JP2014053173A - System for predicting battery deterioration - Google Patents

Abstract

PROBLEM TO BE SOLVED: To predict deterioration of a battery from information about charging by mutually making efficient use of battery information about a plurality of batteries.SOLUTION: In a system 10 for predicting battery deterioration, a prediction unit 13 predicts deterioration of a battery pack 100 by retrieving a plurality of battery information administered by a storage unit 15, extracting battery information including mutually corresponding charge information as a first data group, and mutually comparing charge times on the extracted first data group. Information about battery packs 100 having equal charge modes is collected in the battery information extracted as the first data group. In the plurality of battery packs 100 having equal charge modes, if no deterioration is caused in each individual battery pack 100, their charge times have a tendency to show equal charge times. Therefore, using the plurality of battery packs 100 having equal charge modes as targets, whether their charge times are equal or different is verified, and deterioration of the battery pack 100 can be thereby predicted effectively.

Description

  The present invention relates to a battery deterioration prediction system that predicts battery deterioration.

  In recent years, electric vehicles that use electricity as a power source and hybrid vehicles that run in combination with an engine and a motor have attracted attention, and batteries (batteries) that have high energy density and high output density are known. . As such a high output type battery, for example, an assembled battery configured by electrically connecting a plurality of single cells in series is used.

  For example, Patent Document 1 discloses a management server. When it is determined that the battery has reached the end of its life, the battery usage environment (equipped vehicle type, area of use, usage, travel history, etc.) and usage status information (battery charging and discharging characteristics, total travel distance, total And as a part of the lifetime information database. Since the battery has a different susceptibility to deterioration depending on the usage environment including its vehicle type, usage region, usage, and travel history, the state of the battery that has reached the end of its life depends on the previous usage environment. Therefore, the life information should be made more appropriate by associating the battery usage environment (vehicle type, area, usage, travel history, etc.) with life state information in a database and storing it as life information. Can do.

JP 2011-69693 A

  According to the technique disclosed in Patent Document 1, deterioration (life) of a battery is predicted using information about a single battery, and information regarding a plurality of batteries is mutually utilized to reduce battery deterioration. It is not disclosed until forecasting.

  The present invention has been made in view of such circumstances, and an object of the present invention is to predict battery deterioration from information related to charging of a battery by mutually utilizing battery information related to a plurality of batteries.

  In order to solve this problem, the present invention provides a battery deterioration prediction system including a prediction unit that predicts battery deterioration based on battery information acquired from a plurality of batteries. Here, the battery information includes charging information including battery charging time and other information related to battery charging. And a prediction part searches several battery information, extracts the battery information to which charge information respond | corresponds mutually as a 1st data group, By comparing charge time mutually about this extracted 1st data group, Predict battery degradation.

  According to the present invention, charging information can be compared with each other in battery information regarding a plurality of batteries. As a result, the battery information extracted as the first data group includes information related to the batteries having the same charging mode, so that the deterioration of the battery can be predicted through the difference in charging time. In this way, by utilizing battery information relating to a plurality of batteries, it is possible to predict battery deterioration from information relating to charging of the batteries.

Block diagram schematically showing the overall configuration of the battery deterioration prediction system Explanatory drawing showing the configuration of the assembled battery Explanatory drawing which shows the battery information list which a memory | storage part holds The flowchart which shows the process of the deterioration prediction of the assembled battery which concerns on 1st Embodiment. Explanatory drawing showing the first data group Explanatory drawing showing the second data group Explanatory drawing which shows battery information where deterioration is predicted Explanatory diagram plotting charging time against mileage The flowchart which shows the process of the deterioration prediction of the assembled battery which concerns on 2nd Embodiment. Correlation diagram showing voltage variation and correction factor Correlation diagram showing elapsed time and correction factor after capacity adjustment

(First embodiment)
FIG. 1 is a block diagram schematically showing an overall configuration of a battery deterioration prediction system 10 according to the present embodiment. The battery deterioration prediction system is a system that predicts battery deterioration. In the present embodiment, as an example of a battery to be predicted, an assembled battery that is a battery mounted as a power source in an electric vehicle EV such as a hybrid vehicle or an electric vehicle is illustrated. To do.

