JP2021197862A - Vehicle charging planning system - Google Patents

Abstract

To make a charging plan such that a certain cruising range can be secured in an electric vehicle equipped with a battery that also functions as a member of a smart grid.SOLUTION: In a smart grid that includes a battery and a charging infrastructure of an electric vehicle, a vehicle charging planning system that makes a charging plan to charge the battery includes SOC prediction means (300a) that predicts the minimum SOC of the battery between first and second timings, and plan creation means (300a) that creates a charging plan to charge the battery from the charging infrastructure before the battery's SOC drops to the predicted minimum SOC when the predicted minimum SOC is less than a preset threshold SOC.SELECTED DRAWING: Figure 1

Description

The present invention relates to the technical field of a vehicle charging planning system for systematically charging in a smart grid including a battery and a charging infrastructure provided in an electric vehicle.

As this type of system, for example, within the range of electric power that can be supplied in the area, a charging schedule for the amount of electric power to be charged to the electric vehicle is created and maintained, and an identification number for identifying the electric vehicle is retained and charged. When a charging start signal indicating that the electric vehicle should be started is detected from the electric vehicle specified by the identification number, whether or not charging is possible at the current position and the current time of the electric vehicle based on the charging schedule already created and held. To judge. In this way, the charging of the electric vehicle is managed based on the charging schedule (see Patent Document 1). This system facilitates leveling when powering electric vehicles.

Japanese Unexamined Patent Publication No. 2018-61433

However, according to the system according to Patent Document 1, when power is supplied to the smart grid from the battery side of the electric vehicle, the electric vehicle may not be able to secure a certain cruising range for normal vehicle traveling. There is a technical problem.

The present invention has been made in view of the above-mentioned technical problems, for example, and it is possible to carry out a charging plan so as to secure a certain cruising range in an electric vehicle equipped with a battery that also functions as a member of a smart grid. The challenge is to provide a vehicle charging planning system.

One aspect of the vehicle charging planning system according to the present invention is to charge a vehicle in a smart grid including a battery provided in an electric vehicle and a charging infrastructure in order to solve the above-mentioned problems. In a planning system, an SOC predicting means for predicting the minimum SOC (State Of Charge) of the battery between the first timing and the second timing, and the predicted minimum SOC being less than a preset threshold SOC. In some cases, it comprises a planning means for creating the charging plan so that the battery is charged from the charging infrastructure at a timing prior to the timing at which the SOC of the battery drops to the predicted minimum SOC.

According to one aspect of the vehicle charging planning system according to the present invention, a certain cruising range for normal vehicle traveling in the electric vehicle is secured even when power is supplied to the smart grid from the battery side of the electric vehicle. It will be possible.

Such an action and effect according to the present invention will be further clarified by the embodiment of the invention described below.

It is a block diagram which shows the vehicle charge planning system which concerns on 1st Embodiment together with the element which comprises the smart grid. It is a flowchart which shows the flow of processing in the charge planning algorithm of 1st Embodiment. It is a characteristic diagram which shows the time-dependent change of SOC in 1st Embodiment. It is a block diagram which shows the vehicle charge planning system which concerns on 2nd Embodiment. It is a flowchart which shows the flow of processing in the charge planning algorithm of 2nd Embodiment.

<First Embodiment>
A first embodiment according to the present invention will be described with reference to FIGS. 1 to 3. First, the overall configuration of the embodiment will be described with reference to FIG.

As shown in FIG. 1, the vehicle charging planning system 100 according to the first embodiment is constructed for a smart grid (hereinafter, as appropriate, simply referred to as “grid”). The grid can charge each battery BA or each battery BA with a plurality of electric vehicle EVs (ie, electric vehicle group EVG including EV1, EV2, ...) Each having a battery BA (ie, BA1, BA2, ...). Charger CH (that is, CH1, CH2, ...) Provided to be rechargeable (that is, each battery BA can be discharged), a plurality of consumer CS (that is, a consumer group CSG), and a plurality of power generations. It is configured to include a unit PU (that is, a power generation unit group PUG). The battery BA of each electric vehicle EV, each of which is an example of an "electric vehicle", is configured to appropriately function as a grid power buffer. The power generation unit group PUG, each of which constitutes an example of "charging infrastructure", includes a power generation unit PU for renewable energy power generation such as solar power generation, an internal combustion engine generator such as a cogene, and the like. Each element constituting these grids is always connected or as needed so that power can be transmitted and received via a transmission line (not shown), a substation facility, and the like.

