SOH diagnostic method and SOH diagnostic device

Abstract

To provide an SOH diagnostic method capable of achieving both cost and diagnostic accuracy, while achieving a simple SOH diagnosis method for easily diagnosing the SOH of storage batteries to realize lower prices for reused storage batteries, and to realize an SOH diagnostic method that allows users of electric vehicles to easily know the SOH of the storage battery mounted on the vehicle.SOLUTION: The SOH diagnostic method capable of diagnosing SOH of a vehicle-mounted storage battery includes: an SOH estimation function generation step of generating an SOH estimation function for estimating the SOH based on multiple regression analysis using multiple data items that affect the health of batteries stored in multiple vehicles as explanatory variables; and an SOH estimation step of estimating the current or future SOH of the battery from the SOH estimation function generated in the SOH estimation function generation step and the multiple data items that affect the health state of the battery in the specified vehicle.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an SOH diagnostic method and an SOH diagnostic device that can diagnose the SOH when reusing in-vehicle storage batteries, stationary storage batteries, etc. The present invention also relates to a current and future SOH diagnostic method and an SOH diagnostic device that allow users of electric vehicles and the like to easily know the SOH of storage batteries installed in their vehicles. [Background technology]

[0002] In recent years, in response to the issue of global warming, the introduction of renewable energy sources and the use of electric vehicles (EVs) and hybrid electric vehicles (HEVs), which are said to have a low environmental impact, have rapidly spread.

[0003] Storage batteries are an essential and important component for stabilizing the grid when large amounts of renewable energy are introduced, not only for electric vehicles (EVs) and hybrid electric vehicles (HEVs) but also for renewable energy.

[0004] The price of in-vehicle storage batteries has remained high due to the global trend toward EVs and geopolitical risks, etc. There is also a need to make effective use of the rare metals used in storage batteries to reduce their impact on the environment.

[0005] Against this background, automotive storage batteries are not only being reused as automotive storage batteries, but are also being used as stationary storage batteries for renewable energy.

[0006] When reusing storage batteries, it is important to check the battery's State of Health (SOH) and reuse them according to the state of SOH. While the "charge and discharge method" is known as a SOH diagnostic method, it is costly and time-consuming, and as a result, the price of a reused storage battery is almost the same as that of a new one.

[0007] Non-Patent Document 1 introduces various methods for diagnosing the deterioration of used lithium-ion batteries, and describes a diagnostic method that can diagnose deterioration in a relatively short time, but because it is necessary to directly examine and diagnose the electrical characteristics of the used storage battery, there are limits to how much cost and time can be reduced.

[0008] Patent Document 1 describes methods for reusing used storage batteries and for separating them during distribution, but does not describe a method for diagnosing the SOH in a short time and at low cost. [Prior art documents] [Patent documents]

[0009] [Patent Document 1] Patent Publication No. 2021-48007 [Non-patent literature]

[0010] [Non-Patent Document 1] Deterioration diagnosis method for used LIBs JARI Research Journal (October 2022) Summary of the Invention [Problem to be solved by the invention]

[0011] If we could reuse storage batteries and reduce their prices, we could establish a secondhand battery market and reduce the prices of storage batteries, including new ones. The present invention aims to realize a simple SOH diagnosis method for easily diagnosing the SOH of storage batteries in order to reduce the prices of reused storage batteries. The present invention also aims to realize a SOH diagnosis method that achieves both cost and diagnostic accuracy by combining the simple SOH diagnosis method with a time-consuming but highly accurate SOH diagnosis method, such as a known charge / discharge method. Another aim is to realize a SOH diagnosis method that allows users of electric vehicles and other vehicles to easily know the SOH of the storage batteries installed in their vehicles, from the present to future estimates. [Means for solving the problem]

[0012] This is an SOH diagnosis method capable of diagnosing the state of health (SOH) of a storage battery installed in a vehicle, and includes an SOH estimation function generation step of generating an SOH estimation function that estimates the SOH based on multiple regression analysis using multiple data items that affect the state of health of storage batteries stored in multiple vehicles as explanatory variables, and an SOH estimation step of estimating the current or future SOH of a storage battery of a specified vehicle from the SOH estimation function generated in the SOH estimation function generation step and the multiple data items that affect the state of health of the storage battery.

