Prediction device, prediction method, and prediction program

Abstract

To provide a prediction device capable of further accurately predicting the remaining life of an on-vehicle storage battery.SOLUTION: A prediction device 1 according to the present invention is provided with: a prediction temperature setting unit 10 for dividing an elapsed time from the present time to a predetermined time into a plurality of periods and setting a predicted temperature of the usage environment of a storage battery B for each of the plurality of periods; a usage mode setting unit 20 for setting a usage mode related to charge / discharge of the storage battery B for each of the plurality of periods; a cycle deterioration amount prediction unit 30 for calculating a cycle deterioration amount of the storage battery B based on the usage mode of the storage battery B and the predicted temperature for each of the plurality of periods; and a remaining life prediction unit 40 for accumulating the cycle deterioration amount of each of the plurality of periods in chronological order and calculating the transition of an accumulated deterioration amount of the storage battery B according to the elapsed time from the present time.SELECTED DRAWING: Figure 3

Description

Technical Field

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

Background Art

[0002] In recent years, electric vehicles and hybrid vehicles equipped with storage batteries have attracted attention. Among storage batteries, in particular, lithium-ion batteries have high energy density, are small and lightweight, have a long lifespan, have little self-discharge, and can also be rapidly charged.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] By the way, since lithium-ion batteries deteriorate due to system operation, remaining life diagnosis is an important factor. This is because the numerical value of the lifespan presented by the lithium-ion battery manufacturer as a standard of the battery life is based on cycles under certain conditions and is not necessarily accurate.

[0005] In addition, SOH (State Of Health, also referred to as soundness) is known as an index that quantifies the health state and deterioration state of a lithium-ion battery. SOH is represented by a value with the initial value of the battery capacity set to 100%, for example. And as the deterioration of the lithium-ion battery progresses, the battery capacity decreases. Therefore, the SOH at each time is represented as the value of the battery capacity decreased by the deterioration amount from 100%.

[0006] Lithium-ion batteries are generally considered to be related to degradation called cycle degradation that occurs by repeating charge and discharge, and degradation called storage degradation that occurs as the operation time elapses. That is, the degradation of lithium-ion batteries progresses as the number of charge-discharge cycles increases and as the operation time elapses.

[0007] However, as a result of the inventors' intensive studies of the present application, it has been found that the progress rate of the degradation of lithium-ion batteries strongly depends on the ambient temperature. For example, even within Japan, there is a difference of several years in the number of years to reach the same SOH between lithium-ion batteries used in cold regions and lithium-ion batteries used in warm regions. In particular, lithium-ion batteries mounted on vehicles are used in a state exposed to the outside air, so the difference in remaining life due to the usage environment of each vehicle user is large.

[0008] In Patent Document 1 and the like, in a power storage system mounted on an automobile, each driving mode is classified by a running measurement unit, the usage ratio of each driving mode is calculated, and remaining life diagnosis is performed using the results of a life database of each driving mode measured in advance. However, in the conventional technologies such as Patent Document 1, since the ambient temperature of the lithium-ion battery is not considered for remaining life diagnosis, there is room for improvement in terms of prediction accuracy.

[0009] The present invention has been made in view of the above problems, and an object thereof is to provide a prediction device, a prediction method, and a prediction program capable of realizing more accurate prediction of the remaining life of an in-vehicle battery.

Means for Solving the Problems

[0010] The main invention of the present application for solving the above problems is a prediction device for predicting the remaining life of an in-vehicle battery, a predicted temperature setting unit that divides the elapsed time from the current time to a predetermined period 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; 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 degradation amount prediction unit that calculates a cycle degradation amount of the storage battery based on the usage mode of the storage battery and the predicted temperature; A remaining life prediction unit that accumulates the cycle degradation amounts of each of the plurality of periods in chronological order and calculates the transition of the accumulated degradation amount of the storage battery according to the elapsed time from the current time; A prediction device comprising:

[0011] In another aspect, A prediction method for predicting the remaining life of an in-vehicle storage battery, comprising: Dividing the elapsed time from the current time to a predetermined period into a plurality of periods, and for each of the plurality of periods, setting a predicted temperature of the usage environment of the storage battery; For each of the plurality of periods, performing a process of setting a usage mode related to charging and discharging of the storage battery; For each of the plurality of periods, calculating a cycle degradation 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, accumulating the cycle degradation amounts in chronological order and calculating the transition of the accumulated degradation amount of the storage battery according to the elapsed time from the current time; A prediction method for executing the above.