In FIG. 1, among the electric vehicle group EVG, the electric vehicle EV1 and the electric vehicle EV2 are shown, the charger CH1 is provided for the battery BA1 of the electric vehicle EV1, and the charger CH2 is used for the battery BA2 of the electric vehicle EV2. The provided state is shown. However, the correspondence between the electric vehicle EV and the charger CH is not limited to this. As the charger CH, various types of contact charging type or non-contact charging type can be adopted, including a plug-in type household general-purpose socket, a dedicated charger installed in a charging station, and the like. good.

In the grid, the surplus electric power mainly generated by the power generation unit PU is distributed to the battery BA of each electric vehicle EV. When there is no surplus power, the discharge power from the battery BA of the electric vehicle EV that is parked and has surplus power can be used for the consumer CS and other electric vehicle EVs. Note that this grid may be a smart grid that is highly dependent on grid power.

As will be described later, the vehicle charging planning system 100 initially initializes a long-term charging power distribution plan or charging / discharging plan (hereinafter referred to as “charging plan”) based on the power supply / demand plan in the grid and the usage plan of the electric vehicle EV. When it is predicted that the remaining battery level of the battery BA of the lowest electric vehicle EV in the planned period will be less than a predetermined threshold if necessary (that is, the remaining battery level is expected to be less than a predetermined threshold value). ), Adopt an algorithm that sequentially raises the level by redistributing the charging power. As a result, the battery BA of the electric vehicle EV having surplus power can be used for charging, or the battery BA of the electric vehicle EV having no surplus power can be charged, in other words, the battery BA can be used as a power buffer. Use as. Therefore, even for a grid where the supply and demand of surplus electricity is unstable due to the introduction of a large amount of power generation from renewable energy, the weather, the operation of electric vehicles, and the configuration within the grid (power generation unit group PUG, consumer group CSG, electric power) Robustness can be ensured against variable and uncertain factors such as the number of vehicle EVs), and stable power supply and operation in daily use of electric vehicle EVs can be achieved at a high level. Moreover, in the present embodiment, it is possible to efficiently distribute power over the entire grid by making such a plan change to raise the remaining battery level as soon as possible from the time when the necessity is predicted. Has been done.

The vehicle charging planning system 100 is composed of at least one computer housed in a communication network covering the grid, and is configured to be capable of creating a charging plan in the grid. The charging plan in this example is a plan for each electric vehicle EV, specifically, the battery BA of each electric vehicle EV is charged via each charger CH, or each charge is performed from the battery BA of each electric vehicle EV. It is a plan for charging (in other words, discharging when viewed from the battery BA side) via the device CH. In the charging plan in this example, as described below, the planning period (that is, "" is the period from the start timing as an example of "first timing" to the end timing as an example of "second timing". The charging timing and the amount of charging power for each electric vehicle EV in the "charging planning period" or "power distribution planning period") are determined. According to the charging plan thus created, the battery BA of each electric vehicle EV will be charged (or discharged).

The vehicle charging planning system 100 includes a charging planning execution unit 300 including a prediction unit 200, a planning creation unit 300a, and a planning execution unit 300b. As a result, the charging plan created at the initial stage or earlier is sequentially modified based on the parameters that change with time, and a more appropriate charging plan is sequentially created. As an initial charging plan, for example, a charging plan in which surplus electric power is equally divided into each electric vehicle EV can be considered.

