Battery management system, battery management method, and battery management program

Abstract

To transmit a future state of a storage battery mounted on an electric vehicle to a user.SOLUTION: A battery management system includes: an acquisition unit which acquires storage battery data indicating a state of a storage battery mounted on an electric vehicle; a service life prediction unit which predicts battery life that is a service life of the storage battery, based on the storage battery data; a state prediction unit which predicts secular change in the state of the storage battery in the future, based on the battery life; a generation unit which generates a report indicating the secular change; and an output unit which outputs the report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] One aspect of the present disclosure relates to a battery management system, a battery management method, and a battery management program. [Background technology]

[0002] Patent Document 1 describes a condition monitoring system for lead-acid batteries. This system includes a device for measuring the internal resistance of a lead-acid battery, a device for calculating the average value of the internal resistance for each fixed period and comparing this average value of the internal resistance for each fixed period with the average value for the fixed period immediately before that to calculate the rate of change between the average values, and a device for issuing an alarm or displaying a message indicating that it is time to replace the lead-acid battery when the rate of change exceeds a predetermined value. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 4353653 Summary of the Invention [Problem to be solved by the invention]

[0004] It is desirable to communicate to users the future status of storage batteries installed in electric vehicles. [Means for solving the problem]

[0005] A battery management system according to one aspect of the present disclosure includes an acquisition unit that acquires storage battery data indicating the state of a storage battery mounted on an electric vehicle, a life prediction unit that predicts battery life, which is the lifespan of the storage battery, based on the storage battery data, a state prediction unit that predicts future changes in the state of the storage battery over time based on the battery life, a generation unit that generates a report indicating the changes over time, and an output unit that outputs the report.

[0006] A battery management method according to an aspect of the present disclosure is executed by a battery management system including at least one processor. The battery management method includes the steps of acquiring storage battery data indicating a state of a storage battery mounted on an electric vehicle, predicting a battery life based on the storage battery data, predicting a future change in the state of the storage battery over time based on the battery life, generating a report indicating the change over time, and outputting the report.

[0007] A battery management program according to one aspect of the present disclosure causes a computer to execute the steps of acquiring storage battery data indicating the state of a storage battery mounted on an electric vehicle, predicting a battery life based on the storage battery data, predicting future changes in the state of the storage battery over time based on the battery life, generating a report indicating the changes over time, and outputting the report.

[0008] In this aspect, the battery life of a storage battery mounted on an electric vehicle is predicted. Then, a report showing the future transition of the state of the storage battery is generated based on the battery life. This report can communicate the future state of the storage battery mounted on the electric vehicle to a user. [Effects of the Invention]

[0009] According to one aspect of the present disclosure, the future state of a storage battery mounted on an electric vehicle can be communicated to a user. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 2 is a diagram illustrating an example of a functional configuration of a battery management system according to the embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of a computer that configures a battery management system according to an embodiment. [Figure 3] 4 is a flowchart illustrating an example of processing performed by a battery management system according to an embodiment. [Figure 4]10 is a flowchart showing an example of predicting a battery life based on a characteristic value corresponding to an SOC. [Figure 5] FIG. 10 is a diagram illustrating an example of a graph relating to a reference characteristic value and a target characteristic value. [Figure 6] FIG. 10 is a diagram illustrating an example of a report. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In the description of the drawings, the same or equivalent elements are designated by the same reference numerals, and redundant description will be omitted.

[0012] [System Configuration] A battery management system 1 according to an embodiment is a computer system that predicts the future state of a storage battery (secondary battery) installed in an electric vehicle and provides a user with a report showing the prediction results. Examples of types of storage batteries include, but are not limited to, lead-acid batteries and lithium-ion batteries. The storage battery may also be a battery pack consisting of multiple cells of the same type. An electric vehicle is a vehicle that runs using all or part of the electrical energy stored in a storage battery as its power source. An electric vehicle may be a vehicle for carrying passengers or a vehicle for transporting cargo. An electric vehicle may also be a cargo handling vehicle for transporting cargo, such as a forklift. In one example, the battery management system 1 may provide a user with a report showing the prediction results for the lead-acid battery installed in the cargo handling vehicle.

