current to the battery-mounted machine 20 (load (air conditioner, etc.)) via the measuring device 40, thereby cutting off the current supply to the battery-mounted machine 20 (power load cut-off) is performed.

[0199] (ii-8) Step 1409 The calculation unit 101 obtains the charging and discharging characteristic data (V, I, T values), power load status and measurement current interruption start point (stop point) information after the power load is cut off through the power load monitoring device 502, and sends the information to the battery degradation diagnosis device 70.

[0200] (ii-9) Step 1410 The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the measurement data (charge and discharge characteristic data (V, I, T values)). As a battery degradation diagnosis method, there are methods such as using SOH (State of Health), IR (Internal Resistance), and abnormal degradation degree.

[0201] (ii-10) Step 1411 The calculation unit 101 sends the instruction to cut off the charging and discharging current to the battery-mounted machine 20 (load (air conditioner, etc.)) via the measuring device 40, thereby cutting off the current supply to the battery-mounted machine 20 (power load cut-off) is performed.

[0202] (ii-11) Step 1412 The calculation unit 101 obtains the charging and discharging characteristic data (V, I, T values) after the power load is cut off from the measuring device 40 and sends the information to the battery degradation diagnosis device 70.

[0203] (ii-12) Step 1413 The battery degradation diagnosis device 70 diagnoses the battery subject to diagnosis based on the battery degradation diagnosis method and measurement data (charge and discharge characteristic data (V, I, T values)) determined by the battery degradation diagnosis method determination device 10. For example, battery degradation diagnosis methods may use SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0204] <Example of a battery degradation diagnostic system 1 that provides countermeasures when the measuring device does not have a current interruption control function for the battery-mounted device> (i) System configuration example Figure 15A shows an example of a battery degradation diagnostic system 1 with an additional measuring current interruption signal control device 503. The measuring current interruption signal control device 503 is added as a countermeasure when the measuring device 40 does not have a current interruption control function for the battery-mounted device 20.

[0205] The battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting device 20, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a memory device 710 such as a memory, and a measuring current interruption signal control device 503 (which may also include a charging and discharging device 30). In the battery degradation diagnosis system 1, the internal structure of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 is the same as that of the basic configuration example (Fig. 6A).

[0206] Immediately after the start of operation, the measuring device 40 directly transmits the initial charge-discharge characteristic data (measurement data V, I, T) of the battery-mounted machine (e.g., an air conditioner) 20 detected by the detection unit 401 to the battery degradation diagnosis method determination device 10. The battery degradation diagnosis method determination device 10 calculates the C-rate and data sampling rate (through calculations already explained) based on this charge-discharge characteristic data and identifies the measurement data accuracy prediction pattern (see Figure 1: areas I to IX). If the measurement data accuracy prediction pattern is identified, the communication unit 402 of the measuring device 40 receives the measurement current interruption command from the battery degradation diagnosis method determination device 10 and transmits the command to the measurement current interruption signal control device 503. Furthermore, if a current interruption command is issued for receiving and measuring devices, the measuring device 40 provides the measured data (V, I, T) to the current interruption signal control device 503. The current interruption signal control device 503 responds to the current interruption command by monitoring the measured data (V, I, T) provided by the measuring device 40, appropriately controlling the interruption of the measuring current for the battery-mounted device 20, and sending the current interruption result to the battery degradation diagnosis method determination device 10. If the current supply to the battery-mounted device 20 is interrupted, the measured data (V, I, T) originally sent from the measuring device 40 to the battery degradation diagnosis method determination device 10 is switched by being sent via the current interruption signal control device 503 to the battery degradation diagnosis method determination device 10.

[0207] The measurement current interruption signal control device 503, as an internal component, includes a detection unit 5031 that detects (acquires) the measurement current interruption command and measurement data (V, I, T) provided by the measurement device 40, a calculation unit 5032, a memory unit 5033, and a communication unit 5034. The calculation unit 5032 transmits the measurement data acquired by the detection unit 5031 to the battery degradation diagnosis method determination device 10 via the communication unit 5034. Furthermore, when the measurement data (V, I, T) acquired from the measurement device 40 meets the preset conditions (when the threshold values ​​of V, I, and T are reached), the calculation unit 5022 sends a current interruption control signal to the battery mounting device 20 and stops the current supply. Furthermore, if the current supply to the battery-mounted device 20 is interrupted, the calculation unit 5032, via the communication unit 5034, sends the current interruption result indicating that the current supply has been stopped, along with the measurement data (charge and discharge characteristic data: V, I, T) of the target battery obtained from the measuring device 40, to the battery degradation diagnosis device 70 via the battery degradation diagnosis method determination device 10. Then, the battery degradation diagnosis device 70 diagnoses the degradation state of the target battery according to the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10.

[0208] Furthermore, in Figure 15A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, and the memory device 710 are the same as those in Figure 6A. They can be configured as a cloud server, or the entire system or a part of it can be configured as a local deployment or edge computing configuration without going through a cloud server. Also, it can be configured by integrating the measurement current interruption signal control device 503 into the cloud control device.

[0209] (ii) Operation diagram 15B of the battery degradation diagnosis system 1 is a flowchart for explaining the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG15A. In FIG15B, steps 1501 to 1507 are performed instead of steps 1407 to 1409 in FIG14B. Since the processing contents of steps 1401 to 1406 and steps 1410 to 1413 are the same as those in FIG14B, here, only steps 1501 to 1507 will be described.

[0210] (ii-1) Step 1501 When the C-rate (charge and discharge rate) is less than 0.2C (when step 1406 is YES), the calculation unit 101 of the battery degradation diagnosis method determination device 10 applies (determines to apply) the measurement current cut-off signal control device 503.

[0211] (ii-2) Step 1502 The current interruption signal control device 503 for measuring acquires the charge and discharge characteristic data (voltage V, current I, temperature T) measured by the measuring device 40.

[0212] (ii-3) Step 1503 The calculation unit 5032 of the current interruption signal control device 503 calculates the elapsed time based on the start time of the power load applied at the battery-mounted machine 20.

[0213] (ii-4) Step 1504 The calculation unit 5032 determines whether the calculated elapsed time is greater than the set value (time threshold) or whether the voltage value V of the obtained charge / discharge characteristic data is smaller than the set value (voltage threshold). When the elapsed time is greater than the set value (time threshold) or the voltage value V is smaller than the set value (voltage threshold) (when step 1504 is YES), the processing system moves to step 1505. When the elapsed time is less than the set value (time threshold) and the voltage value V is greater than the set value (voltage threshold) (when step 1504 is NO), the processing system moves to step 1502, and the processing systems of steps 1502 and 1503 are executed again.

[0214] (ii-5) Step 1505 The calculation unit 5032 sends a power load interruption signal to the battery-mounted machine 20 via the communication unit 5034.

[0215] (ii-6) Step 1506 If the battery-equipped machine 20 receives a power load interruption signal, it will interrupt the power load.

[0216] (ii-7) Step 1507 The calculation unit 101 of the battery degradation diagnosis method determination device 10 obtains the charge and discharge characteristic data (voltage V, current I, temperature T) and the current interruption result (voltage V, current I, time t) measured by the measuring device 40 after the power load is interrupted, via the measuring current interruption signal control device 503. Then, in step 1410, the battery degradation diagnosis is performed.

[0217] <Example of a battery degradation diagnosis system 1 that has the function of determining whether the battery output has reached the stable range under low power load and the measuring device does not have the current cut-off control function for the battery-equipped machine> (i) System configuration example Figure 16A is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 that has "the function of determining the stability of the battery output under low power load such as an air conditioner, which is used as a power supply load machine with a C-rate of low speed" and "the same measuring current cut-off signal control device as in Figure 15A".

