JP2017135926A - Charge control system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a charge control system capable of accurately estimating a next scheduled departure time, which is used to determine a charge completion time, in order to optimize a charging by a timer with no need of time setting to be done by a user.SOLUTION: A charge control system determines charge start time tcs so that a required charge amount is completely charged by charge completion time tz which is set according to estimated scheduled departure time tdp# in a charge schedule for a charging by a timer. Actual departure time values of vehicles are learned in respective different learning classes. Errors are calculated for respective learning classes each between an actual departure time value for a next scheduled departure day at previous leaning time and an estimated departure time for the next schedule departure day on the basis of past learning results. A next estimated scheduled departure time tdp# is estimated according to an estimated scheduled departure time in a learning class having a minimum one of said errors.SELECTED DRAWING: Figure 4

Description

  The present invention relates to a charge control system, and more particularly to a charge control system for charging an in-vehicle secondary battery with electric power from a power source outside the vehicle.

  A technique for charging an in-vehicle power storage device such as an electric vehicle or a hybrid vehicle with a power source outside the vehicle (hereinafter also simply referred to as “external power source”) is known. Hereinafter, the charging of the in-vehicle power storage device by the external power source is also simply referred to as “external charging”.

  As an aspect of external charging, so-called timer charging for controlling a time schedule of external charging is described in Japanese Patent Laid-Open No. 2013-215084 (Patent Document 1) and the like. For example, Patent Document 1 describes a method of optimizing a charging plan for external charging of a vehicle by analyzing data representing fluctuations in electricity charges and vehicle behavior trends.

  In Patent Document 1, it is described that the standard departure time and the standard home stay time are estimated from past results as tendency parameters when determining the charge setting including the charge start time and the charge end time as the charge plan. ing.

JP 2013-215084 A

  In the case of external charging, it is required to charge necessary energy until the next departure of the user to avoid incomplete charging. For this reason, as in Patent Document 1, in order to perform timer charging without requiring time setting by the user (input of charging end time) in order to improve user convenience, the next departure time is estimated with high accuracy. It is important to.

  In this regard, Patent Document 1 uses learning of actual values for estimation of the standard departure time, and in particular, in the prediction of normal time from normal sunrise, the average and deviation learned from the normal sunrise time (actual value) Is used.

  However, Patent Document 1 does not describe an overall image of what kind of learning is performed. For this reason, for example, when the learning category prepared in advance does not match the usage mode of the user, there is a possibility that the prediction accuracy of the departure time is lowered. Further, even in a period immediately after the start of use where the number of learning is small, there is a concern that the reliability of the statistical value such as the average value is low, so that the prediction accuracy of the departure time decreases.

  The present invention has been made to solve such problems, and an object of the present invention is to determine a charging end time in order to optimize timer charging that does not require time setting input by a user. It is to estimate the next scheduled departure time with high accuracy.

  According to an aspect of the present invention, a charge control system for charging a power storage device mounted on a vehicle with a power supply external to the vehicle includes a learning unit, an estimation unit, and a control unit. The learning means learns the departure time of the vehicle in each of a plurality of predetermined learning categories. The estimating means estimates the next scheduled departure date and scheduled departure time. The control means creates a charging schedule according to the estimated departure date and estimated departure time. The control means operates a charger that converts electric power from outside the vehicle into charging electric power for the power storage device according to the charging schedule. The estimation unit includes a calculation unit and a setting unit. The calculating means calculates the estimated departure time on the scheduled departure date and the previous actual value of the departure time corresponding to the scheduled departure date in the learning category, which is calculated from the learning results obtained so far in each of the plurality of learning categories. Calculate the error. The setting means sets the scheduled departure time according to the estimated departure time on the scheduled departure date in the learning segment having the smallest absolute value of errors among the plurality of learning segments.