[0012] In another aspect, A prediction program for predicting the remaining life of an in-vehicle storage battery, which causes a computer to: Divide the elapsed time from the current time to a predetermined period into a plurality of periods, and for each of the plurality of periods, set a predicted temperature of the usage environment of the storage battery; For each of the plurality of periods, perform a process of setting a usage mode related to charging and discharging of the storage battery; For each of the plurality of periods, calculate a cycle degradation 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, accumulate the cycle degradation amounts in chronological order and calculate the transition of the accumulated degradation amount of the storage battery according to the elapsed time from the current time; The process of accumulating the cycle degradation amounts for each of the plurality of periods in chronological order and calculating the transition of the accumulated degradation amount of the storage battery according to the elapsed time from the current time, is a prediction program to be executed.

Effect of the Invention

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

Brief Description of the Drawings

[0014]

Figure 1

Figure 2

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Figure 7

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Figure 11

Figure 12

Figure 13

Mode for Carrying Out the Invention

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

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

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

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

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

[0020] The prediction device 1 is a computer mainly including a CPU 101, a ROM 102, a RAM 103, an external storage device (for example, a flash memory) 104, a communication unit (for example, a communication module connected to the Internet) 105, an input unit (for example, a keyboard or a mouse) 106, and a display unit (for example, a liquid crystal display) 107, etc.

[0021] Each function of the prediction device 1 described below, for example, is realized by the CPU 101 referring to processing programs and various data stored in the ROM 102, RAM 103, external storage device 104, etc. Note that the external storage device 104 stores, in addition to a prediction program for realizing each function of the prediction device 1 described below, a cycle degradation map, temperature acceleration data, and the like.

[0022] FIG. 3 is a diagram showing an example of the functional blocks of the prediction device 1.

[0023] The prediction device 1 includes, as its functions, for example, a predicted temperature setting unit 10, a usage mode setting unit 20, a cycle degradation amount prediction unit 30, and a remaining life prediction unit 40. Note that the cycle degradation map D1 and the temperature acceleration data D2 in FIG. 3 are data stored in advance in the external storage device 104.

[0024] The vehicle C is generally used while being exposed to the outside air. Therefore, as described above, the progress rate of the deterioration of the storage battery B (that is, the lithium-ion battery) mounted on the vehicle C varies depending on the location where the vehicle C is used and also varies depending on the period (that is, the season) when the vehicle C is used.

[0025] The prediction device 1 according to the present invention divides the elapsed time from the current time to a predetermined time into a plurality of periods in consideration of the usage mode of the storage battery B mounted on the vehicle C, and accurately predicts the deterioration amount of the storage battery B in each period in consideration of the temperature in each period. Then, the prediction device 1 calculates the deterioration amount of the storage battery B corresponding to the elapsed time from the current time as the cumulative value of the deterioration amounts in each period. Thereby, it becomes possible to accurately predict the remaining life (that is, the usable period) of the storage battery B.

[0026] In this embodiment, the prediction device 1 is configured to calculate the amount of deterioration per month by dividing the elapsed time from the current time to a predetermined future period into intervals of one month each. Therefore, hereinafter, the configuration for calculating the amount of deterioration on a monthly basis will be described. However, the way of dividing the elapsed time from the current time to a predetermined future period does not necessarily have to be in units of one month. For example, it may be in units of one day or two months. Also, the way of dividing the elapsed time does not necessarily have to have the same time width in each period. Also, the predetermined period is a future period that is appropriately set when executing the arithmetic processing and can be any period.

[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 current time to a predetermined future period. Here, the average predicted temperature for each month is used as the predicted temperature for each month.

[0028] However, the mode of setting the predicted temperature can be variously deformed, and the maximum temperature or the minimum temperature in each month may be used. Also, the temperature range between the maximum temperature and the minimum temperature in each month may be used.

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

[0030] The predicted temperature setting unit 10, for example, receives an input of the predicted temperature from the user and sets the input predicted temperature. However, the predicted temperature setting unit 10 may set the predicted temperature based on the history data of a temperature sensor (not shown) for detecting the ambient environment mounted on the vehicle C. In this case, the predicted temperature setting unit 10 may set the predicted temperature based on, for example, the detected temperature data in the same month in the past. Also, the predicted temperature setting unit 10 may acquire statistical data of the average temperature in the usage area of the vehicle C 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 the charge and discharge of the storage battery B for each month from the current time to a predetermined period. Based on the usage mode related to the charge and discharge of the storage battery B, the cycle degradation amount prediction unit 30 calculates the cycle degradation amount 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 and discharge cycles of the storage battery B for each month. This is because the progress of the degradation of the storage battery B typically depends on the number of charge and discharge cycles of the storage battery B (see FIG. 4).