[0218] This battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a memory device 710 such as a memory, a measuring current interruption signal control device 503, and an output stability judgment device 504. The system configuration in FIG16A differs from the aforementioned system configuration (refer to FIG6A, 8A-11A, and FIG13A) and does not include a charging / discharging machine 30. This is because the purpose is to perform degradation diagnosis of the mounted battery during operation under low power load. Furthermore, in this battery degradation diagnosis system 1, the internal configuration of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 is the same as in the basic configuration example (FIG6A). Furthermore, the internal structure and function of the current interruption signal control device 503 are the same as those shown in Figure 15A.

[0219] Immediately after the start of operation, the measuring device 40 directly transmits the initial charge and discharge characteristic data (measurement data V, I, T) of the battery-equipped machine (air conditioner, etc.) 20 detected by the detection unit 401 to the battery degradation diagnosis method determination device 10. The battery degradation diagnosis method determination device 10 calculates the C-rate and data sampling rate (through calculations already explained) based on the charge and discharge characteristic data, and specifies the measurement data accuracy prediction pattern (see Figure 1: Areas I to IX). If the measurement data accuracy prediction pattern is specified, the communication unit 402 of the measuring device 40 receives the battery output monitoring command from the battery degradation diagnosis method determination device 10, and issues an instruction to the output stability determination device 504 to start monitoring of "battery output caused by electrical load". After issuing the instruction to start battery output monitoring, the measuring device 40 provides the measured data (V, I, T) to the output stability judgment device 504.

[0220] The output stability determination device 504, as an internal component, includes a detection unit 5041 that detects (acquires) battery output monitoring commands and measurement data (V, I, T) provided by the measuring device 40, a calculation unit 5042, a memory unit 5043, and a communication unit 5044. The calculation unit 5042 determines whether the measurement data (battery output data) acquired by the detection unit 5041 has reached the stability region (whether the output value has reached a specific threshold value), and sends the determination result (result of the battery output data reaching the stability region) to the battery degradation diagnosis method determination device 10 via the communication unit 5044. In addition, the output stability determination device 504 differs from the power load monitoring device 502 in that it does not perform a determination of "whether the measurement current interruption start point (rest point) has been reached".

[0221] If the battery output data stability range is reached as determined by the output stability determination device 504, the battery degradation diagnosis method determination device 10 sends a measurement current cutoff command to the measurement device 40. If the measurement current cutoff command is received, the communication unit 402 of the measurement device 40 issues an instruction to the battery-mounted device 20 by implementing current cutoff.

[0222] The detection unit 401 of the measuring device 40 detects the charge and discharge characteristic data (measurement data V, I, T) after the current of the battery-mounted device 20 is interrupted. This measurement data is transmitted to the battery degradation diagnosis device 70 via the "communication unit 402 → output stability judgment device 504 → battery degradation diagnosis method judgment device 10". The battery degradation diagnosis device 70 diagnoses the degradation state of the target battery according to the battery degradation diagnosis method determined by the battery degradation diagnosis method judgment device 10.

[0223] Furthermore, in Figure 16A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, the memory device 710, and the output stability determination device 504 can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server. Also, it can be configured by integrating the functions of the output stability determination device 504 into a measuring device (e.g., OBDII).

[0224] In Figure 16A, the output stability judgment device 504 is integrated into the cloud control device. With this configuration, even a battery-equipped machine (e.g., a vehicle) 20 that does not have the output stability judgment device 504 can perform a degradation diagnosis of the target battery after the power is stabilized.

[0225] (ii) Operation diagrams 16B and C of the battery degradation diagnosis system 1 are flowcharts used to explain the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG16A. In FIG16B and C, steps 1501 to 1507 and steps 1601 to 1603 are performed instead of steps 1407 to 1409 in FIG14B. The processing contents of steps 1401 to 1406 and steps 1410 to 1413 are the same as those steps in FIG14B, and the processing contents of steps 1501 to 1507 are the same as those steps in FIG15B. Therefore, here, only steps 1601 to 1603 will be described.

[0226] (ii-1) Step 1601 When the C-rate (charge and discharge rate) is less than 0.2C (when step 1406 is YES), the calculation unit 101 of the battery degradation diagnosis method determination device 10 applies (determines to apply) the output stability judgment device 504.

[0227] (ii-2) Step 1602 The calculation unit 5042 of the output stability judgment device 504 obtains information on the battery output value (current value) at the battery mounting machine 20 from the measuring device (battery degradation state measuring device) 40 via the detection unit 5041.

[0228] (ii-3) Step 1603 The calculation unit 5042 calculates the standard deviation of the battery output value (current value) obtained in step 1602 and determines whether it converges to within 1% (in one example). When the standard deviation of the battery output value (battery value) converges to within 1% (when step 1603 is YES), the process moves to step 1502. On the other hand, when the standard deviation of the battery output value (battery value) is 1% or more (when step 1603 is NO), the process moves to step 1602. Since it is necessary to make the standard deviation of the battery output value (current value) less than 1%, the process of step 1602 → step 1603 is repeated until the battery output becomes stable. In this way, because the system is designed to perform battery degradation diagnosis after the battery output value (current value) has become stable, the system can obtain diagnostic results with high reliability.

[0229] <Example of the configuration of a battery degradation diagnosis system 1 having the function of specifying the start point (rest point) of current cutoff when the data sampling rate is in the medium-low speed range> (i) System configuration example Figure 17A is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 having the function of specifying the start point (rest point) of current cutoff when the data sampling rate is in the medium-low speed range (regions II, IV, VI, VII, VIII, IX in Figure 1). This system configuration is applicable when using an OBDII or BMS, etc., with a data sampling rate in the medium-low speed range as the measuring device 40.

[0230] This battery degradation diagnosis system 1 includes a battery degradation diagnosis method determination device 10, a battery mounting machine 20, a charge / discharge machine 30, a measuring device (battery degradation state measuring device) 40, a battery degradation diagnosis device 70, a memory device 710 such as a memory, and a rest point determination device 505. In this battery degradation diagnosis system 1, the internal structure of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 is the same as in the basic configuration example (Fig. 6A).

[0231] The measuring device 40 directly transmits the initial charge-discharge characteristic data (measurement data V, I, T) of the battery-mounted machine 20 detected by the detection unit 401 to the battery degradation diagnosis method determination device 10. The battery degradation diagnosis method determination device 10 calculates the C-rate and data sampling rate (through calculations already explained) based on the charge-discharge characteristic data, and specifies the measurement data accuracy prediction form (when the system configuration is applicable, it becomes one of regions II, IV, VI, VII, VIII, and IX). If the accuracy prediction of the measurement data is determined, the communication unit 402 of the measurement device 40 receives the pause point specific operation start command and the charge / discharge current cut-off command (current cut-off start command) from the battery degradation diagnosis method determination device 10, and issues an instruction to the pause point determination device 505 to determine the pause point (current cut-off start point), and issues a charge / discharge current cut-off instruction to the charge / discharge machine 30. If the pause point determination device 505 determines the pause point (current cut-off start point), it provides the result to the battery degradation diagnosis method determination device 10. Furthermore, after determining the pause point (current cut-off start point), the pause point determination device 505 sends the charge / discharge characteristic data (measurement data: V, I, T) of the battery to be diagnosed obtained from the measurement device 40 to the battery degradation diagnosis method determination device 10.

