Information processing device and information processing method

Abstract

To provide an information processing device and an information processing method capable of commonly estimating a deterioration in power storage elements for a diversified vehicle system.SOLUTION: An information processing device includes a storage part for storing history data related to a deterioration in each of a plurality of power storage elements, an acquisition part for acquiring history data related to a deterioration in a power storage element mounted on a vehicle, a similarity calculation part for calculating similarity between the acquired history data and the stored history data, an estimation part for estimating a deterioration index on the basis of the stored history data similar to the acquired history data, and a transmission part for transmitting the estimated deterioration index to the vehicle.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing device and an information processing method. [Background technology]

[0002] Traditionally, lead-acid batteries have been used as a power source for vehicles, but in recent years, vehicle powertrains have diversified to include not only gasoline vehicles but also HEVs (Hybrid Electric Vehicles), PHEVs (Plug-in Hybrid Electric Vehicles), and EVs (Electric Vehicles). For example, lead-acid batteries that were previously used as a power source for vehicles are sometimes being replaced with lithium-ion batteries.

[0003] A storage battery mounted on a vehicle is repeatedly charged and discharged as the vehicle travels, and it is known that repeated charging and discharging causes deterioration (for example, capacity deterioration) of the storage battery. Various methods are conceivable for estimating capacity deterioration, such as an internal resistance estimation method and an actual capacity estimation method. Patent Document 1 discloses a method for estimating the degree of deterioration of a storage battery by calculating the internal resistance of the vehicle storage battery. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-142561 Summary of the Invention [Problem to be solved by the invention]

[0005] Although it is possible to use individual degradation estimation methods suited to individual vehicles, a common method for estimating degradation of storage elements that is compatible with a variety of powertrains has not yet been established.

[0006] An object of the present invention is to provide an information processing device and an information processing method that can estimate deterioration of a storage element in common for a variety of vehicle systems. [Means for solving the problem]

[0007] An information processing device according to one aspect of the present invention includes a memory unit that stores historical data related to the deterioration of each of a plurality of storage elements, an acquisition unit that acquires historical data related to the deterioration of the storage elements mounted on a vehicle, a similarity calculation unit that calculates the similarity between the acquired historical data and stored historical data, an estimation unit that estimates a deterioration index based on the stored historical data that is similar to the acquired historical data, and a transmission unit that transmits the estimated deterioration index to the vehicle. [Effects of the Invention]

[0008] According to the present invention, deterioration of an electric storage element can be estimated commonly for a variety of vehicle systems. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating a configuration of an information processing system. [Figure 2] FIG. 2 is a diagram illustrating a configuration of a server. [Figure 3] FIG. 10 is a diagram showing the configuration of a storage element DB. [Figure 4] FIG. 10 is a diagram illustrating an example of time-series data of temperature. [Figure 5] FIG. 10 is a diagram illustrating an example of a temperature histogram. [Figure 6] FIG. 10 is a diagram illustrating an example of calculation of similarity using temperature history. [Figure 7] FIG. 10 is a diagram illustrating an example of time-series data of SOC. [Figure 8] 10A and 10B are diagrams illustrating examples of time-series data of SOC and charge / discharge current. [Figure 9] FIG. 10 is a diagram illustrating an example of weight calculation. [Figure 10] FIG. 10 is a diagram illustrating an example of the configuration of a learning model. [Figure 11]FIG. 10 is a diagram illustrating an example of the configuration of a learning model. [Figure 12] FIG. 10 is a diagram illustrating an example of a deterioration index estimated by an estimation unit. [Figure 13] FIG. 10 is a flowchart showing a processing procedure of the server. DETAILED DESCRIPTION OF THE INVENTION

[0010] (1) The information processing device includes a memory unit that stores historical data related to the deterioration of each of a plurality of storage elements, an acquisition unit that acquires historical data related to the deterioration of the storage elements mounted on the vehicle, a similarity calculation unit that calculates the similarity between the acquired historical data and stored historical data, an estimation unit that estimates a deterioration index based on the stored historical data that is similar to the acquired historical data, and a transmission unit that transmits the estimated deterioration index to the vehicle.

[0011] (12) An information processing method includes storing historical data related to the deterioration of each of a plurality of storage elements in a memory unit, acquiring historical data related to the deterioration of storage elements mounted on a vehicle, calculating the similarity between the acquired historical data and the stored historical data, estimating a deterioration index based on the stored historical data that is similar to the acquired historical data, and transmitting the estimated deterioration index to the vehicle.

[0012] The storage unit stores history data related to the deterioration of each of the multiple storage elements. The history data may be data related to the deterioration of the storage elements, and may include, for example, temperature history, charge / discharge history, and SOC history. The history data may be time-series data of temperature, charge / discharge history, SOC, etc., or may be statistical data calculated based on the time-series data. For example, with regard to temperature, the history data may be time-series data of temperature, or may be statistical data dividing the temperature into categories and showing the usage time for each category. The same applies to charge / discharge and SOC. History data of the storage elements of multiple vehicles is collected from the vehicles, and the storage unit stores the collected history data in association with each vehicle (or BMS (Battery management system)). History data collected from the same vehicle at different times may also be stored. The storage unit makes it possible to construct big data related to deterioration.

[0013] The acquisition unit acquires historical data related to deterioration of a power storage element mounted on the vehicle. The power storage element mounted on the vehicle is a power storage element for which a deterioration index is to be estimated. The vehicle may use any powertrain (vehicle system).

[0014] The similarity calculation unit calculates the similarity between the acquired history data and the stored history data. When calculating the similarity, the history data to be compared may include at least one of temperature history, charge / discharge history, and SOC history. If the temperature history, charge / discharge history, or SOC history can be compared, the similarity can be calculated with high accuracy. For example, the history data stored in the storage unit is searched for patterns similar to the temperature transition pattern, charge / discharge pattern (charge period, charge cycle, discharge period, discharge cycle, rest period, etc.), or SOC transition pattern during use of the energy storage element for which a degradation index is to be estimated.