[0033] However, as such a usage mode, it is preferably set together with the SOC (State Of Charge, representing the charge rate) range information and C-rate (Capacity rate) information of the planned use of the storage battery B (see FIG. 5). Thereby, the cycle degradation amount prediction unit 30 described later can select an appropriate cycle degradation map D1 for calculating the cycle degradation amount 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 progress speed of the degradation of the storage battery B (here, a lithium-ion battery) becomes faster as the used SOC range is wider, and slower as the used SOC range is narrower. In particular, every time the storage battery B is discharged until the SOC reaches "0%" or charged until the SOC reaches "100%", the progress speed of the degradation of the storage battery B tends to increase. Conversely, by restricting the usage mode of the storage battery B to a usage mode with the SOC range limited to "20% - 80%" etc., the progress speed of the degradation of the storage battery B can be slowed down. That is, in order to predict a more accurate cycle degradation amount, it is preferable for the user to set the SOC range of the planned use of how the storage battery B is used.

[0035] In addition, the rate of progress of the deterioration of the storage battery B (here, a lithium-ion battery) increases as the discharge rate (C rate) of the storage battery B used increases, and decreases as the discharge rate used decreases. For example, if the vehicle C frequently performs high-load operations, the discharge rate of the storage battery B increases (that is, the C rate increases), and the rate of progress of the deterioration of the storage battery B tends to increase. That is, in order to predict a more accurate amount of cycle deterioration, it is preferable for the user to set the C rate information of the planned use of how the storage battery B is used.

[0036] Here, the usage mode setting unit 20 preferably sets the usage mode of the storage battery B based on the past charge and discharge history data of the storage battery B. The charge and 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 and discharge history data in the form of the time course of the power consumption and the charging power. The usage mode setting unit 20 preferably calculates, from such past charge and discharge history data, for example, referring to the past history data of the same month as the month of the calculation target, the number of charge and discharge cycles, the SOC range of the planned use, and the average C rate of the planned use.

[0037] The usage mode of the storage battery B (for example, the driving distance per month, the SOC range used, etc.) varies from user to user. In this regard, by making it possible to set the usage mode of the storage battery B based on the past charge and discharge history data of the storage battery B, the cycle deterioration amount prediction unit 30 described later can accurately predict the cycle deterioration amount of the storage battery B at each time period.

[0038] The cycle deterioration amount prediction unit 30 predicts the cycle deterioration amount of the storage battery B caused by the repetition of charge and discharge 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 for each month from the current time to a predetermined time period.

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

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

[0041] The cycle degradation map D1 is a map for calculating the cycle degradation amount caused by the repetition of charge and discharge of the storage battery B. The cycle degradation map D1 stores, for example, the association between the number of charge and discharge cycles and the cycle degradation amount corresponding to the number of charge and discharge cycles. Note that the cycle degradation map D1 is created assuming that the temperature of the usage environment of the storage battery B is the reference temperature (for example, 25°C).

[0042] However, in the present embodiment, classification items related to the SOC range of the planned use and the average C rate of the planned use are set in the cycle degradation map D1 so that the provisional cycle degradation amount calculation unit 31 can appropriately calculate the progress rate of the cycle degradation amount according to the usage mode of the storage battery B. That is, the external storage device 104 stores data of a group of cycle degradation maps D1 in which classification items related to the SOC range of the planned use and the average C rate of the planned use are set.

[0043] FIG. 5 is a diagram showing an example of a group of cycle degradation maps D1. In the group of cycle degradation maps D1 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. Further, in the group of cycle degradation maps D1 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 the cycle degradation map D1 shown in FIG. 5, in each column, the degradation amount [%] per cycle when the number of charge-discharge cycles is up to "0 - 200 cycles", the degradation amount [%] per cycle when the number of charge-discharge cycles is up to "200 - 500 cycles", and the degradation amount [%] per cycle when the number of charge-discharge cycles is up to "500 cycles" are shown.