[0232] The rest point identification device 505 is an internal component that includes a detection unit 5051 that detects (acquires) measurement data (V, I, T) provided by the measuring device 40, a calculation unit 5052, a memory unit 5053, and a communication unit 5054. The calculation unit 5052, upon receiving a rest point identification operation start command from the measuring device 40, determines that the current supply system has been stopped (the current supplied to the battery has been cut off) when the V and I values ​​in the battery's charge / discharge characteristic data (measurement data: V, I, T) decrease by a specific value (held at the memory unit 5053). If a rest point is identified, the calculation unit 5052 sends the rest point (current cut-off) identification result to the battery degradation diagnosis method determination device 10 via the communication unit 5054. Furthermore, the calculation unit 5052 transmits the measurement data (V, I, T) obtained from the measuring device 40 after the rest point to the battery degradation diagnosis method determination device 10 via the communication unit 5054. In addition, the rest point determination device 505 is different from the power load monitoring device 502, and does not monitor the power of the battery being diagnosed, but only determines whether the rest point exists.

[0233] After the specific point of rest (current interruption), the charge and discharge characteristic data (measurement data V, I, T) of the battery-mounted machine 20 detected by the detection unit 401 of the measuring device 40 are sent to the battery degradation diagnosis device 70 via "communication unit 402 → rest point specific device 505 → battery degradation diagnosis method judgment device 10" to diagnose the degradation state of the battery being diagnosed.

[0234] Furthermore, in Figure 17A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, the rest point identification device 505, and the memory device 710 can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server. It can also be configured by integrating the function of the rest point identification device 505 into a measurement device (e.g., OBDII or BMS). If it is configured as shown in Figure 17A, by integrating the rest point identification device 505 into a cloud server, then even a battery-mounted machine (e.g., a vehicle) 20 that does not have the rest point identification device 505 can perform degradation diagnosis of the target battery using the identified rest point as a reference point.

[0235] (ii) Operation diagram 17B of the battery degradation diagnosis system 1 is a flowchart for explaining the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG17A. In FIG17B, the processing contents of steps 801 to 805, 809, and 812 to 814 are the same as those in FIG8B. Therefore, here, only steps 1701 to 1704 will be explained.

[0236] (ii-1) Step 1701 The calculation unit 101 of the battery degradation diagnosis method determination device 10 determines whether the data sampling rate calculated in step 804 is smaller than 100 S / s (in one example) (low or medium speed region, for example, one of regions II, IV, VI, VII, VIII, and IX in FIG1). When the data sampling rate is smaller than 100 S / s (when step 1701 is YES), the processing moves to step 1702. On the other hand, when the data sampling rate is 100 S / s or more (when step 1701 is NO), the processing moves to step 812.

[0237] (ii-2) Step 1702 Calculation unit 101 is applicable (determined to be applicable) rest point specific device 505.

[0238] (ii-3) Step 1703 The calculation unit 5052 of the rest point determination device 505, if it receives a rest point determination operation start command from the measuring device 40, determines that the current supply system has been stopped (the current supply to the battery has been cut off) when the V value and I value in the battery charge-discharge characteristic data (measurement data: V, I, T) decrease by a specific value (held at the memory unit 5053). If a rest point is determined, the calculation unit 5052 sends the rest point (current cut-off) determination result to the battery degradation diagnosis method determination device 10 via the communication unit 5054. Furthermore, the charge-discharge characteristic data (measurement data: V, I, T) obtained from the measuring device 40 after the rest point is sent to the battery degradation diagnosis device 70 via the communication unit 5054 → battery degradation diagnosis method determination device 10.

[0239] (ii-4) Step 1704 The battery degradation diagnosis device 70 diagnoses the battery based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the charge / discharge characteristic data (V, I, T values) obtained after the rest point (current interruption). For example, battery degradation diagnosis methods include those using SOH (State of Health), IR (Internal Resistance), and abnormal degradation degree.

[0240] <Example of a battery degradation diagnostic system 1 equipped with the function of specifying the start point (rest point) of the output current cutoff when the data sampling rate is in the medium-low speed range and the function of correcting the phase difference> (i) System configuration example Figure 18A is a diagram showing an example of the configuration of a battery degradation diagnostic system 1 equipped with the function of specifying the start point (rest point) of the output current cutoff when the data sampling rate is in the medium-low speed range (regions II, IV, VI, VII, VIII, IX in Figure 1) and the function of correcting the phase difference of the measured data. The system configuration is the same as that in Figure 17A, and it can be used when using an OBDII or BMS, etc., with a data sampling rate in the medium-low speed range as the measuring device 40.

[0241] The battery degradation diagnosis system 1 shown in FIG18A, in addition to the configuration of the battery degradation diagnosis system 1 shown in FIG17A, further includes a phase difference correction device 604. In this battery degradation diagnosis system 1, the internal configuration of the battery degradation diagnosis method determination device 10, the measuring device 40, and the battery degradation diagnosis device 70 is the same as in the basic configuration example (FIG. 6A). Furthermore, the internal configuration and operation of the rest point determination device 505 are the same as those of the rest point determination device 505 shown in FIG. 17A.

[0242] The phase difference correction device 604 has the function of "correcting the phase difference caused by irregular sampling rates during data measurement from BMS or OBDII, using the rest point (current interruption start point) specified by the rest point specifying device 505 as a reference". Internally, the phase difference correction device 604 includes, for example, a calculation unit 6041 for performing phase difference correction and a memory unit 6042 for storing correction parameters. The calculation unit 6041 determines whether the acquisition interval of the measurement data is irregular (whether the difference in acquisition intervals converges within a specific value) if it obtains measurement data (V, I, T) and rest point (current interruption start point) information from a battery degradation diagnosis method determination device. Subsequently, when it is determined that the acquisition interval of the measurement data is irregular, the calculation unit 6041 obtains the corrected data sampling rate (data interval) information from the memory unit 6042, corrects the sampling rate of the measurement data (interpolates the data) to create regular measurement data, and submits the measurement data with the corrected data sampling rate to the battery degradation diagnosis device 70. Furthermore, since it is not that "there is an error in the measurement data obtained by the measuring device 40", but simply that there is a shift in the acquisition interval, this shift is corrected by the phase difference correction device 604. Also, the corrected data sampling rate can be the initially obtained data sampling rate (see step 306 in Figure 3) or a pre-set specific data sampling rate.

[0243] Furthermore, in Figure 18A, the battery degradation diagnosis method determination device 10, the battery degradation diagnosis device 70, the rest point identification device 505, the phase difference correction device 604, and the memory device 710 can be configured as a cloud server, or the entire system or a part thereof can be configured as a local deployment or edge computing configuration without going through a cloud server. It can also be configured by integrating the function of the rest point identification device 505 into a measurement device (e.g., OBDII or BMS). If it is configured as shown in Figure 18A, by integrating the rest point identification device 505 into a cloud server, even a battery-mounted machine (e.g., a vehicle) 20 that does not have the rest point identification device 505 can perform degradation diagnosis of the target battery using the identified rest point as a reference point.

[0244] (ii) Operation diagram 18B of the battery degradation diagnosis system 1 is a flowchart for explaining the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in FIG18A. In FIG18B, the processing contents of steps 801 to 805, 809, and 812 to 814 are the same as those steps in FIG8B, and the processing contents of steps 1701 to 1703 are the same as those steps in FIG17B. Therefore, here, only steps 1801 to 1803 will be explained.

[0245] (ii-1) Step 1801 The calculation unit 101 of the battery degradation diagnosis method determination device 10 is applicable (determined to be applicable) to the phase difference correction device 604.

[0246] (ii-2) Step 1802 The calculation unit 6041 of the phase difference correction device 604 corrects the phase difference of the charge and discharge characteristic data generated under irregular sampling speeds, using the rest point (current interruption start point) as a reference. The charge and discharge characteristic data with the phase difference corrected is supplied to the battery degradation diagnosis device 70.