[0013] Furthermore, there is provided a SOH diagnostic method capable of diagnosing the state of health (SOH) of a storage battery mounted on a vehicle, comprising a first step of simply diagnosing the SOH based on a plurality of data items that affect the state of health of the storage battery stored in the vehicle, and a second step of diagnosing the SOH of a storage battery that satisfies predetermined conditions in the first step by directly measuring the electrical characteristics of the storage battery. [Effects of the Invention]

[0014] According to the present invention, a simple SOH diagnostic method for easily diagnosing the SOH of a storage battery can be realized, and further, by combining the simple SOH diagnostic method with a known SOH diagnostic method such as a charge / discharge method, which is time-consuming but has high diagnostic accuracy, a SOH diagnostic method that achieves both low cost and diagnostic accuracy can be realized. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is a diagram for explaining an SOH diagnosis device according to a first embodiment. [Figure 2] FIG. 10 is a diagram for explaining the correlation between the measured SOH and the mileage in the embodiment. [Figure 3] A diagram to explain the absolute value of the correlation coefficient and its interpretation [Figure 4] FIG. 10 is a diagram for explaining the correlation between the measured SOH and the number of charge cycles (number of rapid charge cycles, number of normal charge cycles) in the embodiment. [Figure 5]FIG. 1 is a diagram illustrating the correlation between the measured SOH and the number of charging times (an index combining the number of fast charging times and the number of normal charging times) in the embodiment. [Figure 6] FIG. 10 is a diagram for explaining the correlation between the measured SOH and the number of days since the vehicle was in operation in the embodiment. [Figure 7] FIG. 10 is a diagram showing data on the absolute error and mean absolute error between the measured SOH and the estimated SOH in Application Example 1 of the embodiment. [Figure 8] FIG. 10 is a diagram showing data on the absolute error and mean absolute error between the measured SOH and the estimated SOH in the future (six months later) in Application Example 1 of the embodiment. [Figure 9] FIG. 10 is a diagram showing data on the absolute error and mean absolute error between the measured SOH and the estimated SOH in Application Example 2 of the embodiment. [Figure 10] FIG. 10 is a diagram showing data on the absolute error and mean absolute error between the measured SOH and the estimated SOH in the future (six months later) in application example 2 of the embodiment. [Figure 11] An example of a screen displaying the current estimated SOH, the estimated SOH six months from now, and advice on how to improve SOH. [Figure 12] 10 is a flowchart of a SOH estimation method applied to determining whether to reuse an in-vehicle storage battery according to a second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, embodiments of the present invention will be described, but the present invention is not limited to the following embodiments.

[0017] (Embodiment 1) FIG. 1 is a diagram for explaining an SOH diagnostic device according to a first embodiment of the present invention.

[0018] In FIG. 1, a data acquisition unit 12 acquires vehicle data from each vehicle using an OBD2 (On-Board Diagnostics 2) function, such as an OBD2 diagnostic device (scan tool), and stores the acquired vehicle data in a storage unit 13. The vehicle data is essential for vehicles equipped with on-board storage batteries, such as electric vehicles (EVs) and hybrid electric vehicles (HEVs), and must include data items that affect the health state of the storage batteries stored in the vehicle, but other data items may also be included. The vehicle data is intended to be acquired, for example, when the vehicle is brought to a dealer or inspection center for a vehicle inspection or periodic maintenance, but it may also be acquired in real time via the in-vehicle internet, for example.

[0019] The SOH diagnosis device 1 is a device for simply diagnosing the SOH and includes the following components: In the following explanation, the SOH calculated by this SOH diagnosis device 1 will be referred to as the estimated SOH, and the SOH calculated by directly measuring the electrical characteristics of the storage battery using a "charge and discharge method" or the like will be referred to as the measured SOH.

