BATTERY DIAGNOSIS METHOD, BATTERY DIAGNOSIS DEVICE, BATTERY DIAGNOSIS SYSTEM, BATTERY-EQUIPPED DEVICE, AND BATTERY DIAGNOSIS PROGRAM

Abstract

To provide a battery diagnostic method capable of predicting in advance a time when the probability that the battery capacity of the battery suddenly decreases becomes high.SOLUTION: In a battery diagnostic method according to an embodiment, on the basis of the internal state of the battery at a plurality of different times, the timing at which the internal state of the battery reaches a predetermined state after the plurality of times is estimated.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] An embodiment of the present invention relates to a battery diagnostic method, a battery diagnostic device, a battery diagnostic system, a battery-equipped device, and a battery diagnostic program. [Background technology]

[0002] With the spread of information-related devices and communication devices, secondary batteries are becoming more and more popular as power sources for these devices. Secondary batteries are also being used in fields such as electric vehicles (EVs) and natural energy. In particular, lithium-ion secondary batteries are widely used due to their high energy density and the ability to be miniaturized.

[0003] Secondary batteries such as lithium ion secondary batteries deteriorate as time passes from the start of use, and also deteriorate due to repeated charging and discharging. For this reason, it is important to grasp the deterioration state of batteries such as lithium ion secondary batteries. One of the indicators indicating the deterioration state of a battery is the battery capacity of a battery, and one of the methods for predicting the deterioration state of a battery is to predict the battery capacity of a battery, such as a fully charged capacity. In this case, for example, the battery capacity is predicted using an empirical rule (root rule) that the amount of decrease in the battery capacity of a battery is proportional to the 0.5th power of the elapsed time from the start of use of the battery. On the other hand, in the use of a battery, the rate of decrease in the battery capacity may deviate from the empirical rule (root rule) and may decrease rapidly over time. In the use of a battery, it is required to predict the time when the probability of the battery capacity of the battery decreasing rapidly increases before the probability of the battery capacity decreasing rapidly increases. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2012-251806 A [Patent Document 2] International Publication No. 2017 / 098686 Summary of the Invention [Problem to be solved by the invention]

[0005] The problem that the present invention aims to solve is to provide a battery diagnostic method, a battery diagnostic device, a battery diagnostic system, a battery-equipped device, and a battery diagnostic program that are capable of predicting in advance the time when the probability of a battery's capacity decreasing rapidly will be high. [Means for solving the problem]

[0006] According to an embodiment, a method for diagnosing a battery is provided, in which, for each of a plurality of mutually different time periods, a measurement result of at least one of a voltage and a current of the battery and data indicating a relationship of an internal state of the battery to at least one of the voltage and the current of the battery are calculated as an internal state parameter indicating the internal state of the battery. ,negative Extreme capacity ,negative Extreme capacity maintenance rate and Negative electrode quality Amount One or more of the following are estimated. In the diagnosis method, for the estimated internal state parameter, a value corresponding to the internal state of the battery at a time when the internal state of the battery is more deteriorated than at the multiple time periods when the internal state was estimated and the probability of the battery capacity decreasing rapidly is high is set as the predetermined value. In the diagnosis method, a time after the multiple time periods when the internal state parameter of the battery will reach a predetermined value is estimated based on the estimation results of the internal state parameter of the battery at the multiple time periods. In the diagnosis method, If the internal state parameter is decreasing at any of the multiple periods, The time corresponding to the estimation result of the time when the internal state parameter reaches the predetermined value is predicted as the time when the battery capacity of the battery will be likely to decrease rapidly. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a schematic diagram showing a battery diagnostic system according to a first embodiment. [Diagram 2] FIG. 2 is a schematic diagram for explaining an example of a process performed by the timing prediction unit of the diagnosis device according to the first embodiment to estimate the timing at which a certain internal state parameter will reach a predetermined value. [Diagram 3] FIG. 3 is a flowchart showing an example of processing related to estimation of an internal state, which is performed by the diagnosis device according to the first embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of processing related to prediction of a time when the probability of a sudden decrease in the battery capacity of a battery is high, which is performed by the diagnosis device according to the first embodiment. [Diagram 5] FIG. 5 is a schematic diagram showing the estimation result of the negative electrode capacity, which is one of the internal state parameters of the battery, in the verification related to the embodiment. [Figure 6] FIG. 6 is a schematic diagram showing the measurement results of the battery capacity of a battery in a test related to the embodiment. [Figure 7] FIG. 7 is a schematic diagram showing a prediction result of a time when the probability of a rapid decrease in the battery capacity of a battery becomes high in a verification related to the embodiment. [Figure 8] FIG. 8 is a flowchart showing an example of processing performed by the diagnostic device according to the second embodiment, using a prediction result regarding a time when the probability of the battery capacity of the battery decreasing rapidly increases. [Figure 9] FIG. 9 is a flowchart showing an example of processing related to prediction of a time when the probability of a sudden drop in the battery capacity of a battery is high, which is performed by the diagnosis device according to the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, embodiments will be described with reference to the drawings.

[0009] (First embodiment) First, a first embodiment will be described as an example of an embodiment. Fig. 1 shows a diagnostic system 1 for a battery 5 according to the first embodiment. As shown in Fig. 1, the diagnostic system 1 includes a battery-equipped device 2 and a diagnostic device 3. A battery 5 is mounted on the battery-equipped device 2. Examples of the battery-equipped device 2 include a large-scale power storage device for a power system, a smartphone, a vehicle, a home power supply device, a stationary power supply device, a robot, and a drone, and examples of the vehicle that can be the battery-equipped device 2 include a railroad car, an electric bus, an electric car, a plug-in hybrid car, and an electric motorcycle.

[0010] The battery 5 is, for example, a secondary battery such as a lithium ion secondary battery. The battery 5 may be formed from a single cell (single battery), or may be a battery module or a cell block formed by electrically connecting a plurality of single cells. When the battery 5 is formed from a plurality of single cells, the plurality of single cells may be electrically connected in series in the battery 5, or may be electrically connected in parallel in the battery 5. In addition, the battery 5 may be formed in both a series connection structure in which a plurality of single cells are connected in series, and a parallel connection structure in which a plurality of single cells are connected in parallel. In addition, the battery 5 may be any of a battery string, a battery array, and a storage battery in which a plurality of battery modules are electrically connected.

[0011] The diagnostic system 1 is provided with a power source and a load (indicated by reference symbol 6). The power source can supply power to the battery 5, and the battery 5 is charged by being supplied with power from the power source or the like. The load can be supplied with power from the battery 5, and the battery 5 discharges by supplying power to the load or the like. Examples of the power source include a battery other than the battery 5 and a generator. Examples of the load include an electric motor and a light. In one example, instead of or in addition to the load, a capacitor to which power is supplied from the battery 5 may be provided. In this case, the battery 5 discharges by supplying power to the capacitor. The capacitor can store the power supplied from the battery 5. In another example, a motor generator may be provided. In this case, power can be supplied from the battery 5 to the motor generator, and power can be supplied from the motor generator to the battery 5. That is, the motor generator functions as both a power source and a load. In FIG. 1, the power source and the load are mounted on the battery-mounted device 2, but this is not limited thereto. The battery 5 may be capable of supplying power to a load external to the battery-equipped device 2, or power may be supplied to the battery 5 from a power source external to the battery-equipped device 2.