[0015] The estimation unit estimates a deterioration index based on stored history data similar to the acquired history data. For example, the estimation unit can estimate a decrease in SOH (deterioration index) from time t1 to time tn based on the transition of SOC and the transition of temperature from time t1 to time tn. The transmission unit transmits the deterioration index estimated by the estimation unit to the vehicle.

[0016] With the above-described configuration, even in situations where it is not possible to accurately estimate the deterioration index of a storage element installed in a vehicle, a deterioration index estimated based on historical data similar to historical data reflecting the usage state of the storage element can be provided to the vehicle, and deterioration of the storage element can be estimated in common for a variety of vehicle systems.

[0017] (2) The acquisition unit may acquire the reliability of a deterioration index of a storage element installed in a vehicle, and the transmission unit may transmit the estimated deterioration index to the vehicle if the reliability of the deterioration index estimated by the estimation unit is higher than the reliability acquired by the acquisition unit.

[0018] The reliability of the degradation index depends on the compatibility between the vehicle's powertrain and the degradation estimation method for the vehicle's power storage element. For example, the internal resistance estimation method is considered to be accurate when the current during cranking is large, but is considered to be inaccurate for power storage elements of vehicles without engines or auxiliary batteries. If the reliability of the degradation index estimated by the estimation unit is higher than the reliability of the degradation index of the power storage element installed in the vehicle, a more accurate degradation index can be provided to the vehicle by transmitting the estimated degradation index to the vehicle.

[0019] (3) The memory unit may include a weighting calculation unit that stores the reliability of the deterioration index of each of the plurality of storage elements and calculates a weighting based on the calculated similarity and the stored reliability, and a selection unit that selects a storage element from the plurality of storage elements based on the calculated weighting, and the estimation unit may estimate the deterioration index based on historical data of the selected storage element.

[0020] For example, if the similarity is S and the reliability is R, the weighting W is calculated as W = S × R. The selection unit selects a storage element from among the plurality of storage elements based on the calculated weighting. For example, it may select a storage element whose weighting W is equal to or greater than a predetermined threshold. This makes it possible to select a storage element from among the plurality of storage elements stored in the storage unit, taking into consideration both the similarity of the history data and the reliability of the deterioration index. There may be multiple storage elements to select.

[0021] The estimation unit estimates the deterioration index based on the history data of the selected energy storage element, and therefore can estimate the deterioration index taking into consideration both the similarity of the history data and the reliability of the deterioration index.

[0022] (4) The similarity calculation unit may calculate the similarity between the acquired history data and the history data of a storage element having an operating voltage in the same range as that of the storage element installed in the vehicle, among the multiple storage elements stored in the memory unit.

[0023] The same range of operating voltages may be divided into, for example, low voltages such as 12 V and high voltages such as several hundred V. By limiting the operating voltages to the same range, it is possible to prevent the calculation of similarities between energy storage elements with different characteristics or deterioration progress depending on the operating voltage, thereby improving the estimation accuracy of the deterioration index.

[0024] (5) The similarity calculation unit may calculate the similarity between the acquired history data and the history data of a storage element, among the multiple storage elements stored in the memory unit, that has the same active material as all or part of the active material of a storage element installed in a vehicle.

[0025] By limiting the calculation to energy storage elements that have all or part of the same active material, it is possible to prevent the calculation of similarity between energy storage elements that have different energy storage element characteristics or different degrees of deterioration depending on the active material, thereby improving the estimation accuracy of the deterioration index.

[0026] (6) The similarity calculation unit may calculate the similarity between the acquired history data and the history data of a storage element from the same manufacturer as the storage element installed in the vehicle, among the multiple storage elements stored in the memory unit.

[0027] By limiting the calculation to energy storage devices manufactured by the same manufacturer, it is possible to prevent the calculation of similarity between energy storage devices with different characteristics or deterioration progress depending on the manufacturer, thereby improving the estimation accuracy of the deterioration index.

[0028] (7) The memory unit may store location data related to the area of use of each of the multiple storage elements, the acquisition unit may acquire location data related to the area of use of the vehicle, and the similarity calculation unit may calculate the similarity between the acquired history data and history data of storage elements, among the multiple storage elements stored in the memory unit, that have the same area of use as all or part of the area of use of the storage elements installed in the vehicle.

[0029] The use area may be, for example, one metropolitan area, one prefecture, two metropolitan areas, and 43 prefectures, or may be a division such as the Kanto region or the Kinki region, or may be a division such as urban areas, suburban areas, and mountainous areas. By limiting the use area to energy storage elements of vehicles used in the same use area in whole or in part, it is possible to prevent the calculation of similarity between energy storage elements with different energy storage element characteristics or progress of deterioration depending on the difference in use area. This makes it possible to improve the estimation accuracy of the deterioration index.

[0030] (8) The history data may include a usage time for each temperature range of the power storage element.

[0031] The usage time for each temperature range of the storage element is, for example, the time (frequency) for which the storage element is used within each temperature range when the temperature is divided into predetermined ranges (e.g., 5°C, 10°C, etc.). Depending on the usage state of the vehicle, the temperature of the storage element may differ, and it is thought that the impact on the deterioration of the storage element will also differ. By selecting storage elements with similar usage temperature patterns, the estimation accuracy of the deterioration index can be improved.

[0032] (9) The history data may include a usage time for each SOC category of the energy storage element.

