Prediction device, prediction method, and prediction program

Abstract

To provide a prediction device capable of realizing more accurate remaining life prediction of an in-vehicle storage battery. [Solution] A prediction device 1 of the present invention includes a predicted temperature setting unit 10 that divides the elapsed time from the present time to a predetermined time into a plurality of periods and sets a predicted temperature in the usage environment of storage battery B for each of the plurality of periods, a usage mode setting unit 20 that sets a usage mode related to charging and discharging storage battery B for each of the plurality of periods, a cycle deterioration amount prediction unit 30 that calculates the amount of cycle deterioration of storage battery B based on the usage mode of storage battery B and the predicted temperature for each of the plurality of periods, and a remaining life prediction unit 40 that accumulates the amount of cycle deterioration for each of the plurality of periods in chronological order and calculates the transition of the accumulated deterioration amount of storage battery B according to the time elapsed from the present time.

Description

[Technical field]

[0001] The present disclosure relates to a prediction device, a prediction method, and a prediction program. [Background technology]

[0002] In recent years, electric vehicles and hybrid vehicles equipped with storage batteries have been attracting attention. Among storage batteries, lithium-ion batteries in particular have high performance, such as high energy density, small size and light weight, long life, low self-discharge, and the ability to be quickly charged. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-126788 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, because lithium-ion batteries deteriorate during system operation, remaining life assessment is an important factor, because the life figures provided by lithium-ion battery manufacturers as a guideline for battery life are based on cycles under certain conditions and are not necessarily accurate.

[0005] Incidentally, SOH (State of Health) is known as an index that quantifies the health and deterioration state of a lithium-ion battery. For example, SOH is expressed as a value with the initial value of the battery capacity being 100%. As the deterioration of the lithium-ion battery progresses, the battery capacity decreases, so the SOH at each point in time is expressed as a battery capacity value that is reduced from 100% by the amount of deterioration.

[0006] Lithium-ion batteries are generally considered to be subject to two types of degradation: cycle degradation, which occurs as a result of repeated charging and discharging, and storage degradation, which occurs over time. In other words, lithium-ion batteries deteriorate as the number of charge / discharge cycles increases and as time passes.

[0007] However, as a result of intensive research by the inventors of the present application, it has been found that the rate at which lithium-ion batteries deteriorate strongly depends on the ambient temperature. For example, even in Japan, there is a difference of several years between the number of years it takes for a lithium-ion battery used in a cold region and that used in a warm region to reach the same SOH. In particular, lithium-ion batteries installed in vehicles are used in a state exposed to the outside air, so there is a large difference in remaining lifespan for each user due to the usage environment of the vehicle.

[0008] Patent Document 1 and other publications report a technology for classifying each driving mode in an on-board power storage system using a driving measurement unit, calculating the usage ratio of each driving mode, and diagnosing the remaining lifespan using the results of a lifespan database for each driving mode that has been measured in advance. However, the conventional technology in Patent Document 1 and other publications diagnoses the remaining lifespan without taking into account the ambient temperature of the lithium-ion battery, leaving room for improvement in terms of prediction accuracy.

[0009] The present invention has been made in consideration of the above problems, and aims to provide a prediction device, a prediction method, and a prediction program that are capable of realizing more accurate remaining life prediction of an on-board storage battery. [Means for solving the problem]

[0010] The main object of the present invention to solve the above-mentioned problems is to A prediction device for predicting a remaining life of an in-vehicle storage battery, a predicted temperature setting unit that divides an elapsed time from a current time to a predetermined time into a plurality of periods and sets a predicted temperature of the usage environment of the storage battery for each of the plurality of periods; a usage mode setting unit that sets a usage mode related to charging and discharging of the storage battery for each of the plurality of periods; a cycle deterioration prediction unit that calculates a cycle deterioration amount of the storage battery based on the usage mode of the storage battery and the predicted temperature for each of the plurality of time periods; a remaining life prediction unit that accumulates the amount of cycle deterioration for each of the plurality of periods in chronological order and calculates a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time; The prediction device includes:

[0011] In other respects, A method for predicting a remaining life of an in-vehicle storage battery, comprising: A process of dividing an elapsed time from a current time to a predetermined time into a plurality of periods, and setting a predicted temperature of an environment in which the storage battery is used for each of the plurality of periods; A process of setting a usage mode related to charging and discharging of the storage battery for each of the plurality of periods; calculating an amount of cycle deterioration of the storage battery based on the usage mode of the storage battery and the predicted temperature for each of the plurality of time periods; a process of accumulating the amount of cycle deterioration for each of the plurality of periods in chronological order and calculating a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time; This is a prediction method that performs the following.