  According to the above charging system, in the timer charging (automatic timer charging) for estimating the scheduled departure time and creating the charging schedule, the previous learning in the learning category from the actual learning (estimated map) by the different learning categories. The scheduled departure time can be estimated by selecting the learning category that minimizes the error between the actual value of the departure time at the time and the learning result so far. Therefore, the estimated accuracy of the scheduled departure time can be improved as compared with the case where the estimated departure time is estimated according to a certain learning category. By using the estimated departure time estimated in this way, the user convenience is avoided so as to avoid incomplete charging at the time of departure of the user and to prevent deterioration due to the storage device being left for a long time in a high SOC state. High timer charging can be performed appropriately.

  According to the present invention, the next scheduled departure time for determining the charging end time can be estimated with high accuracy in order to make appropriate the timer charging that does not require time setting input by the user.

1 is an overall configuration diagram of a charging system according to an embodiment of the present invention. It is a whole block diagram which shows the structure of the electric vehicle shown by FIG. FIG. 2 is a block diagram schematically showing the configuration of the data center shown in FIG. 1. It is a conceptual diagram explaining the outline | summary of a charging schedule. It is a flowchart explaining the control process of the timer charge in the charging system according to the present embodiment. It is a flowchart explaining the detail of the estimation process of the scheduled departure time in automatic timer charge. It is the schematic explaining the structure of the estimation map of the scheduled departure time in the charging system according to this Embodiment. It is a graph explaining the 1st specific example of the last track record value of departure time. It is a graph explaining the 2nd specific example of the last track record value of departure time. It is a conceptual diagram explaining the estimation error by each estimation map.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. In the following, the same or corresponding parts in the drawings are denoted by the same reference numerals, and the description thereof will not be repeated in principle.

(Configuration of external charging system)
FIG. 1 is an overall configuration diagram of a charging system according to an embodiment of the present invention. Referring to FIG. 1, a charging system 10 for externally charging an electric vehicle 100 includes an electric vehicle 100, a charging stand 200, a data center 300, and a smartphone 500.

  Electric vehicle 100 is connected to charging station 200 through charging cable 400. Charging stand 200 is shown as an example of an apparatus that supplies electric power from electric power source (for example, commercial power source) outside the vehicle to electric vehicle 100. The charging stand 200 is provided at a plurality of charging locations, and is provided at home as well as outside places such as a service area and a shopping center. In other words, electrically powered vehicle 100 can charge the in-vehicle power storage device with charging stand 20 at a plurality of charging locations.

  Electric vehicle 100 may receive power by mounting on vehicle 10 a power receiving coil that receives power in a non-contact manner through an electromagnetic field from a power transmitting coil to which AC power is supplied from charging station 200. Electric vehicle 100 is, for example, an electric vehicle such as a hybrid vehicle or an electric vehicle that can travel using electric power stored in a power storage device.

  The electric vehicle 100 has a configuration (described later) for external charging for charging a power storage device (described later) mounted on the vehicle with electric power supplied from the charging stand 200. Furthermore, electric vehicle 100 has a function of automatically starting external charging in accordance with a charging schedule set on a time basis. Hereinafter, external charging by such a function is also referred to as “timer charging”.

  The smartphone 500 is a communication device that can communicate with various devices. The user can display information related to external charging of the electric vehicle 100 and input settings through the smartphone 500. For example, using the smartphone 500 as a tool, manual settings related to external charging (immediate charging start instruction, charging start / end time setting, or target charging level setting) and display of the set charging schedule can be performed. It is. The manual setting input and display can be executed using a touch panel or the like disposed in the electric vehicle 100.

  The data center 300 can perform various types of information processing. For example, in the data center 300, management of electricity bill information, estimation processing of a scheduled departure time of a user, which will be described later, and creation of a charging schedule can be performed.

  The electric vehicle 100 has a wireless communication function. The electric vehicle 100 can receive a charging schedule from the data center 300 through the communication network. Hereinafter, the timer charging according to the user setting input via the smartphone 500 or the like is referred to as “manual timer charging”, and the charging automatically created by the data center 300 or the like instead of the charging schedule set by the user. Timer charging according to the schedule is also referred to as “automatic timer charging”. Thus, in charging system 10, electrically powered vehicle 100 can perform automatic timer charging according to the charging schedule received from data center 300.

