Secondary battery deterioration state prediction method and battery information management system

Abstract

The present invention provides a method for highly accurately predicting battery deterioration according to the use environment and / or purpose of use of a battery-equipped device. Provided is a deterioration state prediction method for predicting the deterioration state of a secondary battery, said method comprising: a reception step for receiving operational data pertaining to each of a plurality of secondary batteries; a battery diagnosis step for calculating a battery deterioration history and the battery use environment of each secondary battery on the basis of each item of operational data; a diagnosis result recording step for storing the battery deterioration history and the battery use environment of each secondary battery; a battery deterioration prediction step for, on the basis of the battery deterioration state and the battery use environment of each secondary battery, predicting the future battery deterioration state before and after the change of the use environment or purpose of use of a secondary battery targeted for prediction; and an output step for outputting a result of the prediction.

Description

Method for Predicting Deterioration State of Secondary Battery and Battery Information Management System

[0001] The present invention relates to a method for predicting the deterioration state of a secondary battery and a battery information management system that can also cope with changes in the usage environment and usage applications of the secondary battery.

[0002] A lithium secondary battery, which is a type of storage battery, is used as a power source for various devices, ranging from electric vehicles to portable devices. However, when a lithium secondary battery is repeatedly used, there is a problem that the energy that can be taken out of the battery gradually decreases. In particular, in an electric vehicle, since the energy capacity of the battery pack, which is the power source of the drive motor, is closely related to the cruising range, a technology for accurately grasping the capacity deterioration of the battery pack and predicting future deterioration trends has been demanded.

[0003] As an example of a technology for predicting the deterioration state of a storage battery of an electric vehicle, there is the technology described in Patent Document 1. For example, in the abstract of the same document, the problem is stated as "Considering the user's vehicle usage method, predicting the deterioration state of a used battery after replacement, and searching for a used battery suitable for the user based on the predicted deterioration state." As a solution, "Based on the vehicle information of each of the battery before replacement and the candidate battery for replacement, analyze the user's vehicle usage method (step S2), and from the cruising range at the replacement timing of the candidate battery for replacement and the deterioration curve according to the vehicle usage method of the user of the battery before replacement, predict the deterioration state of the candidate battery for replacement when the user of the battery before replacement uses the candidate battery for replacement (steps S4, S8). Based on the predicted deterioration state of the candidate battery for replacement, select a used battery that has a cruising range suitable for the vehicle usage method of the user of the battery before replacement and whose usable period, price, etc. satisfy the wishes of the user of the battery before replacement (steps S5, steps S9 to S11), and present this as the optimal battery to the user terminal 7 (step S12)." Thus, according to the technology of this document, it is possible to predict the deterioration state of a used battery after replacement and present a used battery that satisfies the user's wishes as the optimal battery.

[0004] Japanese Unexamined Patent Application Publication No. 2013 - 84198

[0005] However, as explained in paragraph 0017 of Patent Document 1, "User-specified information includes information such as the battery ID of the battery to be replaced (the battery before replacement), the timing of replacement with a used battery, the desired driving range, the desired retention period for the replaced battery, the upper limit price, the lower limit price, etc.", the technology in that document was limited to suggesting an optimal battery based on user-specified information such as the desired driving range. In other words, Patent Document 1 did not consider the prediction of the future degradation state of the battery in the event of changes in the battery's usage environment or intended use.

[0006] Therefore, the present invention aims to provide a method for predicting the degradation state of a secondary battery and a battery information management system that can predict the future degradation state of a secondary battery with high accuracy, even if there are changes in the usage environment or application of the secondary battery.

[0007] To solve the above problems, the present invention provides a secondary battery degradation state prediction method comprising: a receiving step of receiving operational data for each of a plurality of secondary batteries; a battery diagnostic step of calculating the battery degradation history and battery usage environment of each secondary battery based on the operational data; a diagnostic result recording step of storing the battery degradation history and battery usage environment of each secondary battery; a battery degradation prediction step of predicting the future battery degradation state of the secondary battery to be predicted before and after a change in the usage environment or usage application of the secondary battery based on the battery degradation state and battery usage environment of each secondary battery; and an output step of outputting the prediction result.