[0020] The vehicle data acquisition unit 2 acquires data items necessary for estimating the state of health (SOH) of the vehicle's storage battery from the aforementioned storage unit 13 and stores them in the vehicle data unit 3. The data items necessary for estimating the state of health (SOH) of the storage battery include, but are not limited to, data related to the vehicle's mileage, the number of times the vehicle's onboard storage battery, such as a lithium-ion battery (LIB), has been charged (e.g., the number of quick charges and the number of normal charges), and the number of days since the vehicle was in operation. The SOH estimation function generation unit 4 generates a function (SOH estimation function) for estimating the SOH from the data used to estimate the SOH stored in the vehicle data unit 3. The SOH estimation function is generated using multiple regression analysis with the vehicle's mileage, the number of quick charges, the number of normal charges, and the number of days since the vehicle was in operation as explanatory variables. Details of the rationale for using the vehicle's mileage, the number of quick charges, the number of normal charges, and the number of days since the vehicle was in operation as explanatory variables will be explained in the examples below. The SOH estimation unit 5 estimates the SOH of the storage battery installed in each vehicle based on the SOH estimation function obtained by the SOH estimation function generation unit 4. The estimated SOH of each vehicle estimated by the SOH estimation unit 5 is stored in the SOH data storage unit 6. The estimated SOH to be stored is the estimated SOH at the current time, but the SOH estimation unit 5 may also estimate a future SOH, for example, an SOH six months from now, and store the estimated SOH six months from now as well.

[0021] When a user or business wants to know the SOH value of a storage battery installed in a certain vehicle, if the SOH of the vehicle is already stored in SOH data storage unit 6, by inputting the vehicle number from input unit 7, SOH estimation unit 5 will obtain the estimated SOH of the vehicle from SOH data storage unit 6 and output the estimated SOH to output unit 8. If the vehicle's storage battery data for which the estimated SOH is desired is not stored in SOH data storage unit 6, by inputting data necessary for calculating the estimated SOH using an SOH estimation function, such as the vehicle's mileage, number of quick charges, number of normal charges, and number of days since the vehicle was last used, from input unit 7, SOH estimation unit 5 will calculate the estimated SOH and output the estimated SOH to output unit 8.

[0022] The estimated SOH is output to the output unit 8 in the form of a screen display, a printout, a data output, etc. In addition to outputting the estimated SOH, the SOH estimation unit 5 may also output advice for improving the SOH, etc.

[0023] The following examples provide further details.

[0024] (Example) <Using actual vehicle data to verify data items that are expected to have a high correlation with SOH> In order to verify data items that are useful for estimating the state of health (SOH) of the storage battery from various data that can be obtained from vehicle 11 in Figure 1 and that are highly correlated with SOH, the following data was collected.

[0025] ·vehicle Data from 26 vehicles that are actually in use was used, with the 26 vehicles being chosen to have as wide a range as possible in terms of usage conditions, mileage, and age.

[0026] Data items Data items expected to be highly correlated with SOH were selected: mileage, battery voltage, number of quick charges, number of normal charges, and age of the vehicle. A linear function (regression equation) was created (simple regression analysis) with the measured SOH as the objective function and one of the data items as an explanatory variable, and the degree of correlation between the measured SOH and each data item was measured. Figure 2 illustrates the correlation between the measured SOH and mileage, Figure 4 illustrates the correlation between the measured SOH and number of charges (number of quick charges, number of normal charges), Figure 5 illustrates the correlation between the measured SOH and number of charges (an index combining the number of quick charges and the number of normal charges), and Figure 6 illustrates the correlation between the measured SOH and age of the vehicle. Figure 3 illustrates the absolute value of the correlation coefficient and its interpretation. The correlation coefficient is expressed by the following formula:

[0027]

number

[0028] Figure 2(b) is a plot of the data from Figure 2(a), with the horizontal axis (X axis) representing distance traveled and the vertical axis (Y axis) representing the measured SOH. A linear function was created (simple regression analysis) using distance traveled as an explanatory variable.

[0029]

number

[0030] 4, Fig. 4(a) shows data showing the relationship between the number of charge attempts and the actually measured SOH for each of vehicles 1 to 26. QC is the number of quick charge attempts, and NC is the number of normal charge attempts.

