Diagnosis program and diagnosis system

Abstract

A diagnosis program (P) causes a processor (10) to use a battery degradation model formula (Ma) to specify a degradation factor of a battery module (2a), wherein the battery degradation model formula (Ma) is obtained by linear regression analysis in which the degradation amount of characteristic data (E) of the battery module (2a) is used as an objective variable (Y) and in which usage history data (D) of the battery module (2a) during measurement of the characteristic data (E) and the power (D') of the usage history data are used as explanatory variables (X), and through modeling in which the coefficient of each term of the explanatory variables (X) is predefined as positive or negative.

Description

Diagnostic program, diagnostic system CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is based on Japanese Application No. 2024-53160, filed on March 28, 2024, the contents of which are incorporated herein by reference.

[0002] The present disclosure relates to techniques for diagnosing batteries.

[0003] Patent Document 1 below discloses a system for estimating the capacity of a secondary battery. This system describes that when estimating the capacity of a secondary battery, a nonlinear regression model such as a folded neural network or an arbitrary model such as a linear regression model is used.

[0004] Patent No. 7276928

[0005] When identifying the cause of battery degradation in a secondary battery or other battery, if a nonlinear regression model is used, the model formula is not readable, so even if the degradation state is known, the cause of the degradation cannot be identified. Therefore, instead of a nonlinear regression model, a linear regression model, whose model formula is readable, can be used. However, simply using a linear regression model makes it difficult to identify the cause of battery degradation because it cannot represent nonlinear degradation reactions.

[0006] The present disclosure aims to provide a technology that is effective in identifying the cause of battery degradation using a readable battery degradation model equation.

[0007] One aspect of the present disclosure is a diagnostic program that causes a processor to identify the cause of deterioration of the battery using a battery deterioration model equation obtained by linear regression analysis with the amount of deterioration of battery characteristic data as a dependent variable, and the battery usage history data at the time of measuring the characteristic data and the exponents of the usage history data as explanatory variables, wherein the coefficient of each term of the explanatory variables is specified in advance as positive or negative.

[0008] Another aspect of the present disclosure is a diagnostic system comprising: a memory unit that stores a battery degradation model equation obtained by linear regression analysis using a deterioration amount of battery characteristic data as a dependent variable, and using history data of the battery at the time of measuring the characteristic data and exponents of the history data of the battery characteristic data as explanatory variables, the battery degradation model equation being modeled by specifying a coefficient of each term of the explanatory variables as positive or negative in advance; and a degradation factor identification unit that uses the battery degradation model equation stored in the memory unit to identify a factor of the battery degradation.

[0009] According to the above-described aspects, it becomes possible to identify the cause of battery deterioration using a readable battery deterioration model equation.

[0010] Note that the symbols in parentheses in the claims indicate the correspondence with the specific means described in the embodiments described below, and do not limit the technical scope of the present disclosure.

[0011] The above and other objects, features, and advantages of the present disclosure will become more apparent from the following detailed description taken in conjunction with the accompanying drawings, in which: Fig. 1 is a block diagram showing the configuration of a diagnostic system according to a first embodiment; Fig. 2 is a flowchart of a model generation process performed by the diagnostic system of Fig. 1; Fig. 3 is a diagram for explaining a battery diagnostic model; Fig. 4 is a flowchart of a degradation influence display process performed by the diagnostic system of Fig. 1; Fig. 5 is a bar graph showing the degradation influence for each explanatory variable; Fig. 6 is a bar graph showing the sum of the degradation influence for each identical explanatory variable; Fig. 7 is a bar graph showing the coefficients for each explanatory variable; Fig. 8 is a bar graph showing the sum of the coefficients for each identical explanatory variable; Fig. 9 is a heat map showing the degradation influence for each SOC-temperature use condition; Fig. 10 is a heat map showing the coefficients of a battery degradation model formula for each SOC-temperature use condition; and Fig. 11 is a heat map showing the frequency of use for each SOC-temperature use condition.