[0008] Furthermore, the battery information management system of the present invention comprises: a receiving unit that receives operational data relating to each of a plurality of secondary batteries; a battery diagnostic unit that calculates the battery degradation history and battery usage environment of each secondary battery based on the operational data; a diagnostic result recording unit that stores the battery degradation history and battery usage environment of each secondary battery; a battery degradation prediction unit that predicts the future battery degradation state of a target secondary battery before and after a change in the usage environment or usage application based on the battery degradation state and battery usage environment of each secondary battery; and an output unit that outputs the prediction results.

[0009] According to the method for predicting the degradation state of a secondary battery and the battery information management system of the present invention, it is possible to predict the future degradation state of a secondary battery with high accuracy, even if there are changes in the usage environment or application of the secondary battery.

[0010] A functional block diagram of a battery information management system according to one embodiment. A schematic diagram of a battery pack according to one embodiment. A diagram showing an example of vehicle data according to one embodiment. A diagram showing an example of basic battery data according to one embodiment. A flowchart of processing by the battery diagnostic unit according to one embodiment. A diagram showing an example of battery degradation diagnosis by the battery diagnostic unit according to one embodiment. A diagram showing an example of battery usage environment extraction by the battery diagnostic unit according to one embodiment. A flowchart of processing by the battery degradation prediction unit according to one embodiment. A diagram showing an example of battery degradation state prediction results when there is no change in the battery usage environment. A diagram showing an example of battery degradation state prediction results when there is a change in the battery usage environment.

[0011] Hereinafter, embodiments of the method for predicting the degradation state of a secondary battery and a battery information management system according to the present invention will be described with reference to the drawings. In the following, an example will be given of predicting the future degradation state of a secondary battery for electric vehicles, but the present invention may also be used to predict the future degradation state of secondary batteries for other applications, such as batteries for HEMS (Home energy management systems), BEMS (Building energy management systems), FEMS (Factory energy management systems), railway batteries, or construction machinery batteries. Furthermore, in the following, an example will be given of predicting the future degradation state of a secondary battery for electric vehicles when a secondary battery used in one environment (e.g., a warm region) is moved to another environment (e.g., a cold region), but the present invention may also be used in situations where a secondary battery used for one application is repurposed for another application.

[0012] [Configuration of the Battery Information Management System] The battery information management system 1 in this embodiment is a system that can communicate with multiple electric vehicles 2 via a wireless communication network such as a mobile phone network, and is a system that predicts the future degradation state of the battery pack 21 installed in each vehicle.

[0013] Specifically, this battery information management system 1 is a server or the like, equipped with hardware such as a CPU or other computing device, a semiconductor memory or other storage device, and a communication device. It is connected to input interfaces (not shown) such as a keyboard, mouse, and touch panel used by the user during operation, and output interfaces (not shown) such as a display for displaying information. The computing device then executes a predetermined program to realize the various functions described later, but in the following explanation, such well-known technologies will be omitted as appropriate.

[0014] Figure 1 is a functional block diagram of the battery information management system 1 in this embodiment. Here, we illustrate how the battery information management system 1 communicates wirelessly with two electric vehicles 2, but the battery information management system 1 is designed to communicate with more electric vehicles 2. In the following, the battery packs 21 of each vehicle are assumed to have the same specifications.

[0015] As shown in Figure 1, the battery information management system 1 includes a receiving unit 11 that receives vehicle data D1, which is battery operation data transmitted by the electric vehicle 2; a battery information storage unit 12 that stores basic battery data D2; a battery diagnostic unit 13 that diagnoses the battery degradation state and extracts the battery usage environment; a diagnostic result recording unit 14 that stores information on the battery degradation state and the battery usage environment; and a battery degradation prediction unit 15 that predicts the future battery degradation state.