[0013] FIG. 1 is a diagram showing an example of the functional configuration of a battery management system 1. In one example, the battery management system 1 includes a server 10. The server 10 can access a database 20, which stores storage battery data indicating the status of storage batteries mounted on electric vehicles 2, via a communication network. The database 20 stores storage battery data for at least one electric vehicle 2. The database 20 may be a component of the battery management system 1, or may be provided in a computer system separate from the battery management system 1. The server 10 is further connected to at least one user terminal 30 via the communication network. The communication network used for the battery management system 1 is, for example, configured by at least one of the Internet and an intranet.

[0014] Each electric vehicle 2 provides battery data to the database 20. The electric vehicle 2 includes a battery management unit (BMU) 3 that monitors and controls the battery. The BMU 3 repeatedly measures the battery status at predetermined intervals and generates battery data indicating the battery status. The BMU 3 then transmits the battery data to the database 20 via a communication network at predetermined times. The battery data is time-series data indicating the battery status. For example, each record of the battery data includes the measurement date and time and at least one physical quantity indicating the battery status. Examples of the physical quantity include, but are not limited to, a measured voltage, a measured current, and a measured temperature. The battery data indicates a physical quantity measured, for example, every 100 milliseconds. In the database 20, the battery data is associated with at least one of a battery ID and an electric vehicle ID. The battery ID is an identifier that uniquely identifies the battery. The electric vehicle ID is an identifier that uniquely identifies the electric vehicle 2.

[0015] The server 10 is a computer that predicts the future state of a storage battery based on storage battery data and provides a user with a report showing the prediction results. The server 10 includes functional modules: a receiving unit 11, an acquiring unit 12, a lifespan predicting unit 13, a state predicting unit 14, a generating unit 15, and a transmitting unit 16. The receiving unit 11 is a functional module that receives a request for generating and providing a report from a user terminal 30. The acquiring unit 12 is a functional module that acquires storage battery data from a database 20 based on the request. The lifespan predicting unit 13 is a functional module that predicts the lifespan of a storage battery based on the storage battery data. In this disclosure, the lifespan of a storage battery is also referred to as "battery life." The state predicting unit 14 is a functional module that predicts future changes in the state of the storage battery over time based on the battery life. The generating unit 15 is a functional module that generates a report showing the changes over time. The transmitting unit 16 is a functional module that transmits the report to the user terminal 30. This transmission is an example of report output, and therefore the transmitting unit 16 functions as an output unit.

[0016] The user terminal 30 is a computer operated by a user of the battery management system 1. Examples of the user include, but are not limited to, a sales representative in charge of selling storage batteries, a service technician who performs maintenance on the storage batteries, and an owner or manager of the electric vehicle 2.

[0017] 2 is a diagram showing an example of a general hardware configuration of a computer 100 constituting the server 10. For example, the computer 100 includes a processor (e.g., a CPU) 101 that executes an operating system, application programs, etc., a main memory unit 102 consisting of ROM and RAM, an auxiliary memory unit 103 consisting of a storage device such as a hard disk or flash memory, a communication control unit 104 consisting of a network card or a wireless communication module, an input device 105 such as a keyboard or a mouse, and an output device 106 such as a monitor.

[0018] Each functional module of server 10 is realized by loading a predetermined program onto processor 101 or main memory unit 102 and having processor 101 execute the program. Processor 101 operates communication control unit 104, input device 105, or output device 106 in accordance with the program, and reads and writes data from and to main memory unit 102 or auxiliary memory unit 103. Data or databases required for processing are stored in main memory unit 102 or auxiliary memory unit 103.

[0019] The server 10 is composed of at least one computer. When multiple computers are used, a single server 10 is logically constructed by connecting these computers via a communication network such as the Internet or an intranet.

[0020] [System Operation] An example of processing by the battery management system 1 (server 10) and an example of a battery management method according to this embodiment will be described with reference to Fig. 3. Fig. 3 is a flowchart showing an example of the processing as a processing flow S1.

[0021] In step S11, the receiving unit 11 receives a report request from the user terminal 30. The report request is a data signal for requesting the server 10 to generate and provide a report. The user terminal 30 generates a report request based on a user operation and transmits the report request to the server 10. In one example, the report request includes at least one electric vehicle ID, for example, the electric vehicle ID of at least one electric vehicle 2 located in a specific location such as a sales office, a work site, or the like.

