Patent Publication Number: US-2021181256-A1

Title: Estimation system, estimation device, estimation method, program, and storage medium

Description:
TECHNICAL FIELD 
     The present invention relates to an estimation system, an estimation device, an estimation method, a program, and a storage medium. 
     Priority is claimed on Japanese Patent Application No. 2018-143635, filed Jul. 31, 2018, the content of which is incorporated herein by reference. 
     BACKGROUND ART 
     A sharing service in which a battery, which is a drive source of an electric vehicle, is removable and shared by a plurality of users is known. In this service, a charging station for storing and charging one or more removable batteries is installed and a mechanism for requesting a user who uses the charging station to pay a fee is constructed. Considering an operational aspect of the sharing service, removable batteries having different usage environments or usage state histories are mixed in one charging station. Also, the batteries used in the sharing service may deteriorate according to usage conditions and environmental conditions. 
     Conventionally, it is general to set a framework of a deterioration estimation method from physical characteristics of a battery, acquire deterioration parameters from durability test results of the battery, and construct battery deterioration prediction technology. 
     For example, a method of estimating a capacity of a lithium ion battery is described in Patent Document 1. In the technology described in Patent Document 1, a plurality of lithium ion batteries having the same structure and specifications as the lithium ion battery whose capacity is to be estimated and having capacities different due to a cause of repeated use or the like are provided. In the technology described in Patent Document 1, using the above, a lithium ion battery is charged in a constant current/constant voltage scheme and a charging current value at a point in time when the charging condition has been switched from a constant current to a constant voltage is measured. In the technology described in Patent Document 1, correlations between capacities of batteries having different capacity values and a charging current value are obtained in advance from a result of the measurement. In the technology described in Patent Document 1, when the lithium ion battery whose capacity is to be estimated is charged in the constant current/constant voltage scheme, a charging current value at a point in time when a preset time period has elapsed from a point in time when the charging condition has been switched from the constant current to the constant voltage is obtained. In the technology described in Patent Document 1, the capacity of the lithium ion battery serving as a target of capacity estimation is estimated from correlations between capacities of batteries having different capacity values obtained in advance and the charging current value using the charging current. 
     In Patent Document 2, technology for ascertaining a value of internal resistance of a secondary battery which has deteriorated after the elapse of a predetermined time period with high accuracy by calculating an amount of change in the internal resistance after the elapse of the predetermined time period in consideration of information of the temperature to which the battery is exposed with respect to an initial value of the internal resistance in relation to the prediction of deterioration of the internal resistance is described. In the technology described in Patent Document 1, for the temperature information, temperature progress information including a battery temperature and time information in which the battery temperature is recorded is acquired over time. In the technology described in Patent Document 1, the temperature progress information is acquired at least at a predetermined frequency when the battery is used, for example, every 1 minute to 12 hours. 
     In Patent Document 3, as another example of a movable object in which a battery can be removably mounted on a main body of the movable object, a portable power charging/supplying device including a battery and a power charging/supplying unit in which a storage chamber for storing the battery is formed is described. 
     PRIOR ART DOCUMENTS 
     Patent Documents 
     [Patent Document 1] 
     
         
         Japanese Unexamined Patent Application, First Publication No. 2001-257008 
       
    
     [Patent Document 2] 
     
         
         Japanese Unexamined Patent Application, First Publication No. 2017-009540 
       
    
     [Patent Document 3] 
     
         
         Japanese Unexamined Patent Application, First Publication No. 2019-068552 
       
    
     SUMMARY OF INVENTION 
     Problems to be Solved by the Invention 
     However, in the conventional technology, deterioration cannot be predicted without knowledge of a battery. Also, in the conventional technology, a framework of a change in deterioration made from physical properties may not actually be suitable. 
     Aspects of the present invention provide an estimation system, an estimation device, an estimation method, a program, and a storage medium capable of estimating a change in deterioration of a battery without having specialized knowledge about deterioration of the battery. 
     Solution to Problem 
     (1) According to an aspect of the present invention, there is provided an estimation system including: a battery removably mounted on an electric power device using electric power; a charging device; and an estimation device, wherein the charging device includes a charger configured to charge the battery; an acquirer configured to acquire first information about deterioration of the battery from the battery; and a transmitter configured to transmit the first information acquired by the acquirer, and wherein the estimation device includes a receiver configured to receive the first information transmitted by the transmitter; and an estimator configured to estimate performance of the battery when the first information received by the receiver is input to a model, the model having been learned to output second information about the performance of the battery at a certain point in time when the first information of the battery is input at the point in time. 
     (2) In the estimation system, the estimation device may further include a learner configured to learn the model based on teacher data in which the second information of the battery at the point in time is associated with the first information of the battery at the point in time. 
     (3) In the estimation system, the first information may include at least one of the number of days elapsed after the battery was produced, the initial capacity of the battery, the initial resistance value of the battery, a histogram of the temperature of the battery, and a histogram of the remaining capacity of the battery. 
     (4) In the estimation system, the second information may include one or both of a capacity of the battery at the time of measurement and a resistance value of the battery at the time of measurement. 
     (5) In the estimation system, the first information may include at least one of the number of days elapsed after the battery was produced, the initial capacity of the battery, the initial resistance value of the battery, the standard deviation and the average value in the temperature of the battery, and the standard deviation and the average value in the remaining capacity of the battery. 
     (6) In the estimation system, the acquirer of the charging device may acquire the first information and the second information when the battery has been mounted on the charging device, and the transmitter of the charging device may transmit the first information and the second information acquired by the acquirer to the estimation device. 
     (7) In the estimation system, the acquirer of the charging device may acquire the first information and the second information for a period from the time when the battery is mounted on the charging device to the time when the battery is removed from the charging device, and the transmitter of the charging device may transmit the first information and the second information acquired by the acquirer to the estimation device. 
     (8) In the estimation system, the second information may include at least one of measured values of a capacity and a resistance value obtained by measuring the performance of the battery and time-series data of each of the voltage, the electric current, and the temperature when the battery is charged. 