(Configuration of electric vehicle)
FIG. 2 is an overall block diagram showing the configuration of the electric vehicle 100.

  Referring to FIG. 2, electrically powered vehicle 100 includes a charging inlet 110, a power storage device 120, a charger 130, a PCU (Power Control Unit) 140, a DCM (Data Communication Module) 150, and a GPS (Global Positioning System). ) Module 160 and ECU (Electronic Control Unit) 170.

  Charging connector 410 of charging cable 400 is connected to charging inlet 110. Then, electric power from a power source outside the vehicle is supplied to the electric vehicle 100 through the charging connector 410 and the charging inlet 110.

  The power storage device 120 is a power storage element configured to be chargeable / dischargeable. The power storage device 120 includes, for example, a secondary battery such as a lithium ion battery, a nickel metal hydride battery, or a lead storage battery, and a power storage element such as an electric double layer capacitor.

  The charger 130 converts an alternating current supplied from a power supply outside the vehicle through the charging inlet 110 into a direct current. Then, the charger 130 increases or decreases the voltage to a desired voltage and supplies the voltage to the power storage device 120. The charger 130 includes, for example, a rectifier circuit that converts an alternating current into a direct current and a converter that steps up or steps down the voltage. Charger 130 is not limited to such a configuration, and may be configured to supply DC power supplied from a power source outside the vehicle to power storage device 120, for example.

  PCU 140 includes an inverter, a motor connected to the inverter, and the like, and generates a traveling driving force of electric vehicle 100 using electric power supplied from power storage device 120.

  The DCM 150 is a communication device that can wirelessly communicate with the outside. The DCM 150 is, for example, a communication module compliant with a communication standard such as W-CDMA (Wideband Code Division Multiple Access), LTE (Long Term Evolution), or a wireless LAN standard such as IEEE (Institute of Electrical and Electronic Engineers) 802.11. including.

  The GPS module 160 is a receiving device used in a satellite positioning system. The GPS module 160 calculates the current position of the electric vehicle 100 based on the received signal.

  ECU 170 includes a CPU (Central Processing Unit) and a memory (not shown), and each device (charger 130, PCU 140) of electric vehicle 100 based on information stored in the memory and information from each sensor (not shown). , DCM 150, GPS module 160). ECU 170 has a built-in timer and can recognize the current time.

  The main function realized by the ECU 170 is an automatic timer charging function. As described above, the automatic timer charging function is timer charging that is performed according to a charging schedule that is automatically created by the data center 300 or the like without the setting of the charging end time by the user. ECU 170 controls the operation and stop of charger 130 so that power storage device 120 is charged in a time zone according to the charging schedule received from data center 300. Thereby, an automatic timer charging function is realized.

  ECU 170 can transmit an instruction to create a charging schedule for automatic timer charging to data center 300 through DCM 150 when charging connector 410 is connected to charging inlet 110.

  Further, ECU 170 can manage travel information of electric vehicle 100. The travel information includes the departure time of electric vehicle 100 by the user. For example, ECU 170 can detect the departure time based on the ON time (IG ON time) of an ignition switch (not shown) or the disconnection time of charging cable 400.

  Further, the travel information may include information on SOC (State of Charge) of power storage device 120 of electric vehicle 100 or travel distance. For example, the SOC change amount (decrease amount) from the time when the user leaves (for example, when the charging cable 400 is disconnected) to the time when the operation is ended after returning (for example, when the charging cable 400 is connected) is used as the daily SOC usage amount. Can be detected. Travel information such as departure time and SOC usage detected in this way can be transmitted to the data center 300 through the DCM 150.

(Data center configuration)
FIG. 3 is a block diagram schematically showing the configuration of the data center 300 shown in FIG. With reference to FIG. 3, the data center 300 includes a communication module 310, a storage device 320, and a controller 330.

  The communication module 310 is a communication module that can communicate with the electric vehicle 100 and the smartphone 500. The communication module 310 receives information on electricity charges for each time zone from a server (for example, managed by an electric power company). Further, the communication module 310 can receive the actual value of the departure time from the electric vehicle 100. The communication module 310 outputs the received information to the controller 330.