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

[0045] The provisional cycle degradation amount calculation unit 31 selects, for example, one cycle degradation map D1 from the group of cycle degradation maps D1 based on the SOC range and the average C rate to be used, set in the usage mode setting unit 20. Then, the provisional cycle degradation amount calculation unit 31 calculates, for example, the provisional cycle degradation amount from the number of charge-discharge cycles per month using the selected cycle degradation map D1. The provisional cycle degradation amount calculated by the provisional cycle degradation amount calculation unit 31 is assumed at the reference temperature (here, 25°C). Therefore, the provisional cycle degradation amount needs to be corrected by the correction processing unit 32 based on the predicted temperature of the ambient 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 formula data D2 based on the predicted temperature set by the predicted temperature setting unit 10. Note that the corrected provisional cycle deterioration amount is output to the remaining life prediction unit 40 as the formal cycle deterioration amount.

[0047] FIG. 7 is a diagram schematically showing the relationship between the deterioration amount and temperature of a lithium-ion battery. As shown in FIG. 7, the deterioration amount per unit time (cycle deterioration amount and storage deterioration amount) of the lithium-ion battery increases as the temperature of the ambient environment is higher, and decreases as the temperature of the ambient environment is lower.

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

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

[0050]

Equation

[0051] In formula (1), α and T2 are values previously held in the external storage device 104, and a at the reference temperature (for example, 25 ° C) T2 is a value derived by the provisional cycle deterioration amount calculation unit 31 (for example, the cycle deterioration amount per cycle obtained from the cycle deterioration map D1).

[0052] The correction processing unit 32 obtains the deterioration rate a at the predicted temperature, for example, by introducing T1, which is the predicted temperature, into formula (1). Then, the correction processing unit 32, for example, the deterioration rate a at the predicted temperature T1 to obtain. And the correction processing unit 32, for example, the deterioration rate a at the predicted temperatureT1 and the deterioration rate a at the reference temperature T2 corrects the provisional cycle deterioration amount from the ratio.

[0053] In this way, the cycle deterioration amount prediction unit 30 calculates the cycle deterioration amount for each month from the current time to a predetermined period by the processes of the provisional cycle deterioration amount calculation unit 31 and the correction processing unit 32.

[0054] FIG. 8 is a diagram schematically showing the arithmetic processing of the remaining life prediction unit 40. FIG. 9 is a diagram showing an example of the display screen data output by the remaining life prediction unit 40.

[0055] As shown in FIG. 8, the remaining life prediction unit 40 accumulates the cycle deterioration amounts 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 current time to a predetermined period. Then, as shown in FIG. 9, the remaining life prediction unit 40 displays and outputs the transition of the accumulated deterioration amount according to the elapsed time from the current time to a predetermined period to the display unit 107. In FIG. 9, the transition of the accumulated deterioration amount is represented as the transition of the capacity retention rate of the storage battery B.

[0056] As a result, the user can recognize the remaining life of the storage battery B.

[0057] Incidentally, the life of the storage battery B is generally considered to be the time when the capacity retention rate of the storage battery B drops to 70% or less. From this point of view, the remaining life prediction unit 40 may notify the user, for example, of the time when the capacity retention rate of the storage battery B reaches 70%.

[0058] Also, in FIGS. 8 and 9, at the current time, the state where the storage battery B is unused (that is, before delivery) is shown. That is, FIGS. 8 and 9 show the transition of the accumulated deterioration amount according to the elapsed time from 100% of the current SOC of the storage battery B.

[0059] On the other hand, when the remaining battery life prediction unit 40 determines that the storage battery B is already in use (i.e., has been delivered), it shows the transition of the accumulated degradation amount according to the elapsed time from the current SOC. In this case, it is preferable for the remaining battery life prediction unit 40 to use the measured value as the current SOC of the storage battery B. The current SOC of the storage battery B can be measured, for example, from the capacity retention rate or the change rate of the internal resistance of the storage battery B.

[0060] [Effect] As described above, the prediction device 1 according to the present embodiment divides the elapsed time from the current time to a predetermined time into a plurality of periods, and a predicted temperature setting unit 10 that 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 degradation amount prediction unit 30 that predicts the cycle degradation amount of the storage battery based on the usage mode and the predicted temperature of the storage battery for each of the plurality of periods, a remaining battery life prediction unit 40 that accumulates the cycle degradation amounts of each of the plurality of periods in chronological order and calculates the transition of the accumulated degradation amount of the storage battery according to the elapsed time from the current time, is provided.