[0247] (ii-3) Step 1803 The battery degradation diagnosis device 70 diagnoses the battery based on the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the charge-discharge characteristic data (V, I, T values) obtained after the rest point (current interruption) and with the phase difference corrected. As a battery degradation diagnosis method, for example, there are methods using SOH (State of Health), IR (Internal Resistance), abnormal degradation degree, etc.

[0248] (iii) Example Figure 18C of charge and discharge characteristic data before and after phase difference correction is a diagram showing the charge and discharge characteristic data before the rest point and before the phase difference correction is applied (as one example, the time change of voltage) and the charge and discharge characteristic data after the phase difference correction is applied (as one example, the time change of voltage).

[0249] As shown in Figure 18C, the phase difference correction device 604 uses the rest point (current interruption start point) specified by the rest point specifying device 505 as a reference point to correct the phase difference in the charge / discharge characteristic data, which is mainly caused by irregular sampling rates, in a way that makes the data sampling rate constant. For example, when the data sampling time of the acquired charge / discharge characteristic data is not consistent with the data sampling period based on the rest point, it is corrected to the time of the closest data sampling period.

[0250] <Summary> (i) According to this embodiment, the battery degradation diagnosis method determination device 10 stores information of a multidimensional vector space (refer to FIG4 and FIG5) in a memory unit (memory device) 102. The information of the multidimensional vector space is distributed in a multidimensional vector space composed of at least two indicators, namely the data sampling rate of the measuring device (battery degradation state measuring device) 40 that measures the battery and the S / N ratio (C-rate) of the power supply load device (charge and discharge machine 30 or air conditioner, etc.), and in a plurality of regions defined by a threshold value of 1 or more set at each indicator. The device 10 performs the following processes: processing information obtained from the memory unit 102 in a multi-dimensional vector space; processing the acquisition of at least two index values ​​(C-rate and data sampling rate) corresponding to the charge-discharge characteristic data of the battery measured by the measuring device 40 (the data sampling rate is calculated based on the charge-discharge characteristic data, and the C-rate is either input from an external source or calculated based on the charge-discharge characteristic data); processing the identification of one region in the multi-dimensional vector space corresponding to the acquired at least two index values; and processing the determination of the battery degradation diagnosis method corresponding to the identified region as the battery degradation diagnosis method. With this configuration, it is possible to determine an appropriate battery degradation diagnosis method for various combinations of C-rate (power supply load machine) and data sampling rate (measuring device 40), thus obtaining highly reliable battery degradation diagnosis results.

[0251] In addition, the above-mentioned multidimensional vector space refers to the S / N ratio (C-rate) of the power supply load machine, and includes a complex region distinguished by a first S / N ratio threshold (e.g., 0.5C) and a second S / N ratio threshold (e.g., 0.2C) that is smaller than the first S / N ratio threshold (e.g., 0.2C) (see Figure 1).

[0252] (ii) When the data sampling rate of the battery degradation diagnosis method determination device 10 is lower than a specific sampling threshold (e.g., lower than 100 S / s), the measurement data smoothing processing device 603 is applied from the device group B60. Therefore, since the charge / discharge characteristic data is smoothed and interpolated for data in areas not yet acquired, battery degradation diagnosis can be performed using charge / discharge characteristic data with fewer variation points (smooth data).

[0253] (iii) When the data sampling rate is lower than a specific sampling threshold (e.g., lower than 100 S / s), the battery degradation diagnosis method determination device 10 applies a rest point specific device 505 from the device group A50. Therefore, since the rest point is specifically the starting point of the interruption of the current supplied to the battery, the necessary processing in battery degradation diagnosis can be performed based on that rest point.

[0254] Furthermore, at this time, the battery degradation diagnosis method determination device 10 can also be further equipped with a phase difference correction device 604 from the device group B60, which corrects the phase difference of charge and discharge characteristic data. By correcting the phase difference of charge and discharge characteristic data based on the rest point, it is possible to set the irregular data sampling rate to a constant data sampling rate (making the irregular data sampling interval a constant interval).

[0255] (iv) In the battery degradation diagnosis method determination device 10, when the S / N ratio of the power supply load machine is smaller than the first S / N ratio threshold (e.g., 0.5C: low and medium speed range), a measurement data amplification device 601 that amplifies charge and discharge characteristic data is applied from the device group B60. Therefore, since the charge and discharge characteristic data is amplified, the measurement sensitivity is improved.

[0256] Furthermore, at this time, the battery degradation diagnosis method determination device 10 can also be equipped with a high-frequency component blocking adjustment device 602, which removes high-frequency components contained in the amplified charge-discharge characteristic data, from among the device groups B60. Therefore, since battery degradation diagnosis is performed on charge-discharge characteristic data that has been amplified (data with improved measurement sensitivity) and for which high-frequency components have been removed, a more accurate (more reliable) diagnostic result can be obtained.

[0257] (v) Battery degradation diagnosis method determination device 10, when the S / N ratio of the power supply load machine is smaller than the aforementioned second S / N ratio threshold (e.g., 0.2C: low speed region), is a power load monitoring device from device group A50 that monitors the power load status of the battery. Since the power load monitoring device can monitor whether the power load status of the battery has become stable, degradation diagnosis can be performed using charge and discharge characteristic data after the battery's power load status has become stable, and a more accurate (more reliable) diagnostic result can be obtained.

[0258] (vi) The battery degradation diagnosis method determination device 10, when the S / N ratio of the power supply load machine is smaller than the second S / N ratio threshold (e.g., 0.2C), is selected from device group A50 and is equipped with a measurement current interruption signal control device that sends a measurement current interruption signal to the battery-mounted machine 20 when the charge / discharge characteristic data meets the preset conditions. This is effective when the measurement device 40 does not have a control function to interrupt the current to the battery-mounted machine 20.

[0259] (vii) The battery degradation diagnosis method determination device 10 is an output stability determination device from the device group A50 that monitors the battery output data and determines whether the battery output has reached the stable region when the S / N ratio of the power supply load machine is smaller than the second S / N ratio threshold (e.g., 0.2C). This allows for degradation diagnosis using charge / discharge characteristic data of the battery after its output has stabilized, resulting in more accurate (more reliable) diagnostic results.

[0260] (viii) The battery degradation diagnosis method determination device 10, when it is impossible to obtain charge / discharge characteristic data because the charge / discharge circuit of the battery-mounted device 20 is in an open circuit state, uses a charge / discharge circuit opening / closing control device 501 from device group A50 to control the opening and closing of the charge / discharge circuit of the battery-mounted device 20. Therefore, even when using a general high-speed data sampling measurement device 40 such as an analyzer or terminal, it is possible to properly measure the battery's condition.

[0261] (ix) The battery degradation diagnosis method determination device 10 can also be configured to calculate the S / N ratio of the power supplied to the load machine based on the capacity information (at least for the battery to be diagnosed) input from the battery data input device (battery information input device) 90, and the charge / discharge characteristic data measured by the measuring device 40. Therefore, when the memory unit 102 of the battery degradation diagnosis method determination device 10 does not hold the capacity information of the battery to be diagnosed (during manufacturing), since the capacity information of the battery to be diagnosed can be obtained from the outside, it is possible to appropriately perform degradation diagnosis on various types of batteries.

[0262] (x) In this embodiment, all or at least part of the constituent elements of the battery degradation diagnosis system 1, such as the battery degradation diagnosis method determination device 10 and the battery degradation diagnosis device 70, can be configured as a cloud server. Alternatively, the battery degradation diagnosis system 1 can be implemented in a local deployment or edge computing configuration instead of as a cloud server. Furthermore, the calculation unit 101 of the battery degradation diagnosis method determination device 10 and the calculation units in other constituent elements (e.g., the calculation unit 701 of the battery degradation diagnosis device 70) can be configured as a single calculation unit (processor), or they can be configured as separate calculation units (processors).