  The storage device 320 stores various information collected from the Internet, a control program executed in the data center 300, and the like. For example, the storage device 320 stores information on the electricity rate for each time period received by the communication module 310, an estimated departure time estimated map, which will be described later, and the like.

  The controller 330 creates a charging schedule using information acquired through the communication module 310.

FIG. 4 is a conceptual diagram illustrating the contents of the charging schedule.
Referring to FIG. 4, the charging schedule includes a charging end time tz and a charging start time tcs. The charging start time tcs is determined so that the charging of the necessary charge amount (ΔSOC) is completed by the charging end time tz. Moreover, since deterioration progresses when the power storage device is left in a high SOC state for a long period of time, it is desirable to consider so that the waiting time in the fully charged state is shortened.

  The charging end time tz is set according to the input value when manual timer charging is applied, that is, when the charging end time or departure time is input by the user. On the other hand, when automatic timer charging is applied, the charging end time tz is set according to the estimation result of the next scheduled departure date and time. The scheduled departure date and time includes a scheduled departure date and a scheduled departure time tdp #. Below, in order to simplify description, description is advanced as what the electric vehicle 100 is used every day. Therefore, the following explanation will be made assuming that the scheduled departure date is always the next day. For example, if the scheduled departure date and time is 8:00 am the next day, the charging end time tz can be set to 7:50 am.

  The required charge amount ΔSOC can be set according to the difference of the current SOC with respect to the fully charged state. Alternatively, the required charge amount ΔSOC in this external charging can be obtained according to the daily SOC usage amount and travel distance included in the travel information. Basically, the required charging time can be obtained by dividing the amount of power corresponding to the required charging amount ΔSOC by the charging power (for example, the rated value) by the charger 130.

  The charging start time tcs can be basically obtained as a time that is traced back by the required charging time from the charging end time tz. However, when there is a time zone with different power unit prices depending on the electricity rate plan, the charging start time tcs is set so that external charging is interrupted in the time zone where the unit price is high.

  When external charging is performed using a part of the time zone in which the power unit price is the same, charging is performed in the time zone on the departure time side as much as possible, so that standby after charging (ie, high SOC state) is possible. It is preferable to set the charging start time tcs so that the time is shortened.

  Thus, by setting the charging end time by timer charging in correspondence with the next scheduled departure date and time, it is possible to prevent the deterioration of the power storage device 120 from being left in the high SOC state.

  In automatic timer charging in which the departure time or charging end time is not input by the user, the estimation accuracy of the scheduled departure time tdp # is important. That is, if the estimation accuracy is low, charging of the required charge amount may not be completed at the user's actual departure time, or deterioration of the power storage device 120 may progress due to a longer standing period after completion of charging. May be a problem. For this reason, in the charging system according to the present embodiment, the departure time estimation process based on the actual learning of the past departure time is improved with the following method.

  FIG. 5 is a flowchart illustrating a timer charging control process in the charging system according to the present embodiment. FIG. 5 shows a process executed by ECU 170 of electric vehicle 100 and a process executed by controller 330 of data center 300.

  With reference to FIG. 5, the process by the side of the vehicle by ECU170 is demonstrated first. ECU 170 determines in step S100 whether timer charging has been activated. For example, after operation of electric vehicle 100 (after IG is turned off), charging stand 200 and electric vehicle 100 are electrically connected by connection of charging cable 400, that is, electric vehicle 100 can be externally charged. Is formed, step S100 is determined as YES.

  ECU 170 executes processing after step S110 when timer charging is started (when YES is determined in S100). On the other hand, when timer charging is not activated (NO in S100), the processing after step S110 is not activated, and timer charging is not activated.

  When the timer charging is activated, ECU 170 determines whether or not the time setting by the user is input in step S110. When the charging end time or the departure time is input by the user, step S110 is determined as YES. In this case, ECU 170 advances the process to step S120, transmits the input time setting to data center 300 (controller 330), and generates a charging schedule creation request in step S130. Thereby, the creation of the charging schedule by manual timer charging is transmitted to the data center 300 (controller 330).