The prediction unit 200 includes a post-travel battery remaining amount prediction unit 210, a charge acceptance capacity prediction unit 220, and a power supply and demand difference prediction unit 230. As shown in FIG. 1, the post-travel battery remaining amount prediction unit 210 and the charge acceptance capacity prediction unit 220 are on a network in which chargers CH1 and CH2, electric vehicles EV1 and EV2, and a vehicle charging planning system 100 are housed. It is connected so that information communication is possible. The same applies to other charger CHs and other electric vehicle EVs. Although not shown in FIG. 1, the electric vehicle EV and the prediction units 210 and 220 may be directly connected to each other so as to be capable of information communication without going through the charger CH. Further, the power supply and demand difference prediction unit 230 is connected to each power generation unit PU included in the power generation unit group PUG and each consumer CS included in the consumer group CSG so as to be capable of information communication. The vehicle charging planning system 100 is configured so that each of the above configurations constituting the grid and their corresponding relationships can be identified.

The post-travel battery remaining amount prediction unit 210 predicts the minimum value of the battery remaining amount (hereinafter, appropriately referred to as “SOC”) of each electric vehicle EV within the planning period of the charging plan. In this example, this minimum value is predicted as the SOC immediately before the running of the electric vehicle EV ends and is connected to the charger CH in the grid in units of one day. The post-driving battery remaining amount prediction unit 210 is used to predict the SOC from each of the running or parked electric vehicle EVs (for example, the SOC, electricity cost, usage history, and / or usage plan of each electric vehicle EV). Etc.), and based on this information, it is configured to obtain the predicted minimum value within the planned period of each electric vehicle EV. The prediction process of the post-travel battery remaining amount prediction unit 210 is executed, for example, when the subroutine process (see FIG. 2) by the plan creation unit 300a, which will be described later, is executed. More specifically, this prediction process is performed regularly or irregularly or appropriately depending on the driving condition of the electric vehicle EV, about once every few seconds to several hours, or every time the electric vehicle EV approaches the charging station, etc. It may be executed sequentially at a relatively low frequency, or it may be executed sequentially at a relatively high frequency as part of various subroutine processes executed on an electric vehicle EV several to several thousand times per second. But it may be.

The charge acceptance capacity prediction unit 220 predicts the charge acceptance capacity of the battery BA of each electric vehicle EV. The charge receiving capacity prediction unit 220 provides information necessary for predicting the charge receiving capacity from each electric vehicle EV (for example, the performance of each charger CH in the grid, the battery performance of each connected electric vehicle EV, and the like. The SOC, connection time, and / or expected start time of use, etc.) are acquired, and based on this information, the charge acceptance capacity prediction that changes over time within the planned period is obtained. The prediction process of the charge receiving capacity prediction unit 220 is sequentially executed, for example, when the process of the plan creation unit 300a (see FIG. 2), which will be described later, is executed.

The electric power supply and demand difference prediction unit 230 predicts the electric power supply and demand difference. The power supply-demand difference forecasting unit 230 is configured to sequentially obtain a power supply-demand difference forecast that changes over time within the planning period from the power supply forecast acquired from the power generation unit group PUG and the power consumption forecast acquired from the consumer group CSG. ing. It should be noted that this power supply-demand difference prediction is a prediction profile excluding the power buffer function by the battery BA of the electric vehicle EV. The prediction process of the power supply and demand difference prediction unit 230 is sequentially executed, for example, when the process of the plan creation unit 300a (see FIG. 2), which will be described later, is executed.

The plan creation unit 300a creates a charging plan by executing a charging planning algorithm (hereinafter, simply referred to as “algorithm” as appropriate). The algorithm focuses on the minimum value of the SOC after driving of each electric vehicle EV acquired from the battery remaining amount prediction unit 210 after driving within the planning period, and raises the minimum value of SOC from the existing charging plans. It is configured to sequentially calculate and calculate the redistribution of charging power and the review of charging timing.

By executing the algorithm, the planning unit 300a predicts the minimum SOC within the planning period based on the information of the predicted minimum value acquired from the post-travel battery remaining amount prediction unit 210. Then, the charging plan before reaching the minimum SOC is modified so as to raise the predicted minimum SOC. At this time, the planning unit 300a modifies the existing charging plan within the range of the power supply and demand difference prediction obtained from the power supply and demand difference prediction unit 230 and the charge receiving capacity prediction obtained from the charge receiving capacity prediction unit 220.