[0033] The usage time for each SOC range of the storage element is, for example, the time (frequency) for which the storage element is used within each SOC range when the SOC is divided into predetermined ranges (e.g., 10%). The transition of the SOC of the storage element may differ depending on the vehicle usage state and the type of vehicle (e.g., HEV, EV, etc.), and the impact on the degradation of the storage element is also thought to differ. By selecting storage elements with similar SOC transition patterns, the estimation accuracy of the degradation index can be improved.

[0034] (10) The history data may include a cumulative charge / discharge amount for each classification of the SOC of the storage element.

[0035] The cumulative charge / discharge amount for each SOC range of a storage element is, for example, the cumulative charge / discharge amount (Ah) (frequency) of the storage element within each SOC range, which is determined by dividing the SOC into predetermined ranges (e.g., 10%). The transition of the SOC of the storage element may differ depending on the vehicle usage conditions and the type of vehicle (e.g., HEV, EV, etc.), and the impact on the deterioration of the storage element is also thought to differ. By selecting storage elements with similar SOC transition patterns, the estimation accuracy of the deterioration index can be improved.

[0036] (11) The estimation unit may predict future degradation of the energy storage element based on the estimated degradation index or a change in the degradation index, and the transmission unit may transmit the prediction of the degradation estimation to the vehicle.

[0037] By predicting future deterioration, it is possible to predict the remaining period until the end of the life of the storage element mounted on the vehicle, and it becomes possible to replace the storage element before it becomes unusable. If a vehicle is equipped with multiple storage elements, it is possible to replace only the storage element approaching the end of its life with a new one, thereby extending the life of the storage elements of the vehicle as a whole.

[0038] Hereinafter, an embodiment of an information processing device and an information processing method will be described with reference to the drawings.

[0039] FIG. 1 is a diagram showing the configuration of an information processing system. The information processing system includes a server 50 as an information processing device. The server 50 is connected to a roadside device 20 via a communication network 1. A vehicle 10 is equipped with an energy storage element (energy storage element) 11 and a BMS (Battery Management System) 12. The energy storage element 11 may be a lithium-ion battery or another secondary battery. The vehicle 10 may be any of a gasoline-powered vehicle, an HEV (Hybrid Electric Vehicle), a PHEV (Plug-in Hybrid Electric Vehicle), and an EV (Electric Vehicle).

[0040] The BMS 12 includes a storage unit (not shown) that collects and stores measurement data obtained by measuring the voltage, current, temperature, etc. of the energy storage element 11 at a predetermined sampling period, and history data related to the deterioration of the energy storage element 11 (e.g., temperature history, charge / discharge history, SOC (State of Charge) history, etc.) based on the measurement data. The BMS 12 estimates the deterioration of the energy storage element 11 using the history data and determines the reliability of the estimated value at the time of estimating the deterioration. The deterioration estimation can use various methods such as an internal resistance estimation method or an actual capacity estimation method. The reliability may be determined each time a deterioration estimation is performed based on the state of the energy storage element 11 and the deterioration estimation method. The reliability may be expressed, for example, as "high," "medium," or "low," or may be expressed as a five-level number, or may be expressed as a numerical value within a range of 0 to 100%.

[0041] The BMS 12 includes a communication module (not shown) for transmitting the measurement data, history data, degradation estimates, and reliability to the server 50 via the roadside device 20, or directly to the server 50. Specifically, the BMS 12 may transmit the measurement data, history data, degradation estimates, and reliability together with the storage element data, storage element ID, and BMS ID to the server 50 using a mobile phone network (e.g., LTE [Long Term Evolution], 4G, 5GG, etc.), a wireless LAN (e.g., WiFi, etc.), or an ITS (Intelligent Transport System) radio.

[0042] Vehicle 10 is equipped with an ECU (Electronic Control Unit) not shown. The ECU may collect location data related to the area in which vehicle 10 is used and transmit the collected location data to server 50 together with the storage element ID and the BMS ID. The location data may be, for example, the driving history of vehicle 10. This makes it possible to identify the area (region) in which vehicle 10 (i.e., storage element 11) is located and the time (period) in which vehicle 10 is located in that area.

[0043] 2 is a diagram showing the configuration of the server 50. When the BMS 12 mounted on the vehicle 10 is unable to accurately estimate the degradation index of the storage element 11, the server 50 estimates the degradation index of the storage element 11 mounted on the vehicle 10 instead of the BMS 12. The server 50 includes a control unit 51, a communication unit 52, a similarity calculation unit 53, an estimation unit 54, a weighting calculation unit 55, and a storage element DB 56. Each unit of the server 50 may be distributed across multiple servers. The control unit 51 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), etc.

[0044] The communication unit 52 includes a communication module and can communicate with the BMS 12 via the roadside device 20 or directly with the BMS 12. The communication unit 52 functions as an acquisition unit and acquires measurement data, history data, estimated values of degradation, and reliability of the storage elements 11 from the BMSs 12 mounted on multiple vehicles 10. The communication unit 52 acquires position data of the vehicles 10 (i.e., the storage elements 11).

[0045] Various data (history data of the storage elements 11, degradation estimation data, usage area data, and storage element data) collected from a large number of vehicles 10 via the communication unit 52 is stored as big data in the storage element DB 56. The communication unit 52 may acquire various data from the same vehicle 10 at different times.

[0046] FIG. 3 is a diagram showing the configuration of the energy storage element DB 56. The energy storage element DB 56 may be provided in the server 50, or may be provided in another data server accessible from the server 50. The energy storage element DB 56 associates history data, degradation estimation data, usage area data, and energy storage element data of the energy storage element 11 with each BMS ID (which may be an energy storage element ID). The history data may include temperature history, SOC history (SOC interval, SOC band), charge / discharge history, etc. The history data may be time-series data such as temperature, charge / discharge, and SOC, or statistical data calculated based on the time-series data. The degradation estimation data may include history of estimated degradation values, history of the reliability of the estimated values, history of the estimated date and time, etc. The usage area data may include history of area information in which the energy storage element 11 was used, the period for each area information, etc. The energy storage element data may include the manufacturer, operating voltage, active material, etc. of the energy storage element 11.