  First, prior to the description of the battery deterioration prediction system 10, the electric vehicle EV will be described. The electric vehicle EV includes an assembled battery 100 as a battery and a load 200.

  As shown in FIG. 2, the assembled battery 100 is mainly composed of a battery group in which a plurality of single cells C1 to CN are connected in series. As each single cell C1-CN, alkaline storage batteries, such as a nickel metal hydride battery, organic electrolyte batteries, such as a lithium ion battery, etc. can be used, In this embodiment, a lithium ion battery is illustrated and demonstrated. The number of the cells C1 to CN constituting the assembled battery 100 is not particularly limited, and can be appropriately set according to desired specifications. Each of the single cells C1 to CN need not be composed of a single battery element, and may be composed of a plurality of battery elements connected to each other.

  A capacity adjustment circuit 400 is connected to each of the single cells C1 to CN. The capacity adjustment circuit 400 is configured by connecting a series circuit of a resistor 401 and a switch 402 in parallel. By closing the switch 402 and performing capacity adjustment discharge of the cells C1 to CN, the capacity of the cells C1 to CN is set. Adjustments can be made. The purpose of the capacity adjustment is to make the voltages of the plurality of single cells C1 to CN uniform, for example. Opening and closing of the switch 402 corresponding to each of the unit cells C1 to CN is controlled by a battery controller 500 described later.

  The assembled battery 100 can be charged by being connected to an external commercial AC power source via an AC outlet or connected to an external dedicated charger, and can be arbitrarily selected depending on the form of the charging facility. A charge form can be selected. For example, the electric vehicle EV includes two charging systems, normal charging and quick charging, and can thereby charge the assembled battery 100 with specifications according to the user's request.

The battery controller 500 has a function of comprehensively managing the assembled battery 100. As the battery controller 500, a microcomputer mainly composed of a CPU, a ROM, a RAM, and an I / O interface can be used. The battery controller 500 receives a detection signal from the temperature sensor 102 for measuring the temperatures of the cells C1 to Cn, and a detection signal from the current sensor 300 for detecting the charge / discharge current of the assembled battery 100. Have been. The battery controller 500 also receives detection signals from voltage sensors (not shown) for measuring the voltages of the individual cells C1 to Cn.

  In the present embodiment, the battery controller 500 stores battery information in the storage unit 501. The battery information is information necessary for predicting battery deterioration by the battery deterioration prediction system 10 and includes use environment information and charging information.

  The usage environment information is information relating to the environment in which the assembled battery 100 is used, and includes “area”, “use”, “travel distance”, and “elapsed time after shipment”.

  “Area” is information indicating an area where the assembled battery 100 is used. For example, a corresponding area classification is set from a plurality of predefined area classifications such as a Kanto plain area and a Kanto mountainous area. ing. The information regarding the “area” can be set in advance in accordance with the region where the electric vehicle EV is used, and information such as a navigation system held by the electric vehicle EV can be stored in the battery controller 500. May be periodically acquired and updated from time to time according to the region classification corresponding to the acquired information.

  “Use” is information indicating the use of the electric vehicle EV on which the assembled battery 100 is mounted. For example, a classification such as whether the use is for personal use or business use for a corporation is set. Has been. The information regarding the “use” can be set in advance in accordance with the use of the electric vehicle EV.

  The “travel distance” is information indicating the travel distance of the electric vehicle EV on which the assembled battery 100 is mounted. For example, a corresponding distance section is set from a plurality of distance sections divided every 5000 km. The information related to “travel distance” can be periodically updated by the battery controller 500 by the battery controller 500 periodically acquiring information related to the travel distance from the controller responsible for controlling the electric vehicle EV, and updated as needed according to the distance classification corresponding to the acquired information.