[0012] The diagnostic system 1 is also provided with a current measurement circuit 7 and a voltage measurement circuit 8. The current measurement circuit 7 and the voltage measurement circuit 8 form a measurement circuit that measures parameters related to the battery 5. The current measurement circuit 7 measures the current flowing through the battery 5 during charging and discharging of the battery 5. The voltage measurement circuit 8 measures the voltage of the battery 5 during charging and discharging of the battery 5. During charging or discharging of the battery 5, the current measurement circuit 7 measures the current at each of a plurality of measurement points, and the voltage measurement circuit 8 measures the voltage at each of a plurality of measurement points. In the example of FIG. 1, the measurement circuits such as the current measurement circuit 7 and the voltage measurement circuit 8 are mounted on the battery-mounted device 2, but may be formed outside the battery-mounted device 2. In one example, similar to the example of FIG. 1, the measurement circuit may include a temperature measurement circuit 9. In this case, the measurement circuit measures the temperature of the battery 5 in addition to the current and voltage of the battery 5 during charging and discharging of the battery 5. The temperature of the battery 5 is also measured at each of a plurality of measurement points.

[0013] The diagnostic device 3 performs diagnosis on the battery 5 by determining the deterioration state of the battery 5, etc. For this reason, the battery 5 is the subject of diagnosis by the diagnostic device 3. In one example such as FIG. 1, the diagnostic device 3 is provided outside the battery-equipped device 2. The diagnostic device 3 includes a transmitter / receiver unit 11, an internal state estimation unit 12, a timing prediction unit 15, and a data storage unit 16. The diagnostic device 3 is, for example, a server capable of communicating with the battery-equipped device 2 (a processing device mounted on the battery-equipped device 2) via a network. In this case, the diagnostic device 3 includes a processor and a storage medium.

[0014] The processor includes any one of a central processing unit (CPU), an application specific integrated circuit (ASIC), a microcomputer, a field programmable gate array (FPGA), and a digital signal processor (DSP). The storage medium may include a main storage device such as a memory, as well as an auxiliary storage device. Examples of the storage medium include a magnetic disk, an optical disk (CD-ROM, CD-R, DVD, etc.), a magneto-optical disk (MO, etc.), and a semiconductor memory. In the diagnostic device 3, the processor and the storage medium may each be one or more. In the diagnostic device 3, the processor performs processing by executing a program or the like stored in the storage medium or the like. The program executed by the processor of the diagnostic device 3 may be stored in a computer (server) connected to the diagnostic device 3 via a network such as the Internet, or in a server in a cloud environment. In this case, the processor downloads the program via the network. In the diagnostic device 3, the transmitting / receiving unit 11, the internal state estimation unit 12, and the timing prediction unit 15 perform part of the processing performed by the processor or the like, and the storage medium functions as the data storage unit 16.

[0015] In one example, the diagnostic device 3 may be a cloud server configured in a cloud environment. The infrastructure of the cloud environment is configured by a virtual processor such as a virtual CPU and a cloud memory. Therefore, when the diagnostic device 3 is a cloud server, part of the processing performed by the virtual processor is performed by the transmitting / receiving unit 11, the internal state estimation unit 12, and the timing prediction unit 15. And the cloud memory functions as the data storage unit 16.

[0016] The data storage unit 16 may also be provided in a computer separate from the battery-equipped device 2 and the diagnostic device 3. In this case, the diagnostic device 3 is connected via a network to a computer in which the data storage unit 16 and the like are provided. The diagnostic device 3 may also be mounted in the battery-equipped device 2. In this case, the diagnostic device 3 is composed of a processing device and the like mounted in the battery-equipped device 2. In addition, when the diagnostic device 3 is mounted in the battery-equipped device 2, one processing device and the like mounted in the battery-equipped device 2 may perform the processing of the diagnostic device 3 described below and may also control the charging and discharging of the battery 5, etc.

[0017] In one example, similar to the example of Fig. 1, the diagnostic system 1 may include a user interface 17. In this case, the user interface 17 includes an operation unit to which an operation command is input by a user of the battery-equipped device 2, and a notification unit that notifies the user of the battery-equipped device 2 of information including warning information. The notification unit notifies the information by, for example, a screen display or a sound. The processing of the diagnostic device 3 will be described below.

[0018] The processor etc. of the diagnostic device 3 periodically estimates the internal state of the battery 5 as a diagnosis of the battery 5 to be diagnosed. The transceiver 11 communicates with processing devices other than the diagnostic device 3, such as the processing device of the battery-equipped device 2, via a network. When the diagnostic device 3 estimates the internal state of the battery 5, the transceiver 11 of the diagnostic device 3 transmits a control command to the processing device etc. of the battery-equipped device 2, and the processing device etc. of the battery-equipped device 2 charges or discharges the battery 5 under predetermined conditions based on the transmitted control command.

[0019] The measurement circuit, including the current measurement circuit 7 and the voltage measurement circuit 8, measures the above-mentioned parameters related to the battery 5 while the battery 5 is being charged or discharged under a predetermined condition. The transmitter / receiver 11 receives, for example, measurement data including the measurement results of the parameters related to the battery 5 by the measurement circuit from the battery-equipped device 2. The measurement data includes the measurement results of the current flowing through the battery 5 by the current measurement circuit 7 and the measurement results of the voltage of the battery 5 by the voltage measurement circuit 8, and may also include the measurement results of the temperature of the battery 5 by the temperature measurement circuit 9. The measurement data includes the measured values ​​of the parameters related to the battery 5 at each of a plurality of measurement points (a plurality of measurements). The measurement data also includes the time changes (time history) of the parameters related to the battery 5 while the battery 5 is being charged or discharged under a predetermined condition. Therefore, the measurement data includes the time changes (time history) of the current of the battery 5, the time changes (time history) of the voltage of the battery 5, and the time changes (time history) of the temperature of the battery 5.

[0020] Furthermore, at least one of the processing device of the battery-equipped device 2 and the processor of the diagnostic device 3 may estimate the charge amount of the battery 5 based on the measurement results of parameters related to the battery 5. In this case, the transceiver 11 of the diagnostic device 3 may acquire the estimated value of the charge amount of the battery 5 and the time change (time history) of the estimated value of the charge amount of the battery 5 as data included in the above-mentioned measurement data. The measurement data may also include data indicating the relationship between the estimated charge amount of the battery 5 and the measured voltage of the battery 5. In this case, for example, data indicating the relationship between the estimated charge amount of the battery 5 and the measured voltage of the battery 5 is included in the measurement data.

[0021] The real-time charge amount of the battery 5 can be calculated based on the charge amount of the battery 5 at the start of charging or discharging under specified conditions, etc., and the change over time of the current of the battery 5 during charging or discharging under specified conditions. In this case, an integrated value of the current of the battery 5 from the start of charging or discharging is calculated based on the change over time of the current. Then, the charge amount of the battery 5 is calculated based on the charge amount of the battery 5 at the start of charging or discharging, etc., and the calculated integrated value. The transmitting / receiving unit 11 stores the received measurement data, etc. in the data storage unit 16.