[0047] As shown in Figure 3, for BMS ID 0001, the following are recorded: temperature history data group T0001, SOC section data group B0001, SOC band data group C0001, charge / discharge history data group D0001, estimated value history data group H0001, reliability history data group R1, estimated date and time data group XXO, area information history data group A01, period data group OO, manufacturer XYZ, operating voltage 12V, and active material X△. A data group refers to a collection of data at each of multiple points in time within the history data. The same applies to other BMS IDs. The energy storage element DB 56 allows for the creation of big data related to the degradation of a large number of energy storage elements 11.

[0048] The storage element DB 56 stores history data related to the deterioration of each of the multiple storage elements 11. The history data may be any data related to the deterioration of the storage elements 11, and may include, for example, temperature history, charge / discharge history, SOC history, etc. The history data may be time-series data of temperature, charge / discharge, SOC, etc., or may be statistical data calculated based on the time-series data. For example, with regard to temperature, the history data may be time-series data of temperature, or may be statistical data obtained by dividing the temperature into categories and indicating the usage time for each category. The same applies to charge / discharge and SOC. History data of the storage elements 11 of multiple vehicles is collected from the vehicles, and the storage element DB 56 stores the collected history data in association with each vehicle (or BMS (Battery management system)). History data collected at different times from the same vehicle may also be stored.

[0049] The communication unit 52 acquires history data related to the deterioration of the storage element 11 that is the degradation estimation target from the vehicle 10. The acquired history data is not data that has already been stored in the storage element DB 56, but is data that the server 50 uses when estimating a deterioration index instead of the BMS 12. The vehicle 10 equipped with the storage element 11 that is the degradation estimation target may use any powertrain (vehicle system).

[0050] The similarity calculation unit 53 calculates the similarity between the history data acquired from the vehicle 10 and the history data stored in the energy storage element DB 56. When calculating the similarity, the history data to be compared may include at least one of a temperature history, a charge / discharge history, and an SOC history. If the temperature history, the charge / discharge history, or the SOC history can be compared, the similarity can be calculated with high accuracy. For example, the history data stored in the energy storage element DB 56 may be searched for patterns similar to the temperature transition pattern, the charge / discharge pattern (charging period, charging cycle, discharging period, discharging cycle, rest period, etc.), or the SOC transition pattern during use of the energy storage element 11 for which a degradation index is to be estimated.

[0051] The estimation unit 54 estimates the deterioration index based on historical data stored in the power storage element DB 56, which is similar to the historical data acquired from the vehicle 10. For example, the estimation unit 54 can estimate a decrease in the State of Health (SOH) (deterioration index) from the difference between the SOH at time t1 and the SOH at time tn, based on the transition of the SOC and the transition of the temperature from time t1 to time tn. The SOH at time t1 may be the SOH estimated by the BMS 12 of the vehicle 10.

[0052] The communication unit 52 transmits the deterioration index estimated by the estimation unit 54 to the vehicle 10.

[0053] With the above-described configuration, even in situations where it is not possible to accurately estimate the deterioration index of the storage element 11 installed in the vehicle 10, a deterioration index estimated based on historical data similar to historical data reflecting the usage state of the storage element 11 can be provided to the vehicle 10, and the deterioration of the storage element 11 can be estimated in common for a variety of vehicle systems.

[0054] If the reliability of the deterioration index estimated by the estimation unit 54 is higher than the reliability of the deterioration index of the storage element 11 obtained from the vehicle 10, the communication unit 52 may transmit the deterioration index estimated by the estimation unit 54 to the vehicle 10.

[0055] The reliability of the degradation index depends on the compatibility between the vehicle's powertrain and the degradation estimation method for the storage element 11 of the vehicle 10. For example, the internal resistance estimation method is considered to be accurate when the current during cranking is large, but is considered to be inaccurate for the storage element 11 of a vehicle that does not have an engine or for an auxiliary battery. If the reliability of the degradation index estimated by the estimation unit 54 is higher than the reliability of the degradation index of the storage element 11 mounted on the vehicle 10, a more accurate degradation index can be provided to the vehicle 10 by transmitting the degradation index estimated by the estimation unit 54 to the vehicle 10.

[0056] The method for calculating the similarity will be specifically described below in the order of temperature history, SOC history, and charge / discharge history.

[0057] FIG. 4 is a diagram showing an example of time-series data of temperature. The horizontal axis represents time, and the vertical axis represents temperature. The temperature scale is an example, and other temperature transitions may be used. The temperature transition may be on various scales, such as one day, three days, one week, one month, three months, six months, or one year. For example, if the time scale is one month, a temperature transition such as that shown in FIG. 4 is obtained for each month. In the case of time-series data of the temperature of the energy storage element 11 whose degradation is to be estimated, the starting point of the temperature transition may be the time of the most recent degradation estimation performed by the BMS 12, or may be the time of a degradation estimation when the reliability of the degradation estimation is relatively high. The starting point may be a time when the degradation index estimated by the BMS 12 is reasonably reliable.

[0058] FIG. 5 shows an example of a temperature histogram. A temperature histogram is a statistical value calculated based on time-series temperature data and is also a temperature history. The temperature histogram shows the usage time (frequency) for each temperature range of the storage element 11 for which degradation is estimated. FIG. 5A divides the temperature into eight ranges of 5°C and shows the usage time for each range as a histogram. FIG. 5B shows a feature vector calculated based on the temperature histogram. The feature vector has eight elements corresponding to the eight ranges, and each element represents a frequency. If the feature vector is represented by V, then the feature vector V can be expressed as follows: V = (0, 7, 191, 316, 166, 37, 3, 0). The feature vector V may be normalized as described below.