  On the other hand, when the time setting by the user is not input (NO determination in S110), ECU 170 skips step S120 and generates a charge schedule creation request in step S130. Thereby, the creation of a charging schedule by automatic timer charging is transmitted to the data center 300 (controller 330).

  Controller 330 determines whether or not the electric vehicle 100 (ECU 170) has requested creation of a charging schedule in step S200. Then, when the creation of a charging schedule is requested (when YES is determined in S200), controller 330 creates a charging schedule and transmits it to electric vehicle 100 (ECU 170) by the processes in steps S210 to S260.

  When ECU 170 receives a charging schedule from data center 300 (controller 330) in step S140, ECU 170 performs external charging in accordance with the charging schedule in step S150. Specifically, the operation and stop of the charger 130 are controlled according to the charging schedule. When the external charging of power storage device 120 is completed, ECU 170 waits until the user starts driving electric vehicle 100 and leaves (when NO is determined in S170).

  ECU 170 detects the actual value of the departure time when the user departs (YES in S170). As described above, the departure time (actual value) can be detected based on the IG on time or the disconnection time of the charging cable 400. Then, ECU 170 outputs the detected departure time (actual value) to data center 300 (controller 330) in step S180.

  Next, the process on the data center 300 side, mainly the estimated departure time estimation process in steps S210 to S250 will be described in detail.

  When the creation of the charging schedule is requested (YES in S200), the controller 330 determines whether or not the automatic timer charging is performed in step S210.

  When a request for creating a charging schedule by manual timer charging is transmitted from ECU 170 (when NO is determined in S210), data center 300 (controller 330) sets time by user input transmitted from ECU 170 in step S230. In accordance with the information, the scheduled departure time tdp # is set.

  On the other hand, when a request for creating a charging schedule by automatic timer charging is transmitted from ECU 170 (when YES is determined in S210), data center 300 (controller 330) executes estimated processing for estimated departure time in step S220. To do.

  FIG. 6 is a flowchart for explaining the details of the estimated departure time estimation process in the automatic timer charging. That is, FIG. 6 shows details of the process in step S220 of FIG.

  Referring to FIG. 6, controller 330 confirms the distinction between the day of the week and the weekday / holiday of the scheduled departure date (here, the next day) in step S221. Further, in step S222, ECU 170 calls a plurality of estimated maps having different learning segments corresponding to the scheduled departure date.

  FIG. 7 is a schematic diagram illustrating a configuration of an estimated departure time estimation map in the charging system according to the present embodiment.

  Referring to FIG. 7, scheduled departure time estimation map 600 includes a day of week estimation map 610, a weekday / holiday estimation map 620, and a day estimation map 630. The estimated departure time estimation map 600 can be stored using the storage device 320 of the data center 300.

  The day-of-week estimation map 610 includes seven estimation maps 611 to 617 corresponding to Monday through Sunday, with the day of the week as a learning category. The estimated maps 611 to 617 accumulate the actual values of departure times on Monday to Sunday, respectively, and the estimated departure time on the next day of the week is calculated from learning processing (for example, calculation of statistical processing values) of the accumulated actual values. Configured to calculate. Of the estimation maps 611 to 617, the estimation map corresponding to the day of the scheduled departure date is called as the day-of-week estimation map 610 in step S222 of FIG.

  The weekday / holiday estimation map 620 includes a weekday estimation map 621 and a holiday estimation map 622 with weekdays and holidays as learning divisions. The weekday estimation map 621 accumulates the actual value of the departure time on weekdays (basically, Monday to Friday), and calculates the estimated departure time on the next weekday from the learning process of the accumulated actual value. Configured. Similarly, the holiday estimation map 622 accumulates actual values of departure times on holidays (for example, Saturdays, Sundays, and holidays), and calculates the estimated departure time on the next holiday from the learning processing of the accumulated actual values. Configured to do.