[0012] In other respects, A prediction program for predicting a remaining life of an in-vehicle storage battery, On the computer, A process of dividing an elapsed time from a current time to a predetermined time into a plurality of periods, and setting a predicted temperature of an environment in which the storage battery is used for each of the plurality of periods; A process of setting a usage mode related to charging and discharging of the storage battery for each of the plurality of periods; calculating an amount of cycle deterioration of the storage battery based on the usage mode of the storage battery and the predicted temperature for each of the plurality of time periods; a process of accumulating the amount of cycle deterioration for each of the plurality of periods in chronological order and calculating a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time; This is a prediction program that executes the above. Effect of the Invention

[0013] According to the prediction device of the present invention, it is possible to realize more accurate remaining life prediction of an in-vehicle storage battery. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing an example of a vehicle equipped with a prediction device and a storage battery according to a first embodiment. [Diagram 2] FIG. 1 is a diagram illustrating an example of a hardware configuration of a prediction device according to a first embodiment. [Diagram 3] FIG. 1 is a diagram showing an example of a functional block of a prediction device according to a first embodiment; [Figure 4] FIG. 13 is a diagram showing an example of predicted temperatures for each month set in the predicted temperature setting unit according to the first embodiment; [Diagram 5] FIG. 1 is a diagram showing an example of a cycle deterioration map group according to the first embodiment; [Figure 6] FIG. 1 is a diagram showing a cycle deterioration amount [%] based on the number of charge / discharge cycles, which is derived from a cycle deterioration map according to the first embodiment; [Figure 7] A schematic diagram showing the relationship between the deterioration of a lithium-ion battery and temperature. [Figure 8] FIG. 4 is a diagram showing a schematic diagram of a calculation process of a remaining life prediction unit according to the first embodiment; [Figure 9] FIG. 13 is a diagram showing an example of display screen data output by a remaining life prediction unit according to the first embodiment; [Figure 10] FIG. 13 is a diagram showing functional blocks of a prediction device according to a second embodiment. [Figure 11] FIG. 13 is a diagram showing an example of a storage deterioration map according to the second embodiment; [Figure 12]FIG. 13 is a diagram showing a schematic diagram of a storage deterioration amount [%] based on a storage period (i.e., non-operating time) of a storage battery, which is derived from a storage deterioration map according to a second embodiment; [Figure 13] FIG. 1 is a diagram showing an embodiment of a process flow for predicting remaining life by a prediction device. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functions are designated by the same reference numerals, and redundant description will be omitted.

[0016] [First embodiment] The configuration of a prediction device according to one embodiment of the present invention (hereinafter, referred to as "prediction device 1") will be described below.

[0017] Fig. 1 is a diagram illustrating an example of a prediction device 1 and a vehicle C equipped with a storage battery B. Fig. 2 is a diagram illustrating an example of a hardware configuration of the prediction device 1.

[0018] The prediction device 1 is a device that predicts the remaining life of a storage battery B mounted on a vehicle C. The prediction device 1 is mounted on the vehicle C together with the storage battery B, for example, as shown in FIG.

[0019] In this case, the vehicle C is, for example, an electric vehicle that runs using power from a storage battery B. Here, a lithium-ion battery is used as the storage battery B.

[0020] The prediction device 1 is a computer having, as its main components, a CPU 101, a ROM 102, a RAM 103, an external storage device (e.g., a flash memory) 104, a communication unit (e.g., a communication module connected to the Internet) 105, an input unit (e.g., a keyboard and a mouse) 106, and a display unit (e.g., an LCD display) 107.

[0021] Each function of the prediction device 1, which will be described later, is realized, for example, by the CPU 101 referring to processing programs and various data stored in the ROM 102, the RAM 103, the external storage device 104, etc. In addition to the prediction programs for the prediction device 1 to realize each function, which will be described later, the external storage device 104 also stores a cycle deterioration map, temperature acceleration equation data, etc.

[0022] FIG. 3 is a diagram illustrating an example of functional blocks of the prediction device 1. As shown in FIG.

[0023] The prediction device 1 includes, as its functions, a predicted temperature setting unit 10, a usage mode setting unit 20, a cycle deterioration amount prediction unit 30, and a remaining life prediction unit 40. The cycle deterioration map D1 and the temperature acceleration data D2 in FIG.

[0024] Vehicle C is generally used in a state exposed to the outside air, and therefore, as described above, the rate at which deterioration of storage battery B (i.e., lithium ion battery) mounted on vehicle C progresses varies depending on the location where vehicle C is used and also varies depending on the time of year (i.e., season) when vehicle C is used.

