Charge scheduling device

Abstract

To provide a charge scheduling device capable of performing charge scheduling adapted to a situation for each of a plurality of electric vehicles.SOLUTION: A charge scheduling device 2 comprises: a variation calculation section 101 which uses travel information relating to travel energy required when an electric vehicle has traveled, to calculate a variation degree including variations of the travel energy to be required when the electric vehicle travels, or variations of influence factors exerting influences upon the travel energy; and a charge scheduling section 102 which uses a travel schedule including an actual travel time zone in which a plurality of electric vehicles travels actually, and a stop time zone in which the plurality of electric vehicles stop, to allocate charging times of the plurality of electric vehicles to the stop time zone so as to allocate a more charging time to the electric vehicle of the relatively larger variation degree than the electric vehicle of the relatively smaller variation degree.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present disclosure relates to a charging planning device. [Background technology]

[0002] As described in Patent Document 1 below, a charging system has been proposed that creates a charging plan for charging a plurality of electric vehicles. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2022-118575 Summary of the Invention [Problem to be solved by the invention]

[0004] The driving energy consumed by an electric vehicle that needs charging varies depending on the conditions under which the electric vehicle is traveling. The driving energy consumed by different electric vehicles, and even by the same electric vehicle, varies depending on the conditions of the driving route. The driving energy consumed varies not only depending on the conditions of the driving route, but also depending on the driving details of the electric vehicle. The charging system described in Patent Document 1 does not take into account such changes in the driving energy of each of the multiple electric vehicles, and therefore cannot create an efficient charging plan.

[0005] The present disclosure provides a charging planning device capable of creating a charging plan adapted to the situation of each of a plurality of electric vehicles. [Means for solving the problem]

[0006] The present disclosure provides a charging innovation device that includes a variance calculation unit (101) that uses driving information related to the driving energy required when the electric vehicle runs to calculate a degree of variation including the variance in the driving energy required when the electric vehicle runs or the variance of influencing factors that affect the driving energy, and a charging planning unit (102) that uses a driving plan that includes actual driving time periods when the multiple electric vehicles actually run and stop time periods when the multiple electric vehicles are stopped to allocate charging time to multiple electric vehicles during stop time periods such that more charging time is allocated to electric vehicles with a relatively large degree of variance than to electric vehicles with a relatively small degree of variance. Effect of the Invention

[0007] According to the present disclosure, it is possible to provide a charging planning device that can create a charging plan adapted to the situation of each of a plurality of electric vehicles. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram for explaining the charging planning device according to this embodiment. [Diagram 2] FIG. 2 is a flowchart for explaining an information processing flow using the charging planning device shown in FIG. [Diagram 3] FIG. 3 is a diagram for explaining an example of a driving plan. [Figure 4] FIG. 4 is a diagram for explaining an example of a charging plan. [Diagram 5] FIG. 5 is a diagram for explaining an example of a charging plan. [Figure 6] FIG. 6 is a flowchart for explaining an information processing flow using the charging planning device shown in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the present embodiment will be described with reference to the accompanying drawings. In order to facilitate understanding of the description, the same components in each drawing are denoted by the same reference numerals as much as possible, and duplicated description will be omitted.

[0010] Fig. 1 is a diagram for explaining functional components of a charging planning device 2 in this embodiment. As shown in Fig. 1, the charging planning device 2 includes a variation calculation unit 101, a charging planning unit 102, a charging execution unit 103, a traveling data accumulation unit 201, and a traveling plan storage unit 202. The charging planning device 2 is a computer system including a CPU, a memory, a communication interface, etc., as a hardware configuration.

[0011] The variation calculation unit 101 is a part that calculates the degree of variation using driving information related to the driving energy required when the electric vehicle runs. The degree of variation includes the variation in the driving energy required when the electric vehicle runs or the variation of influencing factors that affect the driving energy. The driving information is stored in the driving data storage unit 201.

[0012] The charging planning unit 102 is a part that creates a charging plan using a driving plan including an actual driving time period during which the multiple electric vehicles actually drive and a stop time period during which the multiple electric vehicles stop. The charging planning unit 102 allocates charging times of the multiple electric vehicles to the stop time period so that more charging time is allocated to electric vehicles with a relatively large degree of variation than to electric vehicles with a relatively small degree of variation. The driving plan is stored in the driving plan storage unit 202.