[0031] Figure 4(b) is a plot of the data from Figure 4(a), with the horizontal axis (X axis) representing the number of quick charges and the vertical axis (Y axis) representing the measured SOH. A linear function was created (simple regression analysis) using the mileage as an explanatory variable.

[0032]

number

[0033] Figure 4(c) is a plot of the data from Figure 4(a), with the horizontal axis (X axis) representing the number of normal charges and the vertical axis (Y axis) representing the measured SOH. A linear function was created (simple regression analysis) using the mileage as an explanatory variable.

[0034]

number

[0035] Figure 5 shows an example of using the "QC-NC combination index," which is a formula that combines the linear function of the number of fast charge cycles and the measured SOH, and the linear function of the number of normal charge cycles and the measured SOH. Specifically, the data was calculated using the formula shown below.

[0036]

number

[0037]

number

[0038] Figure 5(a) shows data showing the relationship between the "QC-NC combination index" and the measured SOH for each of vehicles 1 to 26. QC (times) is the number of quick charges, NC (times) is the number of normal charges, and the "QC-NC combination index" is the value calculated using the formula shown in Equation 5.

[0039] Figure 5(c) is a plot of the data from Figure 5(a), with the horizontal axis (X axis) representing the "QC-NC combination index" and the vertical axis (Y axis) representing the measured SOH. A linear function was created (simple regression analysis) using the "QC-NC combination index" as an explanatory variable.

[0040]

number

[0041] In Fig. 6, Fig. 6(a) shows data indicating the relationship between the elapsed days (days after vehicle registration) of each of Vehicles 1 to 26 and the measured SOH. Fig. 6(b) is a graph obtained by plotting the data in Fig. 6(a) with the elapsed days on the horizontal axis (X-axis) and the measured SOH on the vertical axis (Y-axis), and a linear function with the elapsed days as an explanatory variable was created (simple regression analysis). In this data,

[0042]

Number

[0043] As described above, in this embodiment, as explanatory variables, the driving distance, the number of rapid charge times, the number of normal charge times, the "QC-NC combination index", and the elapsed days of the vehicle were verified to have a high correlation coefficient with the measured SOH. Therefore, they were used as explanatory variables of the function for estimating the SOH. Regarding the relationship between the voltage of the storage battery and the measured SOH, data was also collected using the same method, but the correlation coefficient was small, so it was decided not to use it this time. Note that if a sufficient correlation coefficient with the SOH is also recognized in other vehicle data, it is possible to use it as an explanatory variable of the function for estimating the SOH.

[0044] <Specific Application Example 1 in the SOH Estimation Function (Multiple Regression Analysis)> Using the data items with high correlation coefficients mentioned above as explanatory variables, a SOH estimation function was constructed using multiple regression analysis. In multiple regression analysis, the more explanatory variables there are, the smaller the overall error. However, there is a possibility that some variables may not actually contribute much to the dependent variable, or that the coefficients may have opposite signs, increasing the error. This is called multicollinearity. Multicollinearity occurs when there is a correlation between explanatory variables. Since the number of days elapsed is expected to be correlated with mileage, QC, and NC, it is necessary to take multicollinearity into consideration. Therefore, while mileage, QC, and NC are used as explanatory variables, the number of days elapsed is not treated as an explanatory variable; rather, it is used to correct the error, assuming that the error between the dependent variable calculated using other explanatory variables and the actual SOH is influenced by the number of days elapsed.

[0045] An SOH estimation function is constructed by setting three explanatory variables: mileage, QC, and NC. Data obtained from 26 vehicles stored in Vehicle Data Unit 3 is used as learning data and trained on Python for analysis. The original data to be estimated is treated as validation data, and the absolute error rate between the estimated SOH and the measured SOH is verified. Additionally, assuming that the absolute error rate is due to the number of days elapsed from the registration date to the data acquisition date, the value obtained by dividing the measured SOH by the estimated SOH was used as the model year correction coefficient. An approximate curve was drawn from the relationship between the model year correction coefficient and the number of days elapsed, and the model year correction coefficient for the number of days elapsed was calculated.