[0059] Fig. 6 is a diagram showing an example of calculating similarity using temperature history. Calculation of similarity using temperature history involves calculating the similarity between the temperature history acquired from vehicle 10 and the temperature history stored in power storage element DB 56. As shown in Fig. 5B, the feature vector V based on the time-series temperature data transmitted from BMS 12 (vehicle 10) is V = (0, 7, 191, 316, 166, 37, 3, 0), and the normalized feature vector V is V' = (0, 0.009722, 0.265278, 0.438889, 0.230556, 0.051389, 0.004167, 0). On the other hand, the normalized feature vectors Vs stored in the storage element DB56 are, for each BMS, Vs1 = (0, 0, 0.009722, 0.231944, 0.45, 0.2625, 0.045833, 0), Vs2 = (0, 0.044444, 0.197222, 0.351389, 0.270833, 0.122222, 0.013889, 0), Vs3 = (0, 0.198611, 0.597222, 0.2, 0.004167, 0, 0, 0), ...

[0060] The similarity calculation unit 53 calculates the similarity between the feature vector V' based on the temperature history acquired from the vehicle 10 and each of the feature vectors Vs1, Vs2, Vs3, ... based on the temperature history stored in the storage element DB 56. The similarity can be calculated, for example, using the distance between each element of the feature vector (for example, Euclidean distance). The closer the distance, the greater the similarity. In the example of FIG. 6, it is determined that the temperature history of BMS00002 is most similar.

[0061] The temperature of the storage element may vary depending on the usage state of the vehicle, and it is thought that the impact on the deterioration of the storage element may also vary. By selecting a storage element 11 whose usage temperature pattern is similar to that of the storage element 11 whose deterioration is to be estimated from the BMS (storage elements 11) stored in the storage element DB 56, the accuracy of estimating the deterioration index can be improved.

[0062] FIG. 7 is a diagram showing an example of time-series data of SOC. The horizontal axis represents time, and the vertical axis represents SOC. The SOC transition may be measured using various scales, such as one day, three days, one week, one month, three months, six months, or one year. For example, if the time scale is one month, the SOC transition shown in FIG. 7 is obtained for each month. In the case of time-series data of the SOC of the storage element 11 whose degradation is to be estimated, the starting point of the SOC transition may be the time of the most recent degradation estimation performed by the BMS 12, or may be the time of a degradation estimation when the reliability of the degradation estimation is relatively high. The starting point may be a time when the degradation index estimated by the BMS 12 is reasonably reliable.

[0063] The SOC interval histogram is a statistical value calculated based on time-series data of the SOC, and is also an SOC history. The SOC interval histogram divides the SOC of the storage element 11 whose degradation is to be estimated into predetermined intervals (for example, a 10% range), and shows the time (frequency) that the storage element 11 was used within each SOC interval. In the case of FIG. 7, the feature vector calculated based on the SOC interval histogram has 10 elements corresponding to 10 intervals, and each element represents a frequency. The similarity can be calculated in the same way as in FIG. 6.

[0064] The transition of the SOC of the storage element may differ depending on the usage state of the vehicle and the type of vehicle (e.g., HEV, EV, etc.), and it is thought that the impact on the deterioration of the storage element will also differ. By selecting, from the BMSs (storage elements 11) stored in the storage element DB 56, a storage element 11 whose SOC transition pattern is similar to that of the storage element 11 whose deterioration is to be estimated, the accuracy of estimating the deterioration index can be improved.

[0065] FIG. 8 is a diagram showing an example of time-series data of the SOC and charge / discharge current. The horizontal axis represents time, and the vertical axis represents the SOC and current. The SOC transition may be measured on various scales, such as one day, three days, one week, one month, three months, six months, or one year. For example, when the time scale is one month, the SOC transition shown in FIG. 8 is obtained for each month. In the case of time-series data of the SOC of the storage element 11 whose degradation is to be estimated, the starting point of the SOC transition may be the time of the most recent degradation estimation performed by the BMS 12, or may be a time of degradation estimation when the reliability of the degradation estimation is relatively high. The starting point may be a time when the degradation index estimated by the BMS 12 is reasonably reliable.

[0066] The SOC band histogram is a statistical value calculated based on time-series data of the SOC, and is also an SOC history. The SOC band histogram divides the SOC of the storage element 11 whose degradation is to be estimated into predetermined SOC bands (for example, a 10% range), and shows the cumulative charge / discharge amount (Ah) of the storage element 11 within each SOC band. In the case of FIG. 8, the feature vector calculated based on the SOC band histogram has five elements corresponding to the five SOC bands, and each element is a cumulative charge / discharge amount. The similarity can be calculated in the same way as in FIG. 6.

[0067] The transition of the SOC of the storage element may differ depending on the usage state of the vehicle and the type of vehicle (e.g., HEV, EV, etc.), and it is thought that the impact on the deterioration of the storage element will also differ. By selecting, from the BMSs (storage elements 11) stored in the storage element DB 56, a storage element 11 whose SOC transition pattern is similar to that of the storage element 11 whose deterioration is to be estimated, the accuracy of estimating the deterioration index can be improved.

[0068] Although not shown, the charge / discharge history can also be expressed in various scales, such as one day, three days, one week, one month, three months, six months, or one year. Within the scale, a feature vector may be obtained using elements such as the number of charges, the charge period, the number of discharges, the discharge period, the number of rest periods, and the rest period. The similarity can be calculated in the same way as in FIG. 6.

[0069] With the above-described configuration, it is possible to select, from among the BMSs (storage elements 11) stored in the storage element DB 56, storage elements 11 having a similar charge / discharge pattern to the storage element 11 whose deterioration is to be estimated.