[0022] In step S12, the acquisition unit 12 selects one electric vehicle 2 (one electric vehicle ID) based on the report request.

[0023] In step S13, the acquisition unit 12 acquires storage battery data of the selected electric vehicle 2. The acquisition unit 12 reads out the storage battery data corresponding to the selected electric vehicle ID from the database 20. When the storage battery ID is used, the life prediction unit 13 identifies the storage battery ID from the electric vehicle ID by referring to given data indicating the correspondence between the electric vehicle ID and the storage battery ID, and reads out the storage battery data corresponding to the storage battery ID from the database 20.

[0024] In step S14, the life prediction unit 13 predicts the battery life of the selected electric vehicle 2 based on the storage battery data. The life prediction unit 13 may calculate the operation rate or discharge capacity per unit time of the electric vehicle 2 from the storage battery data, and predict the battery life based on the operation rate or discharge capacity. Alternatively, the life prediction unit 13 may predict the battery life based on a characteristic value corresponding to the state of charge (SOC) of the storage battery.

[0025] As an example of step S14, the process of predicting the battery life based on a characteristic value corresponding to the SOC will be described below.

[0026] The life prediction unit 13 calculates characteristic values ​​corresponding to the SOC for a reference period and a target period following the reference period. In this disclosure, the characteristic values ​​for the reference period are also referred to as "reference characteristic values," and the characteristic values ​​for the target period are also referred to as "target characteristic values." These characteristic values ​​are not the SOC itself, but values ​​obtained based on the SOC.

[0027] In one example, the reference period and the target period are the time spans from the completion of charging the storage battery to the start of the next charging. In this case, the SOC at the start of both the reference period and the target period is 100%.

[0028] In one example, the life prediction unit 13 may calculate a parameter obtained from the relationship between the SOC and the open circuit voltage (OCV) as the characteristic value. In the present disclosure, this parameter is also referred to as the "OCV-SOC parameter." Alternatively, the life prediction unit 13 may calculate a parameter obtained from the relationship between the SOC and the direct current resistance (DCR) as the characteristic value. In the present disclosure, this parameter is also referred to as the "DCR-SOC parameter." In these examples, the life prediction unit 13 performs calculations based on an equivalent circuit of the storage battery. The equivalent circuit includes a power source whose voltage changes in proportion to the SOC and an internal resistance whose resistance value changes in proportion to the SOC. The calculations based on the equivalent circuit include linear equation (1) which shows the OCV-SOC characteristic, which is the relationship between the SOC and the OCV, and linear equation (2) which shows the DCR-SOC characteristic, which is the relationship between the SOC and the DCR. In these two equations, a indicates the intercept and b indicates the slope. a OCV ,b OCV ,a DCR ,b DCR can be said to be first-order approximation constants. OCV=a OCV +b OCV ·SOC…(1) DCR=a DCR +b DCR ·SOC …(2)

[0029] The life prediction unit 13 calculates b in Equation (1). OCV may be obtained as a characteristic value. OCV is an example of an OCV-SOC parameter. The life prediction unit 13 may obtain the DCR when the SOC is 50% as the characteristic value, which is obtained from equation (2). In the present disclosure, the DCR when the SOC is 50% is referred to as the DCR 50 Also expressed as DCR. 50 is an example of a DCR-SOC parameter.

[0030] The life prediction unit 13 calculates a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value. The reference value indicates how the characteristics of the storage battery have changed over time from the reference period to the target period. The reference value can also be said to represent the state of health (SOH) of the storage battery. The life prediction unit 13 predicts the battery life based on the reference value.

[0031] The prediction of battery life based on characteristic values ​​corresponding to SOC will be described in more detail with reference to Fig. 4. Fig. 4 is a flowchart showing an example of the prediction process. This flowchart shows the details of step S14.

[0032] In step S141, the life prediction unit 13 acquires battery data corresponding to a reference period as reference data. In one example, the reference period corresponds to a period when the battery is new. The life prediction unit 13 selects a record group of the battery data corresponding to the reference period.