[0013] The charging execution unit 103 is a part that executes charging of a plurality of electric vehicles based on the charging plan created by the charging plan unit 102.

[0014] Next, a flow of creating a charging plan by the charging planning device 2 will be described with reference to FIG. 2. In step S001, the variation calculation unit 101 reads the driving information stored in the driving data storage unit 201. The driving information is information about the driving energy required when the electric vehicle drives. The driving information may be a group of information that accumulates the driving energy required for each driving of the electric vehicle. The driving energy is represented by an actual measurement value or an estimated value of the energy used by the electric vehicle. The driving information may include an influencing factor that affects the driving energy. The influencing factor may include at least one of the driving distance of the electric vehicle, the driving time of the electric vehicle, the traffic condition of the road on which the electric vehicle has driven, the number of stops of the electric vehicle, the average vehicle speed of the electric vehicle, the frequency of driver changes of the electric vehicle, the variation in driving operation of the electric vehicle, and the weather condition of the road on which the electric vehicle has driven.

[0015] In step S002 following step S001, the variation calculation unit 101 calculates the variance as the degree of variation. The degree of variation includes the variance of the traveling energy required when the electric vehicle runs or the variance of the influencing factors that affect the traveling energy. Therefore, the variation calculation unit 101 calculates the variance of the traveling energy required when the electric vehicle runs or the variance of the influencing factors that affect the traveling energy as the degree of variation.

[0016] For example, when the variance σ(i) of the required energy in a route i is a combination of two factors a and b, it is calculated by the following formula (f01). σ(i)=σa(i)×σb(i) ···(f01)

[0017] In step S003 following step S002, the charging plan unit 102 reads the driving plan stored in the driving plan storage unit 202. FIG. 3 shows an example of a driving plan. FIG. 3(A) is a driving plan for electric vehicle A. FIG. 3(B) is a driving plan for electric vehicle B. The time period marked "driving" is the time period during which electric vehicles A and B are scheduled to actually drive. The time period marked "not driving" is the time period during which electric vehicles A and B are scheduled to stop, and is a time period during which charging is possible. The time period during which electric vehicles A and B are scheduled to stop is set as the total time Tsum to be considered for division. In this embodiment, an example will be described in which two electric vehicles A and B are charged by one charging device. For convenience of explanation, it is assumed that two electric vehicles A and B drive during the same time period and stop during the same time period.

[0018] In step S004 following step S003, the charging plan unit 102 creates a charging plan. The charging plan unit 102 calculates a charging margin time Tm_sum by the following formula (f02) (see FIG. 4). Tm_sum=Tsum-T(ia)-T(ib) ···(f02) Tsum: Total time to consider division T(ia): Base charging time of vehicle A on route ia T(ib): Base charging time of vehicle B on route ib

[0019] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ia) of the vehicle A according to the following formula (f03). Tm(ia)=Tm_sum*σ(ia) / (σ(ia)+σ(ib)) ···(f03) Tm_sum: Charging time σ(ia): Variance of the required energy on path ia σ(ib): Variance of the required energy on path ib

[0020] The charging planning unit 102 allocates the charging margin time Tm_sum to the charging time Tm(ib) of the vehicle B according to the following formula (f04). Tm(ib)=Tm_sum*σ(ib) / (σ(ia)+σ(ib)) ···(f04)

[0021] As shown in Fig. 5(A), the charging time allocated to vehicle A is T(ia)+Tm(ia). As shown in Fig. 5(B), the charging time allocated to vehicle B is T(ib)+Tm(ib).