[0012] The battery diagnostic techniques according to the above-described aspects will be described in detail below with reference to the drawings.

[0013] 1 shows a diagnostic system 101 according to a first embodiment. The diagnostic system 101 is a system for diagnosing a battery pack 2 mounted on a vehicle 1 such as an electric vehicle or a hybrid vehicle.

[0014] The functions of the diagnostic system 101 are executed by a processor 10. The term "processor 10" as used herein broadly encompasses computer components that perform operations such as data calculations and conversions, program execution, and control of other devices. The processor 10 includes a CPU (Central Processing Unit) that controls the entire computer, or an MPU (Micro Processing Unit) that integrates some of the functions of the CPU.

[0015] As shown in FIG. 1 , the battery pack 2 includes a plurality of battery modules 2a and a main battery management unit 3. Each of the plurality of battery modules 2a includes an assembled battery formed by combining a plurality of battery cells, and a satellite battery management unit 4. The battery cells are secondary batteries that can be recharged and reused. The main battery management unit 3 stores battery information for the plurality of battery modules 2a. In this embodiment, the battery pack 2 and the plurality of battery modules 2a included in the battery pack 2 are also referred to simply as "batteries" for convenience.

[0016] Vehicle 1 is equipped with a battery pack 2, a battery information management unit 5, and a communication unit 6. Battery information management unit 5 has a function of managing battery information stored in a main battery management unit 3 of battery pack 2. Communication unit 6 has a function of transmitting battery information stored in main battery management unit 3 to a diagnostic device 40 and a battery information management database 50. These functions are executed by processor 10A. Processor 10A is included in processor 10 together with processors 10B, 10C, and 10D, which will be described later.

[0017] The diagnostic system 101 includes a display device 20, a diagnostic device 40, and a battery information management database 50. The diagnostic device 40 and the battery information management database 50 are provided in the cloud 30 or a data server 31.

[0018] 1. Configuration of Display Device 20 The display device 20 has a display unit 21. The display unit 21 displays information output from the data management unit 49 of the diagnostic device 40. The function of this display unit 21 is executed by the processor 10B.

[0019] 2. Configuration of Diagnostic Device 40 The diagnostic device 40 has a function of diagnosing the battery pack 2. The diagnostic device 40 includes, as its components, a usage history data acquisition unit 41, a characteristic data acquisition unit 42, an explanatory variable generation unit 43, a target variable generation unit 44, a normalization processing unit 45, a model generation unit 46, a deterioration influence calculation unit 47, a deterioration amount calculation unit 48, and a data management unit 49. The functions of these components are executed by the processor 10C.

[0020] The usage history data acquisition unit 41 acquires usage history data D from the main battery management unit 3. The usage history data D is usage history information that indicates the usage history of each of the plurality of battery modules 2a.

[0021] Examples of the usage history data D include history such as the usage period, the mileage of the vehicle 1, the number of times the vehicle 1 has been started, the accumulated charge amount, the accumulated discharge amount, the temperature frequency distribution, the SOC (State Of Charge) frequency distribution, the SOC fluctuation frequency, the current frequency, the current continuation frequency, and the multidimensional frequency of each feature amount (for example, the two-dimensional frequency of SOC-temperature), which are generated from sensing data such as the usage period, the mileage of the vehicle 1, the number of times the vehicle 1 has been started, the current, the voltage, and the temperature. This usage history data D is stored in the battery information storage unit 55 of the battery information management database 50.

[0022] The characteristic data acquisition unit 42 acquires characteristic data E from the main battery management unit 3. The characteristic data E is battery characteristic information that represents the battery characteristics of each of the plurality of battery modules 2a. The characteristic data E is measured by sampling.

[0023] The characteristic data E may include, for example, the resistance, capacity, and AC impedance of the battery module 2 a. The characteristic data E is stored in the battery information storage unit 55 of the battery information management database 50.