[0061] As described above, according to the prediction device 1 according to the present embodiment, considering the temperature at each time, the cycle degradation amount at each time can be accurately predicted, and as the cumulative value thereof, the degree of degradation of the storage battery B according to the elapsed time from the current time can be calculated. As a result, the user can accurately predict the remaining battery life (i.e., the 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 a functional block of the prediction device 1 according to the second embodiment.

[0064] As described above, the degradation characteristics of a lithium-ion battery are roughly classified into two types: degradation during cycling (cycle degradation) when the lithium-ion battery is doing work on the outside, and degradation of the lithium-ion battery during storage (storage degradation) when the lithium-ion battery is not doing work on the outside. The amount of degradation caused by storage degradation is smaller than the amount of degradation caused by cycle degradation, but from the perspective of performing more accurate life prediction, it is preferable to consider the amount of degradation caused by storage degradation as well.

[0065] From such a perspective, the prediction device 1 according to the present embodiment has a configuration including a storage degradation amount prediction unit 50 in addition to each functional unit described in the above embodiment. Here, in order to distinguish the correction processing unit 32 of the cycle degradation amount prediction unit 30 from the second correction processing unit 52 of the storage degradation amount prediction unit 50, it is referred to as the first correction processing unit 32.

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

[0067] More specifically, the storage degradation amount prediction unit 50 includes a tentative storage degradation amount calculation unit 51 that calculates a tentative storage degradation amount each month based on the usage mode of the storage battery B using the storage degradation map D3 stored in the external storage device 104, and a second correction processing unit 52 that corrects the tentative storage degradation amount using the temperature acceleration formula data D2 based on the predicted temperature to calculate the formal storage degradation amount.

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

[0069] FIG. 12 is a diagram schematically showing the amount of storage degradation [%] depending on the storage period (i.e., non-operating time) of the storage battery B derived from the storage degradation map D3.

[0070] Here, the provisional storage degradation amount calculation unit 51 refers to the storage degradation map D3 as shown in FIG. 11, and calculates the provisional storage degradation amount from the non-operating time specified from the usage mode of the storage battery B. Note that, as described above, the provisional storage degradation amount is the storage degradation amount when the ambient temperature of the storage battery B is the reference temperature (for example, 25°C).

[0071] The second correction processing unit 52 corrects the provisional storage degradation amount calculated by the provisional storage degradation 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 processing unit 52 is the same as the temperature acceleration formula D2 referred to by the first correction processing unit 32. However, in order to perform more accurate temperature correction, different temperature acceleration formulas may be used for the temperature acceleration formula related to the storage degradation amount and the temperature acceleration formula related to the cycle degradation amount.

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

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

[0075] [Example] Next, an example of the processing flow when predicting the remaining life by the prediction device 1 according to the above embodiment will be described. Here, an example of the mode in which the prediction device 1 performs remaining life prediction is shown so that the user can grasp the available years of the storage battery B before starting the use of the vehicle C.

[0076] FIG. 13 is a diagram showing an example of the processing flow when predicting the remaining life by the prediction device 1.

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

[0078] Still, in this step S1, the user inputs information related to "upper SOC", "number of charging times per day", "battery capacity of battery B", "power consumption per day", and "operating hours per day" at the input unit 106, and these pieces of information are set as the usage mode of 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 content of the usage plan of vehicle C, their own experience, the product specifications of battery B, and past charge and discharge history data.

[0079] Still, here, assuming that the usage mode of battery B is substantially the same each month, the same value is set each month. However, in this case, if it is assumed that the usage mode of battery B is different each month, it is preferable to set these items each month.

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

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

[0082] In step S4, the prediction device 1 (usage mode setting unit 20) calculates the "SOC range" that is expected to be used based on the "upper SOC" and "number of charging times per day" set in step S1. That is, here, under the condition that there are restrictions on the "upper SOC" and "number of charging times per day" as the usage method of the user's 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 degradation amount prediction unit 30) selects one corresponding cycle degradation map D1 from the group of cycle degradation maps D1 pre-stored in the external storage device 104 based on the "SOC range" and the "average C rate" planned for use.

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

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

[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 this information is set in the prediction device 1.

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

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

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

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

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

[0092] With the processing flow as described above, the prediction device 1 can present the user with the ability to predict the remaining life of the storage battery B.

[0093] (Other Embodiments) The present invention is not limited to the above-described embodiments and can be applied to various modified forms.