[0022] The internal state estimation unit 12 acquires the above-mentioned measurement data, and estimates the internal state of the battery 5 during a period in which the battery 5 is charged or discharged under predetermined conditions based on the measurement data. In this embodiment, the internal state estimation unit 12 estimates an internal state parameter indicating the internal state of the battery 5. In one example, the internal state estimation unit 12 estimates the internal state of the battery 5 by analyzing data indicating a time change in at least one of the current and voltage of the battery 5 during charging or discharging under the above-mentioned predetermined conditions, that is, by performing a charging curve analysis or a discharging curve analysis of the battery 5.

[0023] Here, in the battery 5, a lower limit potential and an upper limit potential are specified for the positive electrode potential, and the positive electrode potential changes between the lower limit potential and the upper limit potential in response to the change in the charge amount of the positive electrode. The positive electrode potential becomes higher as the charge amount of the positive electrode increases. In addition, the charge amount of the positive electrode in a state where the positive electrode potential is at the lower limit potential is specified as the initial charge amount of the positive electrode, and the charge amount of the positive electrode in a state where the positive electrode potential is at the upper limit potential is specified as the upper limit charge amount of the positive electrode. In the battery 5, the charge amount from the initial charge amount to the upper limit charge amount of the positive electrode is the positive electrode capacity equivalent to the chargeable and dischargeable amount of the positive electrode. The positive electrode capacity may be expressed in units such as (mA·h), or may be expressed as a ratio to the capacity at the start of use of the battery 5, that is, as a positive electrode capacity maintenance rate.

[0024] As with the positive electrode, in the battery 5, a lower limit potential and an upper limit potential are specified for the negative electrode potential, and the negative electrode potential changes between the lower limit potential and the upper limit potential in response to the change in the charge amount of the negative electrode. The negative electrode potential decreases as the charge amount of the negative electrode increases. In addition, the charge amount of the negative electrode in a state in which the negative electrode potential becomes the upper limit potential is specified as the initial charge amount of the negative electrode, and the charge amount of the negative electrode in a state in which the negative electrode potential becomes the lower limit potential is specified as the upper limit charge amount of the negative electrode. In the battery 5, the charge amount from the initial charge amount to the upper limit charge amount of the negative electrode is the negative electrode capacity equivalent to the chargeable and dischargeable amount of the negative electrode. The negative electrode capacity may be expressed in units such as (mA·h), or may be expressed as a ratio to the capacity at the start of use of the battery 5, that is, as the negative electrode capacity maintenance rate.

[0025] The internal state parameters of the battery 5 include the above-mentioned positive electrode capacity, negative electrode capacity, initial charge amount of the positive electrode, and initial charge amount of the negative electrode. The internal state parameters of the battery 5 also include a positive electrode mass, which is a parameter corresponding to the positive electrode capacity, and a negative electrode mass, which is a parameter corresponding to the negative electrode capacity. The positive electrode mass can be calculated based on the positive electrode capacity and the type of material forming the positive electrode. Similarly, the negative electrode mass can be calculated based on the negative electrode capacity and the type of material forming the negative electrode. The internal state parameters of the battery 5 also include an operation window shift (SOW), which is a difference between the initial charge amount of the positive electrode and the initial charge amount of the negative electrode. The internal state parameters of the battery 5 also include a parameter related to the internal resistance of the battery 5, a parameter related to the resistance of the positive electrode, and a parameter related to the resistance of the negative electrode.

[0026] The data storage unit 16 stores data indicating the relationship of the internal state of the battery 5 to at least one of the voltage and current of the battery 5, and for example, stores a calculation formula for calculating at least one of the current and voltage of the battery 5 from one or more of the above-mentioned internal state parameters. Note that the relationship of the internal state to each of the current and voltage of the battery 5 changes depending on the temperature of the battery 5, etc. Therefore, the data storage unit 16 may store data that sets the relationship of the internal state to at least one of the current and voltage of the battery 5 for each of a plurality of temperatures that are different from each other.

[0027] When estimating the internal state of the battery 5 by the above-mentioned charge curve analysis or discharge curve analysis of the battery 5, the internal state estimation unit 12 performs a fitting calculation (regression calculation) using the measurement result of at least one of the voltage and current of the battery 5 included in the measurement data, and data indicating the relationship of the internal state of the battery 5 to at least one of the voltage and current of the battery 5. At this time, the fitting calculation is performed using one or more of the internal state parameters as variables in a calculation formula for calculating at least one of the voltage and current of the battery 5 from one or more of the internal state parameters of the battery 5. Then, the internal state estimation unit 12 estimates the internal state of the battery 5 by calculating one or more internal state parameters that become variables through the fitting calculation. As a result, the internal state of the battery 5 during the period in which the battery 5 is charged or discharged under predetermined conditions is estimated. The internal state estimation unit 12 stores the estimation result of the internal state of the battery 5, including the estimated value of the internal state parameter of the battery 5, in the data storage unit 16 as estimated data.

[0028] A method for estimating the internal state of a battery by analyzing a charging curve is shown in Patent Document 1 (JP Patent Publication 2012-251806A). In Patent Document 1, the internal state of a battery is also estimated by performing a fitting calculation using measurement results of at least one of the current and voltage of the battery and data showing the relationship of the internal state of the battery to at least one of the voltage and current of the battery. In this embodiment, the internal state of the battery 5 may be estimated in the same manner as in Patent Document 1.

[0029] In estimating the internal state of the battery 5, the internal state estimation unit 12 reads data indicating the relationship of the internal state of the battery 5 to at least one of the voltage and current of the battery 5, including a calculation formula for calculating at least one of the voltage and current of the battery 5 from one or more of the internal state parameters, from the data storage unit 16. In addition, in the above-mentioned estimation of the internal state, a provisional estimate value or the like for the internal state parameter may be calculated in the process of obtaining the final estimation result. In this case, the internal state estimation unit 12 may estimate the internal state of the battery 5 using the provisional estimate value for the internal state parameter. In addition, the internal state estimation unit 12 can store in the data storage unit 16 an estimate value that is required for subsequent processing, out of the provisional estimate value and the final estimate value for the internal state parameter.

[0030] In this embodiment, the processor of the diagnostic device 3 periodically estimates the internal state of the battery 5. For this reason, the battery 5 is charged or discharged under a predetermined condition at a plurality of different time periods, and the internal state of the battery 5 is estimated for the plurality of different time periods. That is, for each of the plurality of different time periods, estimated data indicating the estimation result of the internal state of the battery 5 is generated. Therefore, by periodically estimating the internal state of the battery 5 at a plurality of time periods, a plurality of estimated data are generated. In each of the plurality of estimated data, the estimated internal state is associated with time. That is, in each of the plurality of estimated data stored in the data storage unit 16, the estimated internal state is associated with the time period that was the subject of estimation.

[0031] In the following description, when the internal state of the battery 5 is estimated m times and m pieces of estimated data are generated, the estimated data are sorted in order of the time when they were estimated, namely, D0, D1, . . . , D m-1 Here, m indicates the number of pieces of generated estimated data, and is an integer equal to or greater than 1. The number of pieces of data m corresponds to the number of pieces of estimated data stored in the data storage unit 16, and corresponds to the number of times that the internal state of the battery 5 has been estimated. The number of pieces of data m is incremented by 1 each time the internal state of the battery 5 is estimated by the internal state estimation unit 12, that is, each time estimated data is generated.