[0070] When calculating the similarity, it is possible to narrow down and search the BMSs (energy storage elements 11) stored in the energy storage element DB 56 in advance using predetermined conditions. The predetermined conditions used in the search will be described below.

[0071] The similarity calculation unit 53 may calculate the similarity between the history data acquired from the vehicle 10 and the history data of the storage elements 11, among the multiple storage elements 11 stored in the storage element DB 56, that have the same range of operating voltage as the storage element 11 mounted on the vehicle 10. The operating voltages in the same range may be divided into low voltages such as 12 V and high voltages such as several hundred V, for example. By limiting the operating voltages to the same range, it is possible to prevent the calculation of similarity between storage elements that have different storage element characteristics or progress of degradation depending on the level of the operating voltage. This makes it possible to improve the estimation accuracy of the degradation index.

[0072] The similarity calculation unit 53 may calculate the similarity between the historical data acquired from the vehicle 10 and the historical data of the storage elements 11 among the multiple storage elements 11 stored in the storage element DB 56, in which all or part of the active material of the storage elements 11 installed in the vehicle 10 has the same active material.

[0073] The positive electrode active material may be, for example, a lithium transition metal oxide (Li1+aMeO2, a≧1, Me: containing one or more transition metal elements such as Ni, Mn, Co) such as lithium cobalt oxide, lithium nickel manganese cobalt oxide, or lithium nickel cobalt aluminum oxide, spinel-type lithium manganese oxide (LiMe2O4: Me is one or more metal elements including at least Mn), lithium iron phosphate, lithium manganese iron phosphate, or lithium vanadium phosphate, which can absorb and release Li. Two or more of these may be used in combination.

[0074] The negative electrode active material may be any material capable of absorbing and releasing Li, such as graphite, hard carbon, soft carbon, metallic Li, silicon monoxide, silicon or its alloy, tin or its alloy, lithium vanadate, tungsten oxide, titanium oxide, or niobium oxide. Two or more of these materials may be used in combination. By limiting the active materials to energy storage elements that contain all or part of the same active material, it is possible to prevent the calculation of similarity between energy storage elements that differ in energy storage element characteristics or the progress of degradation due to differences in the active material. This improves the accuracy of estimating the degradation index.

[0075] The similarity calculation unit 53 may calculate the similarity between the history data acquired from the vehicle 10 and the history data of the storage elements 11 manufactured by the same manufacturer as the storage elements 11 mounted on the vehicle 10, among the multiple storage elements 11 stored in the storage element DB 56. By limiting the storage elements to those manufactured by the same manufacturer, it is possible to prevent the calculation of similarity between storage elements having different storage element characteristics or progress of deterioration depending on the manufacturer. This can improve the estimation accuracy of the deterioration index.

[0076] The similarity calculation unit 53 may calculate the similarity between the history data acquired from the vehicle 10 and the history data of the storage elements 11 mounted on the vehicle 10, among the plurality of storage elements 11 stored in the storage element DB 56, that are used in the same area, in whole or in part. The area of use may be, for example, one prefecture, two metropolitan areas, and 43 prefectures, or may be a region such as the Kanto region or the Kinki region, or may be an urban area, a suburban area, or a mountainous area. By limiting the area of use to storage elements of vehicles that are used in the same area, in whole or in part, it is possible to prevent the calculation of similarity between storage elements that have different storage element characteristics or a progress of deterioration depending on the difference in the area of use. This improves the accuracy of estimating the degradation index.

[0077] The weighting when selecting, from among the BMSs (storage elements 11) stored in the storage element DB 56, the storage elements 11 having history data similar to the history data of the storage element 11 whose deterioration is to be estimated, will be described.

[0078] The weight calculation unit 55 may calculate the weight based on the similarity calculated by the similarity calculation unit 53 and the reliability stored in the power storage element DB 56.

[0079] Fig. 9 is a diagram showing an example of weighting calculation. Weighting W may be calculated, for example, as W = S × R, where S indicates similarity and R indicates reliability of degradation estimation data. In Fig. 9, weighting W1 for storage element 11 of BMS ID 0001 is calculated as W1 = S1 × R1, where S1 is the similarity calculated by similarity calculation unit 53 and R1 is the reliability of degradation estimation data of BMS ID 0001. The same applies to other BMS IDs.

[0080] The control unit 51 has a function as a selection unit, and may select a storage element 11 from among the plurality of storage elements 11 based on the weighting calculated by the weighting calculation unit 55. The storage element 11 may be selected if the weighting W is equal to or greater than a predetermined threshold value. This allows the storage element 11 to be selected from the plurality of storage elements 11 stored in the storage element DB 56, taking into consideration both the similarity of the history data and the reliability of the deterioration index. There may be a plurality of storage elements 11 to be selected.

[0081] Estimation unit 54 may estimate the deterioration index based on the history data of selected energy storage element 11. This allows estimation of the deterioration index taking into consideration both the similarity of the history data and the reliability of the deterioration index.

[0082] The calculation of the similarity and the weighting may be performed using machine learning such as a neural network.

[0083] Fig. 10 is a diagram showing an example of the configuration of the learning model 53a. The learning model 53a is a neural network model including deep learning, and is composed of an input layer, an output layer, and multiple intermediate layers. For convenience, Fig. 10 shows two intermediate layers, but alternatively, the number of intermediate layers may be three or more.

[0084] The input layer, output layer, and intermediate layer each contain one or more nodes (neurons), and the nodes in each layer are connected in one direction with the nodes in the previous and next layers with desired weights. A vector having the same number of components as the number of nodes in the input layer is provided as input data to the learning model 53a. The input data includes history data transmitted from the BMS 12 (history data acquired from the vehicle 10), the ID of the BMS stored in the server 50, and the history data of the ID (history data stored in the energy storage element DB 56). The output data includes the similarity of the history data of the ID. The input data may contain multiple BMS IDs.