[0025] The prediction device 1 according to the present invention divides the time elapsed from the present time to a predetermined time into multiple periods, taking into consideration the usage mode of the storage battery B mounted on the vehicle C, and accurately predicts the deterioration amount of the storage battery B for each period, taking into consideration the temperature in each period. The prediction device 1 then calculates the deterioration amount of the storage battery B according to the elapsed time from the present time as a cumulative value of the deterioration amount for each period. This makes it possible to accurately predict the remaining lifespan (i.e., the usable period) of the storage battery B.

[0026] In this embodiment, the prediction device 1 is configured to divide the time elapsed from the present time to a predetermined time in the future into monthly intervals and calculate the amount of deterioration for each month. Therefore, the following describes a configuration for calculating the amount of deterioration on a monthly basis, but the time elapsed from the present time to a predetermined time in the future does not necessarily have to be divided into monthly intervals. For example, it may be divided into daily or bimonthly intervals. Furthermore, the time elapsed does not necessarily have to be divided into the same time intervals for each period. Furthermore, the predetermined time is a future time that is appropriately set when performing calculation processing, and is an arbitrary time.

[0027] The predicted temperature setting unit 10 sets the predicted temperature of the usage environment of the storage battery B for each month from the present time to a predetermined time. Here, the predicted temperature for each month is the average predicted temperature for each month.

[0028] However, the setting of the predicted temperature can be modified in various ways, and the maximum or minimum temperature in each month may be used, or the temperature range of the maximum and minimum temperatures in each month may be used.

[0029] FIG. 4 is a diagram showing an example of predicted temperatures for each month set in the predicted temperature setting unit 10. As shown in FIG.

[0030] The predicted temperature setting unit 10, for example, accepts input of a predicted temperature from a user and sets the input predicted temperature. However, the predicted temperature setting unit 10 may set the predicted temperature based on history data of a temperature sensor (not shown) for detecting the surrounding environment mounted on the vehicle C. In this case, the predicted temperature setting unit 10 may set the predicted temperature based on, for example, detected temperature data from the same past month. Furthermore, the predicted temperature setting unit 10 may obtain statistical data on the average temperature in the area where the vehicle C is used from an external device (not shown) and set the predicted temperature based on the statistical data.

[0031] The usage mode setting unit 20 sets the usage mode related to charging and discharging of the storage battery B for each month from the present time to a predetermined time. Based on the usage mode related to charging and discharging of the storage battery B, the cycle deterioration amount prediction unit 30 calculates the amount of cycle deterioration for each month.

[0032] Here, the usage mode of the storage battery B set by the usage mode setting unit 20 includes information related to the number of charge / discharge cycles of the storage battery B in each month. This is because the progress of deterioration of the storage battery B typically depends on the number of charge / discharge cycles of the storage battery B (see FIG. 4).

[0033] However, in this usage mode, it is preferable to set SOC (State Of Charge: indicating the charging rate) range information and C-rate (Capacity rate) information of the storage battery B to be used together (see FIG. 5). This enables the cycle deterioration amount prediction unit 30, which will be described later, to select an appropriate cycle deterioration map D1 for calculating the amount of cycle deterioration, based on the classification items related to the SOC range information and the classification items related to the C-rate information.

[0034] According to the findings of the inventors of the present application, the rate of deterioration of the storage battery B (here, a lithium ion battery) increases as the SOC range used becomes wider, and decreases as the SOC range used becomes narrower. In particular, if the storage battery B is discharged until the SOC becomes "0%" every time, or if the storage battery B is charged until the SOC becomes "100%, the rate of deterioration of the storage battery B tends to increase. Conversely, the rate of deterioration of the storage battery B can be slowed down by limiting the SOC range to "20% to 80%" or the like as a way of using the storage battery B. That is, in order to more accurately predict the amount of cycle deterioration, it is preferable that the user sets the SOC range of the storage battery B to be used, based on how the storage battery B is to be used.

[0035] In addition, the rate at which deterioration of storage battery B (here, a lithium ion battery) progresses increases as the discharge rate (C rate) of storage battery B used increases, and decreases as the discharge rate used decreases. For example, when vehicle C is frequently operated under high load, the discharge rate of storage battery B increases (i.e., the C rate increases), and the rate at which deterioration of storage battery B progresses tends to increase. That is, in order to predict the amount of cycle deterioration more accurately, it is preferable for the user to set information on the C rate to be used, based on how storage battery B is to be used.