  “Elapsed time after shipment” is information indicating an elapsed time since the electric vehicle EV on which the assembled battery 100 is mounted is shipped, and corresponds to, for example, a plurality of time segments divided every year. Time division is set. This “elapsed time after shipment” can be updated by the battery controller 500 at any time corresponding to the elapsed time after shipment.

  The charging information is information relating to charging of the assembled battery 100, and includes “charging time”, “charged electric energy”, “charging start voltage”, and “charging start battery temperature”. These pieces of information are specified by the battery controller 500 when the assembled battery 100 is charged by, for example, rapid charging, and updated to the latest state by the battery controller 500 as needed.

  “Charging time” is information indicating the time required for charging the assembled battery 100, and for example, a corresponding time section is set from a plurality of time sections divided every five minutes.

  “Charged electricity amount” is information indicating the amount of electricity charged in the assembled battery 100. For example, a corresponding electricity amount category is set from among a plurality of electricity amount segments divided every 5 AH.

  The “charging start voltage” is information indicating the average voltage of each of the cells C <b> 1 to CN at the start of charging of the assembled battery 100.

  “Battery temperature at the start of charging” is information indicating the temperatures of the cells C <b> 1 to CN at the start of charging of the assembled battery 100.

  In addition, the assembled battery 100 is provided with a communication device 600 that is a communication interface for performing data communication with the battery deterioration prediction system 10. The battery controller 500 of the assembled battery 100 transmits battery information to the battery deterioration prediction system 10 via the communication device 600, or acquires information transmitted from the battery deterioration prediction system 10 via the communication device 600. be able to. As a method of bidirectional data communication, for example, a wireless LAN (Local Area Network) generally used for data communication can be used, but in addition to this, a method performed via a communication network such as a mobile phone It may be. The communication device 600 may be a dedicated wireless communication device applied to the assembled battery 100, or a communication terminal that uses a wireless paper passing device provided in the electric vehicle EV or has a data communication function such as a mobile phone. May be connected and used.

  The load 200 is used by being electrically connected to the assembled battery 100. For example, a motor and an inverter mounted on the electric vehicle EV correspond to this. In the regenerative control, the battery pack 100 can be charged by being reversely converted into electric energy via a motor and an inverter.

Referring to FIG. 1 again, the battery deterioration prediction system 10 is a computer including a CPU, a memory such as a ROM and a RAM, an HDD (Hard Disk Drive) as an auxiliary storage device, and these elements are connected via a bus. Are connected to each other. Further, the battery deterioration prediction system 10 includes a communication interface for performing data communication with the communication device 600 included in the assembled battery 100.

  The battery deterioration prediction system 10 includes an information acquisition unit 11, an information correction unit 12, a prediction unit 13, a notification unit 14, and a storage unit 15 when this is functionally grasped.

  The information acquisition unit 11 acquires battery information from the assembled battery 100 corresponding to the communication device 600 by communicating with the communication device 600. The information correction unit 12 corrects the battery information as necessary. The prediction unit 13 predicts deterioration of the assembled battery 100 based on a plurality of pieces of battery information managed in the storage unit 15. The notification unit 14 notifies the determination result by the prediction unit 13 to the communication device 600 corresponding to the assembled battery 100.

  FIG. 3 is an explanatory diagram showing a battery information list held by the storage unit 15. The storage unit 15 stores the battery information acquired by the information acquisition unit 11 for each assembled battery 100, thereby forming a battery information list. The battery information is stored in the storage unit 15 in association with the battery ID for identifying the assembled battery 100. For example, when the communication apparatus 600 transmits battery information, the battery ID can be transmitted in association with the battery information. Thus, the memory | storage part 15 bears the function as an information management part which manages the battery information which the information acquisition part 11 acquired from the some assembled battery 100, respectively.