[0032] In addition, for the battery 5, the battery capacity is specified as a parameter indicating the battery characteristics. The battery capacity is equivalent to the amount of charge and discharge of the battery 5, and is indicated, for example, by a fully charged capacity. The battery capacity may be indicated in units such as (mA·h), or may be indicated as a ratio to the capacity at the start of use of the battery 5, that is, by a battery capacity maintenance rate. Until a certain period of time has passed since the start of use of the battery 5, the battery capacity of the battery 5 gradually decreases over time, and decreases, for example, according to an empirical rule (root rule) that the amount of decrease in the battery capacity of the battery is proportional to the 0.5th power of the elapsed time from the start of use of the battery. However, after a certain period of time has passed since the start of use of the battery 5, the battery capacity may suddenly decrease over time. In this case, the rate at which the battery capacity decreases deviates from the empirical rule (root rule). In the use of the battery 5, a sudden decrease in the battery capacity has a large impact on the charge and discharge of the battery 5. For this reason, it is important to predict in advance the time when the probability of the battery capacity of the battery 5 suddenly decreasing is high.

[0033] The timing prediction unit 15 predicts the time when the probability of the battery capacity of the battery 5 decreasing rapidly becomes high. That is, the time when the probability of the battery capacity of the battery 5 decreasing rapidly becomes high is predicted as a prediction result. At this time, the timing prediction unit 15 predicts the above-mentioned time using the internal state of the battery 5 at a plurality of mutually different times. In addition, in this embodiment, a required number of data n is specified in the prediction by the timing prediction unit 15. The required number of data n indicates the number of pieces of estimated data required to predict the time when the probability of the battery capacity decreasing rapidly becomes high, and is an integer of 2 or more. The timing prediction unit 15 performs the prediction using n pieces of estimated data. In one example, the required number of data n is 3.

[0034] In the above-mentioned time prediction, the time prediction unit 15 judges whether the number of data m of the generated estimated data is equal to or greater than the required number of data n. If the number of data m is less than the required number of data n, it judges that there is insufficient data for time prediction and does not predict the time. At this time, the time prediction unit 15 may notify via the user interface 17 that there is insufficient data for time prediction, i.e., that the number of data m of the estimated data does not meet the criteria for time prediction.

[0035] When the number of pieces of generated estimated data, m, is equal to or greater than the number of necessary pieces of data, n, the time prediction unit 15 predicts the time using n pieces of estimated data. At this time, when the number of pieces of data, m, is the same as the number of necessary pieces of data, n, the time prediction unit 15 predicts the time using all of the m pieces of estimated data. Also, when the number of pieces of data, m, is greater than the number of necessary pieces of data, n pieces of estimated data are selected from the m pieces of estimated data, and the time prediction unit 15 predicts the time using the selected n pieces of estimated data. At this time, for example, n pieces of estimated data are selected from the m pieces of estimated data in order of the latest time that they were the subject of estimation. That is, the m pieces of estimated data, D0 to D m-1 The n most recent estimation data D m-n ~D m-1 is selected, and the selected estimation data D m-n ~D m-1 is used to predict the timing.

[0036] Then, based on the n pieces of estimated data, the timing prediction unit 15 estimates the time when the internal state of the battery 5 will become a predetermined state after the time that is the subject of estimation in the n pieces of estimated data. Therefore, based on the internal state of the battery 5 at a plurality of times different from each other, the timing prediction unit 15 estimates the time when the internal state of the battery 5 will become a predetermined state after the above-mentioned plurality of times. Here, the predetermined state is set in advance and is set to a state corresponding to the internal state of the battery 5 at a time when the probability of the battery capacity of the battery 5 decreasing rapidly is high. Then, a state in which the deterioration of the internal state of the battery 5 has progressed compared to the time that is the subject of estimation in the generated m pieces of estimated data is set as the predetermined state. For example, a state in which the positive electrode capacity and the negative electrode capacity of the battery 5 have each decreased compared to the time that is the subject of estimation in the m pieces of estimated data is set as the predetermined state.

[0037] In estimating the time when the internal state becomes a predetermined state, the timing prediction unit 15 sets a predetermined value corresponding to the predetermined state for one or more of the internal state parameters. Then, the timing prediction unit 15 estimates the time when the internal state parameter for which the predetermined value is set will become the predetermined value after the time that was the subject of estimation in the n pieces of estimation data. In one example, the time when the negative electrode capacity becomes a predetermined value is estimated. In another example, the time when the negative electrode capacity and the positive electrode capacity each become a predetermined value is estimated. In this case, the predetermined value for the negative electrode capacity and the predetermined value for the positive electrode capacity may be the same value for each other, or may be different values ​​for each other.

[0038] Here, when a predetermined value is set for each of the positive electrode capacity and the negative electrode capacity, a value lower than the value in the m pieces of estimated data generated is set as the predetermined value. When a predetermined value is set for each of the initial charge amounts of the positive electrode and the negative electrode, two values ​​are set as the predetermined values: a value smaller than the value at the start of use of the battery 5, and a value larger than the value at the start of use of the battery 5. When a predetermined value is set for the SOW, two values ​​are set as the predetermined values: a value when the direction of deviation of the initial charge amount of the negative electrode from the initial charge amount of the positive electrode becomes the same as that at the start of use, and a value when the direction of deviation of the initial charge amount of the negative electrode from the initial charge amount of the positive electrode becomes the opposite to that at the start of use. For the internal state parameters for which two predetermined values ​​are set, the timing prediction unit 15 estimates the time when the internal state parameter becomes one of the two corresponding predetermined values ​​based on the n pieces of estimated data.

[0039] When estimating the time when a certain internal state parameter will reach a predetermined value, the time prediction unit 15 calculates a function indicating the relationship of the internal state parameter with respect to time. Here, when the time t and the internal state parameter y are specified, in one example, a linear function represented by y=at+b is calculated as the function indicating the relationship of the internal state parameter with respect to time. The time prediction unit 15 calculates the function based on n pieces of estimated data. That is, the function indicating the relationship of the internal state parameter with respect to time is calculated based on the internal states of the battery at multiple times different from each other.

[0040] Here, n pieces of estimated data D used for prediction m-n ~D m-1 The value of the internal state parameter y in i , and n pieces of estimated data D m-n ~D m-1 The time t indicates the time period for which the estimation is performed. i where i is an integer from 1 to n, and the estimated data D m-n ~D m-1 The later the estimated data is, the smaller the number will be. Therefore, the estimated data D m-n So, (t,y)=(t n ,yn ) and the estimated data D m-1 Let it be shown that (t, y) = (t1, y1).

[0041] The time prediction unit 15 estimates the data D m-n ~D m-1 The value of the internal state parameter y at i , and the estimated data D m-n ~D m-1 The time t i Based on the above, a function showing the relationship between the internal state parameter y and time t is calculated. i and time t i A fitting calculation (regression calculation) using is performed, and a constant in a function showing the relationship of the internal state parameter y to time t is calculated by the fitting calculation. In one example, the slope a and intercept b, which are constants of a linear function, are calculated by the least squares method, and a linear function (y=at+b) showing the relationship of the internal state parameter y to time t is calculated.