[0024] The explanatory variable generation unit 43 generates an explanatory variable X. In this embodiment, the usage history data D of the battery module 2a at the time of measuring the characteristic data E and the power D' of the usage history data D become the explanatory variable X. Therefore, the explanatory variable generation unit 43 generates the usage history data D as well as the power D' of this usage history data D.

[0025] The response variable generating unit 44 generates a response variable Y. In this embodiment, the deterioration amount of the characteristic data E becomes the response variable Y. The normalization processing unit 45 performs processing to normalize the explanatory variable X generated by the explanatory variable generating unit 43.

[0026] The model generation unit 46 generates a battery degradation model formula Ma. The battery degradation model formula Ma is a model formula obtained by linear regression analysis in which the deterioration amount of the characteristic data E is the response variable Y, and the usage history data D at the time of measurement of the characteristic data E and the power D' of the usage history data D are the explanatory variables X. This battery degradation model formula Ma is modeled by specifying the coefficient of each term of the explanatory variable X as positive or negative in advance. In this way, the battery degradation model formula Ma obtained by linear regression analysis is readable and versatile.

[0027] The battery degradation model formula Ma includes a term for the usage history data D and a term for the power D' of the usage history data D (nonlinear term). For the linear regression analysis, an appropriate algorithm such as lasso regression, ridge regression, or elastic net can be used. The battery degradation model formula Ma is stored in the battery information storage unit 55 of the battery information management database 50. At this time, the battery information storage unit 55 serves as a storage unit that stores the battery degradation model formula Ma.

[0028] The deterioration influence calculation unit 47, the deterioration amount calculation unit 48, and the data management unit 49 constitute a deterioration factor identification unit that uses the battery deterioration model formula Ma stored in the battery information accumulation unit 55 to identify the deterioration factor of the battery module 2a.

[0029] The deterioration influence calculation unit 47 calculates the deterioration influence A. The deterioration influence A is a value indicating the degree of influence that the usage history data D has on the capacity deterioration of the battery module 2a. The deterioration influence A is calculated as the product of the coefficient of each term in the battery deterioration model formula Ma and the usage history data D or the power D' of the usage history data D associated with that coefficient.

[0030] In addition, the deterioration impact calculation unit 47 may calculate the deterioration impact A for the usage history data D requested by the external device 40A, or may calculate the deterioration impact A for predetermined usage history data D.

[0031] The deterioration amount calculation unit 48 calculates the deterioration amount B. The deterioration amount B is the deterioration amount of the characteristic data E set as the objective variable Y. For example, when the objective variable Y is the capacity, the amount of capacity deterioration corresponds to the deterioration amount B, and when the objective variable Y is the resistance, the amount of resistance deterioration corresponds to the deterioration amount B. The deterioration amount B is a value obtained by summing the deterioration influence degree A for a plurality of pieces of usage history data D.

[0032] The data management unit 49 manages the degradation influence degree A calculated by the degradation influence degree calculation unit 47 and the degradation amount B calculated by the degradation amount calculation unit 48 in association with each other. The degradation influence degree A and the degradation amount B are stored in the battery information storage unit 55 of the battery information management database 50. The data management unit 49 also functions as a degradation influence degree output unit that outputs the degradation influence degree A to the display unit 21 of the display device 20. At this time, all of the degradation influence degrees A calculated in response to a request from the external device 40A may be output to the display unit 21, or only a predetermined portion of the degradation influence degrees A may be output to the display unit 21. The data management unit 49 may output the degradation amount B together with the degradation influence degree A to the display unit 21.

[0033] 3. Configuration of Battery Information Management Database 50 The battery information management database 50 is a database used to manage battery information related to the battery pack 2. This battery information management database 50 includes a battery information storage unit 51. The function of the battery information storage unit 51 is executed by the processor 10D. The battery information storage unit 51 stores information received from the battery pack 2 and the diagnostic device 40.