[0036] Here, the usage mode setting unit 20 preferably sets the usage mode of the storage battery B based on past charge / discharge history data of the storage battery B. The charge / discharge state of the storage battery B is generally monitored by a current sensor and a voltage sensor (not shown) mounted on the storage battery B, and is stored in the external storage device 104 as charge / discharge history data in the form of temporal changes in power consumption and charging power. The usage mode setting unit 20 preferably calculates the number of charge / discharge cycles, the SOC range to be used, the average C rate to be used, and the like from such past charge / discharge history data, for example, by referring to past history data for the same month as the calculation target month.

[0037] The usage manner of the storage battery B (e.g., the driving distance each month, the SOC range used, etc.) differs for each user. In this regard, by making it possible to set the usage manner of the storage battery B based on the past charge / discharge history data of the storage battery B, it becomes possible for the cycle deterioration amount prediction unit 30, which will be described later, to accurately predict the cycle deterioration amount of the storage battery B at each time period.

[0038] The cycle deterioration prediction unit 30 predicts the amount of cycle deterioration of the storage battery B caused by repeated charging and discharging for each month from the present time to a specified time based on the usage mode of the storage battery B set in the usage mode setting unit 20 and the predicted temperature set in the predicted temperature setting unit 10.

[0039] Specifically, the cycle deterioration prediction unit 30 includes a provisional cycle deterioration calculation unit 31 and a correction processing unit 32.

[0040] Here, the provisional cycle deterioration amount calculation unit 31 calculates the provisional cycle deterioration amount for each month from the cycle deterioration map D1 based on the usage mode of the storage battery B set in the usage mode setting unit 20 when the temperature of the usage environment of the storage battery B is the reference temperature.

[0041] The cycle deterioration map D1 is a map for calculating the amount of cycle deterioration caused by repeated charging and discharging of the storage battery B. The cycle deterioration map D1 stores, for example, the number of charge / discharge cycles and the amount of cycle deterioration corresponding to the number of charge / discharge cycles in association with each other. Note that the cycle deterioration map D1 is created, for example, on the assumption that the temperature of the environment in which the storage battery B is used is a reference temperature (for example, 25°C).

[0042] However, in this embodiment, as described above, the cycle deterioration map D1 is set with classification items related to the SOC range to be used and the average C rate to be used so that the provisional cycle deterioration amount calculating unit 31 can appropriately calculate the progression rate of the cycle deterioration amount according to the usage mode of the storage battery B. In other words, the external storage device 104 stores data of the cycle deterioration map D1 group in which classification items related to the SOC range to be used and the average C rate to be used are set.

[0043] FIG. 5 is a diagram showing an example of the cycle deterioration map D1 group. In the cycle deterioration map D1 group shown in FIG. 5, classification items related to the SOC range to be used are set as "SOC range 0% to 100%", "SOC range 0% to 80%", "SOC range 20% to 80%", "SOC range 20% to 100%", "SOC range 30% to 100%", etc. In addition, in the cycle deterioration map D1 group shown in FIG. 5, classification items related to the average C rate to be used are set as "0.1C", "0.3C", "0.5C", etc. In addition, in the cycle deterioration map D1 shown in FIG. 5, each column shows the deterioration amount [%] per cycle when the number of charge / discharge cycles is up to "0-200 cycles", the deterioration amount [%] per cycle when the number of charge / discharge cycles is up to "200-500 cycles", and the deterioration amount [%] per cycle when the number of charge / discharge cycles is up to "500 cycles".

[0044] Fig. 6 is a diagram showing the cycle deterioration amount [%] depending on the number of charge / discharge cycles derived from the cycle deterioration map D1. In Fig. 6, the difference in the progress rate of the cycle deterioration amount [%] depending on the selection items related to the SOC range to be used and the average C rate to be used is shown by a dotted line, a solid line, and a dashed line.

[0045] The provisional cycle deterioration amount calculation unit 31 selects one cycle deterioration map D1 from the group of cycle deterioration maps D1 based on, for example, the planned SOC range and average C rate set in the usage mode setting unit 20. Then, the provisional cycle deterioration amount calculation unit 31 calculates the provisional cycle deterioration amount from the number of charge / discharge cycles for each month using, for example, the selected cycle deterioration map D1. Note that the provisional cycle deterioration amount calculated by the provisional cycle deterioration amount calculation unit 31 is calculated assuming a reference temperature (here, 25°C). Therefore, the provisional cycle deterioration amount needs to be corrected by the correction processing unit 32 based on the predicted temperature of the surrounding environment in which the storage battery B is used.

[0046] The correction processing unit 32 corrects the provisional cycle deterioration amount for each month calculated by the provisional cycle deterioration amount calculation unit 31 using the temperature acceleration data D2 based on the predicted temperature set in the predicted temperature setting unit 10. The provisional cycle deterioration amount after correction is output to the remaining life prediction unit 40 as the formal cycle deterioration amount.