[0042] Here, the above-mentioned predetermined value set for the internal state parameter y is the predetermined value y p When a function indicating the relationship between the internal state parameter y and time t is calculated, the timing prediction unit 15 calculates a function indicating the relationship between the internal state parameter y and time t, and determines whether the internal state parameter y is equal to a predetermined value y p The value of t at time t when p For example, when the linear function described above is calculated as a function showing the relationship of the internal state parameter y to the time t, the timing prediction unit 15 calculates the value t p is calculated as shown in equation (1). The timing prediction unit 15 then calculates the calculated value t p The time when the internal state parameter y is a given value y p This is estimated to be the time when

[0043]

number

[0044] FIG. 2 shows a timing prediction performed by the timing prediction unit 15, in which a certain internal state parameter y is set to a predetermined value y p An example of a process for estimating the time when the internal state parameter y becomes y will be described. In the example of FIG. 2, six pieces of estimated data are generated, and the number m of generated data is 6. The number n of required data is set to 3. In the example of FIG. 2, the timing prediction unit 15 predicts the value y of the internal state parameter y in the estimated data D3 to D5 (shown surrounded by a dashed line frame). i (y1 to y3), and the time t corresponding to the estimated data D3 to D5 i Based on (t1 to t3), a function showing the relationship of the internal state parameter y to time t is calculated. At this time, a linear function (y=at+b) showing the relationship of the internal state parameter y to time t is calculated by the least squares method. Then, the timing prediction unit 15 uses the above-mentioned formula (1) to calculate a linear function showing the relationship of the internal state parameter y to a predetermined value y p The value of t at time t when p Then, the timing prediction unit 15 calculates the calculated value t p The time when the internal state parameter y is a given value y p This is estimated to be the time when

[0045] In one example, a certain internal state parameter y is set to a predetermined value y p In estimating the time when the internal state parameter y becomes equal to the time t, a parameter corresponding to the time t may be used instead of the time t. In this case, the time prediction unit 15 calculates a function indicating the relationship of the internal state parameter y to the parameter corresponding to the time t based on n pieces of prediction data. At this time, the function is calculated in the same manner as the calculation of the function indicating the relationship of the internal state parameter y to the time t. Then, the time prediction unit 15 calculates a function indicating the relationship of the internal state parameter y to the time t when the internal state parameter y becomes equal to the predetermined value y p Then, the value of the parameter corresponding to the time t when the internal state parameter y becomes the predetermined value y is calculated. p Here, parameters corresponding to the time t include an integrated value (time integral value) of the discharge amount of the battery 5, and an integrated value (time integral value) of the charge amount of the battery 5.

[0046] In another example, the timing prediction unit 15 calculates the variance of the constant of the above-mentioned function that indicates the relationship of the internal state parameter y to the time t or a parameter corresponding to the time t. Then, the timing prediction unit 15 calculates the time when the internal state parameter y is a predetermined value y based on the variance of the calculated constant in addition to the above-mentioned function. p In this case, the time when the internal state parameter y becomes a given value y p The time when pl Greater than or equal to upper limit t ph The following time range is estimated: p In the above example in which the variance of the constant of the function is not taken into account, the time when the internal state parameter y becomes a predetermined value y p The value t of time t estimated as the time when p is the lower limit t pl Greater than or equal to upper limit t ph Included in the following time range:

[0047] For example, when a linear function showing the relationship of the internal state parameter y to time t is calculated by the least squares method, the timing prediction unit 15 calculates the variance for the slope a and intercept b, which are constants of the linear function. a is calculated as in equation (2), and the variance of the intercept b is b is calculated as shown in formula (3). In formulas (2) and (3), s is the number of n pieces of estimation data D m-n ~D m-1 The parameter s is set to an appropriate value in accordance with the measurement error in measuring the parameters related to the battery 5, such as the current and voltage of the battery 5, and the range of values ​​in estimating the internal state of the battery 5. e is the time t i (t1~t n ) is the average value of the calculated variance. Then, the internal state parameter y is set to a predetermined value y p For the time range in which plCalculate the upper limit t using equation (5). ph Calculate.

[0048]

number

[0049] The timing prediction unit 15 estimates the time when the internal state of the battery 5 will be in the predetermined state based on the estimation result of the time when the internal state parameter will be in the predetermined value. In one example, the time when only one internal state parameter will be in the predetermined value is estimated. Then, the timing prediction unit 15 sets the time when the one internal state parameter is estimated as the time when the internal state of the battery 5 will be in the predetermined state as the time. In another example, the time when a plurality of internal state parameters are in the predetermined value is estimated. Then, the timing prediction unit 15 sets the earliest time among the plurality of estimated times as the time when the internal state of the battery 5 will be in the predetermined state. In addition, when estimating the time when a plurality of internal state parameters are in the predetermined value, the timing prediction unit 15 may set the time corresponding to the average value or the median value of the plurality of estimated times as the time when the internal state of the battery 5 will be in the predetermined state.

[0050] The timing prediction unit 15 predicts the time corresponding to the estimation result of the time when the internal state of the battery 5 will reach a predetermined state as the time when the probability of the battery capacity of the battery 5 decreasing rapidly will be high. In this embodiment, the time estimated as the time when the internal state of the battery 5 will reach a predetermined state is determined as the final prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly will be high. The timing prediction unit 15 stores the prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly will be high in the data storage unit 16. Furthermore, the timing prediction unit 15 may notify the above-mentioned prediction result via the user interface 17. In this embodiment, after the number m of estimated data generated about the internal state becomes equal to or larger than the required number n of data, the timing prediction unit 15 predicts the time when the probability of the battery capacity of the battery 5 decreasing rapidly becomes high every time the internal state of the battery 5 is estimated. Then, the timing prediction unit 15 updates the time when the probability of the battery capacity of the battery 5 decreasing rapidly becomes high to the latest prediction result every time the timing prediction unit 15 predicts the time. As a result, in this embodiment, the time when the internal state of the battery 5 becomes a predetermined state is updated to the latest estimation result.

[0051] FIG. 3 shows an example of a process related to the estimation of the internal state performed by the diagnostic device 3. The process shown in FIG. 3 is performed periodically after the start of use of the battery 5. The number of pieces of estimated data m described above is incremented by 1 each time the process of FIG. 3 is performed. When the process of FIG. 3 is started, the transmitting / receiving unit acquires measurement data including measurement results of parameters related to the battery 5 as described above (S101). Then, the internal state estimation unit 12 estimates the internal state of the battery based on the measurement data (S102). Then, the internal state estimation unit 12 associates the estimated internal state with time (S103). Then, the internal state estimation unit 12 stores the estimation data associated with the time when the estimated internal state became the subject of estimation in the data storage unit 16 (S104).

[0052] Fig. 4 shows an example of processing performed by the diagnostic device 3 related to prediction of the time when the probability of the battery capacity of the battery 5 decreasing rapidly increases. The processing in Fig. 4 is performed, for example, every time the processing in Fig. 3 is performed, and every time the internal state of the battery 5 is estimated. When the processing in Fig. 4 is started, the time prediction unit 15 acquires estimated data estimated on the internal state of the battery 5 (S111). Then, the time prediction unit 15 judges whether the number of data m of the generated estimated data is equal to or greater than the number of required data n (S112). If the number of data m is less than the number of required data n (S112-No), the processing in S113 to S115 is not performed, and no time prediction is performed.