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

[0095] Also, in the above-described embodiment, as an example of the usage mode setting unit 20, a configuration is shown in which the usage mode of the storage battery B is set based on the charge / discharge history data of the currently used vehicle C and the input data by the user. However, when the vehicle C is in an unused state before delivery, etc., the usage mode setting unit 20 may use the charge / discharge history data and the travel history data of other vehicles used by the user in the past to set the usage mode of the storage battery B for the current usage target.

[0096] Further, in the above-described embodiment, as an example of the usage mode setting unit 20, the usage mode of the storage battery B is shown in a mode of uniquely setting the SOC range to be used in units of months for which calculation is to be performed. However, the usage mode setting unit 20 may set the usage mode of the storage battery B in a format such as the ratio of use within the SOC range of "20% to 80%" or the ratio of use within the range of "0% to 100%". Similarly, the usage mode setting unit 20 may set the C-rate information in a format such as the ratio of use at "0.5C" or the ratio of use at "0.3C".

[0097] As described above, specific examples of the present invention have been described in detail, but 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 illustrated above.

Industrial Applicability

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

Explanation of Reference Numerals

[0099] 1 Prediction device 10 Prediction temperature setting unit 20 Usage mode setting unit 30 Cycle deterioration amount prediction unit 31 Provisional cycle deterioration amount calculation unit 32 First correction processing unit 40 Remaining life prediction unit 50 Storage deterioration amount prediction unit 51 Provisional storage deterioration amount calculation unit 52 Second correction processing unit B Storage battery C Vehicle D1 Cycle deterioration map D2 Temperature acceleration formula data D3 Storage deterioration map

Claims

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

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

3. The usage mode setting unit receives input from the user regarding the upper limit SOC, the number of charges per day, the power consumption per day, and the operating hours per day as the usage mode of the battery, and calculates the SOC range of the planned usage, the number of charge and discharge cycles within the SOC range, and the C-rate of the planned usage from these values. The prediction device according to claim 1.

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

5. The predicted temperature setting unit sets the monthly average temperature of the usage environment of the battery. The prediction device according to claim 1.

6. The cycle degradation amount prediction unit includes a provisional cycle degradation amount calculation unit that calculates a provisional cycle degradation amount when the usage environment of the storage battery is at the reference temperature based on the usage mode of the storage battery, and a correction processing unit that corrects the provisional cycle degradation amount based on the predicted temperature to calculate the formal cycle degradation amount. The prediction device according to claim 1, which has .

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

8. Further provided with a storage degradation amount prediction unit that calculates the storage degradation amount 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 the non-operating time specified from the usage mode of the storage battery and the predicted temperature. The remaining life prediction unit accumulates the total values of the cycle degradation amount and the storage degradation amount for each of the plurality of periods in chronological order, and calculates the transition of the accumulated degradation amount of the storage battery according to the elapsed time from the current time. The prediction device according to claim 1.

9. The remaining life prediction unit displays and outputs the transition of the cumulative value of the cycle degradation amount according to the elapsed time from the current time to the predetermined period. The prediction device according to claim 1.

10. A prediction method for predicting the remaining life of an in-vehicle storage battery, comprising: Dividing the elapsed time from the current time to a predetermined period into a plurality of periods, and for each of the plurality of periods, setting a predicted temperature of the usage environment of the storage battery; For each of the plurality of periods, setting a usage mode related to the charge and discharge of the storage battery; For each of the plurality of periods, calculating a cycle degradation amount of the storage battery based on the usage mode and the predicted temperature of the storage battery; Accumulating the cycle degradation amounts of each of the plurality of periods in chronological order, and calculating a transition of the accumulated degradation amount of the storage battery according to the elapsed time from the current time; A prediction method for execution.

11. A prediction program for predicting the remaining life of an in-vehicle battery, for a computer, dividing the elapsed time from the current time to a predetermined period into a plurality of periods, and for each of the plurality of periods, setting a predicted temperature of the usage environment of the battery; for each of the plurality of periods, setting a usage pattern related to charging and discharging of the battery; for each of the plurality of periods, calculating a cycle degradation amount of the battery based on the usage pattern and the predicted temperature of the battery; cumulatively summing the cycle degradation amounts of each of the plurality of periods in chronological order, and calculating a transition of the cumulative degradation amount of the battery according to the elapsed time from the current time; A prediction program for causing the above to be executed.

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