[0046]

number

[0047]

number

[0048] Fig. 7(a) shows data indicating the measured SOH of each of vehicles 1 to 26, the estimated SOH before model year correction calculated by the SOH estimation function, the estimated SOH after model year correction, and the absolute error between the measured SOH and the estimated SOH. Fig. 7(b) shows the average absolute error, which is the average of the absolute errors of the 26 vehicles before and after model year correction. The average absolute error before model year correction is 1.36, and the average absolute error after model year correction is 1.15, both of which are highly accurate. However, the accuracy is further improved by performing model year correction.

[0049] Fig. 8(a) shows data indicating the measured SOH, the estimated SOH before model year correction calculated by the SOH estimation function, the estimated SOH after model year correction, and the absolute error between the measured SOH and the estimated SOH for six of the above-mentioned 26 vehicles when six months have passed for the purpose of verifying the accuracy of future estimation. Fig. 8(b) shows the average absolute error, which is the average of the absolute errors of the six vehicles before and after model year correction. The average absolute error before model year correction is 1.48, and the average absolute error after model year correction is 1.90, both of which are relatively highly accurate. However, the effect of model year correction was not observed.

[0050] <Specific Application Example 2 of the SOH Estimation Function (Multiple Regression Analysis)> In the above Specific Application Example 1 of the SOH estimation function (multiple regression analysis), the problem of multicollinearity, in which the error increases as the number of explanatory variables increases in multiple regression analysis, was explained.

[0051] Therefore, in Application Example 1, the number of days elapsed was used to correct errors rather than being treated as an explanatory variable, but in this Application Example 2, the two explanatory variables of QC and NC were used as a single explanatory variable called the "QC-NC combination index," and the number of days elapsed was used as one of the explanatory variables in the multiple regression analysis.

[0052] Specifically, three explanatory variables are set: mileage, the QC-NC combination index, and the number of days elapsed, and an SOH estimation function is constructed. Data obtained from 26 vehicles stored in the vehicle data unit 3 is used as learning data and trained on Python (registered trademark) for analysis. The original data to be estimated is treated as verification data, and the absolute error rate between the estimated SOH and the actually measured SOH is verified.

[0053] Figure 9(a) shows data showing the measured SOH for each of vehicles 1 to 26, the estimated SOH calculated using the SOH estimation function, and the absolute error between the measured SOH and the estimated SOH. Figure 9(b) shows the mean absolute error, which is the average of the absolute errors for all 26 vehicles. The mean absolute error was 1.29, demonstrating high accuracy.

[0054] Figure 10(a) shows data obtained by measuring the SOH of six of the 26 machines mentioned above after six months had passed, in order to verify the accuracy of future estimation. The data shows the measured SOH, the estimated SOH calculated using the SOH estimation function, and the absolute error between the measured SOH and the estimated SOH. Figure 10(b) shows the mean absolute error, which is the average of the absolute errors of the six machines. The mean absolute error was 1.21, demonstrating high accuracy.

[0055] As explained above in detail, the SOH estimation function of this embodiment using multiple regression analysis was able to estimate the SOH with high accuracy.

[0056] It is desirable to optimize the parameters of the multiple regression analysis used in the SOH estimation function in accordance with the timing of vehicle data accumulation. Optimization can also be implemented using AI functions.

[0057] <User interface: Display of SOH diagnosis results> FIG. 11 is a diagram showing an example of a screen displaying the current estimated SOH, the estimated SOH six months from now, and advice for further improving the SOH, based on input from the input unit 7, when the user wishes to know the SOH of a storage battery installed in a vehicle. In FIG. 11, the user inputs data such as the current mileage, age, QC (number of quick charges), and NC (number of normal charges) from the input unit 7, such as a touch panel or keyboard. Note that for a vehicle whose SOH is already stored in the SOH data storage unit 6, it is sufficient to simply input the vehicle number from the input unit 7. The SOH estimation unit 5 uses an SOH estimation function to display the current estimated SOH and future (six-month) estimated data, including the estimated SOH, mileage, age, QC (number of quick charges), and NC (number of normal charges).