[0053] On the other hand, if the number of data m is equal to or greater than the number of required data n (S112-Yes), the timing prediction unit 15 predicts the timing using n of the m pieces of estimation data in ascending order of time when they were the subject of estimation. At this time, the timing prediction unit 15 estimates the time when one or more of the internal state parameters will become the above-mentioned predetermined value (S113). At this time, the time when the internal state will become the predetermined value is estimated to be a time after the time when it was the subject of estimation in the n pieces of estimation data. Then, the timing prediction unit 15 estimates the time when the internal state of the battery 5 will become the above-mentioned predetermined state based on the estimation result of the time when the internal state parameter will become the predetermined value (S114). Then, the timing prediction unit 15 determines the time when the internal state is estimated to become the predetermined state as the final prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly becomes high (S115).

[0054] As described above, in this embodiment, the time when the internal state of the battery 5 will become a predetermined state after a plurality of times is estimated based on the internal states of the battery 5 at a plurality of times. Therefore, by setting the predetermined state to a state corresponding to the internal state of the battery 5 at a time when the probability of a sudden decrease in battery capacity is high, it becomes possible to predict in advance the time when the probability of a sudden decrease in the battery capacity of the battery 5 is high.

[0055] In addition, the time when the probability of a sudden drop in battery capacity is high is one of the indicators indicating the degree of deterioration of the battery 5. Even if the time-varying changes in battery capacity from the start of use of batteries such as the battery 5 tend to be similar to each other, the trends of the time-varying changes in the internal state from the start of use may differ. In this embodiment, an indicator indicating the degree of deterioration of the battery 5 is predicted based on the internal state of the battery 5 at multiple time periods. Since the prediction is performed taking into consideration the trend of the time-varying changes in the internal state, the time when the probability of a sudden drop in battery capacity is high is appropriately predicted.

[0056] Moreover, in this embodiment, the time when the internal state of the battery 5 will become a predetermined state is estimated based on the estimation result regarding the time when the internal state parameter will become a predetermined value. Therefore, the time when the internal state will become a predetermined state is estimated appropriately. Furthermore, in this embodiment, a function indicating the relationship of the internal state parameter to time or a parameter corresponding to time is calculated based on the internal states of the battery at multiple times. Then, the time when the internal state parameter will become a predetermined value is estimated based on the time or the value of the parameter corresponding to time when the internal state parameter becomes a predetermined value in the calculated function. Therefore, the time when the internal state parameter will become a predetermined value is estimated appropriately.

[0057] In one example, the variance is calculated for the constant of the above-mentioned function indicating the relationship of the internal state parameter to time or a parameter corresponding to time. Then, based on the variance of the calculated constant, the time when the internal state parameter will become a predetermined value is estimated. In this case, the time when the internal state parameter will become a predetermined value is appropriately estimated taking into consideration the measurement error in measuring the parameters related to the battery 5, the range of values ​​in the estimation of the internal state of the battery 5, the range of values ​​in the calculation of the above-mentioned function, and the like.

[0058] The following verification was performed on the prediction of timing performed in the above-mentioned embodiment. In the verification, the internal state of one battery was estimated periodically eight times, and the internal state of the battery was estimated at each of times τ0 to τ7. Therefore, the estimated data D0, D1, ..., D7 were generated in order of earliest. FIG. 5 shows the estimation result in the verification of the negative electrode capacity, which is one of the internal state parameters of the battery. In FIG. 5, the horizontal axis indicates time, and the vertical axis indicates the negative electrode capacity. In the verification, the negative electrode capacity changed between times τ0 to τ7 as shown in FIG. 5.

[0059] In the verification, immediately after each of the estimated data D3 to D6 was generated, a prediction was made of the time when the probability of the battery capacity of the battery suddenly decreasing would be high. That is, the prediction was made at each of the times τ3 to τ6 or immediately after each of the times τ3 to τ6. In the verification, the number of required data n was set to 3. Therefore, for example, the prediction made at or immediately after the time τ3 used the estimated data D1 to D3, and the prediction made at or immediately after the time τ6 used the estimated data D4 to D6.

[0060] In the verification, the time when the negative electrode capacity, which is one of the internal parameters of the battery, reaches a predetermined value was estimated for each of the time predictions. At this time, a function showing the relationship between the negative electrode capacity and time was calculated. In calculating the function, a linear function (y=at+b) was calculated using the least squares method. Then, the time value when the negative electrode capacity reaches a predetermined value in the linear function was estimated as the time when the negative electrode capacity reaches a predetermined value. Then, the time estimated as the time when the negative electrode capacity reaches a predetermined value was determined as the time when the internal state of the battery 5 reaches a predetermined state. Then, the time estimated as the time when the internal state of the battery reaches a predetermined state was determined as the final prediction result of the time when the probability of the battery capacity suddenly decreasing is high.

[0061] In addition, in the verification, the validity of the time predicted as described above was confirmed. For this reason, in the verification, the battery capacity of the battery was measured as a process not performed in the above-mentioned embodiment and the like. At this time, the battery capacity was measured by discharging the battery from a fully charged state to a fully discharged state. FIG. 6 shows the measurement results of the battery capacity in the verification. In FIG. 6, the horizontal axis indicates time and the vertical axis indicates battery capacity. In the verification, the battery capacity changed between times τ0 and τ7 as shown in FIG. 6. As shown in FIG. 6, in the verification, the battery capacity suddenly decreased over time between times τ6 and τ7.

[0062] FIG. 7 shows a prediction result in a verification of the time when the probability of the battery capacity of a battery decreasing rapidly increases. In FIG. 7, the horizontal axis indicates the time when the prediction was made, and the vertical axis indicates the time predicted as the prediction result. As shown in FIG. 7, in the verification, in the predictions made at each of the times τ4 to τ6, values ​​between the times τ6 and τ7 were predicted as the prediction results. Therefore, in the prediction at the time τ4 or immediately thereafter, an appropriate prediction result corresponding to the change in the actual battery capacity was predicted for the time when the probability of the battery capacity of a battery decreasing rapidly increases. Therefore, it was demonstrated that the time when the probability of the battery capacity decreasing rapidly increases can be appropriately predicted in advance by predicting the time in the same manner as in the embodiment, etc. That is, the effectiveness of the prediction of the time in the embodiment, etc. was confirmed.

[0063] Second Embodiment Next, a second embodiment will be described. In the following description, the same parts as those in the first embodiment will not be described. In this embodiment, the timing prediction unit 15 predicts the time when the probability of the battery capacity decreasing rapidly increases in the same manner as in the above-mentioned embodiment.

[0064] However, in this embodiment, after predicting the time, the time prediction unit 15 issues either a warning or a request to inspect the battery 5 through the user interface 17 before the time predicted as the time when the probability of a sudden drop in battery capacity is high. In one example, the time prediction unit 15 obtains the arrival time from the current time to the predicted time as the prediction result. Then, if the arrival time is equal to or shorter than the threshold time, the time prediction unit 15 issues either a warning or inspects the capacity of the battery 5.

