Charging power management device and charging power management method

Abstract

This disclosure addresses the problem that computation becomes cumbersome and resource-intensive as the number of electric vehicles increases in the creation of electric vehicle charging plans. It is characterized by comprising: a grouping unit (203) that groups multiple electric vehicles into electric vehicle groups according to classification rules; a total charging power determination unit (204) that determines the total charging power of the electric vehicle group for each group at each time period based on constraints; and an individual charging power determination unit (205) that determines the individual charging power of each electric vehicle at each time period based on the total charging power.

Description

Technical Field

[0001] This involves technologies for managing charging power. Background Technology

[0002] In the charging of electric vehicles (EVs), there are many constraints, such as dwell time at charging facilities, departure time, available charging power, and electricity costs. A charging plan for EVs needs to be developed considering these constraints. For example, reference 1 discloses the following key points: when calculating the optimal charging and discharging plan, a mixed-integer programming problem is solved with constraints including preventing reverse power flow and limiting the upper limit of electricity consumption by multiple households as a whole.

[0003] Existing technical documents

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2015-220862 Summary of the Invention

[0006] Previously, various constraints existed in the creation of electric vehicle charging plans. When attempting to manage the charging plans of multiple electric vehicles holistically, the following challenges arose: as the number of electric vehicles increased, the computation became massive, and the required computational resources increased. The purpose of this disclosure is to mitigate this increase in required computational resources.

[0007] To address the aforementioned issues, this disclosure is characterized by comprising: a grouping unit that groups multiple electric vehicles into an electric vehicle group according to classification rules; a total charging power determination unit that determines the total charging power of the electric vehicle group for each group at each time according to constraints; and an individual charging power determination unit that determines the individual charging power of each electric vehicle at each time based on the total charging power.

[0008] It can suppress the increase of computing resources. Attached Figure Description

[0009] Figure 1 This is a structural diagram of the charging power management device and facility in Implementation Method 1.

[0010] Figure 2 This is a block diagram of the charging power management device in Implementation Method 1.

[0011] Figure 3 This is a diagram showing an example of device information of a charger managed by the charger information management unit in Embodiment 1.

[0012] Figure 4This is a diagram showing an example of a driving plan managed by the driving plan management department in Implementation Method 1.

[0013] Figure 5 This is a diagram showing the result of grouping performed by the grouping unit in Embodiment 1.

[0014] Figure 6 This is a diagram illustrating an example of device information for a rechargeable battery mounted on an electric vehicle in Embodiment 1.

[0015] Figure 7 This is a flowchart illustrating the processing flow of the charging power management device in Embodiment 1.

[0016] Figure 8 This is a hardware structure diagram of the charging power management device in Implementation Method 1.

[0017] (Symbol Explanation)

[0018] 101: Charging power management device; 201: Charger information management department; 202: Driving plan management department; 203: Grouping department; 204: Overall charging power decision department; 205: Individual charging power decision department; 206: Command value notification department. Detailed Implementation

[0019] Implementation method 1.

[0020] Figure 1 This is a structural diagram of the charging power management device 101 and facility 102 in this embodiment. Facility 102 refers to a facility for charging electric vehicles (hereinafter referred to as "EVs"). Here, facility 102 can be a facility such as a charging station that manages multiple EVs, or a facility such as a private residence; the form is not limited as long as it is a facility capable of charging EVs. The charging power management device 101 manages facility 102 (facility 1, facility 2... facility N), deciding which EV to charge, at what time, and with how much charging power. Here, EV refers to all vehicles that utilize rechargeable electrical energy, not limited to automobiles.

[0021] Figure 2 This is a block diagram of the charging power management device 101 in this embodiment. The charging power management device 101 includes: a charger information management unit 201, which manages the information of the chargers installed in each facility 102; a driving plan management unit 202, which manages the driving plan of each EV; a grouping unit 203, which groups multiple EVs into EV groups according to classification rules; and a total charging power determination unit 204, which determines the total charging power of the EV group for each group and for each time period according to constraints.

[0022] In addition, the charging power management device 101 includes: an individual charging power determination unit 205, which determines the individual charging power for each EV at each time period based on the total charging power; and an instruction value notification unit 206, which notifies each charger of an instruction value indicating the charging power based on the determined individual charging power. Details of each part will be explained next.