     (9) According to another aspect of the present invention, there is provided an estimation system including: a battery removably mounted on an electric power device using electric power; and an estimation device, wherein the battery includes an acquirer configured to acquire first information about deterioration of the battery; and a transmitter configured to transmit the first information acquired by the acquirer, and wherein the estimation device includes a receiver configured to receive the first information transmitted by the transmitter; and an estimator configured to estimate performance of the battery when the first information received by the receiver is input to a model, the model having been learned to output second information about the performance of the battery at a certain point in time when the first information of the battery is input at the point in time. 
     (10) According to further another aspect of the present invention, there is provided an estimation device including: a receiver configured to receive first information about deterioration of a battery transmitted by a charging device configured to charge the battery removably mounted on an electric power device using electric power; and an estimator configured to estimate performance of the battery when the first information received by the receiver is input to a model, the model having been learned to output second information about the performance of the battery at a certain point in time when the first information of the battery is input at the point in time. 
     (11) According to still another aspect of the present invention, there is provided an estimation method including a step of: estimating, by a computer including a receiver configured to receive first information about deterioration of a battery transmitted by a charging device configured to charge the battery removably mounted on an electric power device using electric power, performance of the battery when the first information received by the receiver is input to a model, the model having been learned to output second information about the performance of the battery at a certain point in time when the first information of the battery is input at the point in time. 
     (12) According to still another aspect of the present invention, there is provided a program for causing a computer, which includes a receiver configured to receive first information about deterioration of a battery transmitted by a charging device configured to charge the battery removably mounted on an electric power device using electric power, to execute a step of: estimating performance of the battery when the first information received by the receiver is input to a model, the model having been learned to output second information about the performance of the battery at a certain point in time when the first information of the battery is input at the point in time. 
     (13) According to still another aspect of the present invention, there is provided a computer-readable storage medium storing the program. 
     Advantageous Effects of Invention 
     According to the above-described (1) and (8) to (13), it is possible to estimate a change in deterioration of the battery without having specialized knowledge about the deterioration of the battery. Also, according to the above-described (1) or (7), it is expected that the accuracy of estimation will be further improved when more batteries are released onto the market. Further, according to the above-described (1) or (8) to (13), because it is possible to estimate the capacity and the resistance value of the battery without acquiring much time-series data at certain short time intervals, the cost of collecting data in the market can be reduced. 
     According to the above-described (3) or (4), because machine learning is performed using information for determining deterioration of the battery and information about battery performance, it is possible to generate a model for enabling a change in the deterioration of the battery to be estimated accurately. 
     According to the above-described (5) or (6), when the battery is returned or for a period from the time the battery is returned to the time when the battery is rented, the battery performance can be measured by the charging device and the battery information can be obtained, so that the cost of collecting data in the market can be reduced. 
     According to the above-described (7), because the second information used as a teacher label of the teacher data includes time-series data of various types of parameters representing the battery performance, it is not necessary to actually measure the battery performance for the battery and it is possible to improve the accuracy of prediction as the number of data samples increases. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a diagram showing the overall configuration of a battery deterioration estimation system according to an embodiment. 
         FIG. 2  is a diagram showing an example of a configuration of a removable battery according to the embodiment. 
         FIG. 3  is a diagram showing an example of a configuration of a charging station according to the embodiment. 
         FIG. 4  is a diagram showing an example of a configuration of a management server according to the embodiment. 
         FIG. 5  is a flowchart of a procedure of creating a battery deterioration prediction model according to the embodiment. 
         FIG. 6  is a diagram showing an example in which a battery deterioration prediction model is generated according to the embodiment. 
         FIG. 7  is a flowchart of an example of a process in which battery performance is estimated using the battery deterioration prediction model according to the embodiment. 
         FIG. 8  is a diagram showing an example in which battery performance is estimated using the battery deterioration prediction model according to the embodiment. 
         FIG. 9  is a diagram showing an example of histogram data of the temperature of the removable battery according to the embodiment. 
         FIG. 10  is a diagram showing an example of histogram data of a state of charge (SOC) of the removable battery according to the embodiment. 
         FIG. 11  is a diagram showing an example of data for use in learning according to the embodiment. 
         FIG. 12  is a flowchart of a procedure of creating a battery deterioration prediction model according to a first modified example of the embodiment. 
         FIG. 13  is a diagram showing the overall configuration of a battery deterioration estimation system according to a second modified example of the embodiment. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, embodiments of the present invention will be described with reference to the drawings. Also, in the drawings used in the following description, the scale of each member is appropriately changed to make each member recognizable. 
     [Overall Configuration] 
       FIG. 1  is a diagram showing the overall configuration of a battery deterioration estimation system  10  according to the present embodiment. As shown in  FIG. 1 , the battery deterioration estimation system  10  includes a saddle-riding type motorcycle  12 , a removable battery  14 , a charging station  16 , a management server  18 , and a portable terminal  28 . The motorcycle  12  is an example of an “electric power device,” the charging station  16  is an example of a “charging device,” and the management server  18  is an example of an “estimation device.” 
     The “electric power device” is not limited to the motorcycle  12  and may be, for example, a vehicle (a one-wheel vehicle, a three-wheel vehicle, a four-wheel vehicle, or the like) which can travel with electric power and on which a removable battery  14  can be removably mounted, or an assist-type bicycle. Instead of these vehicle-type movable objects, the “electric power device” may be a portable power charging/supplying device carried by a person or a vehicle described in Japanese Unexamined Patent Application, First Publication No. 2019-068552. Also, the “electric power device” may be a movable robot, an autonomous traveling device, an electric bicycle, an autonomous traveling vehicle, another electric vehicle, a drone flying object, or another electric movable device (electric mobility). Hereinafter, an example in which the “electric power device” is the motorcycle  12  will be described. 
     The battery deterioration estimation system  10  is a system capable of providing a sharing service in which a removable battery  14 , which is a drive source of the motorcycle  12 , is shared by a plurality of users. 