[0085] The output data can be in the form of a vector having components of the same size as the number of nodes in the output layer (size of the output layer). For example, the output node outputs the probability of each of multiple similarities. The multiple similarities may be in a desired range, such as 90% to 100%, 80% to 90%, 70% to 80%, etc., or may be predetermined values (95%, 90%, 85%, etc.).

[0086] The learning model 53a can be constructed by combining hardware such as a CPU (e.g., a multi-processor having multiple processor cores), GPUs (Graphics Processing Units), DSPs (Digital Signal Processors), and FPGAs (Field-Programmable Gate Arrays).

[0087] Fig. 11 is a diagram showing an example of the configuration of the learning model 53b. As shown in Fig. 11, the input data includes history data transmitted from the BMS 12 (history data acquired from the vehicle 10), the ID of the BMS stored in the server 50, the history data of the ID, and the reliability of the history data of the ID (history data stored in the energy storage element DB 56). The output data includes a weighting of the history data of the ID. The input data may include multiple BMS IDs.

[0088] The output data can be in the form of a vector having components of the same size as the number of nodes in the output layer (size of the output layer). For example, the output node outputs the probability of each of a plurality of confidence levels. The plurality of confidence levels may be in a desired range, such as 80% to 100%, 60% to 80%, 40% to 60%, etc., or may be predetermined values (80%, 60%, 40%, etc.).

[0089] Fig. 12 is a diagram showing an example of estimation of a deterioration index by the estimation unit 54. When the estimation unit 54 acquires historical data (e.g., time-series data of SOC and time-series data of temperature) of the selected storage element 11 as input data, it estimates (calculates) a deterioration value of the storage element 11. As shown in Fig. 12, the time-series data of SOC indicates the SOC transition from time t1 to time tn, and the time-series data of temperature indicates the temperature transition from time t1 to time tn.

[0090] The estimation unit 54 can estimate the deterioration of the SOH (deterioration value, deterioration index) from time t1 to time tn based on the SOC transition and temperature transition from time t1 to time tn. The SOH (also called health level) at time t1 is calculated as SOH t1 SOH at time tn is SOH tn Then, the degradation value is (SOH t -SOH tn ) That is, if the SOH at time t1 is known, the SOH at time tn can be calculated based on the degradation value.

[0091] The SOH at time t1 may be the SOH estimated by the BMS 12 of the vehicle 10. Specifically, time t1 may be the time of the most recent degradation estimation performed by the BMS 12 among multiple degradation estimations performed by the BMS 12 for the storage element 11 that is the target of degradation estimation by the server 50, or may be the time of a degradation estimation when the reliability of the degradation estimation is relatively high. Time t1 may be set to a time when the degradation index estimated by the BMS 12 is somewhat reliable.

[0092] The period from time t1 to time tn may be determined appropriately depending on the history data of the selected energy storage element 11. In the example of Fig. 12, a temperature transition is input, but a required temperature (for example, an average temperature from time t1 to time tn) may be input instead of the time-series data of the temperature.

[0093] The degradation value Qdeg of the storage element 11 after the degradation estimation target period (for example, from time t1 to time tn) has elapsed can be calculated by the formula Qdeg=Qcnd+Qcur. Qcnd is the non-energized degradation value, and Qcur is the energized degradation value. The non-energized degradation value Qcnd can be calculated, for example, by Qcnd=K1×√(t). The coefficient K1 is a function of the SOC and temperature T. t is the elapsed time, for example, the time from time t1 to time tn. The energized degradation value Qcur can be calculated, for example, by Qcur=K2×√(t). The coefficient K2 is a function of the SOC and temperature T. The SOH at time t1 is calculated by SOH t1 SOH at time tn is SOH tn Then, SOH tn =SOH t1 The SOH can be estimated using -Qdeg. The coefficient K1 is a degradation coefficient, and the correspondence between the SOC and temperature T and the coefficient K1 may be calculated or stored in table format. The SOC can be time-series data. The coefficient K2 is similar to the coefficient K1. The estimation unit 54 may use machine learning such as a neural network. The estimation unit 54 may estimate the degradation index using an internal resistance estimation method or an actual capacity estimation method according to the history data of the selected storage element 11.

[0094] 13 is a flow diagram showing the processing procedure of the server 50. The server 50 acquires from the vehicle 10 history data and deterioration estimation data related to deterioration of the energy storage element 11 (S11). The history data may include a temperature history, a charge / discharge history, and an SOC history. The deterioration estimation data may include an estimated value of deterioration (deterioration index) and the reliability of the estimated value.

[0095] The server 50 calculates the similarity between the acquired history data and the history data stored in the storage element DB 56 (S12), and calculates a weighting based on the calculated similarity and the reliability of the deterioration estimation data stored in the storage element DB 56 (S13).

[0096] Based on the calculated weighting, the server 50 selects a BMS (storage element 11) from the BMSs (storage elements 11) stored in the storage element DB 56 (S14), and calculates degradation estimation data based on the historical data of the selected BMS (storage element 11) (S15).

[0097] The server 50 determines whether the reliability of the calculated deterioration estimation data is greater than the reliability of the deterioration estimation data acquired from the vehicle 10 (S16), and if it is greater (YES in S16), transmits the calculated deterioration estimation data to the vehicle 10 (S17) and terminates the processing. If the reliability of the calculated deterioration estimation data is not greater than the reliability of the deterioration estimation data acquired from the vehicle 10 (NO in S16), the server 50 terminates the processing.