  In addition, the storage unit 15 holds an address list for performing communication with the communication device 600 corresponding to the assembled battery 100 in order to perform communication with the assembled battery 100 to be predicted for deterioration. doing. This address list is a list in which the battery ID is associated with the address information of the communication device 600 for each assembled battery 100. The notification unit 14 performs communication between the assembled battery 100 having a predetermined battery ID and the corresponding communication device 600 based on the address information described in the address list, so that the group mounted on each electric vehicle EV. It is possible to request the battery 100 to transmit battery information, or to transmit predetermined information to the assembled battery 100 mounted on a specific electric vehicle EV.

FIG. 4 is a flowchart showing a process of predicting deterioration of the assembled battery 100 according to the first embodiment. First, in step 1 (S1), the information acquisition unit 11 acquires battery information regarding each assembled battery 100. Specifically, the information acquisition unit 11 first communicates with the communication device 600 of each assembled battery 100 based on the address list, and requests transmission of battery information. In response to this transmission request, when the battery controller 500 reads battery information from its own storage unit 501, the battery controller 500 transmits the battery information to the battery deterioration prediction system 10 through the communication device 600. Thereby, the information acquisition part 11 acquires the battery information regarding each assembled battery 100. FIG. As described above, the battery information includes usage environment information (area, usage, mileage, and elapsed time after shipment) and charging information (charging time, amount of charge, charge start voltage, and charge start battery temperature). It is information to include. As shown in FIG. 3, the battery information regarding each assembled battery 100 is recorded in the storage unit 15 in association with the battery ID, thereby being managed as a battery information list.

  In step 2 (S2), the prediction unit 13 extracts charging information from the battery information list.

  In step 3 (S3), the predicting unit 13 has all other information (charged electricity amount, charging start voltage and charging start battery temperature) other than the charging time in a plurality of charging information with different battery IDs. Judge whether or not. Specifically, the prediction unit 13 determines that all of the other information (the amount of charge, the voltage at the start of charging, and the battery temperature at the start of charging) match all other information in the plurality of charging information. Judge that it is complete. In the example illustrated in FIG. 3, in a plurality of pieces of charging information corresponding to “Battery ID” “1”, “3”, “100”, “500”, and “1000”, “Charge Electricity” is “35 AH”, “Charge start voltage” is “3.8 V” and “Charge start battery temperature” is “20 degrees”. In this case, the prediction unit 13 has the charging information corresponding to each other in the plurality of assembled batteries 100 in which the “battery ID” corresponds to “1”, “3”, “100”, “500”, and “1000”. Judge. A plurality of pieces of battery information corresponding to the charging information are extracted as a first data group (see FIG. 5).

  If an affirmative determination is made in step 3, that is, if all other information except the charging time is available, the process proceeds to step 6 (S6) described later. On the other hand, if a negative determination is made in step 3, that is, if all other information except the charging time is not complete, the process proceeds to step 4 (S4). In step 3, it is not necessary to request that the other information except for the charging time be completely the same, and even if the information is different within an allowable range, the information is collected. It may be determined that

  In step 4, the prediction unit 13 determines whether or not at least one piece of information other than the charging time is provided among a plurality of pieces of charging information with different battery IDs. If all of the other information except the charging time is not complete, a negative determination is made in step 4, and the process returns to step 2. On the other hand, if at least one piece of information other than the charging time is available, an affirmative determination is made in step 4, and the process proceeds to step 5 (S5).

  When the plurality of pieces of battery information including at least one information among other information are extracted as the first data group by the prediction unit 13, the information correction unit 12 includes a plurality of pieces of information corresponding to the first data group. A first correction process is performed on the battery information. The first correction process is a process of correcting the charging time in the first data group based on the temperature characteristics and charge / discharge characteristics of the assembled battery 100 so that the misaligned information among other information is aligned.

Here, specific contents of the first correction process will be described by taking, as an example, a case where only the battery temperature at the start of charging is uneven among other information other than the charging time. For example, only the “battery temperature at the start of charging” is different between the charging information whose “battery ID” is “1” and the charging information whose “battery ID” is “10000”. In this case, the information correction unit 12 first sets battery information as a reference. For example, the reference battery information is battery information with a “battery ID” of “1”. Next, the information correction unit 12 sets the “battery temperature at the start of charging” for the remaining battery information, that is, the battery information with the “battery ID” being “10000”, as in the case where the “battery ID” is “1”. In the case of “20 degrees”, how much the charging time changes is estimated based on the temperature characteristics and charge / discharge characteristics of the assembled battery 100. And the information correction | amendment part 12 sets the estimated value to the charging time after correction | amendment. By this correction processing, even if there is a difference in some of the other information except for the charging time, it can be handled as data under substantially the same conditions.