     The removable battery  14  is a cassette-type power storage device that is removably mounted on the motorcycle  12  that can travel with at least electric power. The removable battery  14  stores battery information about deterioration of the battery. Hereinafter, for convenience of description, the removable battery  14  required to be charged may be distinguished as a “used battery  14   u ” and the removable battery  14  that has been completely charged may be distinguished as a “charged battery  14   c .” At least one removable battery  14  is mounted on one motorcycle  12 . A configuration of the removable battery  14  will be described below. For example, the battery information includes various types of parameters for enabling deterioration of the removable battery  14  to be diagnosed such as the number of days of use of the removable battery  14 , initial performance (the capacity and the resistance value), a histogram of the temperature, and a histogram of the state of charge (SOC). The battery information may further include identification information (also referred to as a battery ID) of the removable battery  14 . In the present embodiment, it is assumed that there is only one type of removable battery  14  used in the battery deterioration estimation system  10 . The battery information is an example of “first information.” 
     The charging station  16  is a facility for storing and charging one or more removable batteries  14  and is installed at a plurality of places. A housing  20  of the charging station  16  includes a slot unit  21  having a plurality of slots (12 slots in the example of  FIG. 1 ), a display  161  and an authenticator  162 . The charging station  16  is connected to the management server  18  via the network NW so that communication with the management server  18  is enabled. The network NW is a wireless communication network or a wired communication network. A charger  163  ( FIG. 3 ) capable of charging the removable battery  14  and a connector  164  ( FIG. 3 ) are provided on the back side of the slot unit  21 . The charging station  16  acquires battery information from the removable battery  14  inserted into the slot unit  21 . The charging station  16  measures (estimates) battery performance (a capacity and a resistance value) of the removable battery  14  inserted into the slot unit  21 , associates information about the measured (estimated) battery performance (hereinafter referred to as battery performance information) with the acquired battery information, and transmits the associated information to the management server  18 . The charging station  16  transmits the number of used batteries  14   u  and the number of charged batteries  14   c  to the management server  18 . The battery performance information is an example of “second information.” 
     The management server  18  acquires the number of used batteries  14   u  and the number of charged batteries  14   c  transmitted by the charging station  16  and manages the removable battery  14  stored in the charging station  16  based on the acquired information. The management server  18  performs a reservation process for renting the removable battery  14  based on, for example, a result of an operation of a user performed on the portable terminal  28 . The management server  18  transmits a reservation result to the portable terminal  28 . The management server  18  performs a billing process in accordance with a situation in which the user uses the removable battery  14 . A billing method may be an annual contract, a monthly contract, a contract based on a use count, or a one-time contract. The management server  18  is connected to the charging station  16  and the portable terminal  28  via the network NW so that communication is enabled. 
     The management server  18  generates a model for predicting deterioration of the removable battery  14  (a battery) (hereinafter referred to as a battery deterioration prediction model) through machine learning using the battery information associated with the battery performance information acquired from the plurality of removable batteries  14  as the teacher data. When the battery deterioration prediction model is sufficiently learned, the management server  18  predicts the battery capacity and the battery resistance at any timing using the battery deterioration prediction model. The battery deterioration prediction model is a model for all the removable batteries  14  used in the battery deterioration estimation system  10 . A method of generating the battery deterioration prediction model and a method of predicting the battery capacity and the battery resistance will be described below. 
     The portable terminal  28  is a terminal carried by the user, for example, such as a smartphone, a tablet terminal, or a notebook computer. The user operates the portable terminal  28  to select and reserve one of the charging stations  16  from which the removable battery  14  can be rented. The portable terminal  28  transmits an operation result to the management server  18 . 
     [Configuration of Removable Battery  14 ] 
     Next, an example of a configuration of the removable battery  14  will be described. 
       FIG. 2  is a diagram showing the example of the configuration of the removable battery  14  according to the present embodiment. As shown in  FIG. 2 , the removable battery  14  includes a power storage battery  141 , a measurement sensor  142 , a battery management unit (BMU)  143 , a storage device  144 , and a connector  145 . 
     The power storage battery  141  is, for example, any one of a secondary battery, a lead storage battery, a capacitor, a lithium ion battery, and the like. 
     The measurement sensor  142  includes various types of sensors that measure electric current and temperature. The measurement sensor  142  outputs a measured value to the BMU  143 . A temperature sensor measures the temperature of the power storage battery  141 . An electric current sensor measures the value of the electric current which flows through the power storage battery  141 . 
     The BMU  143  controls charging and power supply for the power storage battery  141 . For example, the BMU  143  causes the storage device  144  to store the measured value output by the measurement sensor  142  at each predetermined timing. For example, the BMU  143  causes SOC information of the removable battery  14  to be stored at each predetermined timing. The predetermined timing is, for example, every minute. When the removable battery  14  is inserted into the slot unit  21  of the charging station  16 , the BMU  143  outputs information stored in the storage device  144  to the charging station  16 . The information stored in the storage device  144  after the measurement performed by the BMU  143  is not limited to the temperature and the SOC of the battery and it is only necessary for the information to be an element for estimating deterioration of the removable battery  14 . 
     The storage device  144  stores identification information of the removable battery  14 , information about the initial performance of the removable battery  14 , (temperature information including) measured values of the measurement sensor  142  from the time of renting to the time of returning, and SOC information of the removable battery  14  from the time of renting to the time of returning, and the like. The initial performance information is about the initial capacity of the power storage battery  141  and the initial resistance value of the power storage battery  141 . The storage device  144  stores a program that causes the BMU  143  to execute a process. 
     The connector  145  is a connector associated with the slot unit  21  of the charging station  16  and has a communication function. 
     [Configuration of Charging Station  16 ] 
     Next, an example of a configuration of the charging station  16  will be described. 
       FIG. 3  is a diagram showing the example of the configuration of the charging station  16  according to the present embodiment. As shown in  FIG. 3 , the charging station  16  includes a display  161 , an authenticator  162 , a charger  163 , a connector  164 , a communicator  165 , a storage device  166 , and a controller  167 . 