[0016] Here, the schematic configuration of the battery pack 21 will be explained using Figure 2. The battery pack 21 illustrated here is a lithium secondary battery that incorporates multiple modules 21b, each module having multiple cells 21a connected in series. In this embodiment, the specifications of the battery pack 21 are defined as N being the number of series-connected cells 21a and M being the number of parallel-connected series-connected modules. Therefore, this battery pack 21 contains N × M cells 21a. In addition, this battery pack 21 incorporates L temperature sensors 21c, and current sensors (not shown) and voltage sensors (not shown) for measuring the current and voltage values ​​of the battery pack 21 are also provided inside or outside the battery pack 21.

[0017] [Vehicle Data D1] Next, an example of vehicle data D1 will be explained using Figure 3. This vehicle data D1 is data that is wirelessly transmitted from the electric vehicle 2 to the battery information management system 1 at predetermined intervals (for example, every 10 seconds), and includes the following information.

[0018] Information No. 1 is a unique vehicle ID used to identify electric vehicle 2.

[0019] Information No. 2 is the date and time when the current, voltage, and temperature of the battery pack 21 were measured.

[0020] Information No. 3 is a vehicle status flag indicating the status of electric vehicle 2. For example, if electric vehicle 2 is charging, the flag "Charging" is registered; if it is stationary, the flag "Stationary" is registered; and if it is in motion, the flag "In Motion" is registered.

[0021] Information No. 4 represents the current value measured by the current sensor at the time indicated in No. 2. In this embodiment, the charging current value is shown as a positive value, and the discharging current value is shown as a negative value.

[0022] Information No. 5 is the State of Charge (SOC) of the battery pack 21.

[0023] Information No. 6 represents the number of cells 21a in series within the battery pack 21.

[0024] Information No. 7 is the number of temperature sensors 21c inside the battery pack 21.

[0025] Information No. 8 represents the initial Ah capacity of the battery pack 21, and shows the calculated value of the initial Ah of each cell 21a multiplied by the number of parallel cells M.

[0026] Information No. 9 represents the initial energy capacity of the battery pack 21, and shows the calculated value of the initial energy capacity of each cell 21a × number of series connections N × number of parallel connections M.

[0027] The information for No. 10 to N+9 represents the voltage values ​​of each cell 21a within the series connection of cells 21a, measured at time No. 2.

[0028] The information for No. N+10 to N+M+9 represents the temperature measured at each location by the respective temperature sensor 21c at time No. 2.

[0029] Furthermore, if information No. 6 to 9 regarding the specifications of the battery pack 21 can be obtained from the battery basic data D2, etc., as described later, then that information may be omitted from the vehicle data D1.

[0030] [Battery Basic Data D2] Next, an example of battery basic data D2 pre-stored in the battery information storage unit 12 will be explained using Figure 4. This battery basic data D2 is data that registers the initial characteristics of the battery pack 21, and specifically is a table that records the open-circuit voltage, charging resistance, and discharge resistance for each SOC. Note that information No. 6 to 9 (information regarding the specifications of the battery pack 21) from the vehicle data D1 exemplified in Figure 3 may also be registered in this battery basic data D2.

[0031] Needless to say, if the specifications of the battery pack 21 are different, their initial characteristics will also be different. Therefore, in an environment where there are many different specifications for the battery pack 21, it is sufficient to prepare battery basic data D2 for each specification, and the battery information management system 1 can select and use the appropriate battery basic data D2 as needed. However, in this embodiment, since the battery pack 21 of each vehicle has the same specifications, it is assumed that only one type of battery basic data D2 is stored in the battery information storage unit 12 of this embodiment.

[0032] [Battery Degradation Diagnosis and Battery Usage Environment Extraction Process] Next, using the flowchart in Figure 5, an example of the battery degradation state diagnosis process and the battery usage environment extraction process performed by the battery diagnosis unit 13 will be explained.

[0033] First, in S11, the battery diagnostic unit 13 reads all of the vehicle data D1 from the electric vehicle 2 that is transmitted at predetermined intervals by the electric vehicle 2 to be diagnosed and stored in the receiving unit 11.