[0058] The estimated SOH for six months ahead is estimated from estimated data for six months ahead, including mileage, age, number of quick charges (QC), and number of normal charges (NC). In this example, advice for improving the SOH is displayed, such as the extracted issue of "too many quick charges" and the corresponding solution, "combining normal charges." These displays can be generated by the SOH estimation unit 5 extracting a data item (in this example, the number of quick charges) that deviates from the normal range by a predetermined amount, and pre-storing the relationship between the extracted data item and a complementary data item (in this example, the number of normal charges). Advice for improving the SOH may be generated and displayed by using an AI function to generate optimal measures for improving the SOH based on the results of learning data stored in the vehicle data unit 3.

[0059] Although the data required for SOH diagnosis, such as the current mileage, age, QC (number of quick charges), and NC (number of normal charges), are manually input by the user via the input unit 7, the data may also be input automatically from the vehicle via the Internet.

[0060] As described above in detail, according to this embodiment, it is not necessary to directly measure the electrical characteristics of a storage battery mounted on a vehicle each time, and it is possible to simply diagnose the SOH of the storage battery, thereby saving inspection time and costs. Furthermore, users of electric vehicles (EVs) and hybrid electric vehicles (HEVs) can easily know the predicted future SOH, so they can use their electric vehicles (EVs) and hybrid electric vehicles (HEVs) with peace of mind.

[0061] (Embodiment 2) In this second embodiment, we will explain a method of estimating SOH that is applied to determining whether to reuse an on-board storage battery, by combining the simple method of estimating SOH described in the first embodiment with the "charge and discharge method," which is a time-consuming method that has traditionally been used to estimate SOH but can estimate SOH with high accuracy.

[0062] FIG. 12 is a flowchart of the SOH estimation method applied to determining whether to reuse an in-vehicle storage battery according to the second embodiment.

[0063] <First step: Simple diagnosis of SOH based on vehicle data> (Step S801) A simple diagnosis of SOH is performed using the SOH measurement method described in the first embodiment.

[0064] First, the estimated SOH is compared with a reference value 1 (step S802). The reference value 1 is the value at which the storage battery can be reliably deemed unreusable, for example, an estimated SOH of 65%. If the estimated SOH is equal to or less than the reference value 1 (if the determination in step S802 is Yes), the battery is disassembled and determined to be recyclable (step S803).

[0065] <Second step: Diagnosis of SOH based on data obtained by directly measuring the electrical characteristics of the storage battery> (Step S804) If the estimated SOH is equal to or greater than the reference value 1 (if the determination in step S802 is No), the electrical characteristics of the storage battery are directly measured to obtain the SOH. Specifically, the actual measured SOH is obtained using a charge / discharge method that can obtain a highly accurate SOH.

[0066] The estimated SOH is compared with reference value 2 (step S805). Reference value 2 is a value at which the battery can be reliably considered healthy, for example, an estimated SOH of 95%. If the estimated SOH is equal to or greater than reference value 2 (if the determination in step S805 is Yes), it is determined that the vehicle battery is usable (step S806).

[0067] The measured SOH is compared with a reference value 3 (step S807). The reference value 3 is a value at which the storage battery is generally considered healthy, for example, a measured SOH of 85%. If the measured SOH is equal to or greater than the reference value 3 (if the determination in step S807 is Yes), it is determined that the battery can be used as a stationary storage battery 1 (step S808). The stationary storage battery 1 is intended for applications requiring high functionality (such as a storage battery for effectively utilizing renewable energy power).

[0068] If the measured SOH is less than the reference value 3 (if the determination in step S807 is No), the SOH is compared with the reference value 4 (step S809). The reference value 4 is a boundary value at which it is determined that the storage battery has deteriorated and is difficult to use as a stationary storage battery 1, but can still be used as a low-load stationary storage battery (hereinafter referred to as "stationary storage battery 2") for, for example, LED road lighting, and is, for example, 65% of the measured SOH. If the measured SOH is equal to or greater than the reference value 4 (if the determination in step S809 is Yes), it is determined that it can be used as a stationary storage battery 2 (step S810). If the measured SOH is less than the reference value 4, it is difficult to use as a stationary storage battery, and it is therefore determined that it should be recycled and used (step S811).