The planning unit 300a sequentially and repeatedly executes the algorithm while the electric vehicle EV, which is the target of the charging plan, is running or parked / stopped. If the electric vehicle EV is running, it depends on the power consumption, or even if it is parked or stopped, it depends on the charging / discharging status of other charging infrastructure or other electric vehicle EV that changes sequentially over time. , The charging plan created optimally by the algorithm changes. As a result, the existing charging plan is sequentially modified, and a charging plan more suitable for the grid situation is sequentially created. Further, by considering the prediction information by the prediction unit 200, the charging power is re-charged within the range of this restriction so that the charging plan that exceeds the hardware limitation such as the grid and the electric vehicle EV is not executed. Allows adjustment of distribution and charging timing.

The plan execution unit 300b is configured to execute the plan according to the latest plan among the plans to be sequentially updated. Therefore, stable power is secured even when power is supplied to the grid from the battery BA side of the electric vehicle EV on a grid in which a large amount of power generation by renewable energy or the like is introduced or a grid that is highly dependent on grid power. By distributing the power, it becomes possible to secure a certain cruising distance for normal vehicle running in the electric vehicle EV. Specifically, the plan execution unit 300b gives an instruction to charge (or discharge) the battery BA of each electric vehicle EV according to the charging plan created by the plan creation unit 300a as described above for each component of the grid. It is configured to be issued as appropriate.

An example of a specific process executed by the planning unit 300a will be described with reference to FIG. In this example, the algorithm is configured such that the planning period is N days, the modification is started from i = 1, and the modification is performed on the i-day when it is determined that the charging plan can be modified. In this way, the planning unit 300a is configured to make corrections from the latest charging plan, which has a great influence on the future. The unit of "timing" in this example is one day. Therefore, the unit of N and i is a day, but the unit of timing is not limited to a day and can be set as appropriate.

In FIG. 2, the plan creation unit 300a first predicts the minimum SOC of the electric vehicle group EVG in the grid during the planning period based on the information acquired from the after-travel battery remaining amount prediction unit 210 (step S10). Specifically, information on the predicted minimum value of the post-travel SOC of each electric vehicle EV within the planned period is acquired from the post-travel battery remaining amount prediction unit 210, and the lowest lowest SOC is specified from the information. By this specification, in addition to the minimum SOC value, the electric vehicle EV, timing, etc. corresponding to the minimum SOC are also specified. In this way, the plan creation unit 300a functions as an example of the “SOC prediction means”.

Next, it is determined whether or not the predicted minimum SOC is less than the preset threshold SOC (step S11). The "threshold SOC" is the SOC of each electric vehicle EV, and the functions of the grid and the electric vehicle EV (that is, the original functions of the electric vehicle such as the driving function) during the planned period or longer. This is the lowest value that can be considered necessary to secure it. This threshold SOC is an uncertain factor related to the running of the electric vehicle EV (for example, momentary weather change, planned mileage, running time, running speed, road surface and congestion / congestion situation, operation of charging infrastructure). Appropriate charging infrastructure with a value that can ensure the function of the electric vehicle EV (typically, with some margin before the battery BA becomes empty) for situations, congestion, power outages, etc. (To reach) preset. The threshold SOC may be set as an expression or table value that can change with time according to the situation of uncertain factors. Specifically, empirical, experimental, simulation, machine learning, etc. are used to ensure appropriate charging. The SOC value deemed appropriate for reaching the infrastructure may be appropriately determined as the formula or table value using various parameters that can be acquired at present as input parameters.

When it is determined that the predicted minimum SOC has not reached the threshold SOC, that is, it is determined to be less than the threshold SOC (step S11: Yes), it is determined whether or not the charging plan on the i-day day can be modified (step S12). .. For example, if i = 1, it is determined whether or not the correction is possible on the first day, which is the start timing of the planning period. Specifically, it is determined whether or not the charging plan corresponding to the minimum SOC can be modified within the range of the acquired predictions (charge acceptance capacity prediction and power supply / demand difference prediction).