[0034] Next, in S12, the battery diagnostic unit 13 extracts vehicle data D1 with the "driving" vehicle status flag from the data group read in S11, and extracts the battery usage environment of the electric vehicle 2 to be diagnosed based on these. Here, for example, the average value of the measured temperature recorded in the "driving" vehicle data D1 is extracted as a component of the battery usage environment of the electric vehicle 2, or the ΔSOC, which is the difference between the SOC before and after driving recorded in the "driving" vehicle data D1, is extracted as a component of the battery usage environment. Note that information other than temperature and ΔSOC may also be extracted as components of the battery usage environment.

[0035] In S13, the battery diagnostic unit 13 extracts vehicle data D1 with a vehicle status flag of "charging" from the data set read in S11.

[0036] In S14, the battery diagnostic unit 13 compares and examines the charging data (time, current, voltage of each cell, temperature, etc.) for each cell 21a in the data set extracted in S13 with the basic battery data D2 to calculate the change from the initial state (degradation state) for each cell 21a. Here, a method can be used to calculate the battery capacity by optimizing the charging data, open-circuit voltage curve, and resistance curve to match, using fitting parameters such as the starting point of the SOC, capacity, and resistance multiplier.

[0037] In S15, the battery diagnostic unit 13 calculates the capacity degradation of the battery pack 21, taking into account the variation between each cell 21a within the battery pack 21, based on the capacity degradation and voltage data calculated for each cell 21a.

[0038] [Results of Battery Degradation Diagnosis and Battery Usage Environment Extraction] Figure 6A is a graph illustrating the results of battery degradation diagnosis for five vehicles obtained using the processing shown in Figure 5. The horizontal axis of this graph shows the elapsed time since the introduction of the battery pack 21, and the vertical axis shows the battery health status (SOH: State of health). Here, SOH is the value obtained by dividing the current battery capacity by the initial battery capacity and multiplying by 100. From Figure 6A, it can be seen that in all vehicles, the SOH of the battery pack 21 tends to decrease with the number of years elapsed since the introduction (year 0). This indicates that, if there are no changes in the usage environment or application of the secondary battery, the longer the secondary battery is used, the more gradually the battery capacity will decrease.

[0039] Figure 6B is a table illustrating the battery usage environments for five vehicles, obtained through the processing shown in Figure 5. This table contains information used by the battery degradation prediction unit 15 to identify other battery packs 21 that have been used in similar battery usage environments (similar temperature, similar ΔSOC, etc.) to the future usage environment of the battery pack 21 being predicted. In the example in Figure 6B, the battery usage environment is defined by a combination of temperature information and ΔSOC information. However, if a more precise definition of the battery usage environment is desired, other information can also be extracted in step S12 of Figure 5 and registered in the table in Figure 6B.

[0040] [Battery Degradation Prediction] Next, the flow of predicting the future battery degradation state performed by the battery degradation prediction unit 15 will be explained using the flowchart in Figure 7.

[0041] In S21, the battery degradation prediction unit 15 reads the vehicle ID to be predicted (hereinafter, let's assume ID 4 is entered), the battery usage environment, and the prediction conditions (prediction period, end SOH, etc.) that the user has entered into the system. The following explanation will cover the cases where the entered battery usage environment is the same as the current environment and where it is different.

[0042] [If there are no changes to the battery usage environment] First, let's explain using the example where the input information in S21 was: Battery usage environment [Continue current usage environment], Prediction conditions [Up to 6 years from now].

[0043] In S22, the battery degradation prediction unit 15 reads information corresponding to ID4 from the battery degradation history (FIG. 6A) and the battery usage environment (FIG. 6B) stored in the diagnosis result recording unit 14. As shown in the figure, the information regarding ID4 is based on the vehicle data D1 acquired during the elapsed years of 0 to 3 years.

[0044] In S23, the battery degradation prediction unit 15 extracts the battery degradation history and the battery usage environment of vehicles having a battery degradation history up to 6 years later, which is the input prediction period. In the example of FIG. 6A, since the vehicles in which 6 years or more have elapsed since the start of use of the battery pack 21 are the three vehicles of ID1, ID2, and ID3, the battery degradation history and the battery usage environment of these are extracted. On the other hand, the battery degradation history of ID5 in which 6 years have not elapsed since the start of use is not extracted.