[0034] The functions of each component of the diagnostic system 101 are realized by having the processor 10 execute a diagnostic program P for diagnosing the battery pack 2. Therefore, the diagnostic program P is a program that causes the processor 10 to realize the functions of each component. The processor 10 is composed of all or part of the aforementioned processors 10A, 10B, 10C, and 10D. The diagnostic program P is stored in a non-transitory storage medium 11. The non-transitory storage medium 11 is depicted separately in the drawings to avoid clutter. However, the non-transitory storage medium 11 is included in the vehicle 1, the display device 20, the diagnostic device 40, and the battery information management database 50. At least a portion of the diagnostic program P is stored in the non-transitory storage medium 11 included in each of these components. Various types of non-transitory storage media 11, such as memory type, disk type, and tape type, can be used as the non-transitory storage medium 11. The diagnostic program P may be stored in the cloud 30 or the data server 31. A configuration may be adopted in which at least a portion of the diagnostic program P is downloaded from the cloud 30 or the data server 31 to the vehicle 1, the display device 20, the diagnostic device 40, and the battery information management database 50.

[0035] The allocation of the multiple components (functional components) of the diagnostic system 101 to the display device 20, the cloud 30, or the data server 31 is not limited to that shown in Fig. 1 and can be changed as appropriate as necessary. In addition, other devices or facilities may be used as the allocation destination of each component (functional component).

[0036] 4. Model Generation Processing The model generation processing by the diagnostic system 101 will be described with reference to Figures 1 to 3. This model generation processing is executed sequentially in accordance with steps S101 to S105 of the flowchart shown in Figure 2. Note that one or more steps may be added to these steps as necessary, or multiple steps may be appropriately integrated.

[0037] Step S101 is a step of measuring characteristic data E of the battery module 2a. Step S102 is a step of acquiring usage history data D at the time of measuring characteristic data E. Step S103 is a step of generating a power D' of the usage history data D acquired in step S102.

[0038] Step S104 is a step for generating a battery degradation model formula Ma. In this step S104, modeling is performed by linear regression, with the deterioration amount of the characteristic data E acquired by the measurement in step S101 as the objective variable Y, and the usage history data D acquired in step S102 and the power D' generated in step S103 as explanatory variables X. At this time, the coefficient of each term of the explanatory variable X is specified as positive or negative before solving the regression analysis. As a result, a battery degradation model formula Ma is generated.

[0039] Step S105 is a step of temporarily storing the battery deterioration model formula Ma generated in step S104 in the battery information storage unit 51.

[0040] As shown in Fig. 3, the multiple battery modules 2a included in the battery pack 2 are diagnosed by a battery diagnostic model M including a battery degradation model formula Ma. In this battery diagnostic model M, usage history data D and a power D' of the usage history data D are used as input information, and a diagnostic result R (e.g., the amount of degradation) of the battery module 2a is used as output information. This battery diagnostic model M is constructed using a theoretical model, a machine learning model, or the like. In the battery diagnostic model M, for example, a battery degradation model formula Ma expressed by the following formula (1) is used.

[0041] Y = α １ ×X １ +β １ ×X ２ +γ １ ×X ３ +α ２ ×X ４ +β ２ ×X ５ +γ ２ ×X ６ +α ３ ×X ７ +β ３ ×X ８+γ ３ ×X ９ …(1)

[0042] However, in formula (1), X １ The term is the term of the first usage history data D, and X ２ and X ３ The term is the term of the power D' of the first usage history data D. X ４ The term is the term of the second usage history data D, and X ５ and X ６ The term is the term of the power D' of the second usage history data D. ７ The term is the term of the third usage history data D, and X ８ and X ９ The term is the term of the power D' of the third usage history data D. For convenience, the intercept term is omitted from equation (1).

[0043] For example, X ２ The term X １ The nth power term (where n is an integer of 2 or more) of X ３ The term X １ Similarly, X can be a (-m)th power term (where m is an integer of 1 or more). ５ The term X ４ Let it be the nth power term of X ６ The term X ４ Let it be the (-m)th power term of X ８ The term X ７ Let it be the nth power term of X ９ The term X ７ It can be a (-m)th power term of .