  Of the weekday estimation map 621 and the holiday estimation map 622, the estimation map corresponding to the weekday / holiday classification of the scheduled departure date is called as the weekday / holiday estimation map 620 in step S222 of FIG. Note that the distinction between weekdays and holidays (day of week setting, etc.) can be changed by user input to the operation screen of the smartphone 300 or the like.

  The daily estimation map 630 stores the actual value of each sunrise departure time without distinguishing between day of the week and weekday / holiday. The daily estimation map 630 is configured to calculate the estimated departure time on the next day from the learning process of the accumulated performance value. The daily estimation map 630 is also called in step S222 in FIG.

  For example, when the scheduled departure date is “Monday”, the estimation map 611 corresponding to Monday is called as the estimation map 610 for each day of the week (S222), and the estimation for weekdays is performed as the estimation map 620 for each weekday / holiday. The map 621 is called (S222). When the scheduled departure date is “Saturday”, the estimation map 616 corresponding to Saturday is called as the estimation map 610 for each day of the week (S222), and the estimation for holidays is performed as the estimation map 620 for each weekday / holiday. The map 622 is called (S222). In the description of the present embodiment, “Monday” is classified as a weekday, and “Saturday” is classified as a holiday.

  As described above, the day-by-day estimation map 610, the weekday / holiday estimation map 620, and the day-to-day estimation map 630 are obtained from the learning results of the past departure time actual values in each of the different learning sections. The estimated departure time on the next scheduled departure date is calculated.

  Further, every time the user starts driving the electric vehicle and departs, the estimated map is updated so that the actual value of the detected departure time is newly reflected in the learning result. That is, each estimated map is learned each time it is updated with the actual value of the new departure time. For example, when the departure time (actual value) on Monday is obtained, the estimation map 611 corresponding to Monday in the day-of-day estimation map 610, the weekday estimation map 621 in the weekday / holiday estimation map 620, and the day The estimation map 630 is updated.

  Referring to FIG. 6 again, ECU 70 calculates estimated departure times td1 to td3 using each called map in step S223. The estimated departure time td1 is the estimated departure time according to the learning result by the day-of-day estimation map 610. That is, the estimated departure time td1 is calculated from the map corresponding to the scheduled departure date among the estimated maps 611 to 617.

  Similarly, the estimated departure time td2 is the estimated departure time according to the learning result by the weekday / holiday estimated map 620. That is, the estimated departure time td2 is calculated from one of the weekday estimation map 621 and the holiday estimation map 622 corresponding to the scheduled departure date. The estimated departure time td3 is calculated by the daily estimation map 630.

  Further, in step S224, the controller 330 calculates the previous actual values ta1 to ta3 of the departure time in the called day of week estimation map 610, weekday / holiday estimation map 620, and daily estimation map 630. The previous actual value indicates the actual value of the departure time at the latest learning time in each estimated map called in step S222.

  8 and 9 show specific examples of the previous actual value of the departure time. FIG. 9 shows a specific example of the previous actual value of the departure time when the scheduled departure date is “Monday (category on weekdays)”.

  Referring to FIG. 8, when the scheduled departure date is Monday, the estimated map 611 for Monday called as the estimated map 610 for each day of the week is the actual value when last learned, that is, the same day immediately before. The actual value of the departure time on Monday last week is calculated as the previous actual value ta1.

  Similarly, the actual value when the weekday estimation map 621 called as the weekday / holiday estimated map 620 was learned last time, that is, the actual value of the departure time on the previous Friday, which is the previous weekday, is the previous actual value ta2. Is calculated as Also, the actual value when the daily estimation map 630 was last learned, that is, the actual value of the departure time on Sunday yesterday is calculated as the previous actual value ta3.

  FIG. 9 shows a specific example of the previous actual value of the departure time when the scheduled departure date is “Saturday (classified as holiday)”.

  Referring to FIG. 9, when the scheduled departure date is Saturday, the estimated map 616 for Monday called as the estimated map 610 for each day of the week is the actual value when last learned, that is, the same day immediately before. The actual value of the departure time on last Saturday is calculated as the previous actual value ta1.