[0047] Fig. 7 is a diagram showing a schematic diagram of the relationship between the deterioration amount and temperature of a lithium ion battery. As shown in Fig. 7, the deterioration amount (cycle deterioration amount and storage deterioration amount) of a lithium ion battery per unit time increases as the temperature of the surrounding environment increases, and decreases as the temperature of the surrounding environment decreases.

[0048] The temperature acceleration data D2 (hereinafter, "temperature acceleration formula D2") is an arithmetic formula or map for correcting, based on the predicted temperature, the cycle deterioration amount (and the storage deterioration amount described later) predicted by the cycle deterioration amount prediction unit 30. As described above, the rate at which deterioration of the storage battery B progresses strongly depends on the temperature of the ambient environment in which the storage battery B is used, and typically, the higher the ambient temperature is, the faster the deterioration progresses, and the lower the ambient temperature is, the slower the deterioration progresses.

[0049] For example, the temperature acceleration equation D2 is expressed by the following equation (1).

[0050]

number

[0051] In formula (1), α and T2 are values ​​stored in advance in the external storage device 104, and are a T2 is a value derived by the provisional cycle deterioration amount calculation unit 31 (for example, the amount of cycle deterioration per cycle obtained from the cycle deterioration map D1).

[0052] For example, the correction processing unit 32 calculates the deterioration rate a at the predicted temperature by introducing T1, which is the predicted temperature, into the formula (1). T1 Then, the correction processing unit 32 calculates, for example, the deterioration rate aT1 and the degradation rate at the reference temperature a T2 The provisional cycle deterioration amount is corrected based on the ratio of

[0053] In this manner, the cycle deterioration amount predicting section 30 calculates the amount of cycle deterioration for each month from the present time to a predetermined time period through the processing of the provisional cycle deterioration amount calculating section 31 and the correction processing section 32.

[0054] Fig. 8 is a diagram showing a schematic diagram of the calculation process of the remaining life prediction unit 40. Fig. 9 is a diagram showing an example of display screen data output by the remaining life prediction unit 40.

[0055] As shown in Fig. 8, remaining life prediction unit 40 accumulates the amount of cycle deterioration for each month in chronological order from the current SOC, and calculates the transition of the accumulated deterioration amount of the storage battery according to the elapsed time from the present time to a predetermined time. Then, remaining life prediction unit 40 displays and outputs the transition of the accumulated deterioration amount according to the elapsed time from the present time to the predetermined time on display unit 107, as shown in Fig. 9. Note that in Fig. 9, the transition of the accumulated deterioration amount is represented as the transition of the capacity maintenance rate of storage battery B.

[0056] This allows the user to recognize the remaining life of the storage battery B.

[0057] The life of the storage battery B is generally considered to be the time when the capacity maintenance rate of the storage battery B falls to 70% or less. From this perspective, the remaining life prediction unit 40 may notify the user of the time when the capacity maintenance rate of the storage battery B will reach 70%, for example.

[0058] 8 and 9 show a state in which storage battery B is unused (i.e., before delivery) at the present time. That is, Fig. 8 and Fig. 9 show the transition of the accumulated deterioration amount according to the elapsed time from when the SOC of storage battery B at the present time is 100%.

[0059] On the other hand, when storage battery B is already in use (i.e., already delivered), remaining life prediction unit 40 indicates a transition in the amount of accumulated deterioration according to the elapsed time from the current SOC. In this case, remaining life prediction unit 40 preferably uses an actual measured value as the current SOC of storage battery B. The current SOC of storage battery B can be measured, for example, from the capacity maintenance rate or the rate of change in internal resistance of storage battery B.

[0060] [effect] As described above, the prediction device 1 according to this embodiment: A predicted temperature setting unit 10 that divides an elapsed time from a current time to a predetermined time into a plurality of periods and sets a predicted temperature of the usage environment of the storage battery for each of the plurality of periods; a usage mode setting unit (20) that sets a usage mode related to charging and discharging of the storage battery for each of the plurality of periods; a cycle deterioration prediction unit that predicts a cycle deterioration amount of the storage battery based on the usage mode of the storage battery and the predicted temperature for each of the plurality of periods; a remaining life prediction unit that accumulates the amount of cycle deterioration for each of the plurality of periods in chronological order and calculates a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time; Equipped with.

[0061] As described above, the prediction device 1 according to this embodiment can accurately predict the amount of cycle deterioration at each time period, taking into account the temperature at each time period, and calculate the degree of deterioration of the storage battery B according to the elapsed time from the present time as the cumulative value. This enables the user to accurately predict the remaining lifespan (i.e., usable period) of the storage battery B.