[0033] In step S142, the life prediction unit 13 calculates a reference characteristic value based on the reference data. In one example, the life prediction unit 13 calculates a moving average of the measured voltage and the measured current for each of a plurality of intervals set along a time axis. For example, if the time interval between records is 100 milliseconds, the life prediction unit 13 sets the interval to 10 seconds and calculates the average value of 100 physical quantities within the interval every 10 seconds. Furthermore, the life prediction unit 13 calculates the SOC for each interval. Next, the life prediction unit 13 selects a group of intervals in which the moving average of the measured current is equal to or greater than a given threshold. This threshold may be a value for distinguishing whether the electric vehicle 2 is idling. Then, the life prediction unit 13 calculates an IV characteristic for the reference period using a statistical method based on the data of the selected group of intervals, and obtains a reference characteristic value based on the IV characteristic. In this disclosure, the IV characteristic refers to the relationship between the measured current, the measured voltage, and the SOC. In this disclosure, the "data of the selected group of intervals" is also referred to as "partial data."

[0034] Using the measured voltage at low currents results in large errors in the OCV calculation and, ultimately, in the calculation of the characteristic value. Furthermore, depending on the current sensor, offset errors due to temperature and hysteresis errors due to residual magnetism can become large at low currents, which increases the error in the SOC calculation. By excluding the section corresponding to the idling state, where the current is small, these errors can be reduced or avoided, allowing the characteristic value to be calculated with high accuracy. The idling state refers to the state in which the electric vehicle 2 is operating without load.

[0035] The threshold value for determining whether the electric vehicle 2 is idling may be a threshold value resulting from an offset error of the current sensor, and may be set to, for example, 1 (A). In this case, the error in the SOC can be reduced. Alternatively, the threshold value for determining whether the electric vehicle 2 is idling may be a threshold value resulting from battery characteristics, and may be set to, for example, 0.05 (CA). In this case, the IV characteristics can be determined with higher accuracy.

[0036] The life prediction unit 13 calculates SOC(k) for each interval k for which the moving average is obtained, using equation (3). SOC(k) = [W bat -Σ{I(k) / α}] / W bat …(3) where W bat indicates the rated capacity of the battery, and I(k) indicates the measured current in section k. α is a coefficient for converting current (A) to capacity (Ah). If the length of the section is 10 seconds, α = 360. Σ{I(k) / α} indicates the consumed capacity of the battery up to section k.

[0037] As a result, the life prediction unit 13 obtains the measured current I(k), the measured voltage MV(k), and the SOC(k) for each of the n intervals k (k=1 to n). That is, the life prediction unit 13 obtains time-series data on the moving average of the current, the moving average of the measured voltage, and the corresponding SOC.

[0038] Next, the life prediction unit 13 uses a statistical method to calculate the linear approximation constant a in the equations (1) and (2) based on n sets of the measured current, the measured voltage, and the SOC. OCV ,b OCV ,a DCR ,b DCR As an example, the life prediction unit 13 may use the Marquardt method, which is a nonlinear least squares method, as the statistical method. The life prediction unit 13 uses the Marquardt method to calculate a linear approximation constant a that minimizes the mean square error between the measured voltage MV and the theoretical voltage CV. OCV ,b OCV ,a DCR ,b DCR In one example, the theoretical voltage CV(k) in section k is obtained by equation (4). Equation (4) can be said to represent the IV characteristics of the battery based on the equivalent circuit of the battery, and can also be said to be a formula for calculating the theoretical voltage. CV(k)=OCV(k)-I(k)·DCR(k)={a OCV +b OCV ·SOC(k)}-I(k)·{a DCR +b DCR ·SOC(k)} …(4)

[0039] Alternatively, the life prediction unit 13 may use multivariate analysis as a statistical method. In one example, the life prediction unit 13 calculates a linear approximation constant a based on Equation (4). OCV ,b OCV ,a DCR ,b DCR may be calculated.

[0040] That is, the life prediction unit 13 uses a statistical method such as the Marquardt method or multivariate analysis to calculate the IV characteristics so that the mean square error between the measured voltage MV and the theoretical voltage CV is minimized, and calculates a first-order approximation constant a obtained from this IV characteristics. OCV ,b OCV ,a DCR ,b DCR Calculate.