[0044] In the regression analysis when generating the battery deterioration model formula Ma, it is preferable to model the coefficients α, β, and γ of each term of the explanatory variable X by defining them as positive or negative depending on the content of the usage history data D. For example, in formula (1), if the first usage history data D is the "number of days of use," the second usage history data D is the "frequency of high temperature," and the third usage history data D is the "frequency of low temperature," then X ３ and X ６ and X ９In all of the terms, the amount of capacity degradation of the battery module 2a increases as the corresponding value decreases. That is, these terms decrease in value as the capacity degradation of the battery module 2a increases. In contrast, the remaining terms increase in value as the capacity degradation of the battery module 2a increases. Therefore, the coefficient γ １ and coefficient γ ２ and coefficient γ ３ are all specified to be negative values, and the coefficients of the remaining terms are all specified to be positive values.

[0045] On the other hand, if we add X to equation (1), ３ and X ６ and X ９ If the term (a term whose value decreases with the capacity degradation of the battery module 2a) is not included, the coefficients of all terms are specified to be positive values ​​and modeled.

[0046] As described above, by defining the coefficients α, β, and γ of each term of the explanatory variable X as positive or negative depending on the content of the usage history data D, it becomes possible to obtain a highly reliable diagnosis result R using the modeled battery deterioration model formula Ma.

[0047] The battery diagnosis model M may be stored in the cloud 30 or the data server 31, or may be stored in the display device 20 or the external device 40A. The battery diagnosis model M may also be updated as appropriate.

[0048] 5. Deterioration Impact Display Processing The deterioration impact display processing by the diagnostic system 101 will be described with reference to Figures 1 and 4 to 11. This deterioration impact display processing is executed sequentially in accordance with steps S201 to S204 of the flowchart shown in Figure 4. Note that one or more steps may be added to these steps as necessary, or multiple steps may be integrated as appropriate.

[0049] Step S201 is a step of reading out the battery deterioration model formula Ma generated in the above-described model generation process from the battery information storage unit 51.

[0050] Step S202 is a step for calculating the degradation influence degree A. In this step S203, the usage history data D acquired in step S102 and the power D' of the usage history data D generated in step S103 are input to the battery degradation model formula Ma read out in step S201, and the product of the coefficient of each term and the usage history data D or the power D' of the usage history data D linked to that coefficient is calculated. According to step S203, for example, in formula (1), the degradation influence degree A of the first usage history data D is (α １ ×X １ +β １ ×X ２ +γ １ ×X ３ ) The deterioration influence degree A of the second usage history data D is a value represented by (α ２ ×X ４ +β ２ ×X ５ +γ ２ ×X ６ ) The deterioration influence degree A of the third usage history data D is a value represented by (α ３ ×X ７ +β ３ ×X ８ +γ ３ ×X ９ ) is the value shown.

[0051] Step S203 is a step for calculating the deterioration amount B. In this step S203, the plurality of deterioration influence degrees A calculated in step S202 are summed up.

[0052] Step S204 is a step of outputting the deterioration influence degree A calculated in step S202 to the display unit 21. This step S204 allows the deterioration influence degree A to be displayed on the display unit 21. The deterioration influence degree A may be displayed only as a numerical value on the display unit 21, or, for example, as shown in FIG. 5, the deterioration influence degree A for each explanatory variable X in equation (1) may be displayed as a bar graph on the display unit 21. This allows the user to consider and propose how to use the battery pack 2 (battery module 2a) according to the deterioration influence degree A. For example, the following battery usage suggestions can be made:

[0053] In the case of a service that operates a plurality of vehicles 1, the vehicles 1 can be allocated according to their intended use (e.g., required mileage, required input / output power, required operating time, etc.) so that deterioration factors are leveled out. Also, the vehicles 1 can be allocated according to their area of ​​use (e.g., the local ambient temperature, the required mileage in the area of ​​use, required input / output power, required operating time in the area of ​​use, etc.) so that deterioration factors are leveled out. Also, the order of the vehicles 1 (e.g., the order of charging, the order of use, etc.) can be determined so that deterioration factors are leveled out.