[0022] The charging planning unit 102 may create a charging plan by considering not only the magnitude of the variation but also the amount of energy that needs to be charged as a base energy amount. The base energy of vehicle A in charge of route ia is Ea, and the base energy of vehicle B in charge of route ib is Eb. The charging planning unit 102 allocates the charging margin time Tm_sum to the charging time Tm(ia) of vehicle A by the following formula (f05). Tm(ia)=Tm_sum*(Ea / (Ea+Eb))*σ(ia) / (σ(ia)+σ(ib)) ···(f05) Tm_sum: Charging time Ea: Base energy of vehicle A Eb: Base energy of vehicle B σ(ia): Variance of the required energy on path ia σ(ib): Variance of the required energy on path ib

[0023] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ib) of the vehicle B according to the following formula (f06). Tm(ib)=Tm_sum*(Eb / (Ea+Eb))*σ(ib) / (σ(ia)+σ(ib)) ···(f06)

[0024] In this way, by considering the amount of energy that needs to be charged to each vehicle as the base energy amount, it is possible to allocate more of the charging margin time Tm_sum to vehicles with larger variations and larger base charging amounts.

[0025] As described above, the travel information stored in the travel data storage unit 201 may be a group of information that accumulates the travel energy required for each travel of each electric vehicle. In step S002, the variation calculation unit 102 calculates the variance σ(j,i) of the required energy for vehicle j on route i by the combination of two factors a and b using the following formula (f07). σ(j,i)=σa(j,i)×σb(j,i) ···(f07)

[0026] In step S004, the charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ia) of the vehicle A according to the following formula (f08). Tm(ia)=Tm_sum*σ(a,ia) / (σ(a,ia)+σ(b,ib)) ···(f08) Tm_sum: Charging time σ(a,ia): Variance of required energy for vehicle A on route ia σ(b, ib): Variance of required energy for vehicle B on route ib

[0027] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ib) of the vehicle B according to the following formula (f09). Tm(ib)=Tm_sum*σ(b,ib) / (σ(a,ia)+σ(b,ib)) ···(f09)

[0028] In this case, the charging plan unit 102 may create a charging plan by considering not only the magnitude of the variation but also the amount of energy that needs to be charged as a base energy amount. The charging plan unit 102 allocates the charging margin time Tm_sum to the charging time Tm(ia) of the vehicle A by the following formula (f10). Tm(ia)=Tm_sum*(Ea / (Ea+Eb))*σ(a,ia) / (σ(a,ia)+σ(b,ib)) ···(f10) Tm_sum: Charging time Ea: Base energy of vehicle A Eb: Base energy of vehicle B σ(a,ia): Variance of required energy for vehicle A on route ia σ(b, ib): Variance of required energy for vehicle B on route ib

[0029] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ib) of the vehicle B according to the following formula (f11). Tm(ib)=Tm_sum*(Eb / (Ea+Eb))*σ(b,ib) / (σ(a,ia)+σ(b,ib)) ···(f11)

[0030] The charging planning device 2 may perform energy prediction for a planned route, and execute a charging plan using the reliability as a degree of variation. A process flow for calculating the reliability and executing a charging plan will be described with reference to FIG. 6. In step S101, the variation calculation unit 101 predicts the travel energy of a planned travel route or an influencing factor that influences the travel energy. The travel energy is represented by a predicted value of the energy used by the electric vehicle. The influencing factor that influences the travel energy may include at least one of the travel distance of the electric vehicle, the travel time of the electric vehicle, the traffic condition of the road on which the electric vehicle has traveled, the number of stops of the electric vehicle, the average vehicle speed of the electric vehicle, the frequency of driver changes of the electric vehicle, the variation in driving operation of the electric vehicle, and the weather condition of the road on which the electric vehicle has traveled.

[0031] The prediction by the variation calculation unit 101 is performed by obtaining a prediction result such as an average value based on past data. The variation calculation unit 101 sets the prediction result for the path i as Xprd(i).

[0032] In step S102 following step S101, the variation calculation unit 101 obtains an actual measurement value of the travel energy of the planned travel route or an influencing factor that affects the travel energy. This actual measurement value is obtained from the result of the vehicle actually traveling on the planned route. The variation calculation unit 101 sets the actual measurement result of the route i as Xmes(i).