[0041] In one example, the life prediction unit 13 OCV and DCR 50 At least one of the above is obtained as a reference characteristic value.

[0042] In step S143, the life prediction unit 13 acquires storage battery data corresponding to a target period as target data. For example, the target period corresponds to a past period including the present time. The life prediction unit 13 selects a record group of storage battery data corresponding to the target period.

[0043] In step S144, the life prediction unit 13 calculates the target characteristic value based on the target data. In one example, the life prediction unit 13 calculates the target characteristic value using the same method as for the reference characteristic value. That is, the life prediction unit 13 calculates the moving average of the measured voltage and the measured current for each predetermined interval. Furthermore, the life prediction unit 13 calculates the SOC for each interval. Next, the life prediction unit 13 selects a group of intervals in which the moving average of the measured current is equal to or greater than a given threshold. Then, the life prediction unit 13 calculates the IV characteristics for the target period using a statistical method based on the data of the selected group of intervals, i.e., the partial data, and obtains the target characteristic value based on the IV characteristics. The intervals for calculating the moving average and the threshold for selecting the intervals are both the same as when calculating the reference characteristic value. In one example, the life prediction unit 13 uses the Marquardt method or multivariate analysis to calculate the IV characteristics so as to minimize the mean square error between the measured voltage MV and the theoretical voltage CV, and obtains a first-order approximation constant a obtained from the IV characteristics. OCV ,b OCV ,a DCR ,b DCR Calculate.

[0044] In step S145, the life prediction unit 13 calculates a reference value based on the reference characteristic value and the target characteristic value. The life prediction unit 13 calculates a ratio indicating the relationship between the reference characteristic value and the target characteristic value as the reference value. The life prediction unit 13 calculates at least one reference value.

[0045] The life prediction unit 13 may calculate the ratio of the OCV-SOC parameter as a reference value. OCV and b during the target period OCVThe life prediction unit 13 calculates a ratio indicating the relationship between b and OCV b for the target period, relative to the inverse of OCV The ratio of the reciprocal of b may be used as a reference value. In the present disclosure, this reference value is also referred to as "SOH-Q." OCV The reciprocal of b OCV -1 It can also be expressed as:

[0046] The life prediction unit 13 may calculate the ratio of the DCR-SOC parameter as a reference value. 50 DCR for the target period 50 The ratio of SOH-R to SOH-R may be used as a reference value, which is also referred to as "SOH-R" in the present disclosure.

[0047] SOH-Q and SOH-R, which are examples of reference values, will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of a graph relating to the reference characteristic value and the target characteristic value.

[0048] Example (a) shows the OCV-SOC characteristic shown by the linear equation (1) above. The horizontal axis shows SOC (%), and the vertical axis shows OCV (V). Graphs 201 and 202 both show the OCV-SOC characteristic shown by the linear equation (1) above. Graph 201 shows the OCV-SOC characteristic in a reference period, and graph 202 shows the OCV-SOC characteristic in a target period. In this example, the reference period corresponds to the time when the storage battery is new, and the target period corresponds to the time when the storage battery has deteriorated. As can be seen from graphs 201 and 202, as the storage battery deteriorates, the characteristic value b OCV becomes larger, and the inverse b OCV -1 Therefore, the reference value SOH-Q gradually decreases from 100% (or 1.0) as the battery deteriorates. OCV -1 A decrease in the reference value SOH-Q indicates a decrease in the capacity of the battery, so a decrease in the reference value SOH-Q indicates a decrease in the capacity of the battery.

[0049] Example (b) shows the DCR-SOC characteristic shown by the linear equation (2) above. The horizontal axis shows SOC (%), and the vertical axis shows DCR (mΩ). Graphs 211 and 212 both show the DCR-SOC characteristic shown by the linear equation (2) above. Graph 211 shows the DCR-SOC characteristic in the reference period, and graph 212 shows the DCR-SOC characteristic in the target period. In this example, too, the reference period corresponds to the time when the storage battery is new, and the target period corresponds to the time when the storage battery has deteriorated. As can be seen from graphs 211 and 212, as the storage battery deteriorates, the characteristic value DCR 50 Therefore, the reference value SOH-R gradually increases from 100% (or 1.0) as the storage battery deteriorates.