  In step 6, the predicting unit 13 refers to the battery information corresponding to the first data group, extracts the pieces of usage environment information that are aligned with each other, and compares the charging times with respect to the plurality of extracted battery information. . Specifically, as illustrated in FIGS. 5 and 6, the prediction unit 13 extracts battery information in which usage environment information is aligned with each other from a plurality of pieces of battery information corresponding to the first data group. In the example shown in FIG. 5, in the plurality of charging information corresponding to the first data group, the usage environment information corresponding to “1”, “100”, and “1000” in “battery ID” is “area” is “ “Kanto Plains”, “Usage” is “Individual”, “Mileage” is “25000 km to 30000 km”, and “Elapsed time after shipment” is “2 years to 3 years”. The prediction unit 13 determines that the usage environment information is available in the plurality of assembled batteries 100 in which the “battery ID” corresponds to “1”, “100”, and “1000”. A plurality of pieces of battery information having the same usage environment information are extracted as the second data group. In extracting the second data group, it is not necessary to request that the usage environment information match, and even if the usage environment information is different within an allowable range, the state of the information is complete. You may judge.

  In step 7 (S7), the prediction unit 13 compares the plurality of pieces of battery information extracted as the second data group with each other, and whether there is battery information that is determined to have a charging time longer than the other charging times. Judge whether or not. For example, the average value of the charging time in the second data group is calculated and determined based on the requirement that the deviation from the average value is a predetermined value or more, or based on the standard deviation. Statistical methods can be used. If an affirmative determination is made in step 7, that is, if there is battery information whose charging time is longer than other charging times, the process proceeds to step 8 (S8). On the other hand, if a negative determination is made in step 7, that is, if there is no battery information whose charging time is longer than the other charging times, the processing returns to step 1 (S1).

  In step 8, the prediction unit 13 predicts the deterioration of the assembled battery 100 corresponding to the battery information for the battery information determined to have a longer charging time than the other battery information. In the example illustrated in FIG. 6, the battery information in which “battery ID” corresponds to “1” has a charging time other charging time, specifically, “battery ID” is “1”, “100”, “ It is longer than the charging time of the battery information corresponding to “100”. In this case, the prediction unit 13 predicts deterioration of the assembled battery 100 for the battery information in which the “battery ID” is “1” (see FIG. 7).

  Here, the reason for predicting the deterioration of the assembled battery 100 with a long charging time is as follows. Charging of the assembled battery 100 of the electric vehicle EV according to the present embodiment assumes a constant current and constant voltage method. In constant current charging, a large amount of charging can be performed in a short time with a relatively large current, but in constant voltage charging, the amount of current is gradually reduced. ). In this case, as the assembled battery deteriorates, the internal resistance increases, so that the charging time with a minute current after shifting to constant voltage charging becomes extremely long. Therefore, it can be determined that as the charging time is longer, the internal resistance increases, that is, the battery is deteriorated. Here, FIG. 8 is an explanatory diagram plotting the charging time against the travel distance. The battery information with “1” as the “battery ID” shown in FIG. 7 indicates that the charging time is about 36 minutes in the vicinity of about 29000 km in FIG.

  In step 9 (S9), the notification unit 14 searches the address list based on the “battery ID” associated with the battery information determined that the assembled battery 100 is deteriorated. Then, the notification unit 14 confirms that the deterioration of the assembled battery 100 corresponding to the “battery ID” is predicted via the communication device 600 based on the address information corresponding to the “battery ID”. Notice. In addition, if the battery pack 100 corresponding to the specific use environment information or the charge information is predicted to be deteriorated frequently, the notification unit 14 uses the prediction result and the use environment in which the deterioration is predicted. It is preferable to notify the charging mode.