[0033] In step S103 following step S102, the variation calculation unit 101 calculates the prediction reliability R(i) for each route and stores it in the travel data accumulation unit 201. The prediction reliability R(i) is calculated by the following equation (f12). R(i)=Xprd(i) / Xmes(i) ···(f12)

[0034] Next, similarly to step S004, the charging plan unit 102 creates a charging plan. The charging plan unit 102 reads out the prediction reliability R(i) of the route i. The charging plan unit 102 allocates the charging margin time Tm_sum to the charging time Tm(ia) of the vehicle A by the following formula (f13). Tm(ia)=Tm_sum*R(ib) / (R(ia)+R(ib)) ···(f13) Tm_sum: Charging time R(ia): Prediction reliability for route ia R(ib): Predicted reliability of route ib

[0035] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ib) of the vehicle B according to the following formula (f14). Tm(ib)=Tm_sum*R(ia) / (R(ia)+R(ib)) ···(f14)

[0036] As shown in FIG. 5(A), the charging time assigned to vehicle A is T(ia) + Tm(ia). As shown in FIG. 5(B), the charging time assigned to vehicle B is T(ib) + Tm(ib). If R(ia) < R(ib), the allocation of the charging margin time to the vehicle B side with a high prediction reliability becomes smaller, and the allocation of the charging margin time to the vehicle A side with a low prediction reliability becomes larger. If R(ia) > R(ib), the allocation of the charging margin time to the vehicle B side with a low prediction reliability becomes larger, and the allocation of the charging margin time to the vehicle A side with a high prediction reliability becomes smaller. For vehicles on a route with a high prediction reliability, the allocation of the charging margin time becomes smaller, while for vehicles on a route with a low prediction reliability, the allocation of the charging margin time becomes larger. This is because a route with a high prediction reliability is considered to have a low degree of variation, and a route with a low prediction reliability is considered to have a high degree of variation.

[0037] The charging plan unit 102 may create a charging plan by considering not only the high or low prediction reliability but also the amount of energy that needs to be charged as the base energy amount. Let the base energy Ea of vehicle A responsible for route ia and the base energy Eb of vehicle B responsible for route ib. The charging plan unit 102 allocates the charging margin time Tm_sum to the charging time Tm(ia) of vehicle A according to the following formula (f15). Tm(ia) = Tm_sum * (Ea / (Ea + Eb)) * R(ib) / (R(ia) + R(ib)) ···(f15) Tm_sum: Charging margin time Ea: Base energy of vehicle A Eb: Base energy of vehicle B R(ia): Prediction reliability on route ia R(ib): Prediction reliability on route ib

[0038] The charging plan unit 102 allocates the charging margin time Tm_sum to the charging time Tm(ib) of vehicle B according to the following formula (f16). Tm(ib) = Tm_sum * (Eb / (Ea + Eb)) * R(ia) / (R(ia) + R(ib)) ···(f16)

[0039] In this way, by considering the amount of energy that needs to be charged to each vehicle as the base energy amount, it is possible to allocate more of the charging margin time Tm_sum to vehicles with larger variations and larger base charging amounts.

[0040] The prediction reliability may be set for each electric vehicle and for each trip. The variance calculation unit 101 sets the prediction result of the driving energy or the influencing factor of the vehicle j on the route i as Xprd(j,i). The variance calculation unit 101 sets the measurement result of the driving energy or the influencing factor of the vehicle j on the route i as Xmes(j,i). The variance calculation unit calculates the prediction reliability R(j,i) of the vehicle j on the route i by the following formula (f17). R(j,i)=Xprd(j,i) / Xmes(j,i) ···(f17)

[0041] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ia) of the vehicle A according to the following formula (f18). Tm(ia)=Tm_sum*R(b,ib) / (R(a,ia)+R(b,ib)) ···(f18) Tm_sum: Charging time R(a,ia): predicted reliability of vehicle A on route ia R(b, ib): predicted reliability of vehicle B on route ib

[0042] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ib) of the vehicle B according to the following formula (f19). Tm(ib)=Tm_sum*R(a,ia) / (R(a,ia)+R(b,ib)) ···(f19)

[0043] In this case, the charging plan unit 102 may create a charging plan by considering not only the magnitude of the variation but also the amount of energy that needs to be charged as a base energy amount. The charging plan unit 102 allocates the charging margin time Tm_sum to the charging time Tm(ia) of the vehicle A by the following formula (f20). Tm(ia)=Tm_sum*(Ea / (Ea+Eb))*R(b,ib) / (R(a,ia)+R(b,ib)) ···(f20) Tm_sum: Charging time Ea: Base energy of vehicle A Eb: Base energy of vehicle B R(a,ia): predicted reliability of vehicle A on route ia R(b, ib): predicted reliability of vehicle B on route ib