[0054] In the case of replaceable batteries, the battery packs 2 can be allocated according to the intended use (e.g., required driving distance, required input / output power, required operating time, etc.) so that deterioration factors are leveled out. Also, the battery packs 2 can be allocated according to the area of ​​use (e.g., the local ambient temperature, the required driving distance in the area of ​​use, required input / output power, required operating time in the area of ​​use, etc.) so that deterioration factors are leveled out. Also, the order of the battery packs 2 (e.g., the order of charging, the order of use, etc.) can be determined so that deterioration factors are leveled out. Furthermore, when multiple battery packs 2 are mounted on the vehicle 1, the combination of the multiple battery packs 2 can be determined so that the deterioration factors are the same.

[0055] In the case of secondary use, if the deterioration factors can be leveled out by stationary use, the battery pack 2 is allocated for reuse. Also, when reusing, the battery pack 2 can be allocated so that the deterioration factors are leveled out, taking into consideration the intended use and the characteristics of the area of ​​use. When rebuilding, if the capacity deterioration of the battery module 2a can be leveled out by changing the intended use or area of ​​use, the battery pack 2 including the battery module 2a can be allocated for rebuilding.

[0056] In step S204, it is preferable to display all of the deterioration influence degrees A calculated in response to a request from the external device 40A on the display unit 21. This allows the user to efficiently check the deterioration influence degrees A that the user wants to check. Since all of the deterioration influence degrees A can be checked, the readability of the battery deterioration model formula Ma is improved.

[0057] In step S204, instead of or in addition to Fig. 5, as shown in Fig. 6, the sum of the deterioration influence A for each identical explanatory variable X in equation (1) is displayed in the form of a bar graph on the display unit 21. Note that the sum of the deterioration influence A may be for each explanatory variable X of the same type.

[0058] Furthermore, information other than the information related to the deterioration influence degree A may be displayed on the display unit 21. For example, as shown in Fig. 7, the coefficients α, β, and γ for each explanatory variable X in equation (1) can be displayed in the form of a bar graph on the display unit 21. Furthermore, as shown in Fig. 8, the sum of the coefficients α, β, and γ for each identical explanatory variable X in equation (1) can be displayed in the form of a bar graph on the display unit 21. Note that the sum of the coefficients α, β, and γ may be for each explanatory variable X of the same type.

[0059] A heat map may be used in step S204. As shown in FIG. 9, for example, the degradation influence A for each SOC-temperature usage condition can be displayed in a heat map. In FIG. 9, each rectangle in the heat map corresponds to a term in the battery degradation model formula Ma. This allows the magnitude of the degradation influence A to be visualized using different colors in the heat map, allowing the user to easily identify the cause of degradation.

[0060] In relation to FIG. 9 , coefficients for each SOC-temperature usage condition may be displayed in a heat map as shown in FIG. 10 , or the usage frequency for each SOC-temperature usage condition may be displayed in a heat map as shown in FIG. 11 . The coefficients of each term in the battery degradation model formula Ma correspond to the "coefficients" in FIG. 10 , and the explanatory variable X of each term in the battery degradation model formula Ma corresponds to the "usage frequency" in FIG. 11 . This makes it possible to visualize the magnitude of the coefficients and the frequency of use as information related to the degradation influence A using different colors in the heat map. By visualizing the magnitude of the coefficients, the user can confirm the validity of the battery degradation model formula Ma.

[0061] 6. Effects According to the first embodiment, it is possible to identify the deterioration factors of the battery module 2a by using the readable and versatile battery deterioration model formula Ma. By identifying the deterioration factors, it becomes possible to use the battery module 2a in a way that takes the deterioration factors into consideration, thereby realizing a longer lifespan of the battery module 2a.