     The controller  167  includes a charging controller  1671 , a measurement sensor  1672 , an information acquirer  1673 , and a processor  1674 . 
     The display  161  is any one of a liquid crystal display device, an organic electro luminescence (EL) display device, an electronic ink display device, and the like. The display  161  displays information output by the controller  167 . For example, the displayed information is about the remaining capacity of the removable battery  14 , a usage fee of the removable battery  14 , and the like. 
     The authenticator  162  is equipment that reads recorded information of a near field communication (NFC) card (not shown) carried by a user using, for example, NFC. Thereby, the charging station  16  authenticates a user who has the authority to use the sharing service using a user ID included in the recorded information. The authenticator  162  outputs the acquired user ID to the controller  167 . 
     The charger  163  is equipment that charges the removable battery  14  in accordance with control of the controller  167  under a state in which the connector  145  of the removable battery  14  and the connector  164  of the charging station  16  are connected. A power supply (not shown) for supplying electric power to the removable battery  14  is connected to the charger  163 . The charger  163  is an example of a “charger.” 
     The connector  164  is a connector associated with the removable battery  14  and supplies electric power and transmits and receives information. The connector  164  acquires battery information from the removable battery  14  and outputs the acquired battery information to the information acquirer  1673 . 
     The communicator  165  transmits the battery performance information and the battery information to which the identification information of the charging station  16  output by the controller  167  is added to the management server  18  via the network NW. The communicator  165  receives reservation information of the removable battery  14  transmitted by the management server  18  and outputs the received reservation information of the removable battery  14  to the controller  167 . The communicator  165  transmits the number of rentable removable batteries  14  given by the charging station  16  and output by the controller  167  and the like to the management server  18 . The communicator  165  transmits the user ID to which the identification information of the charging station  16  is added output by the controller  167  to the management server  18 . 
     The storage device  166  stores the identification information of the charging station  16 , the battery information of each of the removable batteries  14  inserted into the slot unit  21 , and the like. The storage device  166  stores a program that causes the controller  167  to execute the process. 
     The controller  167  acquires the battery performance information and the battery information output by the connector  164  and causes the storage device  166  to store the acquired battery performance information and the acquired battery information. The controller  167  measures or estimates the battery performance (the capacity and the resistance value) of the removable battery  14  that has been returned and inserted into the slot unit  21  of the charging station  16 . The controller  167  adds the identification information of the charging station  16  to the battery information and the battery performance information stored in the storage device  166  and outputs the battery information and the battery performance information to the communicator  165 . The controller  167  functions as the charging controller  1671 , the information acquirer  1673 , and the processor  1674  by reading and executing the program stored in the storage device  166 . The controller  167  detects the number of rentable removable batteries  14  based on the information stored in the storage device  166 , adds the identification information of the charging station  16  to the detected number of rentable removable batteries  14 , and outputs the detected number of rentable removable batteries  14  to the communicator  165 . 
     The controller  167  permits the user to rent the removable battery  14  when the user has been successfully authenticated by the authenticator  162 . The controller  167  acquires the user ID output by the authenticator  162 , adds the identification information of the charging station  16  to the acquired user ID, and outputs the user ID to the communicator  165 . 
     The charging controller  1671  controls the charger  163  based on the measured value of the measurement sensor  1672  and controls a process of charging the removable battery  14  inserted into the slot unit  21 . 
     The measurement sensor  1672  is, for example, an electric current sensor or a voltage sensor. The measurement sensor  1672  measures an electric current value and a voltage value when an electric current flows through the removable battery  14  and outputs measured values thereof to the charging controller  1671 . 
     The information acquirer  1673  acquires battery information from the removable battery  14  connected to the connector  164  and causes the storage device  166  to store the acquired battery information. 
     The processor  1674  estimates the battery performance (the capacity and the resistance value) of the removable battery  14  based on the measured value of the measurement sensor  1672 . The processor  1674  generates histogram data of the temperature of the battery based on the temperature information included in the battery information acquired by the information acquirer  1673 . The processor  1674  generates histogram data of an SOC of the battery based on the temperature information included in the battery information acquired by the information acquirer  1673 . In the case of a normal distribution, the histogram data of the temperature of the battery can be replaced with an average value and the standard deviation therein. In the case of a normal distribution, the histogram data of the SOC of the battery can be replaced with the average value and the standard deviation therein. 
     [Configuration of Management Server  18 ] 
     Next, an example of a configuration of the management server  18  will be described. 
       FIG. 4  is a diagram showing the example of the configuration of the management server  18  according to the present embodiment. As shown in  FIG. 4 , the management server  18  includes a communicator  181 , a storage device  182 , a controller  183 , and an outputter  184 . The controller  183  includes an information acquirer  1831 , a model generator  1832 , and an estimator  1833 . The model generator  1832  is an example of a “learner.” 
     The communicator  181  receives the battery performance information and the battery information transmitted by the charging station  16  via the network NW and outputs the received battery performance information and the received battery information to the controller  183 . The communicator  181  transmits the reservation information of the removable battery  14  output by the controller  183  to the portable terminal  28  via the network NW. 
     The storage device  182  stores the battery deterioration prediction model generated by the model generator  1832 . The storage device  182  stores a program that causes the controller  183  to execute the process. 
     The controller  183  acquires the battery performance information and the battery information output by the communicator  181  and generates a battery deterioration prediction model through machine learning using the acquired battery performance information and the acquired battery information. The controller  183  estimates the battery capacity and the battery resistance using the generated battery deterioration prediction model. The controller  183  outputs the estimated battery capacity and the estimated battery resistance to the outputter  184 . 
     The information acquirer  1831  acquires the battery performance information and the battery information output by the communicator  181  and outputs the acquired battery performance information and the acquired battery information to the model generator  1832 . 