[0263] (xi) The functions of the embodiments disclosed herein can also be implemented by software code. In this case, a memory medium storing the code is provided to the system or device, and the computer (or CPU or MPU) of the system or device reads the code stored in the memory medium. In this case, the code itself, which is read from the memory medium, performs the functions of the aforementioned embodiments, and the code itself and the memory medium storing the code constitute this disclosure. Examples of such memory media for supplying code include floppy disks, CD-ROMs, DVD-ROMs, hard disks, optical discs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, etc.

[0264] Furthermore, the system can also be configured to perform part or all of the actual processing by the OS (operating system) or the like operating on the computer based on the instructions of the program code, and to realize the functions of the aforementioned implementation through such processing. Moreover, the system can also be configured to, after the program code read from the memory medium is written into the computer's memory, perform part or all of the actual processing by the CPU or the like based on the instructions of the program code, and to realize the functions of the aforementioned implementation through such processing.

[0265] Furthermore, the program code of the software that implements the function can also be distributed through the Internet and stored in the system or device's hard drive or memory or in the memory medium such as CD-RW or CD-R. When in use, the computer (or CPU or MPU) of the system or device will read out and execute the program code stored in the memory or memory medium.

[0266] Finally, the processes and technologies described herein are not inherently associated with any specific device, and can be installed regardless of the corresponding combination of components. Furthermore, various forms of devices for general purposes can be used according to the descriptions in the embodiments. Dedicated devices can also be constructed to perform the various processes described in the embodiments. Moreover, various inventions can be formed by appropriate combinations of the plurality of constituent elements disclosed in the embodiments. For example, several constituent elements can be removed from all the constituent elements shown in the embodiments. Furthermore, constituent elements can be appropriately combined across different embodiments. Although this disclosure is described in relation to specificity, from any point of view, these descriptions are not intended to limit this disclosure, but are merely illustrative. Those skilled in the art will recognize that a variety of suitable combinations of hardware, software, and firmware exist for implementing this disclosure. For example, the software described can be installed using a wide range of programming or scripting languages ​​such as assembly languages, C / C++, Perl, Shell, PHP, and Java (registered trademark).

[0267] (xii) In this embodiment, control lines or information lines represent those deemed necessary for the description. Not all control lines or information lines are necessarily marked on the product. This allows all components to be interconnected.

[0268] Furthermore, those skilled in the art can clearly understand other installation configurations of this disclosure based on the description and examination of the embodiments disclosed herein. The diverse configurations and / or combinations of the described embodiments can be used individually or in any combination. The description and specific examples are merely typical configurations, and the scope and spirit of this disclosure are shown based on the scope of the subsequent patent applications. [Simplified Explanation of the Diagram]

[0013] [Figure 1] is a diagram illustrating the general outline of the battery degradation diagnosis method determination process according to this embodiment. [Figure 2] is a diagram showing an overall configuration example of the battery degradation diagnosis system 1 including the battery degradation diagnosis method determination device 10 according to this embodiment. [Figure 3] is a flowchart illustrating the processing (overall processing) at the battery degradation diagnosis system 1 from the start of battery degradation diagnosis to the end of diagnosis. [Figure 4] is a diagram showing the "information on the battery degradation diagnosis method and applicable device corresponding to the measurement data accuracy prediction mode (hereinafter also referred to as "measurement data accuracy prediction mode applicable information")" 400 held by the battery degradation diagnosis method determination device 10. [Figure 5] is a diagram showing the management information 500 held by the battery degradation diagnosis device 70, which records the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device 10 and the indicators used in the diagnosis. [Figure 6A] is a diagram showing a basic configuration example of the battery degradation diagnosis system 1. [Figure 6B] is a flowchart explaining the processing of the battery degradation diagnosis system 1 as shown in the basic configuration example. [Figure 7A] is a diagram showing a configuration example of the battery degradation diagnosis system 1 that does not include a measuring device (battery degradation state measuring device) 40. [Figure 7B] is a flowchart explaining the operation of the battery degradation diagnosis system 1 as shown in the configuration example of Figure 7A. [Figure 8A] is a diagram showing a configuration example of the battery degradation diagnosis system 1 that has the function of improving measurement sensitivity when the charging / discharging speed is in a low-speed region (e.g., regions III, IV, and VIII: see Figure 1). [Figure 8B] is a flowchart explaining the operation of the battery degradation diagnosis system 1 as shown in the configuration example of Figure 8A. [Figure 9A] is a diagram illustrating an example of the configuration of a battery degradation diagnosis system 1 that amplifies the measurement data when the charging / discharging speed is in a low-speed region (e.g., regions III, IV, and VIII: see Figure 1) and extracts only the low-frequency components of the amplified data (components needed in battery degradation diagnosis). [Figure 9B] is a flowchart illustrating the operation of the battery degradation diagnosis system 1 as shown in Figure 9A. [Figure 10A] is a diagram illustrating an example of the configuration of a battery degradation diagnosis system 1 that smooths the measurement data and interpolates regions where the data is not acquired when the data sampling speed is in a low-speed region (e.g., regions II, IV, and IX: see Figure 1). [Figure 10B] is a flowchart illustrating the operation of the battery degradation diagnosis system 1 as shown in Figure 10A. [Figure 10C] is a diagram illustrating an example of charge / discharge characteristic data before and after smoothing processing.[Figure 11A] is a diagram illustrating an example of the configuration of a battery degradation diagnostic system 1, which has the function of obtaining battery characteristic data or manufacturing ID information of the battery to be diagnosed from an external source. [Figure 11B] is a flowchart explaining the operation of the battery degradation diagnostic system 1 caused by the configuration shown in Figure 11A. [Figure 12] is a diagram illustrating the management information 1200 held by the battery degradation diagnostic device 70 and containing the battery degradation diagnostic method determined by the battery degradation diagnostic method determination device 10, the indicators used in the diagnosis, and manufacturing information. [Figure 13A] is a diagram illustrating an example of the configuration of a battery degradation diagnostic system 1, which has the function of measuring the battery degradation state when the data sampling rate is in a high-speed region (e.g., regions I, III, and V in Figure 1). [Figure 13B] is a diagram illustrating an example of the configuration of a battery degradation diagnostic system 1 that has the function of measuring the battery degradation state when the data sampling rate is in a high-speed region (e.g., regions I, III, and V in Figure 1) and is intended to be an electric vehicle 20' as a battery-equipped device. [Figure 13C] is a flowchart explaining the operation of the battery degradation diagnostic system 1 caused by the configuration shown in Figure 13A or Figure 13B. [Figure 14A] is a diagram illustrating an example of the configuration of a battery degradation diagnostic system 1 that has the function of monitoring the power load of a low-power load, such as an air conditioner, when used as a power supply load device in a low-speed region. [Figure 14B] is a flowchart explaining the operation of the battery degradation diagnostic system 1 caused by the configuration shown in Figure 14A. [Figure 15A] is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 with an additional measuring current interruption signal control device 503. [Figure 15B] is a flowchart explaining the operation of the battery degradation diagnosis system 1 caused by the configuration shown in Figure 15A. [Figure 16A] is a diagram showing an example of the configuration of a battery degradation diagnosis system 1 that has a function of "determining the battery output stability of a low-power load such as an air conditioner when it is used as a power supply load machine with a low C-rate in the low-speed range" and "the same measuring current interruption signal control device as in Figure 15A". [Figure 16B] is a flowchart (first half) explaining the operation of the battery degradation diagnosis system 1 caused by the configuration shown in Figure 16A. [Figure 16C] is a flowchart (second half) explaining the operation of the battery degradation diagnosis system 1 caused by the configuration example shown in Figure 16A.[Figure 17A] is a diagram illustrating an example of the configuration of a battery degradation diagnostic system 1 that has the function of specifying the start point (rest point) of current interruption when the data sampling rate is in the medium-low speed region (regions II, IV, VI, VII, VIII, IX of Figure 1). [Figure 17B] is a flowchart illustrating the operation of the battery degradation diagnostic system 1 caused by the configuration shown in Figure 17A. [Figure 18A] is a diagram illustrating an example of the configuration of a battery degradation diagnostic system 1 that has the function of specifying the start point (rest point) of current interruption when the data sampling rate is in the medium-low speed region (regions II, IV, VI, VII, VIII, IX of Figure 1) and the function of correcting the phase difference of the measured data. [Figure 18B] is a flowchart illustrating the operation of the battery degradation diagnostic system 1 caused by the configuration shown in Figure 18A. [Figure 18C] is a graph showing the charge-discharge characteristics before the rest point and before the application of phase difference correction (as an example, the time-varying voltage) and the charge-discharge characteristics after the application of phase difference correction (as an example, the time-varying voltage).