  Upon receiving a notification from the battery deterioration prediction system 10 via the communication device 600, the battery controller 500 of the assembled battery 100 receives the deterioration prediction and notifies the display of the electric vehicle EV of that fact. In addition, when the current use environment is a specific environment where deterioration of the assembled battery 1000 is predicted, the fact is notified on a display or the like.

  As described above, in the present embodiment, in the battery deterioration prediction system 10, the prediction unit 13 searches for a plurality of pieces of battery information managed by the storage unit 15, and extracts battery information corresponding to each other as charging information as a first data group. Then, the deterioration of the assembled battery 100 is predicted by comparing the charging times of the extracted first data group with each other.

  According to such a configuration, the charging information can be compared with each other in the battery information regarding the plurality of assembled batteries 100. Thereby, the battery information extracted as the first data group includes information on the assembled battery 100 having the same charging mode. In the plurality of assembled batteries 100 having the same charging mode, when the individual assembled batteries 100 are not deteriorated, their charging times tend to show the same time. Therefore, the deterioration of the assembled battery 100 can be effectively predicted by verifying the difference in the charging time for a plurality of assembled batteries 100 having the same charging mode. That is, by utilizing the battery information regarding the plurality of assembled batteries 100, it is possible to predict the deterioration of the battery based on the charging time of the assembled battery 100.

  Further, in the present embodiment, the prediction unit 13 extracts, from the battery information corresponding to the first data group, battery information in which the usage environment information is aligned with each other as the second data group. The deterioration of the battery is predicted by comparing the charging times for the two data groups.

  As described above, the battery information extracted as the second data group includes information on the assembled battery 100 having the same charging mode and equivalent usage environment. In the plurality of assembled batteries 100 in the same usage environment and in the same usage environment, when the individual assembled batteries 100 are not deteriorated, the charging time tends to show the same time. Therefore, the deterioration of the assembled battery 100 can be effectively predicted by verifying the difference in the charging time for a plurality of assembled batteries 100 that have the same charging mode and are in the same usage environment.

  In the present embodiment, the prediction unit 13 determines the deterioration of the assembled battery 100 when it is determined that the charging time is longer than the other charging times.

  As the deterioration of the assembled battery 100 proceeds, the internal resistance increases, and the charging time tends to be prolonged even in the same charging mode and usage environment. Therefore, it is possible to effectively predict the deterioration of the assembled battery 100 by determining that the charging time is longer than other charging times.

Further, in the present embodiment, the prediction unit 13 uses the first battery information including at least one other information (amount of charge electricity, a voltage at the start of charging, and a battery temperature at the start of charging) other than the charging time as the first information. It may be extracted as a data group. Here, the information correction part 12 is based on the temperature characteristic of the assembled battery 100, or the charging / discharging characteristic of the assembled battery 100, when all the information other than charging time is not prepared in the first data group. The charging time in the first data group is corrected so that the irregular information is aligned with each other.

  According to such a configuration, since all charging information may not be prepared, even when some information is different, the information is extracted as the first data group on the premise of correction processing. Thereby, since the parameter of the battery information extracted as the first data group can be secured, the prediction accuracy can be secured. Further, by performing correction processing, even when some information is different, it can be handled as charging information under substantially the same conditions, so that it is possible to suppress a decrease in prediction accuracy.

  Moreover, the notification part 14 notifies a prediction result with respect to the assembled battery 100 which acquired the said battery information based on the battery information by which the prediction part 13 estimated degradation of the assembled battery 100. FIG.

  Thereby, the user of the electric vehicle EV equipped with the assembled battery 100 can be notified that the deterioration of the assembled battery 100 is predicted. Therefore, it is possible to prompt attention to those that may cause an abnormality in the future.

  In addition, the notification unit 14 can notify the usage environment and the charging mode in which deterioration of the assembled battery 100 is predicted together with the prediction result.