If it is determined that the charging plan can be modified here (step S12: Yes), the charging plan on the i-day day is modified (step S13). Here, since the algorithm is started with "i = 1st day" in the initial setting, the possibility of modification is sequentially determined as shown below from the day closest to the planning time (that is, the 1st day). , If the correction is possible, the correction will be executed. In the example of FIG. 3 to be described in detail later, since the minimum SOC reaches the threshold SOC on the second day, it is first determined whether or not the charging plan on the first day can be modified. Depending on the result, the charging plan for the first day is modified if possible, or the charging plan for the second day is modified if not possible.

After the process of step S13, the algorithm is repeatedly executed from step S10 with the charging plan modified (in this example, from the first day).

If it is determined in the determination of step S12 that the charging plan on the i-day cannot be modified (step S12: No), whether or not i <N (that is, whether or not the modification regarding the last day of the plan has been completed) has been completed. Is determined (step S14). If i <N is determined in step S14, that is, if the determination of whether or not to modify the last day of the plan has not yet been reached (step S14: Yes), 1 is added to i (step S15). On the other hand, in step S14, when it is determined that i <N (step S14: No), that is, when i reaches N (that is, it is determined that the correction is impossible until the final day of the charging plan). The algorithm is finished. By the processing in steps S14 and S15, as long as i <N, the correction candidate dates of the charging plan are sequentially updated one day at a time, and the correction possibility determination is continued and the correction is executed when possible.

On the other hand, when it is determined in the determination of step S11 that the minimum SOC is not less than the threshold value SOC (step S11: No), no plan change is required, and the algorithm ends. In this way, the plan creation unit 300a ends the correction process of the charging plan of the electric vehicle EV when the candidate date for correction of the charging plan finally becomes the Nth day or the minimum SOC becomes the threshold SOC or more. It is configured as follows.

Next, how the SOC of the electric vehicle EV can be modified by the algorithm will be described by a concrete example. Here, in the charging plan of the electric vehicle EV1 and the electric vehicle EV2, the process in which the minimum SOC is improved by the algorithm will be described as an example with reference to FIG. In FIG. 3, graph G1α (solid line) is the SOC profile of the battery BA1 of the electric vehicle EV1 predicted by the existing driving prediction and charging plan, and graph G2α (broken line) is predicted by the existing driving prediction and charging plan. It is the SOC profile of the battery BA2 of the electric vehicle EV2.

As shown in FIG. 3, the minimum SOC within the planned period predicted based on the existing charging plans of the electric vehicle EV1 and the electric vehicle EV2 is the point P1 in the graph G2α of the electric vehicle EV2. Since the SOC of the point P1 is less than the planned end threshold value, that is, the threshold value SOC, the charging plan is modified by the processing of the above-mentioned algorithm. The SOC profile of the electric vehicle EV2 obtained as a result of this modification is graph G2β (dotted line).

As shown in the graph G2β, the SOC of the battery BA2 of the electric vehicle EV2 increases at the timing t1 (day 1), so that the point P1 that was less than the threshold SOC is raised to be equal to or higher than the threshold SOC. This indicates that it is determined that the charging plan can be modified at the timing t1 before the timing t2 (the second day) when the minimum SOC is reached.

In FIG. 3, the amount of charging power on the first day corresponding to the electric vehicle EV2 is simply increased, but if there is not enough surplus power during the planning period, for example, the battery BA1 of another electric vehicle EV1 The discharge power from is also used as the distributed power.

After the modification for the point P1, the predicted minimum SOC moves to the point P2 of the graph G1α of the electric vehicle EV1. Since the point P2 is less than the threshold SOC, the algorithm is executed again for this minimum SOC (point P2), and the charging plan is modified again. The SOC profile of the electric vehicle EV1 obtained as a result of this modification is graph G1β (thick line).

As shown in the graph G1β, the SOC of the battery BA1 of the electric vehicle EV1 increases at the timing t3 (day 2), so that the point P2 that was less than the threshold SOC is raised to be equal to or higher than the threshold SOC. This is the timing before the timing t4 (3rd day) when the minimum SOC is reached, and it is determined that the charging plan cannot be modified at the timing t5 (1st day) before the timing t3, and the charging plan at the timing t3. Is the result of the judgment that it can be corrected.