[0062] Although the present disclosure has been described based on the above-described embodiments, it is understood that the present disclosure is not limited to these forms and structures. The present disclosure also encompasses various modifications and modifications within the scope of equivalents. In addition, various combinations and forms, as well as other combinations and forms including only one element, more than one element, or less than one element, are also within the scope and spirit of the present disclosure. For example, the following forms can be implemented by applying the above-described forms.

[0063] In the above embodiment, an example is given of diagnosing a battery pack 2 mounted on a vehicle 1, but this diagnostic technique may also be applied to diagnosing a battery pack 2 mounted on consumer equipment or industrial equipment other than the vehicle 1.

Claims

1. A diagnostic program (P) that causes a processor (10) to identify the cause of deterioration of a battery (2, 2a) using a battery deterioration model formula (Ma) obtained by linear regression analysis with the deterioration amount of characteristic data (E) of the battery (2, 2a) as a dependent variable (Y), and with usage history data (D) of the battery at the time of measurement of the characteristic data and the power (D') of the usage history data as explanatory variables (X), the battery deterioration model formula (Ma) being modeled by specifying the coefficients (α, β, γ) of each term of the explanatory variables as positive or negative in advance.

2. The diagnostic program according to claim 1, wherein the battery deterioration model equation is modeled by pre-specifying the coefficients of the explanatory variable terms whose values ​​increase with the deterioration of the battery as positive, and pre-specifying the coefficients of the explanatory variable terms whose values ​​decrease with the deterioration of the battery as negative.

3. The diagnostic program according to claim 1, wherein the battery deterioration model formula is modeled by previously defining the coefficients of all of the explanatory variable terms as positive.

4. A diagnostic program according to any one of claims 1 to 3, which causes a processor (10) to calculate a degradation influence (A) indicating the degree of influence that the usage history data has on the capacity degradation of the battery from the product of the coefficient of each term of the battery degradation model equation and the usage history data or the exponentiation of the usage history data linked to that coefficient, and outputs the degradation influence to a display unit (21).

5. The diagnostic program according to claim 4, which causes the processor (10) to calculate the deterioration influence degree (A) requested by an external device (40A), and to output all of the deterioration influence degrees calculated in response to the request to the display unit.

6. A diagnostic system (101) comprising: a memory unit (51) that stores a battery degradation model formula (Ma) obtained by linear regression analysis using a deterioration amount of characteristic data (E) of a battery (2, 2a) as a dependent variable (Y), and using usage history data (D) of the battery at the time of measuring the characteristic data and a power (D') of the usage history data as explanatory variables (X), the battery degradation model formula (Ma) being modeled by specifying coefficients (α, β, γ) of each term of the explanatory variables as positive or negative in advance; and a degradation factor identification unit (47, 48, 49) that identifies a degradation factor of the battery using the battery degradation model formula stored in the memory unit.

7. The diagnostic system according to claim 6, wherein the battery deterioration model equation is modeled by specifying in advance the coefficients of the explanatory variable terms whose values ​​increase with the deterioration of the battery as positive, and specifying in advance the coefficients of the explanatory variable terms whose values ​​decrease with the deterioration of the battery as negative.

8. The diagnostic system according to claim 6, wherein the battery deterioration model equation is modeled by previously defining the coefficients of all of the explanatory variable terms as positive.

9. The diagnostic system according to any one of claims 6 to 8, wherein the degradation factor identification unit comprises: a degradation influence calculation unit (47) that calculates a degradation influence (A) indicating the degree of influence that the usage history data has on the capacity degradation of the battery from the product of the coefficient of each term in the battery degradation model equation and the usage history data or the exponentiation of the usage history data linked to that coefficient; and a degradation influence output unit (49) that outputs the degradation influence calculated by the degradation influence calculation unit to a display unit (21).

10. The diagnostic system described in claim 9, wherein the deterioration influence calculation unit calculates the deterioration influence requested by an external device (40A), and the deterioration influence output unit outputs all of the deterioration influence calculated by the deterioration influence calculation unit in response to the request to the display unit.

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