[0069] The specific values ​​of the above-mentioned reference values ​​1, 2, 3, and 4 are provisional values, and may be individually and specifically set according to the technical level and business environment related to storage batteries at the time of implementing the present invention.

[0070] Furthermore, in the second step, the charge / discharge method is used as the method for measuring the SOH, but any other measurement method may be used as long as it directly measures the electrical characteristics of the storage battery and can accurately measure the SOH.

[0071] As explained in detail above, according to the second embodiment, the method for simply estimating SOH explained in the first embodiment is combined with the "charge and discharge method," which is one of the methods that has been used conventionally to estimate SOH and is capable of estimating SOH with high accuracy, although it takes time, and these are applied to the determination of whether to reuse an on-board storage battery. Therefore, it is possible to strike a balance between inspection time and cost and diagnostic accuracy, and it becomes easier to utilize the method in the business of reusing on-board storage batteries. [Explanation of symbols]

[0072] 1 SOH diagnostic device 2 Vehicle data acquisition unit 3 Vehicle Data Department 4. SOH estimation function generator 5 SOH estimation section 6 SOH data storage section 7 Input section 8 Output section 11 vehicles 12 Data Acquisition Section 13 Storage section

Claims

1. A method for diagnosing the state of health (SOH) of a storage battery mounted on a vehicle, comprising: an SOH estimation function generation step of generating an SOH estimation function that estimates the SOH based on multiple regression analysis using a plurality of data items that affect the health of storage batteries stored in a plurality of vehicles as explanatory variables; and an SOH estimation step of estimating the current or future SOH of a storage battery of a specified vehicle from the SOH estimation function generated in the SOH estimation function generation step and a plurality of data items that affect the health state of the storage battery.

2. 2. The SOH diagnosis method according to claim 1, wherein the data items used in the SOH estimation function include at least a plurality of data items selected from the group consisting of a mileage, a number of quick charges, a number of normal charges, and an age of the storage battery.

3. The SOH diagnosis method according to claim 2 , wherein the SOH estimation function includes at least the following explanatory variables based on the number of quick charges (QC) and the number of normal charges (NC): (Slope of QC-SOH) ÷ (Slope of NC-SOH) × QC + NC Here, the slope of QC-SOH is the slope of the regression equation calculated from the number of quick charges (QC) and the measured SOH, and the slope of NC-SOH is the slope of the regression equation calculated from the number of normal charges (NC) and the measured SOH.

4. an input step of inputting a vehicle number or a plurality of data affecting the health of the battery; 2. The SOH diagnosis method according to claim 1, further comprising an output step of estimating the current or future SOH of the storage battery based on the data input in the input step and displaying the estimated SOH on a screen.

5. 5. The SOH diagnosis method according to claim 4, wherein the screen display includes advice for improving the SOH.

6. A SOH diagnosis device capable of simply diagnosing the state of health (SOH) of a storage battery mounted on a vehicle, a vehicle data unit that stores a plurality of data items that affect the health status of storage batteries stored in a plurality of vehicles; an SOH estimation function generation unit that generates an SOH estimation function that estimates the SOH based on multiple regression analysis using the plurality of data items stored in the vehicle data unit as explanatory variables; and an SOH estimation unit that estimates at least one current or future SOH of a storage battery of a specified vehicle from the SOH estimation function generated by the SOH estimation function generation unit and a plurality of data items that affect the health state of the storage battery.

7. A method for diagnosing the state of health (SOH) of a storage battery mounted on a vehicle, comprising: A first step of simply diagnosing the SOH based on a plurality of data items that affect the state of health of the storage battery stored in the vehicle; a second step of diagnosing the SOH of a storage battery that satisfies the predetermined conditions in the first step by directly measuring the electrical characteristics of the storage battery.

8. One or more programs that cause an electronic device such as a computer to execute the SOH diagnosis method according to claim 1 or 7.

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