[0098] The server 50 may predict future degradation estimates of each storage element 11 based on the calculated degradation estimate data and the transition trend of the degradation estimate data, and transmit the predictions to the vehicle 10 (BMS 12). By predicting future degradation estimates, it is possible to predict the remaining period until the lifespan of the storage elements 11 mounted on the vehicle 10, and it becomes possible to replace the storage elements 11 before they become unusable. When the vehicle 10 is mounted with multiple storage elements 11, it is possible to replace only the storage elements 11 that are approaching the end of their lifespan with new ones, and it becomes possible to extend the lifespan of all the storage elements of the vehicle 10.

[0099] The present invention is intended to be illustrative and not restrictive in all respects, and the scope of the present invention is defined by the claims, and includes all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0100] 1. Communication Network 10 vehicles 11 Energy storage element 12 BMS 20 Roadside equipment 50 servers 51 Control section 52 Communications Department 53 Similarity calculation unit 53a, 53b Learning Model 54 Estimation part 55 Weighting calculation unit 56 Energy Storage Element DB

Claims

1. a storage unit that stores history data related to deterioration of each of the plurality of storage elements; an acquisition unit that acquires history data related to deterioration of a storage element mounted on the vehicle; a similarity calculation unit that calculates a similarity between the acquired history data and the stored history data; an estimation unit that estimates a deterioration index based on stored history data that is similar to the acquired history data; a transmitter that transmits the estimated deterioration index to the vehicle; Equipped with The acquisition unit Obtaining the reliability of the deterioration index of the storage element mounted on the vehicle; The transmission unit If the reliability of the deterioration index estimated by the estimation unit is higher than the reliability acquired by the acquisition unit, the estimated deterioration index is transmitted to the vehicle. Information processing device.

2. A storage unit that stores history data related to the deterioration of each of a plurality of storage elements; an acquisition unit that acquires history data related to deterioration of a storage element mounted on the vehicle; a similarity calculation unit that calculates a similarity between the acquired history data and the stored history data; an estimation unit that estimates a deterioration index based on stored history data that is similar to the acquired history data; a transmitter that transmits the estimated deterioration index to the vehicle; Equipped with The storage unit storing the reliability of the deterioration index for each of the plurality of storage elements; a weight calculation unit that calculates a weight based on the calculated similarity and the stored reliability; a selection unit that selects a storage element from the plurality of storage elements based on the calculated weighting; Equipped with The estimation unit Estimating a degradation index based on historical data of the selected energy storage element; Information processing device.

3. A storage unit that stores history data related to the deterioration of each of a plurality of storage elements; an acquisition unit that acquires history data related to deterioration of a storage element mounted on the vehicle; a similarity calculation unit that calculates a similarity between the acquired history data and the stored history data; an estimation unit that estimates a deterioration index based on stored history data that is similar to the acquired history data; a transmitter that transmits the estimated deterioration index to the vehicle; Equipped with The storage unit storing location data relating to the area of use of each of the plurality of storage elements; The acquisition unit obtaining location data relating to an area where the vehicle is used; The similarity calculation unit calculating a similarity between the acquired history data and history data of storage elements, among the plurality of storage elements stored in the storage unit, that are used in the same area as all or part of the area in which the storage element mounted on the vehicle is used; Information processing device.

4. The similarity calculation unit calculating a similarity between the acquired history data and history data of a storage element having a working voltage in the same range as that of the storage element mounted on the vehicle, among the plurality of storage elements stored in the storage unit; The information processing device according to claim 1 .

5. The similarity calculation unit calculating a similarity between the acquired history data and history data of a storage element, among the plurality of storage elements stored in the storage unit, that has the same active material as all or part of the active material of the storage element mounted on the vehicle; The information processing device according to claim 1 .

6. The similarity calculation unit calculating a similarity between the acquired history data and history data of a storage element manufactured by the same manufacturer as the storage element mounted on the vehicle, among the plurality of storage elements stored in the storage unit; The information processing device according to claim 1 .

7. The estimation unit predicting future degradation of the energy storage element based on the estimated degradation index or a change in the degradation index; The transmission unit transmitting the degradation estimation prediction to the vehicle; The information processing device according to claim 1 .

8. storing historical data relating to the deterioration of each of the plurality of storage elements in a storage unit; Acquire historical data related to deterioration of a storage element mounted on the vehicle; Calculating the similarity between the acquired history data and the stored history data; estimating a degradation index based on stored historical data similar to the acquired historical data; transmitting the estimated deterioration indicator to the vehicle; Obtaining the reliability of the deterioration index of the storage element mounted on the vehicle; If the reliability of the estimated deterioration indicator is higher than the acquired reliability, transmitting the estimated deterioration indicator to the vehicle. Information processing methods.

9. A storage unit stores history data relating to the deterioration of each of the plurality of storage elements, Acquire historical data related to deterioration of a storage element mounted on the vehicle; Calculating the similarity between the acquired history data and the stored history data; estimating a degradation index based on stored historical data similar to the acquired historical data; transmitting the estimated deterioration indicator to the vehicle; storing the reliability of the deterioration index for each of the plurality of storage elements; Calculating a weight based on the calculated similarity and the stored confidence; selecting a storage element from the plurality of storage elements based on the calculated weighting; Estimating a degradation index based on historical data of the selected energy storage element; Information processing methods.

10. A storage unit stores history data relating to the deterioration of each of a plurality of storage elements, Acquire historical data related to deterioration of a storage element mounted on the vehicle; Calculating the similarity between the acquired history data and the stored history data; estimating a degradation index based on stored historical data similar to the acquired historical data; transmitting the estimated deterioration indicator to the vehicle; storing location data relating to the area of use of each of the plurality of storage elements; obtaining location data relating to an area where the vehicle is used; calculating a similarity between the acquired history data and history data of storage elements, among the plurality of storage elements stored in the storage unit, that are used in the same area as all or part of the area in which the storage element mounted on the vehicle is used; Information processing methods.

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