[0062] [Second embodiment] Next, the configuration of the prediction device 1 according to the second embodiment will be described.

[0063] FIG. 10 is a diagram showing functional blocks of a prediction device 1 according to the second embodiment.

[0064] As described above, the degradation characteristics of a lithium ion battery are roughly divided into two types: degradation during cycles when the lithium ion battery is performing work on the outside (cycle degradation), and degradation of the lithium ion battery during storage when the lithium ion battery is not performing work on the outside (storage degradation). The amount of degradation caused by storage degradation is smaller than the amount of degradation caused by cycle degradation, but from the viewpoint of performing more accurate life prediction, it is preferable to also take into consideration the amount of degradation caused by storage degradation.

[0065] From this viewpoint, the prediction device 1 according to this embodiment has a configuration including, in addition to each functional unit described in the above embodiment, a storage deterioration amount prediction unit 50. Note that, here, the correction processing unit 32 of the cycle deterioration amount prediction unit 30 is referred to as a first correction processing unit 32 in order to distinguish it from the second correction processing unit 52 of the storage deterioration amount prediction unit 50.

[0066] The storage deterioration amount prediction unit 50 predicts the amount of storage deterioration of the storage battery B that progresses when the storage battery B is in a non-operating state for each month based on the non-operating time identified from the usage mode of the storage battery B and the predicted temperature.

[0067] More specifically, the storage deterioration amount prediction unit 50 is composed of a provisional storage deterioration amount calculation unit 51 that uses a storage deterioration map D3 stored in the external storage device 104 to calculate a provisional storage deterioration amount for each month based on the usage mode of the storage battery B, and a second correction processing unit 52 that corrects the provisional storage deterioration amount to calculate a formal storage deterioration amount using temperature accelerated data D2 based on the predicted air temperature.

[0068] 11 is a diagram showing an example of a storage deterioration map D3. The storage deterioration map D3 shown in FIG 11 shows the amount of deterioration [%] per day when the ambient temperature of the storage battery B is 25°C.

[0069] FIG. 12 is a diagram illustrating the amount of storage deterioration [%] derived from the storage deterioration map D3 and dependent on the storage period (ie, non-operating time) of the storage battery B.

[0070] 11, the provisional storage deterioration amount calculation unit 51 calculates the provisional storage deterioration amount from the non-operating time identified from the usage mode of the storage battery B. As described above, the provisional storage deterioration amount is the storage deterioration amount when the ambient temperature of the storage battery B is a reference temperature (e.g., 25°C).

[0071] The second correction processing unit 52 corrects the provisionally stored deterioration amount calculated by the provisionally stored deterioration amount calculation unit 51 in the same manner as the correction processing by the first correction processing unit 32 .

[0072] Here, the temperature acceleration formula D2 referred to by the second correction processor 52 is the same as the temperature acceleration formula D2 referred to by the first correction processor 32. However, in order to perform temperature correction with higher accuracy, the temperature acceleration formula related to the storage deterioration amount and the temperature acceleration formula related to the cycle deterioration amount may be different from each other.

[0073] The remaining life predicting unit 40 accumulates the total value of the storage deterioration amount and the cycle deterioration amount for each month in chronological order, and predicts the transition of the accumulated deterioration amount of the storage battery B according to the elapsed time from the present time.

[0074] As described above, the prediction device 1 according to this embodiment makes it possible to more accurately predict the remaining life of a storage battery.

[0075] [Example] Next, an example of a process flow for predicting a remaining lifespan by the prediction device 1 according to the above embodiment will be described. Here, an aspect is shown in which the prediction device 1 predicts a remaining lifespan so that a user can know the number of usable years of the storage battery B before starting to use the vehicle C.

[0076] FIG. 13 is a diagram showing an embodiment of a process flow when the prediction device 1 predicts the remaining life.

[0077] In step S1, the prediction device 1 (usage mode setting unit 20) sets a usage mode of the storage battery B for each month from the present time to a predetermined time in the future.

[0078] In step S1, the user inputs information related to the "upper SOC", "number of charges per day", "battery capacity of storage battery B", "power usage per day", and "operation time per day" through the input unit 106, and this information is set as the usage mode of storage battery B in the usage mode setting unit 20. The user sets this information in the usage mode setting unit 20 based on, for example, the planned use of vehicle C, the user's own rules of thumb, the product specifications of storage battery B, and past charge / discharge history data.

[0079] In this case, the usage of the storage battery B is assumed to be substantially the same each month, and the same values ​​are set for each month. However, in this case, if the usage of the storage battery B is expected to differ from month to month, it is preferable to set these items for each month.