[0044] The charging planning unit 102 allocates the marginal charging time Tm_sum to the charging time Tm(ib) of the vehicle B according to the following formula (f21). Tm(ib)=Tm_sum*(Eb / (Ea+Eb))*R(a,ia) / (R(a,ia)+R(b,ib)) ···(f21)

[0045] [Note] Notes 1 to 8 below can be combined in any way as long as there is no technical contradiction.

[0046] [Appendix 1] A variation calculation unit 101 that calculates a variation degree including a variation in the driving energy required when the electric vehicle runs or a variation of an influencing factor that affects the driving energy, using driving information related to the driving energy required when the electric vehicle runs; and a charging planning unit (102) that allocates charging times for a plurality of electric vehicles to stop time periods using a driving plan including actual driving time periods when the plurality of electric vehicles actually drive and stop time periods when the plurality of electric vehicles stop, such that more charging time is allocated to electric vehicles with a relatively large degree of variation than to electric vehicles with a relatively small degree of variation.

[0047] According to Supplementary Note 1, since more charging time is allocated to electric vehicles with a relatively large degree of variation than to electric vehicles with a relatively small degree of variation, more charging time can be allocated to electric vehicles that are likely to have a smaller than expected decrease in charge amount, thereby reducing the possibility of insufficient charge.

[0048] [Appendix 2] The variation calculation unit 101 calculates the amount of charging energy required by the electric vehicle, The charging planning device described in Appendix 1, wherein the charging planning unit 102 allocates charging times to a plurality of electric vehicles during a stop time period such that a longer charging time is allocated to an electric vehicle having a relatively larger degree of variation than an electric vehicle having a relatively smaller degree of variation, and a longer charging time is allocated to an electric vehicle having a relatively larger amount of charging energy than an electric vehicle having a relatively smaller amount of charging energy.

[0049] According to Supplementary Note 2, since more charging time can be allocated to electric vehicles with a relatively large amount of charging energy in accordance with the degree of variation, the possibility of insufficient charging can be reduced.

[0050] [Appendix 3] Driving energy is expressed as the actual or estimated value of the energy used by the electric vehicle, The charging planning device according to claim 1 or 2, wherein the influencing factors include at least one of a mileage of the electric vehicle, a driving time of the electric vehicle, a traffic condition of a road on which the electric vehicle has traveled, a number of stops of the electric vehicle, an average vehicle speed of the electric vehicle, a frequency of driver changes of the electric vehicle, a variation in driving operation of the electric vehicle, and a weather condition of a road on which the electric vehicle has traveled.

[0051] [Appendix 4] The variation calculation unit 101 calculates the variance of the driving energy or the variance of the influencing factor as a variation degree, The charging planning device according to any one of appendixes 1 to 3, wherein the charging planning unit 102 allocates a longer charging time to an electric vehicle having a relatively large variance than to an electric vehicle having a relatively small variance.

[0052] [Appendix 5] 4. The charging planning device according to claim 1, wherein the variation calculation unit 101 calculates the degree of variation separately for each of a plurality of electric vehicles.

[0053] [Appendix 6] 6. The charging planning device according to claim 5, wherein the variation calculation unit 101 distinguishes between each of a plurality of electric vehicles and calculates a variation in driving energy or a variation in influencing factors as a degree of variation.

[0054] [Appendix 7] The charging planning device according to any one of appendices 1 to 3, wherein the variation calculation unit 101 generates predicted driving information that predicts the driving energy required when the electric vehicle runs or an influencing factor that affects the driving energy, and calculates a degree of deviation between the driving information and the predicted driving information as a degree of variation.

[0055] The degree of deviation in Supplementary Note 7 can be calculated, for example, by the ratio between the predicted driving information and the driving information.

[0056] [Appendix 8] The charging planning device according to claim 7, wherein the variation calculation unit 101 generates predicted travel information separately for each of the plurality of electric vehicles, and calculates a degree of deviation between the travel information for each of the plurality of electric vehicles and the predicted travel information as a degree of variation.