     The model generator  1832  generates a battery deterioration prediction model through machine learning using the battery performance information and the battery information output by the information acquirer  1831  and causes the storage device  182  to store the generated battery deterioration prediction model. A method of generating the battery deterioration prediction model will be described below. 
     The estimator  1833  estimates the battery capacity and the battery resistance using the battery deterioration prediction model at any timing. 
     The outputter  184  is an image display device, a printing device, or the like. The outputter  184  displays or prints the battery capacity and the battery resistance output by the controller  183 . 
     [Procedure of Generating Battery Deterioration Prediction Model] 
     Next, a procedure for generating a battery deterioration prediction model will be described. 
       FIG. 5  is a flowchart of the procedure of generating the battery deterioration prediction model according to the present embodiment. The following process is performed, for example, when a user visits the charging station  16  and replaces the removable battery  14 . 
     (Step L 1 ) The replacement work of the removable battery  14  starts. 
     (Step S 1 ) The controller  167  of the charging station  16  detects that the removable battery  14  has been returned to the slot unit  21 . For example, the controller  167  recognizes that the removable battery  14  has been returned when the connector  164  can acquire information from the removable battery  14 . 
     (Step S 2 ) The measurement sensor  1672  measures a capacity and a resistance value of the removable battery  14  returned to the slot unit  21  and the processor  1674  acquires battery performance information including the capacity and the resistance value of the removable battery  14  measured by the measurement sensor  1672  when the removable battery  14  has been returned (when the removable battery  14  has been inserted into the slot unit  21 ) from the measurement sensor  1672 . 
     (Step S 3 ) The information acquirer  1673  acquires the battery information stored in the storage device  144  of the removable battery  14  via the connector  164 . The acquired battery information includes at least a part or preferably all of information indicating a production date of the removable battery  14 , initial performance (a capacity and a resistance value) of the removable battery  14 , histogram data of the temperature of the removable battery  14 , and histogram data of an SOC of the removable battery  14 . 
     (Step S 4 ) The controller  167  adds identification information of the charging station to the battery performance information including the capacity and the resistance value when the removable battery  14  is returned and the battery information stored in the storage device  144  of the removable battery  14  and transmits the battery performance information and the battery information to the management server  18 . 
     (Step S 5 ) The information acquirer  1831  of the management server  18  acquires the battery information and the battery performance information transmitted by the charging station  16  via the communicator  181 . 
     Subsequently, the model generator  1832  of the management server  18  calculates the number of days elapsed after the removable battery  14  was produced based on the production date included in the acquired battery information and the present date. 
     Subsequently, the model generator  1832  of the management server  18  generates data in which the acquired battery information is associated with the acquired battery performance information serving as a teacher label as teacher data (training data) and generates a battery deterioration prediction model through machine learning using the generated teacher data. Thereby, the battery deterioration prediction model is learned to output the battery performance information of the removable battery  14  at the same time when the battery information of the removable battery  14  at a certain point in time is input. 
     Subsequently, the model generator  1832  causes the storage device  182  to store the generated battery deterioration prediction model. 
     (Step L 2 ) The replacement work of the removable battery  14  is completed. 
     The controller  183  of the management server  18  acquires battery information and battery performance information including measured values such as a capacity and a resistance value every time the removable battery  14  is replaced and updates the battery deterioration prediction model through machine learning. 
     Although an example in which the battery performance is measured when the removable battery  14  is replaced and the battery information is acquired and transmitted to the management server  18  has been described in the example shown in  FIG. 5 , the present invention is not limited thereto. A timing when the battery performance is measured, a timing when the battery information is acquired, or a timing of transmission to the management server  18  may be a timing when the removable battery  14  is returned or after the return. 
     Next, machine learning of the battery deterioration prediction model will be described. 
       FIG. 6  is a diagram showing an example in which the battery deterioration prediction model is generated according to the present embodiment. 
     Reference sign g 101  denotes an example of battery information (hereinafter also referred to as model learning data) with which the teacher label is associated. In the example denoted by reference sign g 101 , the model learning data includes the number of days elapsed after the removable battery  14  was produced, the initial performance (the capacity and the resistance value) of the removable battery  14 , the histogram data of the temperature of the removable battery  14 , the histogram data of the SOC of the removable battery  14 , and the like. 
     Reference sign  102  denotes an example of battery performance information (hereinafter also referred to as correct answer data of model learning) serving as the teacher label. In the example denoted by reference sign g 102 , the correct answer data of the model learning represents a battery capacity at the time of measurement and a battery resistance value at the time of measurement. 
     Reference sign  103  denotes an example of a learning engine for machine learning. As denoted by reference sign g 103 , an engine of the machine learning includes, for example, deep learning, random forest regression (random forest), extreme gradient boosting (XG-boosting), a support vector machine, ensemble learning thereof, and the like. The ensemble learning is, for example, bagging, boosting, or the like. 
     As shown in  FIG. 6 , the model generator  1832  learns the battery deterioration prediction model according to a learning method such as deep learning using teacher data in which the correct answer data of the model learning is associated with the model learning data. Thereby, the battery deterioration prediction model is generated. 
     [Battery Performance Estimation Process Using Battery Deterioration Prediction Model] 
     Next, an example of a process of estimating battery performance using the generated battery deterioration prediction model will be described.  FIG. 7  is a flowchart of the example of the process of estimating the battery performance using the battery deterioration prediction model according to the present embodiment. The management server  18  performs the process of  FIG. 7  at any timing. 
     (Step S 11 ) The controller  183  of the management server  18  executes the battery deterioration prediction model stored in the storage device  182  using the input data. The input data includes information indicating the number of days elapsed after the removable battery  14  was produced, initial performance (a capacity and a resistance value) of the removable battery  14 , histogram data of the temperature of the removable battery  14 , and histogram data of an SOC of the removable battery  14 . That is, the input data may be the battery information itself acquired by the management server  18  from the charging station  16 . Alternatively, the input data may be data obtained by performing appropriate processing or the like on the battery information acquired from the charging station  16  by the management server  18 . 