[0065] FIG. 8 shows an example of a process using a prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly increases, which is performed by the diagnostic device 3. The process of FIG. 8 is performed periodically after the aforementioned time is predicted. When the process of FIG. 8 is started, the time prediction unit 15 judges whether the arrival time from the current time to the time predicted as the prediction result is equal to or less than a threshold time (S121). If the arrival time is equal to or less than the threshold time (S121-Yes), the time prediction unit 15 performs either a warning or an inspection of the capacity of the battery 5 (S122). On the other hand, if the arrival time is longer than the threshold time (S121-No), the process of S122 is not performed. In this embodiment, the user of the battery-equipped device 2 can easily know that the time when the probability of the battery capacity of the battery 5 decreasing rapidly increases is approaching.

[0066] (Third embodiment) Next, a third embodiment will be described. In the following description, the same parts as those in the first embodiment will not be described. In this embodiment, the timing prediction unit 15 estimates the time when the internal state of the battery 5 will become a predetermined state in the same manner as in the above-mentioned embodiments.

[0067] However, in this embodiment, the timing prediction unit 15 uses n pieces of estimated data D m-n ~D m-1 Among them, the latest estimated data D m-1 is generated. That is, the usage history of the battery 5 from the start of use of the battery 5 to the mth estimation of the internal state is acquired by the timing prediction unit 15. Then, based on the acquired usage history of the battery 5, the timing prediction unit 15 determines whether or not to correct the estimation result regarding the time when the internal state will become a predetermined state.

[0068] That is, in the present embodiment, similarly to the above-mentioned embodiment, the time when the internal state of the battery 5 will become a predetermined state after a plurality of time periods is estimated based on the internal state of the battery 5 at a plurality of mutually different time periods. However, in the present embodiment, it is determined whether or not to correct the estimation result of the time when the internal state of the battery 5 will become a predetermined state based on the usage history of the battery 5 up to the latest time period among the above-mentioned plurality of time periods.

[0069] In this embodiment, the timing prediction unit 15 estimates the data D m-n ~D m-1 The second latest estimated data D m-2 The usage status of the battery 5 after the generation of the estimated data D m-2 is generated. That is, the usage status of the battery 5 from the (m-1)th internal state estimation to the mth internal state estimation is compared with the usage status of the battery 5 before the (m-1)th estimation. Then, the timing prediction unit 15 determines whether the change in the usage status of the battery 5 between before the (m-1)th internal state estimation and after the (m-1)th internal state estimation is within a reference range.

[0070] In this embodiment, the timing prediction unit 15 also predicts the time corresponding to the estimation result of the time when the internal state of the battery 5 becomes a predetermined state as the time when the probability of the battery capacity of the battery 5 decreasing rapidly increases. However, in this embodiment, only when the change in the usage status of the battery 5 described above is within a reference range, the timing prediction unit 15 determines the time estimated as the time when the internal state of the battery 5 becomes a predetermined state as the final prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly increases. Then, when the change in the usage status of the battery 5 exceeds the reference range, the timing prediction unit 15 corrects the time estimated as the time when the internal state becomes a predetermined state in response to the change in the usage status. Then, the timing prediction unit 15 determines the corrected time in response to the change in the usage status as the final prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly increases.

[0071] In one example, one or more items related to the usage status of the battery 5 are set in the comparison of the usage status of the battery 5. Then, the timing prediction unit 15 compares the one or more set items before the (m-1)th estimation of the internal state with those after the (m-1)th estimation of the internal state. As the item related to the usage status of the battery 5, any of the average current value and the maximum current value in charging the battery 5 and the integrated value (time integrated value) of the charge amount of the battery 5 may be used, or any of the average current value and the maximum current value in discharging the battery 5 and the integrated value (time integrated value) of the discharge amount of the battery 5 may be used. In addition, as the item to be compared, any of the average temperature, the maximum temperature, and the minimum temperature of the battery 5 may be used, or the time during which charging and discharging of the battery 5 was stopped may be used.

[0072] In the use of the battery 5, for example, one or more of the set items may be significantly different between after the (m-1)th estimation of the internal state and before the (m-1)th estimation of the internal state. In this case, the timing prediction unit 15 determines that the change in the usage status of the battery 5 exceeds the reference range. Then, the timing prediction unit 15 corrects the time estimated as the time when the internal state will become a predetermined state in accordance with the change in the usage status.

[0073] In one example, the average temperature of the battery 5 is set as the item to be compared. Here, it is assumed that the average temperature of the battery 5 is about 10° C. higher after the (m-1)th estimation of the internal state than before the (m-1)th estimation of the internal state. In this case, the timing prediction unit 15 determines that the battery 5 was used in a high temperature environment only from the (m-1)th estimation of the internal state to the mth estimation of the internal state. In addition, the timing prediction unit 15 makes a determination on the assumption that the battery 5 is used in the same temperature environment as before the (m-1)th estimation of the internal state after the mth estimation of the internal state. Then, the timing prediction unit 15 corrects the time estimated as the time when the internal state becomes a predetermined state based on an empirical rule or the like. As a result, the time of the final prediction result becomes later than the time estimated as the time when the internal state becomes a predetermined state.

[0074] FIG. 9 shows an example of a process related to prediction of a time when the probability of the battery capacity of the battery 5 decreasing rapidly increases, which is performed by the diagnostic device 3 of this embodiment. When the process of FIG. 9 is started, the timing prediction unit 15 performs the processes of S111 and S112, similar to the process of FIG. 4. Then, if the number of data m is equal to or greater than the number of required data n (S112-Yes), the timing prediction unit 15 performs the processes of S113 and S114. However, in this embodiment, when the time when the internal state of the battery 5 becomes a predetermined state is predicted, the timing prediction unit 15 acquires the usage history of the battery 5 from the start of use of the battery 5 to the m-th estimation of the internal state (S131). Then, the timing prediction unit 15 determines whether or not the change in the usage status of the battery 5 before the (m-1)th estimation of the internal state and after the (m-1)th estimation of the internal state is within a reference range (S132).

[0075] When the change in the usage status of the battery 5 is within the reference range (S132-Yes), the timing prediction unit 15 determines the time estimated as the time when the internal state of the battery 5 becomes a predetermined state as the final prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly is high (S133). On the other hand, when the change in the usage status of the battery 5 exceeds the reference range (S132-No), the timing prediction unit 15 corrects the time estimated as the time when the internal state becomes a predetermined state in response to the change in the usage status (S134). Then, the timing prediction unit 15 determines the corrected time as the final prediction result of the time when the probability of the battery capacity of the battery 5 decreasing rapidly is high (S135). In this embodiment, the prediction result corresponding to the usage history of the battery 5 is appropriately predicted for the time when the probability of the battery capacity of the battery 5 decreasing rapidly is high.

[0076] In at least one of the above-described embodiments or examples, a time when the internal state of the battery will become a predetermined state after a plurality of times is estimated based on the internal state of the battery at a plurality of mutually different times. This makes it possible to provide a battery diagnostic method, a battery diagnostic device, a battery diagnostic system, a battery-equipped device, and a battery diagnostic program that can predict in advance the time when the probability of a sudden decrease in the battery capacity of the battery is high.