[0057] The control unit and the method described in the present disclosure may be realized by a dedicated computer provided by configuring a processor and a memory programmed to execute one or more functions embodied in a computer program. Alternatively, the control unit and the method described in the present disclosure may be realized by a dedicated computer provided by configuring a processor with one or more dedicated hardware logic circuits. Alternatively, the control unit and the method described in the present disclosure may be realized by one or more dedicated computers configured by a combination of a processor and a memory programmed to execute one or more functions and a processor configured with one or more hardware logic circuits. In addition, the computer program may be stored in a computer-readable non-transitory tangible recording medium as instructions executed by a computer.

[0058] The present embodiment has been described above with reference to specific examples. However, the present disclosure is not limited to these specific examples. Any design modifications made by a person skilled in the art to these specific examples are also included within the scope of the present disclosure as long as they have the features of the present disclosure. The elements of each of the above-mentioned specific examples and their arrangements, conditions, shapes, etc. are not limited to those exemplified and can be changed as appropriate. The combination of each of the elements of each of the above-mentioned specific examples can be changed as appropriate as long as no technical contradiction occurs. [Explanation of symbols]

[0059] 2: Charging planning device 101: Variation calculation section 102: Charging Planning Department 103: Charging execution unit 201: Driving data storage unit 202: Travel plan storage unit

Claims

1. a variation calculation unit (101) that calculates a variation degree including a variation in the traveling energy required when the electric vehicle runs or a variation of an influencing factor that affects the traveling energy, using traveling information related to the traveling energy required when the electric vehicle runs; and a charging planning unit (102) that uses a driving plan including actual driving time periods when the plurality of electric vehicles actually drive and stop time periods when the plurality of electric vehicles stop, and allocates charging times of the plurality of electric vehicles to the stop time periods such that more charging time is allocated to electric vehicles having a relatively large degree of variation than to electric vehicles having a relatively small degree of variation.

2. The variation calculation unit calculates an amount of charging energy required by an electric vehicle, 2. The charging planning device according to claim 1, wherein the charging planning unit allocates charging times for the plurality of electric vehicles during the stop time period such that a longer charging time is allocated to an electric vehicle having a relatively larger degree of variation than an electric vehicle having a relatively smaller degree of variation, and a longer charging time is allocated to an electric vehicle having a relatively larger amount of charging energy than an electric vehicle having a relatively smaller amount of charging energy.

3. The traveling energy is expressed as an actual measured value or an estimated value of energy used by the electric vehicle, 3. The charging planning device according to claim 1 or 2, wherein the influencing factors include at least one of a traveling distance of the electric vehicle, a traveling time of the electric vehicle, a traffic condition of a road on which the electric vehicle has traveled, a number of stops of the electric vehicle, an average vehicle speed of the electric vehicle, a frequency of changes of drivers of the electric vehicle, a variation in driving operation of the electric vehicle, and a weather condition of a road on which the electric vehicle has traveled.

4. The variation calculation unit calculates a variance of the traveling energy or a variance of the influencing factor as the degree of variation, The charging planning device according to claim 1 , wherein the charging planning unit allocates a longer charging time to an electric vehicle having a relatively large variance than to an electric vehicle having a relatively small variance.

5. The charging planning device according to claim 1 , wherein the variation calculation unit calculates the degree of variation separately for each of a plurality of electric vehicles.

6. The charging planning device according to claim 5 , wherein the variation calculation unit distinguishes between a plurality of electric vehicles and calculates a variance of the traveling energy or a variance of the influencing factor as the degree of variation.

7. 2. The charging planning device according to claim 1, wherein the variation calculation unit generates predicted travel information that predicts travel energy required when an electric vehicle travels or an influencing factor that affects the travel energy, and calculates a degree of deviation between the travel information and the predicted travel information as a degree of variation.

8. 8. The charging planning device according to claim 7, wherein the variation calculation unit generates the predicted travel information separately for each of a plurality of electric vehicles, and calculates a degree of deviation between the travel information for each of the plurality of electric vehicles and the predicted travel information as a degree of variation.

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