[0023] First, let's explain the details of the Charger Information Management Unit 201. The Charger Information Management Unit 201 manages the equipment information of the chargers held by the facilities 102 where the EV is expected to be charged. For example, the Charger Information Management Unit 201 manages information such as determining the facilities where chargers are installed, determining the charger itself, and the charger's rated output. Here, rated output refers to the output power that the charger can output to charge the EV. The Charger Information Management Unit 201 can also obtain the charger's equipment information from external servers or directly from users; the management method is not limited.

[0024] Figure 3 This diagram illustrates an example of device information for chargers managed by the Charger Information Management Department 201. Figure 3 In this example, information about facility 102, charger, and rated output of the charger are managed. Figure 3 According to the equipment information of the chargers, Charger 1 and Charger 2 are installed in Facility 1. Charger 1 has a rated output of 6.00kW, and Charger 2 has a rated output of 6.00kW. Charger 1, Charger 2, and Charger 3 are installed in Facility 2. Charger 1 has a rated output of 10.00kW, Charger 2 has a rated output of 10.00kW, and Charger 3 has a rated output of 7.00kW. The Charger Information Management Department 201 manages the information for each Facility 102 (Facility 1, Facility 2...Facility N).

[0025] Next, the Driving Plan Management Unit 202 will be explained. The Driving Plan Management Unit 202 manages the driving plan of the EV. Here, the driving plan refers to information related to the driving of the EV, such as information on the facility 102 where the EV is expected to be charged, the expected arrival time at the facility 102, the expected departure time from the facility 102, and the minimum amount of energy required for departure from the facility 102.

[0026] Furthermore, in this embodiment, the driving plan is registered using a dedicated application for setting the driving plan. This dedicated application can be a function of a car navigation system or an application usable from a portable terminal. Here, the driving plan management unit 202 can either receive the driving plan directly from the user or obtain it from an external server. The method of managing the driving plan is not limited.

[0027] Figure 4 This diagram illustrates an example of a driving plan managed by Driving Plan Management Department 202. Figure 4 In the example, the driving plan includes information such as EV name, estimated arrival date, estimated arrival time, estimated departure date, estimated departure time, expected destination, and required battery level at departure.

[0028] exist Figure 4 The example shows that vehicle 1 arrived at facility 1 at 12:00 on November 14, 2019, and departed from facility 1 at 18:00 on the same day. Vehicle 1's required battery capacity at departure was 20.0 kWh. Information is then managed for each vehicle.

[0029] Figure 4 An example was given, which also manages information about chargers that EVs are expected to connect to; the content is not limited as long as the information pertains to EV-related plans. Next, details of the grouping unit 203 will be explained. The grouping unit 203 groups multiple EVs into EV groups according to classification rules.

[0030] Classification rules refer to the rules used to group EVs, and any rules can be set. For example, rules that group EVs with the same expected charging facility 102 in the driving plan into the same group can be considered, and these rules utilize information such as the expected arrival time, expected departure time, and required battery power at departure time in the driving plan.

[0031] Furthermore, the classification rules can be grouped together by the expected arrival time of EVs with similar driving plans, by the expected departure time, or by the required battery charge at departure. Alternatively, a combination of these rules is also possible; there are no limitations on the classification rules.

[0032] The classification rules listed here are based on driving plans, but are not limited to them. They can be rules that randomly group EVs without using driving plans, or rules that classify EVs according to their vehicle type. The classification rules are not limited.

[0033] In addition, in this embodiment, the grouping unit 203 groups the EVs, but it can also group the EVs by driving plans. As long as it is a method of substantially grouping the EVs, the method of division is not limited.

[0034] The determination of whether the expected arrival times are close is made by calculating the absolute value of the difference between the values ​​(if the expected arrival time of vehicle 1 is 12:00 and the expected arrival time of vehicle 2 is 8:00, the time difference is 4 hours, and the absolute value is used). The determination is made based on whether it is below the determined threshold (e.g., within 1 hour or within 2 hours).

[0035] Furthermore, the method for determining whether the estimated arrival times are close is not limited to this. It could be a method of counting the absolute values ​​of the differences between the estimated arrival times in ascending order until a predetermined number is reached, thus determining that they are close. Any method that determines the closeness of values ​​can be used. The same method is used to determine the estimated departure time, the required energy storage at departure, etc.

[0036] Next, the process of assigning EVs to groups when groups have already been defined will be explained. Here, the key points for determining which group to belong to are explained based on the expected arrival time, but it is not limited to this. Grouping can also be determined based on information such as the expected departure time and / or the required charge at departure, and is not limited to this.