[0050] Returning to FIG. 4, in step S146, the life prediction unit 13 predicts the battery life based on the reference value. For example, the life prediction unit 13 may predict the battery life from the reference value based on a correspondence table or a calculation formula showing the relationship between the reference value and the usage period of the storage battery. When the SOH-Q is used as the reference value, the life prediction unit 13 may determine the battery life as the time when the SOH-Q reaches a given threshold value between 50 and 80%. When the SOH-R is used as the reference value, the life prediction unit 13 may determine the battery life as the time when the SOH-R reaches a given threshold value between 200 and 300%. The life prediction unit 13 may predict the battery life based on both the SOH-Q and the SOH-R.

[0051] Returning to FIG. 3 , in step S15, the state prediction unit 14 predicts future changes in the state of the storage battery over time based on the battery life. For example, the state prediction unit 14 may predict this change over time using multiple categories. The multiple categories may represent at least one of a normal period during which the storage battery can be used normally, a budgeting recommendation period during which it is recommended to set up a budget for replacing the storage battery, a replacement recommendation period during which it is recommended to replace the storage battery, and a life end period during which the life of the storage battery will expire. When multiple categories represent these four types of periods, the change over time in the state of the storage battery progresses in the order of the normal period, the budgeting recommendation period, the replacement recommendation period, and the life end period. The state prediction unit 14 may predict the change over time in the state of the storage battery based on a correspondence table or a formula indicating the relationship between the time remaining until the battery life and each category.

[0052] As shown in step S16, the server 10 repeats the processing of steps S12 to S15 until all electric vehicles 2 indicated in the report request have been processed. When the processing is repeated, the next electric vehicle 2 is selected in step S12, and the change over time in the state of the storage battery of that electric vehicle 2 is predicted by the series of processing of steps S13 to S15.

[0053] In step S17, the generation unit 15 generates a report showing the change over time of each storage battery. This report is visualized electronic data. For example, the generation unit 15 may generate a report that shows the change over time of the state of each storage battery using four sections corresponding to a normal period, a budgeting recommended period, a replacement recommended period, and a life end period.

[0054] In step S18, the transmitter 16 transmits the report to the user terminal 30. The user terminal 30 receives and displays the report. When the report is expressed using four sections corresponding to the normal period, the period for which budgeting is recommended, the period for which replacement is recommended, and the end of life period, the user can obtain from this report information useful for managing the storage battery, such as when to replace the storage battery and when to budget for that replacement.

[0055] 6 is a diagram showing an example of a report. In this example, the report 300 includes a time-series heat map 301 showing the change over time in the future state of the storage batteries for each of the ten electric vehicles 2, and a bar graph 302 showing the number of electric vehicles 2 (storage batteries) for which budgeting for or replacement of the storage batteries is recommended.

[0056] The time-series heat map 301 represents the changes over time for each storage battery in four sections: normal period (1), budgeting recommended period (2), replacement recommended period (3), and end-of-life period (4). For example, the following can be predicted for the storage battery in car 1 from this time-series heat map 301: The storage battery will be usable normally until April 2022. Budgeting for replacement is recommended between May 2022 and April 2023. Battery replacement is recommended between May and July 2023. The storage battery will reach the end of its life after August 2023.

[0057] The bar graph 302 shows the number of electric vehicles 2 for which budgeting is recommended and the number of electric vehicles 2 for which battery replacement is recommended, by quarter. For example, from this bar graph 302, it is predicted that in the third quarter of 2022 (July to September 2022), budgeting will be recommended for seven electric vehicles 2, and battery replacement will be recommended for one electric vehicle 2.

[0058] The report 300 allows the user to plan for battery replacement, for example, to properly budget for battery replacement or to decide when to sell or buy a new battery.

[0059] [program] A battery management program for causing a computer or computer system to function as the battery management system 1 or server 10 includes program code for causing the computer or computer system to function as a receiver 11, an acquirer 12, a lifespan predictor 13, a state predictor 14, a generator 15, and a transmitter 16. This battery management program may be provided by being non-temporarily recorded on a tangible recording medium such as a CD-ROM, a DVD-ROM, or a semiconductor memory. Alternatively, the battery management program may be provided via a communication network as a data signal superimposed on a carrier wave. The provided battery management program is stored in, for example, an auxiliary storage unit 103. The processor 101 reads and executes the battery management program from the auxiliary storage unit 103, thereby realizing each of the above-mentioned functional modules.