[0080] In step S2, the prediction device 1 (usage mode setting unit 20) calculates the number of charge / discharge cycles that need to be performed per day based on the “battery capacity of storage battery B” and the “power usage per day” set in step S1.

[0081] In step S3, the prediction device 1 (usage mode setting unit 20) calculates an expected "average C rate" during discharge based on the "power usage per day" and the "operation time per day" set in step S1.

[0082] In step S4, the prediction device 1 (usage mode setting unit 20) calculates an expected "SOC range" for use based on the "upper limit SOC" and "number of charges per day" set in step S1. That is, here, under the condition that there are restrictions on the "upper limit SOC" and the "number of charges per day" as the user's way of using the storage battery B, the usage mode setting unit 20 calculates the "SOC range" that needs to be used.

[0083] In step S5, the prediction device 1 (cycle deterioration prediction unit 30) selects one corresponding cycle deterioration map D1 from the cycle deterioration maps D1 pre-stored in the external storage device 104 based on the “SOC range” to be used and the “average C rate” to be used.

[0084] In step S6, the prediction device 1 (cycle deterioration prediction unit 30) refers to the cycle deterioration map D1 selected in step S5 and calculates the "cycle deterioration amount per day" from the "number of charge / discharge cycles per day". Then, it calculates the "provisional cycle deterioration amount for the corresponding month" from the "cycle deterioration amount per day" and the number of operating days of the storage battery B.

[0085] Here, the prediction device 1 calculates the "provisional cycle deterioration amount for that month" for each month from the current time to the predetermined time.

[0086] In step S7, the prediction device 1 (predicted temperature setting unit 10) sets the average temperature for each month. Here, the user inputs the average temperature for each month through the input unit 106, and the information is set in the prediction device 1.

[0087] In step S8, the prediction device 1 (cycle deterioration amount prediction unit 30) corrects the provisional cycle deterioration amount for each month calculated in step S6 from the average temperature for each month, using the temperature acceleration formula D2 stored in advance in the external storage device 104.

[0088] In step S9, the prediction device 1 (storage deterioration amount prediction unit 50) calculates the storage time (that is, non-operation time) for each month from the "operation time per day" set in step S1.

[0089] In step S10, the prediction device 1 (storage deterioration amount prediction unit 50) refers to the storage deterioration map D3 pre-stored in the external storage device 104, and calculates the storage deterioration amount (i.e., the provisional storage deterioration amount) for each month from the storage time for each month.

[0090] In step S11, the prediction device 1 (storage deterioration amount prediction unit 50) uses the temperature acceleration formula D2 stored in advance in the external storage device 104 to correct the provisional storage deterioration amount for each month calculated in step S10 from the average temperature for each month.

[0091] In step S12, the prediction device 1 (remaining life prediction unit 40) sums up, for each month, the amount of cycle deterioration corrected in step S8 and the amount of storage deterioration corrected in step S11. Then, the prediction device 1 (remaining life prediction unit 40) accumulates the total value of the amount of cycle deterioration and the amount of storage deterioration for each month in chronological order, and calculates the trend in the amount of accumulated deterioration for each month. The prediction device 1 (remaining life prediction unit 40) displays and outputs the trend in the amount of accumulated deterioration calculated in this way (remaining life prediction curve) (see FIG. 9).

[0092] Through the above-described process flow, the prediction device 1 can present the remaining life of the storage battery B in a predictable manner to the user.

[0093] (Other embodiments) The present invention is not limited to the above-described embodiment, but can be applied to various modified aspects.

[0094] For example, in the above embodiment, the prediction device 1 is mounted on the vehicle C, but the prediction device 1 may be installed in a maintenance location or a sales location of the vehicle C.

[0095] Also, in the above embodiment, a configuration has been shown in which the usage mode of the storage battery B is set based on charge / discharge history data of the vehicle C currently in use and data input by the user, as an example of the usage mode setting unit 20. However, in cases such as when the vehicle C is in an unused state prior to delivery, the usage mode setting unit 20 may set the usage mode of the storage battery B to be used this time by using charge / discharge history data and driving history data of other vehicles previously used by the user.

[0096] In the above embodiment, as an example of the usage mode setting unit 20, a usage mode of the storage battery B is uniquely set by setting a planned SOC range for each month to be calculated. However, the usage mode setting unit 20 may set the usage mode of the storage battery B in the form of a SOC range in the form of a usage rate of "20% to 80%" or a usage rate of "0% to 100%", etc. Similarly, the usage mode setting unit 20 may set the C rate information in the form of a usage rate of "0.5C" or a usage rate of "0.3C", etc.