Due to the above modification of point P2, the minimum SOC for all electric vehicles EV1 and EV2 in the grid becomes the modified point P1, but since the modified point P1 is equal to or higher than the threshold SOC, the algorithm-based modification of the charging plan is not possible. finish.

As described above, according to the vehicle charging planning system 100, the existing SOC is reached until the minimum SOC is reached so that the grid and the electric vehicle EV can be operated and the functions can be secured during the planning period or longer. By sequentially updating the charging plan, it is possible to realize a charging plan that is robust against uncertainties and can secure the functions of the grid / electric vehicle group EVG.

<Second Embodiment>
A second embodiment according to the present invention will be described with reference to FIGS. 4 and 5. Here, FIGS. 4 and 5 are drawings having the same meaning as those of FIGS. 1 and 2 according to the first embodiment, respectively, and are the same as those of the first embodiment shown in FIGS. 1 and 2 in FIGS. 4 and 5, respectively. The same reference numerals are given to the components of the above, and the description thereof will be omitted as appropriate.

The vehicle charging planning system 100 according to the second embodiment is provided for a grid further provided with a power supply stabilizing function in the grid of the first embodiment. In the example shown in FIG. 4, in the vehicle charging planning system 100 according to the second embodiment, in addition to the configuration of the first embodiment, the power buffer group PBG including the power buffer PB in which the power supply and demand difference prediction unit 230 has at least one. The other configurations are the same as those of the first embodiment.

The power buffer PB is a large storage battery installed in, for example, a charging / discharging station, various facilities or equipment, and a home, in addition to the battery BA of the electric vehicle EV, and is provided on the grid as a dedicated power buffer capable of temporary storage and discharging. Has been done. The power buffer PB is provided to stabilize the power supply in the grid by the discharge power. Therefore, for example, the power supply and demand difference prediction unit 230 can predict the power supply and demand difference with an adjustment margin based on the charge / discharge capacity of each power buffer PB. Then, the plan creation unit 300a can more flexibly distribute the charge and adjust the timing by utilizing the increase in power supply due to the discharge of the power buffer group PBG. Since the power buffer PB itself cannot generate power, it is necessary to charge the same amount as the amount of discharged power when there is surplus power.

Further, when the fluctuation of the power supply and demand in the grid is remarkable, a plan may be made to temporarily discharge the battery BA of the electric vehicle group EVG to the power buffer in order to solve the power shortage. In this case, the planning unit 300a of the vehicle charging planning system 100 also performs a process of formulating a discharge plan from the battery BA of the electric vehicle group EVG in the grid. A charging planning algorithm (hereinafter, simply referred to as “algorithm”) as an example of the processing is shown in FIG.

In the algorithm of FIG. 5, first, the plan creation unit 300a establishes a discharge plan for solving the power shortage by the battery BA of the electric vehicle group EVG (step S20). When a power shortage occurs, it has a great influence on the operation of the grid such as blackout. Therefore, it is necessary to solve the power shortage with the highest priority by the power buffer group PBG. Therefore, here, at the beginning of the algorithm, a discharge plan for each battery BA in the grid is made and finalized. In particular, in the second embodiment, it is possible to create a plan by using the charge / discharge capacity of the power buffer, and the degree of freedom in creating a plan is increased as compared with the case of the first embodiment.

After that, the processes after step S10 are performed as in the first embodiment, and the series of processes is completed. Therefore, according to the second embodiment, more flexible charge distribution and timing adjustment are possible by utilizing the increase in power supply by charging / discharging the power buffer group PBG.

Additional Notes The following additional notes will be further disclosed with respect to the embodiments described above.

[Appendix 1]
The vehicle charging planning system according to Appendix 1 is a vehicle charging planning system that performs a charging plan for charging the battery in a smart grid including a battery and a charging infrastructure included in an electric vehicle, and is a vehicle charging planning system from the first timing. The SOC predicting means for predicting the minimum SOC of the battery until the second timing, and when the predicted minimum SOC is less than the preset threshold SOC, the SOC of the battery is up to the predicted minimum SOC. The vehicle charging planning system is provided with a planning means for creating a charging plan so as to charge the battery from the charging infrastructure at a timing before the lowering.