[0045] In S24, the battery degradation prediction unit 15 extracts the battery degradation history of the vehicle to be predicted and the battery degradation history of the other vehicle that is closest. In the example of FIG. 6A, when comparing the battery degradation history of ID4, which is the vehicle to be predicted, with the battery degradation histories of ID1, ID2, and ID3, the battery degradation history closest to that of ID4 is the battery degradation history of ID1. Therefore, in this step, the battery degradation history of D1 is extracted. As a method for selecting the closest battery degradation history, a method such as calculating the root mean square error between the SOH value at each elapsed year of the vehicle ID to be predicted and the SOH value at the same elapsed year of other vehicles, and selecting the other vehicle with the minimum error can be used.

[0046] In S25, the battery degradation prediction unit 15 determines whether there is a change in the battery usage environment during the process. In this example, since there is no change, the process proceeds to S26.

[0047] In S26, the battery degradation prediction unit 15 predicts the future battery degradation of the vehicle of ID4 based on the battery degradation history of ID1 extracted in S24, and outputs the result.

[0048] An example of the output result when there is no change in the battery usage environment is shown in FIG. 8A. Here, the battery degradation history of ID4 is indicated by ◆, and the predicted value following the battery degradation history of vehicle ID1 is indicated by a dotted line. This predicted value is obtained by arranging the curve based on the battery degradation history of ID1 so as to overlap the battery degradation history of ID4.

[0049] [[When the battery usage environment is changed midway]] Next, we will explain using the example where the input information in S21 is: battery usage environment [continue the current usage environment until the 4th year, and from the 5th year change to a usage environment with a temperature of 44°C and a ΔSOC of 49], and prediction conditions [up to 8 years from now]. Note that only the processes that differ from the above will be explained below.

[0050] In S23, the battery degradation prediction unit 15 extracts the battery degradation history and battery usage environment of vehicles that have a battery degradation history up to the input prediction period of 8 years from now. In the example in Figure 6A, there are two vehicles, ID1 and ID2, that have been in use for more than 8 years since the start of use of the battery pack 21, so their battery degradation history and battery usage environment are extracted. On the other hand, the battery degradation history of ID3 and ID5, which have not been in use for 8 years, is not extracted.

[0051] In S25, the battery degradation prediction unit 15 determines whether there is a change in the usage environment. In this example, there is a change, so the process proceeds to S27.

[0052] In S27, the battery degradation prediction unit 15 extracts other vehicles with the battery usage environment closest to the battery usage environment of the vehicle being predicted after the change, and then extracts the battery degradation history of those vehicles. In this example, the battery usage environment for ID4 from the 5th year onward was input as [temperature 44, ΔSOC 49], so this is compared with the battery usage environments of ID1 and ID2 extracted in S23. Between the battery usage environment of ID2 [temperature 45, ΔSOC 50] and the battery usage environment of ID2 [temperature 20, ΔSOC 50], the latter is closer, so in this step, the battery usage environment for ID2 is extracted from Figure 6A.

[0053] In S28, the battery degradation prediction unit 15 predicts battery degradation before and after the change in the usage environment, and then combines the two to predict battery degradation for a specified period. In this example, for years 0 to 4, battery degradation is predicted based on the battery degradation history of ID 1, and for years 4 to 8, battery degradation is predicted based on the battery degradation history of ID 2. Then, by overlapping the end point of the former and the start point of the latter, the battery degradation history for years 0 to 8 is predicted.

[0054] An example of the output result of S28 is shown in Figure 8B. Here, the battery degradation history of the battery pack 21 of vehicle ID4, which is the vehicle to be predicted, is indicated by ◆, the predicted value following the battery degradation history of vehicle ID1 is shown by a dotted line, and the predicted value following the battery degradation history of vehicle ID2 is shown by a solid line.