[0097] Although the specific examples of the present invention have been described in detail above, these are merely examples and do not limit the scope of the claims. The technology described in the claims includes various modifications and changes of the specific examples exemplified above. [Industrial Applicability]

[0098] According to the prediction device of the present invention, it is possible to realize more accurate remaining life prediction of an in-vehicle storage battery. [Explanation of symbols]

[0099] 1 Prediction device 10 Predicted temperature setting section 20 Usage setting section 30-cycle deterioration prediction section 31 Provisional cycle deterioration amount calculation unit 32 First correction processing section 40 Remaining Life Prediction Department 50 Storage deterioration prediction section 51 Temporary storage deterioration amount calculation unit 52 Second correction processing section B. Storage battery C Vehicle D1 cycle deterioration map D2 Temperature Acceleration Data D3 Preservation Degradation Map

Claims

1. A prediction device for predicting a remaining life of an in-vehicle storage battery, a predicted temperature setting unit that divides an elapsed time from a current time to a predetermined time into a plurality of periods and sets a predicted temperature of the usage environment of the storage battery for each of the plurality of periods; a usage mode setting unit that sets a usage mode related to charging and discharging of the storage battery for each of the plurality of periods; a cycle deterioration prediction unit that calculates a cycle deterioration amount of the storage battery based on the usage mode of the storage battery and the predicted temperature for each of the plurality of time periods; a remaining life prediction unit that accumulates the amount of cycle deterioration for each of the plurality of periods in chronological order and calculates a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time; A prediction device comprising:

2. The usage mode of the storage battery includes information on a planned SOC range, a number of charge / discharge cycles within the SOC range, and information on a planned C rate. The prediction device according to claim 1 .

3. The usage mode setting unit receives user input regarding an upper limit SOC, a number of charging times per day, a power consumption per day, and an operating time per day as the usage mode of the storage battery, and calculates the planned SOC range, the number of charge / discharge cycles within the SOC range, and the planned C rate from these values. The prediction device according to claim 2 .

4. The usage mode setting unit sets the usage mode of the storage battery based on past charge / discharge history data of the storage battery. The prediction device according to claim 1 .

5. The predicted temperature setting unit sets a monthly average temperature of the environment in which the storage battery is used. The prediction device according to claim 1 .

6. the cycle deterioration prediction unit includes a provisional cycle deterioration calculation unit that calculates a provisional cycle deterioration amount when the storage battery is used at a reference temperature based on the usage mode of the storage battery; and a correction processing unit that corrects the provisional cycle deterioration amount based on the predicted temperature to calculate an official cycle deterioration amount. The prediction device according to claim 1 , further comprising:

7. The storage battery is a lithium ion battery. The prediction device according to claim 1 .

8. a storage deterioration amount prediction unit that calculates an amount of storage deterioration of the storage battery that progresses when the storage battery is in a non-operating state for each of the plurality of periods based on a non-operating time specified from the usage mode of the storage battery and the predicted temperature, The remaining life prediction unit accumulates a total value of the amount of cycle deterioration and the amount of storage deterioration for each of the plurality of periods in chronological order, and calculates a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time. The prediction device according to claim 1 .

9. The remaining life prediction unit displays and outputs a transition of an accumulated value of the amount of cycle deterioration according to an elapsed time from the present time to the predetermined time. The prediction device according to claim 1 .

10. A method for predicting a remaining life of an in-vehicle storage battery, comprising: A process of dividing an elapsed time from a current time to a predetermined time into a plurality of periods, and setting a predicted temperature of an environment in which the storage battery is used for each of the plurality of periods; A process of setting a usage mode related to charging and discharging of the storage battery for each of the plurality of periods; calculating an amount of cycle deterioration of the storage battery based on the usage mode of the storage battery and the predicted temperature for each of the plurality of time periods; a process of accumulating the amount of cycle deterioration for each of the plurality of periods in chronological order and calculating a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time; A forecasting method to perform.

11. A prediction program for predicting a remaining life of an in-vehicle storage battery, On the computer, A process of dividing an elapsed time from a current time to a predetermined time into a plurality of periods, and setting a predicted temperature of an environment in which the storage battery is used for each of the plurality of periods; A process of setting a usage mode related to charging and discharging of the storage battery for each of the plurality of periods; calculating an amount of cycle deterioration of the storage battery based on the usage mode of the storage battery and the predicted temperature for each of the plurality of time periods; a process of accumulating the amount of cycle deterioration for each of the plurality of periods in chronological order and calculating a transition of the amount of accumulated deterioration of the storage battery according to the elapsed time from the present time; A prediction program that executes the following:

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