     (Step S 12 ) The controller  183  estimates battery performance (a capacity and a resistance value) at present according to the processing of step S 11 . For example, the controller  183  inputs battery information as input data to the battery deterioration prediction model sufficiently learned using the teacher data and estimates the battery performance such as the capacity and the resistance value of the removable battery  14  at present based on battery performance information output by the battery deterioration prediction model to which the battery information is input. 
     Subsequently, the controller  183  outputs the estimated battery performance to the outputter  184 . Subsequently, the outputter  184  displays, for example, the battery performance. 
     For example, the management server  18  may acquire the number of days after the production of the removable battery  14  and the initial performance (the capacity and the resistance value) among a plurality of information elements included in the input data from a production factory (not shown) of the removable battery  14  that manages the production date and the initial performance of the removable battery  14  in association with identification information of the removable battery  14 . Alternatively, in relation to the number of days after the production of the removable battery  14  and the initial performance (the capacity and the resistance value), for example, the production factory of the removable battery  14  may cause the storage device  144  of the removable battery  14  to store the production date and the initial performance of the removable battery  14 . In this case, the charging station  16  may read the production date and initial performance of the removable battery  14  stored in the storage device  144  of the removable battery  14  when the removable battery  14  is returned and transmit the read production date and the read initial performance of the removable battery  14  to the management server  18 . 
     In relation to the temperature histogram data and the SOC histogram, for example, a program for causing the BMU  143  to create a histogram for a usage history from the production date to a present point in time may be implemented in the storage device  144  of the removable battery  14  at a production factory or the like. The charging station  16  may read the histogram data from the storage device  144  when the removable battery  14  has been inserted into the slot unit  21  and transmit the read histogram data to the management server  18 . The controller  167  of the charging station  16  may create the histogram. In this case, the controller  167  may create the histogram when the removable battery  14  has been inserted into the slot unit  21 . Alternatively, when the motorcycle  12  includes a communicator as described below, the communicator provided in the motorcycle  12  may transmit SOC data and temperature data to the management server  18  at predetermined time intervals. In this case, the management server  18  may create the histogram using the above-described time-series data received from the communicator provided in the motorcycle  12 . 
     The estimator  1833  may further perform the estimation of the time when the currently used removable battery  14  will be replaced with a new one based on the estimated battery performance, the initial performance of the battery, the number of days elapsed from production, and the like. 
     Next, the estimation of the battery performance using the battery deterioration prediction model will be described.  FIG. 8  is a diagram showing an example in which the battery performance is estimated using the battery deterioration prediction model according to the present embodiment. 
     Reference sign gill denotes an example of input data. As denoted by reference sign g 111 , the input data includes information indicating the number of days elapsed after the removable battery  14  was produced, the initial performance (the capacity and the resistance value) of the removable battery  14 , the histogram data of the temperature of the removable battery  14 , the histogram data of the SOC of the removable battery  14 , and the like. 
     Reference sign  112  denotes a battery deterioration prediction model generated through machine learning. Reference sign g 113  denotes estimated values output by the battery deterioration prediction model. As denoted by reference sign g 113 , the estimated values are the current battery capacity and the current resistance value of the battery. 
     An example of the histogram data of the temperature of the removable battery  14  and an example of the histogram data of the SOC of the removable battery  14  will be described.  FIG. 9  is a diagram showing an example of the histogram data of the temperature of the removable battery  14  according to the present embodiment. In  FIG. 9 , the horizontal axis represents the temperature [degrees] and the vertical axis represents a frequency. 
       FIG. 10  is a diagram showing an example of the histogram data of the SOC of the removable battery  14  according to the present embodiment. In  FIG. 10 , the horizontal axis represents an SOC [%] and the vertical axis represents a frequency. 
     Next, an example of data used for learning will be described.  FIG. 11  is a diagram showing an example of data used for learning according to the present embodiment. As shown in  FIG. 11 , the controller  183  of the management server  18  acquires battery information (the number of days of use and initial performance (a capacity and a resistance value), a temperature histogram, and an SOC histogram) and battery performance (a capacity and a resistance value) corresponding to actually measured values every time the removable battery  14  is replaced. For example, assuming that there are 1000 removable batteries  14  used in the battery deterioration estimation system  10 , the controller  183  acquires the battery information and the actually measured values every time the removable battery  14  is replaced with respect to each of 1000 removable batteries  14 ( 1 ) to  14 ( 1000 ) (not shown). Thus, the controller  183  acquires data used for learning a plurality of times for one removable battery  14 ( 1 ) for each replacement. A variation in how the removable battery  14  is used is included in temperature histogram information and SOC histogram information in the battery information, and the battery performance (the capacity and the resistance value) corresponding to the actually measured values. 
     The controller  183  does not use the identification information of the removable battery  14  and the identification information of the charging station  16  for the learning data. However, when there are a plurality of types of the removable battery  14  used in the battery deterioration estimation system  10 , the controller  183  associates the type of battery with the identification information of the removable battery  14  used in the battery deterioration estimation system  10  in advance and causes the storage device  182  to store an association result. In this case, it is assumed that a mechanism of deterioration differs according to each type of removable battery  14 . Thus, the controller  183  may identify the type of removable battery  14  based on the identification information of the removable battery  14  included in the acquired battery information and the information stored in the storage device  182  and generate a battery deterioration prediction model for each type of removable battery  14 . 
     As described above, in the present embodiment, the battery deterioration prediction model is learned using the battery information and the battery performance information. In the present embodiment, the capacity and the resistance value of the removable battery  14  are estimated using the learned battery deterioration prediction model. 
     Thereby, according to the present embodiment, it is possible to predict a change in deterioration of the removable battery  14  without any specialized knowledge about the deterioration of the battery. Also, according to the present embodiment, it is expected that the larger number of removable batteries  14  in the market will be associated with the higher prediction accuracy. 
     Further, according to the present embodiment, it is possible to predict the capacity and the resistance value of the removable battery  14  without acquiring much time-series data at certain short time intervals, so that the cost of collecting data in the market can be reduced. 