  Similarly, the actual value when the estimated map for holidays 622 called as the weekly / holiday estimated map 620 was learned last time, that is, the actual value of the departure time on the last Sunday, which is the immediately preceding holiday, is the previous actual value ta2. Is calculated as In addition, the actual value when the daily estimation map 630 was last learned, that is, the actual value of the departure time on Friday yesterday is calculated as the previous actual value ta3.

  Referring to FIG. 6 again, in step S225, controller 330 calculates estimation errors Δtd1 to Δtd3 based on each of day-by-day estimation map 610, weekday / holiday estimation map 620, and daily estimation map 630, in step S225. To do.

FIG. 10 is a conceptual diagram illustrating an estimation error due to each estimation map.
Referring to FIG. 10, in day-by-day estimation map 610, estimated error Δtd1 is calculated according to the time difference between estimated departure time td1 calculated in step S223 and previous actual value ta1 calculated in step S224. That is, the estimation error Δtd1 corresponds to a learning error in the learning section by the day-of-week estimation map 610.

  Similarly, in the weekday / holiday estimation map 620, the estimation error Δtd2 is calculated according to the time difference between the estimated departure time td2 calculated in step S223 and the previous actual value ta2 calculated in step S224. Further, in the daily estimation map 630, the estimation error Δtd3 is calculated according to the time difference between the estimated departure time td3 calculated in step S223 and the previous actual value ta3 calculated in step S224. As described above, the estimation errors Δtd2 and Δtd3 correspond to the learning errors in the respective learning sections of the weekday / holiday estimation map 620 and the daily estimation map 630.

  Referring again to FIG. 6, in step S226, controller 330 determines whether day of week estimated map 610, weekday / holiday estimated map 620, or daily estimated map 630 is based on estimated errors Δtd 1 to Δtd 3 (S 225). To select an estimation map having the smallest estimation error. That is, the learning category with the smallest estimation error on the next scheduled departure date is selected.

  In step S227, ECU 170 sets the estimated departure time based on the estimation map selected in step S226 to scheduled departure time tdp #. For example, in the example of FIG. 10, since the estimation error Δtd2 is smaller than the estimation errors Δtd1 and Δtd3, the estimated departure time td2 based on the weekday / holiday estimated map 620 is set as the scheduled departure time tdp #.

  Referring to FIG. 5 again, when the scheduled departure time tdp # is set in step S220 (when automatic timer charging is applied) or step S230 (when manual timer charging is applied), controller 330 performs external charging in step S240. Calculate the required charge. As described with reference to FIG. 4, the necessary charge amount can be calculated according to the SOC shortage amount until the fully charged state or the predicted use energy in the next vehicle travel based on the past travel information.

  In step S250, controller 330 creates a charging schedule including charging start time tcs and charging end time tz based on scheduled departure time tdp # (S220 or S230) and required charge amount (S240). As described in FIG. 4, the charging end time tz is set according to the scheduled departure time tdp #.

  Controller 330 transmits the created charging schedule to electrically powered vehicle 100 (ECU 170) in step S260. In step S150, ECU 170 operates charger 130 according to this charging schedule to charge power storage device 120. When the user starts driving the electric vehicle 100 and departs, the actual value of the departure time is detected and transmitted to the data center 300 (controller 330).

  After transmitting the charging schedule (S260), controller 330 waits for processing until the departure time (actual value) from electric vehicle 100 (ECU 170) is received (NO in S270). When controller 330 receives the departure time (actual value) from electrically powered vehicle 100 (ECU 170) (when YES in S270), controller 330 receives the received departure for each of the estimated maps called in step S222. Learning is performed using the time (actual value). As a result, the estimated map is updated according to the respective learning sections of the day of week estimated map 610, the weekday / holiday estimated map 620, and the daily estimated map 630. When automatic timer charging is applied thereafter, the scheduled departure time tdp # is estimated based on the latest updated learning result.