[0060] [effect] As described above, a battery management system according to one aspect of the present disclosure includes an acquisition unit that acquires storage battery data indicating the state of a storage battery mounted on an electric vehicle, a life prediction unit that predicts battery life, which is the lifespan of the storage battery, based on the storage battery data, a state prediction unit that predicts future changes in the state of the storage battery over time based on the battery life, a generation unit that generates a report indicating the changes over time, and an output unit that outputs the report.

[0061] A battery management method according to an aspect of the present disclosure is executed by a battery management system including at least one processor. The battery management method includes the steps of acquiring storage battery data indicating a state of a storage battery mounted on an electric vehicle, predicting a battery life based on the storage battery data, predicting a future change in the state of the storage battery over time based on the battery life, generating a report indicating the change over time, and outputting the report.

[0062] A battery management program according to one aspect of the present disclosure causes a computer to execute the steps of acquiring storage battery data indicating the state of a storage battery mounted on an electric vehicle, predicting a battery life based on the storage battery data, predicting future changes in the state of the storage battery over time based on the battery life, generating a report indicating the changes over time, and outputting the report.

[0063] In this aspect, the battery life of a storage battery mounted on an electric vehicle is predicted. Then, a report showing the future transition of the state of the storage battery is generated based on the battery life. This report can communicate the future state of the storage battery mounted on the electric vehicle to a user.

[0064] In the battery management system according to another aspect, the state prediction unit may predict a change over time using a plurality of categories, and the generation unit may generate a report representing the change over time using the plurality of categories. In this case, the plurality of categories can be used to clearly present the change over time of the state of the storage battery to a user.

[0065] In a battery management system according to another aspect, the plurality of sections may include a first section representing a period during which it is recommended to set up a budget for replacing the storage battery, which makes it easier to raise funds for the cost of replacing the storage battery.

[0066] In a battery management system according to another aspect, the plurality of segments may further include at least one of a second segment representing a period during which the storage battery can be used normally, a third segment representing a period during which replacement of the storage battery is recommended, and a fourth segment representing a period during which the storage battery is at the end of its life. In this case, the change in the state of the storage battery over time can be displayed in detail to the user.

[0067] In another aspect of the battery management system, the life prediction unit may acquire reference data indicating the state of the storage battery during a reference period and target data indicating the state of the storage battery during a target period after the reference period, calculate a characteristic value corresponding to the state of charge of the storage battery during the reference period as a reference characteristic value based on the reference data, calculate a characteristic value corresponding to the state of charge of the storage battery during the target period as a target characteristic value based on the target data, calculate a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value, and predict the battery life based on the reference value. In this case, the degree of change in the characteristic value corresponding to the state of charge of the storage battery over the passage of time from the reference period to the target period is obtained as a reference value, and the battery life is predicted based on the reference value. This method accurately predicts the battery life, allowing useful reports to be provided to the user. For example, because the battery life is accurately predicted, the accuracy of the change in the state of the storage battery over time shown in the report can be expected to be improved.

[0068] In a battery management system according to another aspect, the electric vehicle may be a cargo vehicle. In this case, the future state of the storage battery mounted on the cargo vehicle can be communicated to a user.

[0069] In the battery management system according to another aspect, the storage battery may be a lead-acid battery. In this case, the future state of the lead-acid battery mounted on the electric vehicle can be communicated to the user.

[0070] [Variations] The present invention has been described in detail above based on the embodiments. However, the present invention is not limited to the above embodiments. Various modifications of the present invention are possible without departing from the spirit and scope of the present invention.

[0071] The life prediction unit 13 may calculate the reference characteristic value and the target characteristic value by a method other than a statistical method. For example, the life prediction unit 13 may calculate the characteristic values ​​using a Kalman filter every time measurement data is obtained.

[0072] The reference value may be calculated by a computer or device other than the server 10. For example, each BMU 3 may calculate the reference value for the corresponding storage battery.