     First Modified Example 
     Although an example in which the battery performance such as the resistance value of the removable battery  14  on the charging station  16  side is obtained through measurement has been described in the example described with reference to  FIG. 5 , the present invention is not limited thereto. The battery performance may be estimated on the management server  18  side. 
       FIG. 12  is a flowchart of a procedure of creating a battery deterioration prediction model according to the first modified example of the present embodiment. Processing similar to that in  FIG. 5  is denoted by the same reference signs and a description thereof will be omitted. 
     (Step L 1 ) The replacement work of the removable battery  14  starts. 
     (Step S 1 ) The controller  167  of the charging station  16  performs the processing of step S 1  and the process proceeds to the processing of step S 101  after the processing of step S 1 . 
     (Step S 101 ) The information acquirer  1673  acquires the battery information stored in the storage device  144  of the removable battery  14  via the connector  164 . The acquired battery information includes information indicating a production date of the removable battery  14 , initial performance (a capacity and a resistance value) of the removable battery  14 , histogram data of the temperature of the removable battery  14 , and histogram data of an SOC of the removable battery  14 . The information acquirer  1673  acquires time-series data of a battery voltage and an electric current at the time of charging. 
     (Step S 102 ) The controller  167  adds identification information of the charging station to the acquired battery information and information about battery performance and transmits the information to the management server  18 . The battery information includes, for example, the number of days after the removable battery  14  was produced, initial performance (a capacity and a resistance value), histogram data of the temperature of a battery, and histogram data of an SOC of the battery. The information about the battery performance includes time-series data (a voltage, an electric current, and the temperature) at the time of charging at the charging station  16 . 
     (Step S 103 ) The information acquirer  1831  of the management server  18  acquires the battery information and the information about the battery performance transmitted by the charging station  16  via the communicator  181 . Subsequently, the estimator  1833  estimates the battery performance (the capacity and the resistance value) according to a well-known method using the time-series data included in the acquired information about the battery performance After the processing in the estimator  1833 , the process proceeds to the processing of step S 5 . 
     (Step S 5 ) The management server  18  performs the processing of step S 5 . 
     (Step L 2 ) The replacement work of the removable battery  14  is completed. 
     A procedure of processing machine learning of the battery deterioration prediction model is the same as that of  FIG. 6 . The management server  18  performs machine learning using the estimated battery performance (the capacity and the resistance value). 
     As described above, even if the teacher data at the time of machine learning is about battery performance actually measured by the charging station  16  as in the embodiment, current battery performance values (a capacity and resistance) estimated by the management server  18  may be used as in the first modified example. 
     When the battery performance is estimated by the management server  18 , the information about the battery performance acquired by the charging station  16  may be time-series data of each of a voltage, an electric current, and the temperature at the time of charging. 
     As described above, in the first modified example, the battery performance is estimated. Thereby, according to the first modified example, it is not necessary to actually measure the battery performance of the battery. Also, according to the first modified example, the prediction accuracy can be improved each time the number of samples of data received from the charging station  16  increases. 
     As described in the first embodiment, the charging station  16  may transmit at least one of the measured values obtained by measuring the battery performance of the removable battery  14  and the time-series data at the time of charging to the management server  18 . In this case, the management server  18  may perform machine learning using the received information about the battery performance (the measured values and the time-series data). 
     Second Modified Example 
     Also, although an example in which the charging station  16  transmits battery performance and battery information to the management server  18  has been described in the above-described example, the present invention is not limited thereto. For example, when the motorcycle  12  includes a telematics communication unit (TCU) having a communication function, the TCU may transmit the battery performance and the battery information to the management server  18  at a predetermined time or at a predetermined time interval. 
       FIG. 13  is a diagram showing the overall configuration of a battery deterioration estimation system  10 A according to a second modified example of the present embodiment. As shown in  FIG. 13 , the battery deterioration estimation system  10 A includes a saddle-riding type motorcycle  12  (a saddle-riding type vehicle), a TCU  13 , a removable battery  14  (a battery), a charging station  16 , a management server  18 , and a portable terminal  28 . Components having functions similar to those of the battery deterioration estimation system  10  are denoted by the same reference signs and a description thereof will be omitted. 
     The TCU  13  is a communication unit mounted within the motorcycle  12 . The TCU  13  acquires battery information (a production date, initial performance (a capacity and a resistance value), the temperature histogram, and an SOC histogram), which is stored in the storage device  144  ( FIG. 2 ) of the removable battery  14 , and transmits the acquired battery information to the management server  18  via a network NW. A transmission timing is, for example, a predetermined time or a predetermined time interval (for example, every minute). 
     In this case, as in the battery deterioration estimation system  10 , the charging station  16  also estimates the battery performance (the capacity and the resistance value) when the removable battery  14  has been returned and reads the battery information from the removable battery  14 . The charging station  16  transmits the battery information regarding the estimated battery performance to the management server  18 . 
     The controller  183  ( FIG. 4 ) of the management server  18  receives battery information from the TCU  13  via the communicator  181  ( FIG. 4 ). The controller  183  of the management server  18  receives the battery performance and the battery information from the charging station  16  via the communicator  181 . The model generator  1832  ( FIG. 4 ) of the management server  18  generates a battery deterioration prediction model through machine learning using the battery information (a time-series history) received from the TCU  13  and the charging station  16  and the battery performance received from the charging station  16 . 
     In the above-described second modified example, it is also possible to obtain effects similar to those of the battery deterioration estimation system  10 . In the modified example, because the battery information (the time-series history) can also be obtained from the TCU  13 , so that more accurate learning can be performed using the battery information. 