  Thus, according to the charging system according to the present embodiment, by selecting and using a plurality of estimated maps with different learning categories in automatic timer charging that does not require time input by the user and has high user convenience, The estimation accuracy of the scheduled departure time can be improved. Thereby, it is possible to improve the estimation accuracy as compared with the case where the estimated departure time is estimated according to a certain learning category. In particular, it is possible to suppress a decrease in the estimation accuracy of the scheduled departure time even in a period immediately after the start of use where the reliability of statistical values such as the average value is low because the number of learning times is small.

  For example, if the estimated departure time is estimated according to the learning only for weekdays / holidays, the characteristics of each day of the week cannot be reflected in the estimation, but if learning for each day of the week is fixed, the number of learnings for each day of the week is Until it is secured, there is a concern that the estimation error does not decrease over a relatively long period due to the difference between the learning initial value and the user's usage mode. On the other hand, in the present embodiment, by extracting and using an estimation map that minimizes the error during the previous learning in the learning segment from a plurality of estimation maps 610, 620, and 630 having different learning segments. The accuracy of the estimated departure time can be improved. For example, during the period immediately after the start of use, when the number of learning increases while suppressing the estimation error by the weekday / holiday map 620 or the daily map 630, the difference in the usage pattern of the user for each day of the week by learning in the day map 610 It is possible to cope with the details.

  As a result, using the estimated scheduled departure time, it is possible to avoid incomplete charging at the time of departure of the user and to prevent deterioration caused by leaving the power storage device in a high SOC state for a long time. It is possible to appropriately execute highly automatic timer charging.

  In the present embodiment, the configuration executed in the data center 300 for the creation of the charging schedule, the estimation of the scheduled departure time when the automatic timer charging is applied, and the storage and update of each estimation map used for the estimation are exemplified. However, some and all of these functions may be executed not by the data center 300 but by the electric vehicle 100 (ECU 170) or the smartphone 500. That is, in the application of the present invention, the function relating to the creation of the charging schedule may be realized by any of the electric vehicle 100, the smartphone 500, and the data center 300, or may be realized cooperatively among a plurality of elements. Also good. The charging schedule for manual timer charging and the charging schedule for automatic timer charging may be created by different elements.

  The embodiment disclosed this time should be considered as illustrative in all points and not restrictive. The scope of the present invention is defined by the terms of the claims, rather than the description above, and is intended to include any modifications within the scope and meaning equivalent to the terms of the claims.

  DESCRIPTION OF SYMBOLS 10 Charging system, 100 Electric vehicle, 110 Charging inlet, 120 Power storage device, 130 Battery charger, 140 PCU, 150 DCM, 160 GPS module, 170 ECU, 200 Charging stand, 300 Data center, 310 Communication module, 320 Storage device, 330 Controller, 400 charging cable, 410 charging connector, 500 smartphone, 600 scheduled departure time estimation map, 610 day of week estimation map, 611 to 617 estimation map (each day of the week), 620 weekday / holiday estimation map, 621 weekday estimation map, 622 holiday estimation map, 630 daily estimation map, ta1 to ta3 previous actual value, tcs charge start time, tdp # scheduled departure time, td1 to td3 departure estimated time, tz charge end time, Δtd1 td3 estimation error (learning categories).

Claims (1)

A charging control system for charging a power storage device mounted on a vehicle by a power source outside the vehicle,
Learning means for learning the departure time of the vehicle in each of a plurality of predetermined learning sections;
An estimation means for estimating the next scheduled departure date and scheduled departure time;
Creating means for creating a charging schedule according to the estimated scheduled departure date and the scheduled departure time;
Control means for operating a charger that converts electric power from outside the vehicle into charging electric power for the power storage device according to the charging schedule;
The estimation means includes
In each of the plurality of learning segments, the estimated departure time on the scheduled departure date calculated from the learning results so far and the previous actual value of the departure time corresponding to the scheduled departure date in the learning segment Means for calculating the error;
A charging control system comprising: a means for setting the scheduled departure time according to the estimated departure time on the scheduled departure date in the learning segment in which the absolute value of the error is the smallest among the plurality of learning segments.

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