[0077] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims. The following are additional notes. [1] A battery diagnostic method comprising: and estimating a time when the internal state of the battery will become a predetermined state after a plurality of times different from each other based on the internal state of the battery at the plurality of times. Diagnostic methods. [2] In the estimation of the time when the internal state of the battery will reach the predetermined state, setting predetermined values ​​for one or more internal state parameters of the battery corresponding to the predetermined state; estimating a time after the plurality of times at which the internal state parameter will reach the predetermined value; estimating the time when the internal state of the battery will reach the predetermined state based on an estimation result regarding the time when the internal state parameter will reach the predetermined value; [1] Diagnostic method. [3] In the estimation of the time when the internal state of the battery will reach the predetermined state, calculating a function indicating a relationship of the internal state parameter to time or a parameter corresponding to time based on the internal state of the battery at the plurality of time periods; estimating the time when the internal state parameter becomes the predetermined value based on the time when the internal state parameter becomes the predetermined value in the calculated function or a value of the parameter corresponding to the time; [2] Diagnostic method. [4] In the estimation of the time when the internal state of the battery will reach the predetermined state, calculating a variance for a constant of the function that indicates a relationship of the internal state parameter to the time or to the parameter corresponding to the time; estimating the time when the internal state parameter will reach the predetermined value based on the calculated variance of the constant; [3] Diagnostic method. [5] Any one of the diagnostic methods of [1] to [4], further comprising determining whether to correct the estimated result regarding the time when the internal state of the battery will reach the specified state based on the usage history of the battery up to the latest of the plurality of times. [6] Any one of the diagnostic methods of [1] to [5], further comprising predicting a time corresponding to an estimated result regarding the time when the internal state of the battery will reach the specified state as a time when the battery capacity of the battery will be highly likely to decrease rapidly. [7] The diagnostic method of [6], further comprising issuing either a warning or requesting inspection of the battery before the time predicted as the time when the probability of a rapid decrease in battery capacity is high. [8] A battery diagnostic device comprising: A diagnostic device comprising: a processor that estimates, based on the internal state of the battery at a plurality of times that are different from one another, a time when the internal state of the battery will become a predetermined state after the plurality of times. [9][8] diagnostic equipment and the battery to be diagnosed by the diagnostic device; The diagnostic system for the battery comprises:

[10] The diagnostic system of [9], further comprising a battery-mounted device in which the battery is mounted.

[11] [8] diagnostic equipment and the battery to be diagnosed by the diagnostic device; A battery-equipped device comprising:

[12] A battery diagnostic program, comprising: A diagnostic program that estimates a time when the internal state of the battery will become a specified state after a plurality of times that are different from each other, based on the internal state of the battery at the plurality of times. [Explanation of symbols]

[0078] 1...diagnosis system, 2...battery-equipped device, 3...diagnosis device, 5...battery, 11...transmitter / receiver, 12...internal state estimation unit, 15...timing prediction unit, 16...data storage unit.

Claims

1. estimating one or more of a negative electrode capacity, a negative electrode capacity retention rate, and a negative electrode mass as an internal state parameter indicating the internal state of the battery, based on a measurement result of at least one of a voltage and a current of the battery and data indicating a relationship of an internal state of the battery with respect to at least one of the voltage and the current of the battery, for each of a plurality of mutually different time periods; setting, as a predetermined value, a value corresponding to the internal state of the battery at a time when the internal state of the battery is more deteriorated than at the multiple times when the internal state was estimated and when the probability of the battery capacity being rapidly reduced is high; estimating a time, after the plurality of time periods, at which the internal state parameter of the battery will reach a predetermined value, based on the estimation results of the internal state parameter of the battery at the plurality of time periods; When the internal state parameter is decreasing at any of the plurality of times, predicting a time corresponding to the estimation result of the time when the internal state parameter becomes the predetermined value as the time when the probability of the battery capacity of the battery being rapidly decreased is high; The battery diagnostic method comprises:

2. In the estimation of the time when the internal state parameter of the battery reaches the predetermined value, calculating a function indicating a relationship of the internal state parameter to time or a parameter corresponding to time based on the estimation results of the internal state parameter of the battery at the plurality of time periods; estimating the time when the internal state parameter becomes the predetermined value based on the time when the internal state parameter becomes the predetermined value in the calculated function or a value of the parameter corresponding to the time; The diagnostic method of claim 1.

3. In the estimation of the time when the internal state parameter of the battery reaches the predetermined value, calculating a variance for a constant of the function that indicates a relationship of the internal state parameter to the time or to the parameter corresponding to the time; estimating the time when the internal state parameter will reach the predetermined value based on the calculated variance of the constant; The diagnostic method of claim 2.

4. 4. A diagnostic method according to claim 1, further comprising: determining whether or not to correct an estimated result of the time when the internal state parameter of the battery will reach the specified value based on a usage history of the battery up to the latest of the multiple times, in predicting the time when the battery capacity of the battery will be highly likely to decrease rapidly.

5. 5. The diagnostic method according to claim 1, further comprising issuing a warning or requesting inspection of the battery before the time predicted as the time when the probability of a rapid decrease in battery capacity is high.

6. estimating one or more of a negative electrode capacity, a negative electrode capacity retention rate, and a negative electrode mass as an internal state parameter indicating the internal state of the battery based on a measurement result of at least one of a voltage and a current of the battery and data indicating a relationship of an internal state of the battery with respect to at least one of the voltage and the current of the battery for each of a plurality of mutually different time periods; For the estimated internal state parameter, a value corresponding to the internal state at a time when the internal state of the battery is more deteriorated than at the multiple time periods when the internal state is estimated and the probability of the battery capacity decreasing rapidly is high is set as a predetermined value; based on the estimation results of the internal state parameter of the battery at the multiple time periods, a time after the multiple time periods when the internal state parameter of the battery will reach a predetermined value; When the internal state parameter is decreasing at any of the plurality of times, a time corresponding to an estimation result of the time when the internal state parameter becomes the predetermined value is predicted as the time when the probability of the battery capacity of the battery being rapidly decreased is high. The battery diagnostic device includes a processor.

7. The diagnostic device of claim 6 ; the battery to be diagnosed by the diagnostic device; The diagnostic system for the battery comprises:

8. The diagnostic system according to claim 7 , further comprising a battery-mounted device in which the battery is mounted.

9. The diagnostic device of claim 6 ; the battery to be diagnosed by the diagnostic device; A battery-equipped device comprising:

10. On the computer, estimating one or more of a negative electrode capacity, a negative electrode capacity retention rate, and a negative electrode mass as internal state parameters indicating the internal state of the battery based on a measurement result of at least one of a voltage and a current of the battery and data indicating a relationship of an internal state of the battery with respect to at least one of the voltage and the current of the battery for each of a plurality of mutually different time periods; For the estimated internal state parameter, a value corresponding to the internal state at a time when the internal state of the battery is more deteriorated than at the multiple times at which the internal state is estimated and the probability of a rapid decrease in the battery capacity of the battery is high is set as a predetermined value; estimating a time, after the plurality of time periods, at which the internal state parameter of the battery will reach a predetermined value, based on the estimation results of the internal state parameter of the battery at the plurality of time periods; When the internal state parameter is decreasing at any of the plurality of times, a time corresponding to an estimation result of the time when the internal state parameter becomes the predetermined value is predicted as the time when the probability of the battery capacity of the battery being rapidly decreased is high. A diagnostic program for the battery.

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