Claims

1. A battery degradation diagnosis method determination apparatus, which determines a battery degradation diagnosis method suitable for a battery to be diagnosed, and comprises: a memory device; and a processor, wherein the memory device stores information in a multi-dimensional vector space, the information of which is allocated with respect to a battery degradation diagnosis method in a multi-dimensional vector space composed of at least two indices, namely, the data sampling rate of the measuring device measuring the battery and the S / N ratio of the power supply load machine of the battery, and a plurality of regions defined by threshold values ​​of 1 or more set at each indices; the processor performs: The process of obtaining information from the aforementioned multidimensional vector space from the aforementioned memory device; the process of obtaining the values ​​of the aforementioned at least two indicators corresponding to the measured charge and discharge characteristics data of the aforementioned battery; the process of identifying a region in the aforementioned multidimensional vector space corresponding to the aforementioned at least two indicator values; and the process of determining the battery degradation diagnosis method corresponding to the aforementioned identified region as the battery degradation diagnosis method for the aforementioned battery.

2. The battery degradation diagnosis method and determination device as described in claim 1, wherein, The aforementioned processor calculates the values ​​of at least two of the aforementioned indicators based on the aforementioned charge and discharge characteristic data measured during the charging and discharging of the aforementioned battery.

3. The battery degradation diagnosis method and determination device as described in claim 1, wherein, The system is further configured such that when the aforementioned data sampling rate is lower than a specific sampling threshold, the aforementioned processor, during battery degradation diagnosis, determines to perform at least one of the following processes: smoothing processing to smooth the charging and discharging characteristic data of the aforementioned battery, specific processing to determine the rest point of the charging and discharging current interruption start point of the aforementioned battery during battery degradation diagnosis, or phase difference correction processing to correct the phase difference of the aforementioned data sampling rate.

4. The battery degradation diagnosis method and determination device as described in claim 1, wherein, The aforementioned multidimensional vector space refers to the S / N ratio of the aforementioned power supply load machine, and includes the region of the aforementioned complex number distinguished by the first S / N ratio threshold and the second S / N ratio threshold, which is smaller than the first S / N ratio threshold.

5. The battery degradation diagnosis method and determination device as described in claim 1, wherein, When the aforementioned S / N ratio is lower than the first S / N ratio threshold, the aforementioned processor decides to perform amplification processing to amplify the aforementioned battery charge and discharge characteristic data during battery degradation diagnosis.

6. The battery degradation diagnosis method and determination device as described in claim 5, wherein, The aforementioned processor further decided to implement a high-frequency component blocking adjustment process, which removes the high-frequency components of the aforementioned battery's charge and discharge characteristic data, which has been improved in sensitivity through the aforementioned amplification process.

7. The battery degradation diagnosis method and determination device as described in claim 4, wherein, When the aforementioned S / N ratio is lower than the second S / N ratio threshold, the aforementioned processor, during battery degradation diagnosis, decides to perform at least one of the following processes: power load monitoring process to monitor the power load of the aforementioned battery, current interruption control process to stop the current supply to the aforementioned battery, or output stability determination process to determine whether the output of the aforementioned battery has reached the stable region.

8. A battery degradation diagnosis system for diagnosing the degradation state of a battery, comprising: a power supply load machine for the battery; a measuring device for measuring charge-discharge characteristic data of the battery; a battery degradation diagnosis method determination device for determining a battery degradation diagnosis method based on the charge-discharge characteristic data obtained by the measuring device; and a battery degradation diagnosis device for diagnosing the degradation state of the battery according to the battery degradation diagnosis method determined by the battery degradation diagnosis method determination device. The aforementioned battery degradation diagnosis method determination device stores information of a multidimensional vector space in a memory device. This information is distributed in a multidimensional vector space consisting of at least two indices: the data sampling rate of the measuring device used to measure the aforementioned battery and the S / N ratio of the power supply load machine of the aforementioned battery. It is further divided into multiple regions defined by threshold values ​​of 1 or more set at each indices, each corresponding to a battery degradation diagnosis method. The aforementioned battery degradation diagnosis method determination device performs the following steps: processing to obtain the information of the aforementioned multidimensional vector space from the memory device; processing to obtain the values ​​of the aforementioned at least two indices corresponding to the charge / discharge characteristic data of the aforementioned battery measured by the aforementioned measuring device; processing to identify one region in the aforementioned multidimensional vector space corresponding to the values ​​of the aforementioned at least two indices; and processing to determine the battery degradation diagnosis method corresponding to the identified region as the battery degradation diagnosis method for the aforementioned battery.

9. The battery degradation diagnostic system as described in claim 8, wherein, Furthermore, the system includes: a measurement data smoothing processing device, which smooths the aforementioned charge-discharge characteristic data and interpolates the data for areas not acquired; and a battery degradation diagnosis method determination device, which is configured to: when the data sampling rate calculated based on the aforementioned charge-discharge characteristic data is lower than a specific sampling threshold, apply the aforementioned measurement data smoothing processing device and provide the smoothed charge-discharge characteristic data to the aforementioned battery degradation diagnosis device.

10. The battery degradation diagnostic system as described in claim 8, wherein, Furthermore, the system includes: a rest point determination device, which determines a rest point based on the aforementioned measured charge-discharge characteristic data of the battery. The rest point is the starting point at which the current supplied to the battery is cut off. The aforementioned battery degradation diagnosis method determination device is configured to apply the aforementioned rest point determination device when the data sampling rate calculated based on the aforementioned charge-discharge characteristic data is lower than a specific sampling threshold value.

11. The battery degradation diagnostic system as described in claim 10, wherein, Furthermore, the system includes: a phase difference correction device for correcting the phase difference of the aforementioned charge-discharge characteristic data; and a battery degradation diagnosis method determination device configured to: use the aforementioned phase difference correction device to correct the phase difference of the aforementioned charge-discharge characteristic data based on the aforementioned rest point.

12. The battery degradation diagnostic system as described in claim 8, wherein, The aforementioned multidimensional vector space refers to the S / N ratio of the aforementioned power supply load machine, and includes the region of the aforementioned complex number distinguished by the first S / N ratio threshold and the second S / N ratio threshold, which is smaller than the first S / N ratio threshold.