  According to such a configuration, since battery information of the plurality of assembled batteries 100 is statistically processed, it is possible to associate use environments and charging modes of the assembled batteries 100 that are predicted to deteriorate. As a result, it is possible to advise usage that is unlikely to deteriorate with respect to the usage of the user. Furthermore, if it is found that resistance degradation is likely to occur with respect to the usage environment or charging mode of a certain assembled battery 100, the system can also take measures such as increasing the detection sensitivity of degradation by lowering the threshold value.

(Second Embodiment)
FIG. 9 is a flowchart showing a process of predicting deterioration of the assembled battery 100 according to the first embodiment. The deterioration prediction process according to the second embodiment is different from that of the first embodiment in that the second correction process is further executed. Note that the description of the configuration common to the first embodiment will be omitted, and the following description will focus on the differences.

  In the first embodiment, following the affirmative determination in step 3 or following the process in step 5, the process proceeds to step 6. In the present embodiment, before the process proceeds to step 6, step 10 is performed. The processing of (S10) and step 11 (S11) is to be executed.

  Specifically, in step 10, the prediction unit 13 specifies a factor that affects the charging time. The charging time is compared in the process of step 6. When comparing the charging time, the charging time is not determined by comparing the numbers as they are, but the factors affecting the charging time are specified and taken into consideration. Are preferably compared. For example, even if the deterioration state of the assembled battery 100 is the same, the voltage variation that is the difference between the maximum voltage and the minimum voltage of the assembled battery 100, or the elapsed time from when the capacity adjustment is completed by the capacity adjustment circuit 400 Depending on the difference, the charging time also differs. Therefore, it is necessary to correct the charging time in consideration of factors such as voltage variation and elapsed time after capacity adjustment.

Therefore, in step 10, the prediction unit 13 specifies the voltage variation and the elapsed time after capacity adjustment for each assembled battery 100 corresponding to the battery information extracted as the first data group. The voltage variation and the elapsed time after capacity adjustment for each assembled battery 100 may be requested at the same time when obtaining battery information (step 1), or the address list from the “battery ID” corresponding to the first data group. , And based on the searched address information, the battery pack 100 corresponding to the “battery ID” may be inquired about the voltage variation and the elapsed time after the capacity adjustment.

  In step 11, the prediction unit 13 performs a second correction process for correcting the charging time for each piece of battery information extracted as the first data group based on the voltage variation and the elapsed time after capacity adjustment.

  Specifically, the prediction unit 13 holds a map or an arithmetic expression that defines a correction coefficient corresponding to the voltage variation, and specifies a correction coefficient corresponding to the voltage variation. Here, as shown in FIG. 10, the correction coefficient is set to a value less than 1, and the correction coefficient tends to be smaller as the voltage variation is larger. Then, the prediction unit 13 calculates the corrected charging time by multiplying the charging time corresponding to the battery information by the correction coefficient. The charging time in the battery information is updated with the corrected charging time. That is, with this correction process, the assembled battery 100 having a large voltage variation is corrected so as to reduce the charging time.

  The prediction unit 13 holds a map or an arithmetic expression that defines a correction coefficient corresponding to the elapsed time after capacity adjustment, and specifies a correction coefficient according to the elapsed time after capacity adjustment. Here, as shown in FIG. 11, the correction coefficient is set to a value less than 1, and the correction coefficient tends to be smaller as the elapsed time after capacity adjustment is larger. Then, the prediction unit 13 calculates the corrected charging time by multiplying the charging time corresponding to the battery information by the correction coefficient. The charging time in the battery information is updated with the corrected charging time. That is, with this correction process, the assembled battery 100 having a long elapsed time after capacity adjustment is corrected so as to reduce the charging time.

  Thus, in this embodiment, the information correction | amendment part 12 equalizes the voltage dispersion | variation which is the difference of the maximum voltage of each cell C1-CN of the assembled battery 100, and the minimum voltage, or the voltage of several cell C1-CN. The charging time in the first data group is corrected based on the elapsed time from the time when the capacity adjustment for performing the adjustment is completed. Thereby, the difference in charging time can be predicted with higher accuracy, and the accuracy of deterioration prediction can be improved.