     Other Modified Examples 
     Although a case in which the information acquirer  1831  of the management server  18  indirectly acquires the battery information and the battery performance information of the removable battery  14  from the charging station  16  via the communicator  181  has been described in the description of the above-described embodiment, the present invention is not limited thereto. For example, the information acquirer  1831  of the management server  18  may acquire the battery information and the battery performance information of the removable battery  14  directly from the removable battery  14  via the communicator  181 . In this case, the removable battery  14  may include a communicator  146  that communicates with the management server  18  or the like in addition to the power storage battery  141 , the measurement sensor  142 , the BMU  143 , the storage device  144 , and the connector  145 . The communicator  146  of the removable battery  14  transmits the battery information and the battery performance information to the management server  18 . Thereby, the management server  18  can directly communicate with the removable battery  14  without involving the charging station  16 , so that a change in the deterioration of the removable battery  14  can be predicted. 
     The above-described embodiment can be expressed as follows. 
     (1) According to an embodiment of the present invention, the battery deterioration estimation system  10  includes the removable battery  14  removably mounted on the motorcycle  12  movable using electric power and configured to store battery information including a parameter for enabling deterioration of the battery to be determined; the charging station  16  configured to charge the removable battery  14 , acquire the battery information from the removable battery  14 , acquire battery performance information about battery performance of the removable battery  14 , and transmit the battery performance information and the battery information to a server device; and the management server  18  configured to designate the battery information received from the charging station  16  as learning data, designate the battery performance as teacher data, generate a battery deterioration prediction model of the removable battery  14  according machine learning, designate the battery information as input data, and estimate battery performance at the time of estimation using the battery deterioration prediction model. 
     (2) In the battery deterioration estimation system  10 , the battery information may include the number of days elapsed after the removable battery  14  was produced, the initial capacity of the removable battery  14 , and the initial resistance value of the removable battery  14 , a histogram of the temperature of the removable battery  14 , and a histogram of the remaining capacity of the removable battery  14  and the battery performance may include a capacity of the removable battery  14  at the time of measurement and a resistance value of the removable battery  14  at the time of measurement. 
     (3) In the battery deterioration estimation system  10 , the battery information may include the number of days elapsed after the removable battery  14  was produced, the initial capacity of the removable battery  14 , and the initial resistance value of the removable battery  14 , the standard deviation and the average value in the temperature of the removable battery  14 , and the standard deviation and the average value in the remaining capacity of the removable battery  14  and the battery performance may include the capacity of the removable battery  14  at the time of measurement and the resistance value of the removable battery  14  at the time of measurement. 
     (4) In the battery deterioration estimation system  10 , the charging station  16  may acquire battery performance information and battery information when the removable battery  14  has been returned and transmit the battery performance information and battery information to the management server  18 . 
     (5) In the battery deterioration estimation system  10 , the charging station  16  may acquire the battery performance information and the battery information for a period in which the removable battery  14  is rented out after the removable battery  14  is returned and transmit the battery performance information and the battery information to the management server  18 . 
     (6) In the battery deterioration estimation system  10 , the battery performance information may be at least one of measured values of a capacity and a resistance value obtained by measuring the battery performance of the removable battery  14  and time-series data of the voltage, the electric current, and the temperature when the removable battery  14  is charged. 
     (7) According to another aspect of the present invention, the management server  18  includes the information acquirer  1673  configured to acquire battery information from the removable battery  14  which is removably mounted on the motorcycle  12  movable using electric power and which stores the battery information including a parameter for enabling deterioration of the battery to be determined and acquire battery performance information about battery performance of the removable battery  14  measured by the charging station  16  configured to charge the removable battery  14 ; the model generator  1832  configured to designate the battery information as learning data, designate the battery performance information as teacher data, and generate a battery deterioration prediction model of the removable battery  14  through machine learning; and the estimator  1833  configured to estimate battery performance at the time of measurement using the deterioration prediction model. 
     Also, all or a part of processing to be performed by the management server  18  may be performed by recording a program for implementing all or some of the functions of the management server  18  according to the present invention on a computer-readable recording medium and causing a computer system to read and execute the program recorded on the recording medium. Also, the “computer system” used here is assumed to include an operating system (OS) and hardware such as peripheral devices. In addition, the computer system is assumed to include a homepage providing environment (or displaying environment) when a World Wide Web (WWW) system is used. In addition, the “computer-readable recording medium” refers to a storage device, including a flexible disk, a magneto-optical disc, a read only memory (ROM), a portable medium such as a compact disc (CD)-ROM, and a hard disk embedded in the computer system. 
     Further, the “computer-readable recording medium” is assumed to include a computer-readable recording medium for retaining the program for a predetermined time period as in a volatile memory (a random access memory (RAM)) inside the computer system including a server and a client when the program is transmitted via a network such as the Internet or a communication circuit such as a telephone circuit. 
     Also, the above-described program may be transmitted from a computer system storing the program in a storage device or the like via a transmission medium or transmitted to another computer system by transmission waves in a transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (a communication network) like the Internet or a communication circuit (a communication line) like a telephone circuit. Also, the above-described program may be a program for implementing some of the above-described functions. Further, the above-described program may be a program capable of implementing the above-described function in combination with a program already recorded on the computer system, i.e., a so-called differential file (differential program). 
     Although modes for carrying out the present invention have been described above using the embodiments, the present invention is not limited to the embodiments and various modifications and replacements can be applied without departing from the spirit and scope of the present invention. 
     DESCRIPTION OF REFERENCE NUMERALS 
     
         
         
           
               10 ,  10 A Battery deterioration estimation system 
               12  Motorcycle (electric power device) 
               13  TCU 
               14  Removable battery 
               16  Charging station (charging device) 
               18  Management server (estimation device) 
               28  Portable terminal 
               20  Housing 
               21  Slot unit 
               141  Power storage battery 
               142  Measurement sensor 
               143  BMU 
               144  Storage device 
               145  Connector 
               161  Display 
               162  Authenticator 
               163  Charger 
               164  Connector 
               165  Communicator 
               166  Storage device 
               167  Controller 
               1671  Charging controller 
               1672  Measurement sensor 
               1673  Information acquirer 
               1674  Processor 
               181  Communicator 
               182  Storage device 
               183  Controller 
               184  Outputter 
               1831  Information acquirer 
               1832  Model generator (learner) 
               1833  Estimator