13. The battery degradation diagnostic system as described in claim 12, wherein, Furthermore, the system includes: a measurement data amplification device for amplifying the aforementioned charge-discharge characteristic data; and a battery degradation diagnosis method determination device configured such that when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge-discharge characteristic data is smaller than the aforementioned first S / N ratio threshold, the aforementioned measurement data amplification device is used, and the amplified charge-discharge characteristic data is provided to the aforementioned battery degradation diagnosis device.

14. The battery degradation diagnostic system as described in claim 13, wherein, Furthermore, the system includes: a high-frequency component blocking adjustment device, which removes high-frequency components contained in the aforementioned amplified charge-discharge characteristic data; and a battery degradation diagnosis method determination device, which is configured to: when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge-discharge characteristic data is smaller than the aforementioned first S / N ratio threshold value, apply the aforementioned high-frequency component blocking adjustment device and provide the aforementioned amplified charge-discharge characteristic data in which the aforementioned high-frequency components have been removed to the aforementioned battery degradation diagnosis device.

15. The battery degradation diagnostic system as described in claim 12, wherein, Furthermore, the system includes: a power load monitoring device for monitoring the power load status of the aforementioned battery; a battery degradation diagnosis method determination device configured to: apply the aforementioned power load monitoring device when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned second S / N ratio threshold value; and a battery degradation diagnosis device that uses the aforementioned charge / discharge characteristic data after the power load status of the aforementioned battery has stabilized to diagnose the degradation status of the aforementioned battery.

16. The battery degradation diagnostic system as described in claim 12, wherein, Furthermore, the system includes: a measurement current interruption signal control device, which sends a measurement current interruption signal to the battery-mounted machine that interrupts the current to the battery-mounted machine when the aforementioned charge / discharge characteristic data meets the preset conditions; a battery degradation diagnosis method determination device, which is configured to apply the measurement current interruption signal control device when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned second S / N ratio threshold value; and a battery degradation diagnosis device that uses the aforementioned charge / discharge characteristic data after the current to the aforementioned battery-mounted machine is interrupted to diagnose the degradation state of the aforementioned battery.

17. The battery degradation diagnostic system as described in claim 12, wherein, Furthermore, the system includes: an output stability determination device that monitors the output data of the aforementioned battery and determines whether the battery output has reached a stable range; a battery degradation diagnosis method determination device configured to apply the aforementioned output stability determination device when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned second S / N ratio threshold value; and a battery degradation diagnosis device that uses the aforementioned charge / discharge characteristic data after the output of the aforementioned battery has become stable to diagnose the degradation state of the aforementioned battery.

18. The battery degradation diagnostic system as described in claim 8, wherein, Furthermore, the system includes a charge / discharge circuit opening / closing control device, which controls the opening and closing of the charge / discharge circuit of the battery-mounted device equipped with the aforementioned battery. The aforementioned charge / discharge circuit opening / closing control device is configured to control the opening and closing of the charge / discharge circuit of the aforementioned battery-mounted device in response to a charge / discharge start / stop signal, thereby enabling the measurement of the charge / discharge characteristic data of the aforementioned battery caused by the aforementioned measuring device.

19. The battery degradation diagnostic system as described in claim 8, wherein, Furthermore, the system includes: a battery information input device that inputs baseline capacity information for at least the battery to be diagnosed; and a battery degradation diagnosis method determination device that calculates the S / N ratio of the battery's power supply to the load machine based on the baseline capacity information and the charge / discharge characteristic data measured by the aforementioned measuring device.

20. A battery degradation diagnosis system for diagnosing the degradation state of a battery, comprising: a data input device for inputting power supply load machine power ratio information of the battery, data sampling speed information of a measuring device measuring the battery, and charge / discharge characteristic data of the battery; a battery degradation diagnosis method determination device for determining a battery degradation diagnosis method based on the power supply load machine power ratio information and the data sampling speed information input via the data input device; and a battery degradation diagnosis device for diagnosing the battery degradation state according to the degradation diagnosis method determined by the battery degradation diagnosis method determination device. The aforementioned battery degradation diagnosis method determination device stores information of a multidimensional vector space in a memory device. This information is distributed in a multidimensional vector space consisting of at least two indices: the data sampling rate of the measuring device used to measure the aforementioned battery and the S / N ratio of the power supply load machine of the aforementioned battery. It is assigned battery degradation diagnosis methods corresponding to each of the multiple regions defined by threshold values ​​of 1 or more set at each indices. The aforementioned battery degradation diagnosis method determination device performs the following steps: processing to obtain the information of the aforementioned multidimensional vector space from the aforementioned memory device; processing to identify one region in the aforementioned multidimensional vector space corresponding to the values ​​of the aforementioned S / N ratio and the aforementioned data sampling rate; and processing to determine the battery degradation diagnosis method corresponding to the identified region as the battery degradation diagnosis method for the aforementioned battery.

21. A battery degradation diagnosis system for diagnosing the degradation state of a battery, comprising: a power supply load machine for the battery; a measuring device for measuring charge-discharge characteristic data of the battery; and a server device having a battery degradation diagnosis method determination function and a battery degradation diagnosis function, wherein the battery degradation diagnosis method determination function determines a battery degradation diagnosis method based on the charge-discharge characteristic data obtained by the measuring device, and the battery degradation diagnosis function diagnoses the degradation state of the battery by following the battery degradation diagnosis method determined by the battery degradation diagnosis method determination function. The aforementioned server device stores information in a multidimensional vector space in a memory device. This information is distributed in a multidimensional vector space consisting of at least two indices: the data sampling rate of the measuring device used to measure the battery and the S / N ratio of the battery's power supply to the load machine. A battery degradation diagnosis method is assigned to each of these indices in a corresponding manner to multiple regions defined by threshold values ​​of 1 or more set at each indices. Furthermore, the aforementioned server device, as the battery degradation diagnosis method determination function, performs the following steps: processing to obtain the information in the multidimensional vector space from the memory device; processing to obtain the values ​​of the at least two indices corresponding to the charge / discharge characteristic data of the battery measured by the measuring device; processing to identify one region in the multidimensional vector space corresponding to the obtained at least two indices; and processing to determine the battery degradation diagnosis method corresponding to the identified region as the battery degradation diagnosis method for the aforementioned battery.

22. A method for determining a battery degradation diagnostic method suitable for a battery subject to diagnosis, comprising: The steps include: preparing a memory device for storing information in a multidimensional vector space, wherein the information in the multidimensional vector space is allocated with battery degradation diagnosis methods corresponding to at least two indices: the data sampling rate of the measuring device measuring the aforementioned battery and the S / N ratio of the power supply load machine of the aforementioned battery; the steps include: having a processor obtain the information in the aforementioned multidimensional vector space from the aforementioned memory device; having the processor obtain the values ​​of the aforementioned at least two indices corresponding to the measured charge / discharge characteristic data of the aforementioned battery; having the processor identify one region in the aforementioned multidimensional vector space corresponding to the values ​​of the aforementioned at least two indices; and having the processor determine the battery degradation diagnosis method corresponding to the identified region as the battery degradation diagnosis method for the aforementioned battery.

23. The method as described in claim 22, wherein, The aforementioned processor calculates the values ​​of at least two of the aforementioned indicators based on the aforementioned charge and discharge characteristic data measured during the charging and discharging of the aforementioned battery.

24. The method as described in claim 22, wherein, The system further includes the following steps: when the aforementioned data sampling rate is lower than a specific sampling threshold, the aforementioned processor, during battery degradation diagnosis, determines to perform smoothing processing to smooth the charging and discharging characteristic data of the aforementioned battery, specific processing to determine the rest point of the charging and discharging current interruption start point of the aforementioned battery during battery degradation diagnosis, or phase difference correction processing to correct the phase difference of the aforementioned data sampling rate, at least one of these processing steps.