  Specifically, the information correction unit 12 can perform correction so that the charging time becomes shorter as the voltage variation is larger. Or the information correction | amendment part 12 can correct | amend so that charge time may become so small that the elapsed time from the time after capacity adjustment is completed is large. Thereby, the difference in charging time can be predicted with higher accuracy by appropriately correcting the charging time.

  Although the battery deterioration prediction system according to the embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and various modifications can be made within the scope of the invention. Absent. Further, in the above-described embodiment, the description has been made with respect to the assembled battery, but the battery may be configured by only a single battery.

DESCRIPTION OF SYMBOLS 10 Battery deterioration prediction system 11 Information acquisition part 12 Information correction part 13 Prediction part 14 Notification part 15 Storage part 100 Battery assembly 102 Temperature sensor 200 Load 300 Current sensor 400 Capacity adjustment circuit 401 Resistance 402 Switch 500 Battery controller 501 Storage part 600 Communication apparatus

Claims (10)

An information acquisition unit that acquires battery information necessary for predicting deterioration of the battery from the battery;
An information management unit for managing battery information acquired from a plurality of batteries by the information acquisition unit;
A prediction unit that predicts deterioration of the battery based on a plurality of pieces of battery information managed by the information management unit,
The battery information includes charging information including battery charging time and other information related to battery charging,
The prediction unit searches for a plurality of pieces of battery information managed by the information management unit, extracts the battery information corresponding to the charging information as a first data group, and the extracted first data group A battery deterioration prediction system that predicts battery deterioration by comparing the charging times with each other. The battery information includes use environment information that is information related to an environment in which the battery is used, in addition to the charge information,
The prediction unit extracts, as a second data group, battery information in which the use environment information is aligned from the battery information corresponding to the first data group, and the extracted second data group The battery deterioration prediction system according to claim 1, wherein the battery deterioration is predicted by comparing the charging times with each other.   The battery deterioration prediction system according to claim 1, wherein the prediction unit determines battery deterioration when it is determined that the charging time is longer than other charging times. The charging information includes, as other information related to the charging of the battery, the temperature of the battery at the start of charging, the voltage of the battery at the start of charging, and the amount of electricity charged in the battery,
The said prediction part extracts the said battery information in which at least 1 or more is gathered in the other information regarding charge of the said battery as said 1st data group, The one in any one of Claim 1 to 3 characterized by the above-mentioned. The battery degradation prediction system described.   In the first data group, when not all of the other information related to the charging of the battery is prepared, the non-uniform information is aligned with each other based on the temperature characteristics of the battery or the charge / discharge characteristics of the battery. The battery deterioration prediction system according to claim 4, further comprising an information correction unit that corrects a charging time in the first data group. The battery is an assembled battery composed of a plurality of single cells,
Based on the voltage variation that is the difference between the maximum voltage and the minimum voltage of each unit cell of the assembled battery, or the elapsed time from when the capacity adjustment to make the voltage of the plurality of unit cells uniform is completed, the first. 5. The battery deterioration prediction system according to claim 1, further comprising an information correction unit that corrects a charging time in one data group.   The battery deterioration prediction system according to claim 6, wherein the information correction unit performs correction so that the charging time decreases as the voltage variation increases.   The battery deterioration prediction system according to claim 6, wherein the information correction unit performs correction so that the charging time becomes shorter as the elapsed time from the time when the capacity adjustment is completed is longer.   9. The notification unit according to claim 1, further comprising: a notification unit that notifies a prediction result to the battery that has acquired the battery information based on the battery information in which deterioration of the battery is predicted by the prediction unit. The battery deterioration prediction system described in the above.   10. The battery deterioration prediction system according to claim 9, wherein the notifying unit notifies together with the prediction result, a usage environment in which deterioration of the battery is predicted and a charging mode in which deterioration is predicted.

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