[0073] The BMU 3 may calculate moving averages of the measured voltage and measured current, and transmit storage battery data indicating these moving averages to the database 20. Alternatively, the BMU 3 may transmit to the database 20 only data on sections where the moving average of the measured current is equal to or greater than a given threshold. As in the above embodiment, the threshold may be a value for distinguishing whether the electric vehicle 2 is idling. In these cases, the amount of communication between the BMU 3 and the database 20 can be reduced, and the processing load on the server 10 can also be reduced.

[0074] The processing procedure of the method executed by at least one processor is not limited to the examples in the above embodiment. For example, some of the steps (processing) described above may be omitted, or the steps may be executed in a different order. Furthermore, any two or more of the steps described above may be combined, or some of the steps may be modified or deleted. Alternatively, other steps may be executed in addition to the steps described above.

[0075] In the present disclosure, when comparing the magnitude relationship of two numerical values, either of the two criteria "greater than or equal to" and "greater than" may be used, or either of the two criteria "less than or equal to" and "under." The selection of such criteria does not change the technical significance of the process of comparing the magnitude relationship of two numerical values.

[0076] In this disclosure, the expression "at least one processor executes a first process, executes a second process, ... executes an nth process" or a corresponding expression indicates a concept including a case where the entity executing the n processes from the first process to the nth process (i.e., the processor) changes midway through. In other words, this expression indicates a concept including both a case where all n processes are executed by the same processor and a case where the processor changes among the n processes according to an arbitrary policy. [Explanation of symbols]

[0077] 1...battery management system, 2...electric vehicle, 3...BMU, 10...server, 11...receiving unit, 12...acquisition unit, 13...life expectancy prediction unit, 14...state prediction unit, 15...generation unit, 16...transmission unit, 20...database, 30...user terminal, 300...report.

Claims

1. an acquisition unit that acquires storage battery data indicating the state of a storage battery mounted in the electric vehicle; a life prediction unit that predicts a battery life, which is a life of the storage battery, based on the storage battery data; a state prediction unit that predicts future changes in the state of the storage battery over time based on the battery life using a plurality of categories determined according to the time remaining until the battery life; a generating unit that generates a report showing the change over time using the plurality of categories simultaneously; an output unit that outputs the report; A battery management system comprising:

2. the plurality of segments includes a first segment representing a period during which it is recommended to budget for replacing the storage battery; The battery management system of claim 1 .

3. the plurality of segments further includes at least one of a second segment representing a period during which the storage battery can be used normally, a third segment representing a period during which replacement of the storage battery is recommended, and a fourth segment representing a period during which the storage battery will reach the end of its life; The battery management system according to claim 2 .

4. The life prediction unit acquiring reference data indicating a state of the storage battery during a reference period and target data indicating a state of the storage battery during a target period after the reference period; calculating a characteristic value corresponding to the state of charge of the storage battery during the reference period as a reference characteristic value based on the reference data; calculating a characteristic value corresponding to the state of charge of the storage battery during the target period as a target characteristic value based on the target data; calculating a ratio indicating the relationship between the reference characteristic value and the target characteristic value as a reference value; predicting the battery life based on the reference value; The battery management system according to any one of claims 1 to 3.

5. The electric vehicle is a cargo handling vehicle. The battery management system according to any one of claims 1 to 4.

6. The storage battery is a lead storage battery. The battery management system according to any one of claims 1 to 5.

7. 1. A battery management method executed by a battery management system comprising at least one processor, comprising: acquiring storage battery data indicating a state of a storage battery mounted on the electric vehicle; predicting a battery life based on the storage battery data; a step of predicting future changes in the state of the storage battery over time based on the battery life using a plurality of categories determined according to time remaining until the battery life; generating a report showing the change over time using the plurality of segments simultaneously; outputting the report; A battery management method including:

8. acquiring storage battery data indicating a state of a storage battery mounted on the electric vehicle; predicting a battery life based on the storage battery data; a step of predicting future changes in the state of the storage battery over time based on the battery life using a plurality of categories determined according to time remaining until the battery life; generating a report showing the change over time using the plurality of segments simultaneously; outputting the report; A battery management program that causes a computer to execute the following.

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