25. The method as described in claim 22, wherein, The aforementioned multidimensional vector space refers to the S / N ratio of the aforementioned power supply load machine, and includes the region of the aforementioned complex number distinguished by the first S / N ratio threshold and the second S / N ratio threshold, which is smaller than the first S / N ratio threshold.

26. The method as described in claim 25, wherein, The system includes the following steps: when the aforementioned S / N ratio is lower than the first S / N ratio threshold, the aforementioned processor, during battery degradation diagnosis, decides to perform an amplification process to amplify the aforementioned battery's charge and discharge characteristic data.

27. The method as described in claim 26, wherein, The system includes a step of causing the aforementioned processor to further determine to perform a high-frequency component blocking adjustment process that removes high-frequency components from the aforementioned battery charge-discharge characteristic data whose sensitivity has been improved by the aforementioned amplification process.

28. The method as described in claim 25, wherein, The system includes at least one of the following steps: when the aforementioned S / N ratio is lower than the second S / N ratio threshold, the aforementioned processor, during battery degradation diagnosis, decides to perform power load monitoring processing to monitor the power load of the aforementioned battery, current interruption control processing to stop the current supply to the aforementioned battery, or output stability determination processing to determine whether the output of the aforementioned battery has reached a stable region.

29. A method for diagnosing the degradation state of a battery subject to diagnosis, comprising: preparing a power supply load machine for the battery subject to diagnosis and a measuring device for measuring charge-discharge characteristic data of the battery; and having a first processor determine the degradation state of the battery based on the charge-discharge characteristic data obtained by the measuring device; and having the first processor or a second processor different from the first processor diagnose the degradation state of the battery according to the degradation diagnosis method, wherein the step of determining the degradation state of the battery includes: The steps include: 1) enabling the first processor to acquire information about the multidimensional vector space from a memory device storing information about the multidimensional vector space, wherein the information about the multidimensional vector space is assigned to a battery degradation diagnosis method in a multidimensional vector space composed of at least two indices, namely the data sampling rate of the aforementioned measuring device and the S / N ratio of the aforementioned power supply load machine; 2) enabling the first processor to acquire values ​​of the aforementioned at least two indices corresponding to the charge / discharge characteristic data of the aforementioned battery measured by the aforementioned measuring device; 3) enabling the first processor to identify one region in the aforementioned multidimensional vector space corresponding to the acquired values ​​of the aforementioned at least two indices; and 4) enabling the first processor to determine the battery degradation diagnosis method corresponding to the identified region as the battery degradation diagnosis method for the aforementioned battery.

30. The method as described in claim 29, wherein, The system further includes: when the sampling rate of the aforementioned data calculated based on the aforementioned charge-discharge characteristic data is lower than a specific sampling threshold, the aforementioned first processor decides to apply a measurement data smoothing process that smooths the aforementioned charge-discharge characteristic data and interpolates the data in the unacquired area. The aforementioned first processor or the aforementioned second processor uses the charge-discharge characteristic data that has been smoothed by the aforementioned measurement data smoothing process to diagnose the degradation state of the aforementioned battery.

31. The method as described in claim 29, wherein, The system further includes a step of determining, when the sampling rate of the data calculated based on the aforementioned charge-discharge characteristic data is lower than a specific sampling threshold, that the aforementioned first processor applies rest point determination processing based on the aforementioned measured charge-discharge characteristic data of the battery to determine the rest point of the current cut-off start point supplied to the aforementioned battery.

32. The method as described in claim 31, wherein, The system further includes a step of: when the sampling rate of the aforementioned data calculated based on the aforementioned charge-discharge characteristic data is lower than a specific sampling threshold, the aforementioned first processor decides to apply a phase difference correction process to correct the phase difference of the aforementioned charge-discharge characteristic data based on the aforementioned rest point. The aforementioned first processor or the aforementioned second processor uses the charge-discharge characteristic data that has been phase-corrected by the aforementioned phase difference correction process to diagnose the degradation state of the aforementioned battery.

33. The method as described in claim 29, wherein, The aforementioned multidimensional vector space refers to the S / N ratio of the aforementioned power supply load machine, and includes the region of the aforementioned complex number distinguished by the first S / N ratio threshold and the second S / N ratio threshold, which is smaller than the first S / N ratio threshold.

34. The method as described in claim 33, wherein, Furthermore, the method includes a measurement data amplification device. The method further includes: when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned first S / N ratio threshold value, the aforementioned first processor determines to apply a measurement data amplification processing to amplify the aforementioned charge / discharge characteristic data. The aforementioned first processor or the aforementioned second processor uses the charge / discharge characteristic data amplified by the aforementioned measurement data amplification processing to perform the aforementioned battery degradation diagnosis.

35. The method as described in claim 34, wherein, The system further includes: when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned first S / N ratio threshold, the aforementioned first processor determines to apply a high-frequency component blocking adjustment process that removes high-frequency components contained in the aforementioned amplified charge / discharge characteristic data. The aforementioned first processor or the aforementioned second processor uses the charge / discharge characteristic data that has been amplified by the aforementioned measurement data amplification process and whose high-frequency components have been removed by the aforementioned high-frequency component blocking adjustment process to perform the aforementioned battery degradation diagnosis.

36. The method as described in claim 33, wherein, The system further includes: when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned second S / N ratio threshold, the aforementioned first processor determines to apply a power load monitoring process to monitor the power load status of the aforementioned battery. The aforementioned first processor or the aforementioned second processor uses the aforementioned charge / discharge characteristic data after the power load status of the aforementioned battery has become stable to perform the aforementioned battery degradation diagnosis.

37. The method as described in claim 33, wherein, The system further includes: when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned second S / N ratio threshold, the aforementioned first processor determines to apply a measurement current interruption signal control processing step, which sends a measurement current interruption signal to the aforementioned battery-equipped power-equipped machine to interrupt the current to the aforementioned battery-equipped machine. The aforementioned first processor or the aforementioned second processor uses the aforementioned charge / discharge characteristic data after interrupting the current to the aforementioned battery-equipped machine to perform the aforementioned battery degradation diagnosis.

38. The method as described in claim 33, wherein, The system further includes: when the S / N ratio of the aforementioned power supply load machine corresponding to the aforementioned charge / discharge characteristic data is smaller than the aforementioned second S / N ratio threshold, the aforementioned first processor decides to apply the step of monitoring the output data of the aforementioned battery and determining whether the battery output has reached the stable region for output stability determination processing. The aforementioned first processor or the aforementioned second processor uses the aforementioned charge / discharge characteristic data after the output of the aforementioned battery has become stable to perform the aforementioned battery degradation diagnosis.

39. The method as described in claim 29, wherein, The system further includes: a step of preparing a charge / discharge circuit opening / closing control device for opening and closing the charge / discharge circuit of a battery-mounted machine equipped with the aforementioned battery; and a step of causing the aforementioned charge / discharge circuit opening / closing control device to respond to a charge / discharge start / stop signal from the charge / discharge machine performing the charge / discharge operation for the aforementioned battery, and to open and close the charge / discharge circuit of the aforementioned battery-mounted machine.

40. The method as described in claim 29, wherein, The system further includes the following steps: enabling the aforementioned first processor to obtain capacity information input from the outside, which serves as a reference for the aforementioned battery; and enabling the aforementioned first processor to calculate the S / N ratio of the power supplied by the aforementioned battery to the load machine based on the aforementioned reference capacity information and the aforementioned charge-discharge characteristic data measured by the aforementioned measuring device.

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