[0055] One way to combine two types of predicted values ​​is as follows: For ID4, the predicted values ​​for years 0 to 4 use the battery degradation history of ID1 for the same period. Then, the number of years elapsed for ID2 that matches the SOH of ID1 at year 4, which is 80%, is calculated. In the example in Figure 6A, the SOH of ID4 becomes 80% after 2 years, so it can be seen that year 4 in the usage environment of ID1 corresponds to year 2 in the usage environment of ID2. Therefore, the battery degradation history of ID2 is used for the predicted battery degradation values ​​for the period when the SOH of ID4 falls below 80%. In this way, the battery degradation of ID4 from years 0 to 8 can be predicted by using the battery degradation history of ID1 for the same period for years 0 to 4, and the battery degradation history of ID2 from years 2 to 6 for years 4 to 8.

[0056] [System Application Methods] The battery information management system of this embodiment, as described above, stores information such as battery degradation history and battery usage environment, and can be used in various use cases. For example, for leasing companies that use a large number of vehicles, the battery degradation prediction results can be used to determine whether to re-lease vehicles after the initial lease and to select customers. Furthermore, by providing prediction results not only for the battery pack but also for each cell, it is possible to reduce maintenance costs by addressing degraded batteries with partial replacement rather than replacing the entire battery pack.

[0057] <Effects of this embodiment> According to this embodiment described above, even if there are changes in the usage environment or application of the secondary battery, the future battery degradation state of the secondary battery can be predicted with high accuracy. Furthermore, this system has the characteristic that its accuracy improves as the number of operational cases increases.

[0058] 1: Information management system 11: Receiving unit 12: Battery information storage unit 13: Battery diagnostic unit 14: Diagnostic result recording unit 15: Battery degradation prediction unit 2: Electric vehicle 21: Battery pack 21a: Cell 21b: Module 21c: Temperature sensor D1: Vehicle data D2: Basic battery data

Claims

1. A method for predicting the degradation state of a secondary battery, comprising: a receiving step of receiving operational data for each of a plurality of secondary batteries; a battery diagnostic step of calculating the battery degradation history and battery usage environment of each secondary battery based on the operational data; a diagnostic result recording step of storing the battery degradation history and battery usage environment of each secondary battery; a battery degradation prediction step of predicting the future battery degradation state of the secondary battery to be predicted, before and after a change in the usage environment or usage application, based on the battery degradation state and battery usage environment of each secondary battery; and an output step of outputting the prediction result.

2. A battery information management system for predicting the degradation state of secondary batteries, comprising: a receiving unit that receives operational data for each of a plurality of secondary batteries; a battery diagnostic unit that calculates the battery degradation history and battery usage environment of each secondary battery based on the operational data; a diagnostic result recording unit that stores the battery degradation history and battery usage environment of each secondary battery; a battery degradation prediction unit that predicts the future battery degradation state of a target secondary battery before and after a change in the usage environment or usage application based on the battery degradation state and battery usage environment of each secondary battery; and an output unit that outputs the prediction results.

3. A battery information management system according to claim 2, wherein the battery degradation prediction unit, when predicting future battery degradation of a secondary battery before a change in the usage environment or usage application, uses the battery degradation history of another secondary battery that is closest to the battery degradation history of the secondary battery to be predicted, which is stored in the diagnostic result recording unit.

4. A battery information management system according to claim 3, wherein the battery degradation prediction unit, when predicting future battery degradation after a change in the usage environment or usage application of the secondary battery to be predicted, utilizes the battery degradation history of other secondary batteries stored in the diagnostic result recording unit that are in the battery usage environment closest to the battery usage environment of the secondary battery to be predicted.

5. A battery information management system according to claim 2, characterized in that it outputs prediction results of battery degradation status at the battery pack, module, and cell level.

6. A battery information management system according to claim 2, characterized in that it outputs a prediction result of the battery degradation state when multiple battery packs are combined.

7. A battery information management system according to claim 2, wherein the battery information management system determines whether the prediction results are suitable for the second method of use.

8. A battery information management system according to claim 2, wherein the battery information management system uses prediction results to search for a battery suitable for a second method of use.

9. A battery information management system according to claim 2, characterized in that it outputs the time-series changes of the battery's Wh capacity, Ah capacity, resistance increase, and their percentages, as well as future prediction results thereof.

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