According to the vehicle charging planning system described in Appendix 1, the minimum SOC of the battery in the period from the first timing to the second timing (that is, the "charging planning period" or the "power distribution planning period") is the SOC predicting means. Therefore, it is predicted sequentially while the electric vehicle is running or parked / stopped. Here, if the predicted minimum SOC is less than the preset threshold SOC, the charging plan is to charge the battery from the charging infrastructure before the timing when the battery SOC drops to the predicted minimum SOC. , It is created sequentially by the planning means while the electric vehicle is running or parked / stopped. That is, the charging plan is reviewed one by one.

Therefore, according to the charging plan created by this vehicle charging planning system, even when power is supplied to the smart grid from the battery side of the electric vehicle as a member of the smart grid, the normal vehicle running in the electric vehicle It is possible to secure a certain cruising range for the purpose. In other words, power supply and demand in the smart grid is achieved by using the battery of the electric vehicle as a power buffer in the smart grid while preventing or suppressing the hindrance to the cruising range of normal driving, which is the original function of the vehicle in the electric vehicle. Can be leveled. Robustness against uncertainties such as weather can also be improved. Moreover, deterioration of the battery due to unreasonable charging / discharging of the battery with the smart grid can be prevented or suppressed.

[Appendix 2]
The vehicle charging planning system according to Appendix 2 is characterized in that the smart grid is configured to include a chargeable / dischargeable power buffer in addition to the battery and the charging infrastructure. It is a planning system.

According to the vehicle charging planning system described in Appendix 2, in addition to the battery that also functions as a power buffer, an increase in power supply due to discharge from a separately dedicated or dual-purpose power buffer can be used. It is possible to create a charging plan with flexible charge distribution and timing survey adjustment. Charging of the power buffer may be performed when there is surplus power in the entire smart grid.

[Appendix 3]
The vehicle charging planning system according to Appendix 3 further includes a charging receiving capacity predicting means for predicting the charging receiving capacity of the battery, and the planning means prepares the charging plan in consideration of the predicted charge receiving capacity. The vehicle charging planning system according to the appendix 1, characterized in that it is created.

According to the vehicle charging planning system described in Appendix 3, the charging plan is created so that the battery is appropriately charged from the charging infrastructure in consideration of the predicted charging capacity of the battery, so that the smart grid as a whole can be used as a whole. More efficient redistribution of charging power is possible.

[Appendix 4]
The vehicle charging planning system according to Appendix 4 further includes a power supply / demand difference predicting means for predicting a power supply / demand difference in the smart grid, and the planning means takes the predicted power supply / demand difference into consideration to perform the charging plan. The vehicle charging planning system according to the appendix 1, wherein the vehicle charging planning system is characterized in that.

According to the vehicle charging planning system described in Appendix 4, the charging plan is created so that the battery is appropriately charged from the charging infrastructure in consideration of the predicted power supply-demand difference in the smart grid, so that the smart grid as a whole , More efficient redistribution of charging power becomes possible.

The present invention can be appropriately modified within the scope of the claims and within the scope not contrary to the gist or idea of the invention which can be read from the entire specification, and the vehicle charging planning system accompanied by such modification is also included in the technical idea of the present invention. Will be.

100 ... Vehicle charging planning system 200 ... Prediction unit 210 ... After-travel battery remaining amount prediction unit 220 ... Charge acceptance capacity prediction unit 230 ... Electric power supply / demand difference prediction unit 300 ... Charging plan execution unit 300a ... Plan creation unit 300b ... Plan execution unit EV , EV1, EV2 ... Electric vehicle (electric vehicle)
BA, BA1, BA2 ... Battery CH, CH1, CH2 ... Charger

Claims (1)

A vehicle charging planning system for charging a battery in a smart grid including a battery and charging infrastructure of an electric vehicle.
An SOC predicting means for predicting the minimum SOC of the battery between the first timing and the second timing,
If the predicted minimum SOC is less than a preset threshold SOC, the charging infrastructure charges the battery before the timing when the battery's SOC drops to the predicted minimum SOC. A vehicle charging planning system characterized by providing a planning means for creating a charging plan.

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