[0037] If a group already exists, the grouping unit 203 calculates the average of the expected arrival times of each vehicle in the group, i.e., the group's expected arrival time, and determines whether to assign the vehicle to the group based on whether the absolute value of the difference between the group's expected arrival time and the EV's expected arrival time is below a threshold.

[0038] For example, if there are two vehicles in the group, vehicle 1 (estimated arrival time 12:00) and vehicle 2 (estimated arrival time 8:00), the estimated arrival time of the group is 10:00. Here, the average value is used to describe the estimated arrival time of the group, but it is not limited to this. The minimum value in the group can also be used as the estimated arrival time of the group, or the maximum value can be used as the estimated arrival time of the group. The method for determining the estimated arrival time of the group is not limited.

[0039] To illustrate with a specific example, if a group contains vehicle 1 (estimated arrival time 12:00) and vehicle 2 (estimated arrival time 8:00), the estimated arrival time of the group can be either the smaller value (8:00) or the larger value (12:00). These are just examples; the method is not limited. The same method is used to determine the estimated departure time, the required battery power at departure, etc.

[0040] Next, the grouping of EVs when the driving plan is expressed as vector information will be explained. By expressing the various pieces of information about the driving plan as vectors, the grouping unit 203 can not only compare information of a single type with each other, but also combine multiple different types of information to group EVs.

[0041] The grouping unit 203 groups EVs based on the Euclidean distance between vectors. More specifically, the grouping unit 203 groups EVs based on whether the Euclidean distances between vectors are close to each other. Here, the determination of the magnitude of the Euclidean distance between vectors can be made using the same method as described above, such as whether the absolute value of the difference between the Euclidean distances is within a threshold.

[0042] The method for determining the magnitude of Euclidean distance is not limited. The grouping unit 203 quantifies information that is not expressed numerically, such as the expected arrival facility. For example, the location of the expected arrival facility is quantified using the latitude and longitude of a representative point.

[0043] In addition, when there is information that should be prioritized in the calculation of Euclidean distance when multiple pieces of information are combined for judgment (for example, even if the expected arrival time is slightly off, it is desirable to prioritize the one with the same EV of facility 102), the grouping unit 203 weights the information corresponding to the priority, calculates the weighted Euclidean distance, and determines whether the distance is close.

[0044] In addition, regarding vector-based grouping methods, grouping can be performed until the distance (similarity) between each group becomes a certain value. Alternatively, clustering methods such as k-means can be effectively used to perform grouping until the number of groups becomes a predetermined number.

[0045] If grouping already exists and vectors are used to assign EVs to groups, the assignment can be performed in the same way as previously described. More specifically, the average Euclidean distance of each feature in a group can be used as the group value, or the minimum value can be used as the group value, or the maximum value can be used as the group value, or the centroid of the vector of each EV can be used as the group value.

[0046] Figure 5 This is a diagram showing the results of grouping based on grouping unit 203. Figure 5 In the example, the result was obtained by grouping EVs with similar expected arrival times. Figure 5 In the example, vehicles 1 and 5 are assigned to group A, vehicles 2 and 7 to group B, vehicles 3, 8, and 9 to group C, vehicles 4 and 10 to group D, and vehicle 6 to group E. Figure 5 In the example, information such as the expected arrival date, expected arrival time, expected departure date, expected departure time, expected arrival facility, required energy storage at departure, and grouping of each EV is managed, but the information managed is not limited to this.

[0047] Next, details of the total charging power determination unit 204 will be explained. The total charging power determination unit 204 determines the charging power (i.e., the total charging power) for each EV group at each time period, based on constraints. The total charging power can also be calculated by determining the charging power of all chargers connected to the EVs in the group at each time period.

[0048] The total charging power for all chargers connected to the grouped EVs is determined by identifying the chargers that the EVs can connect to and adding up the charging power of those chargers. More specifically, although not shown, the total charging power determination unit 204 determines the chargers the EVs should connect to based on information related to the expected destination facilities determined by the EV's driving plan and the equipment information of the chargers installed at the facilities, and calculates the total charging power. Alternatively, the method by which the total charging power determination unit 204 calculates the total charging power can be managed within the EV's driving plan itself, specifying which EVs will connect to which chargers.

[0049] The total charging power determination unit 204 determines the total charging power for each time period, and thus creates a charging plan that plans the total charging power for each group. Here, the charging plan is set as a charging power plan, but it is not limited to this. For example, the energy storage capacity can be calculated based on the charging power, and the energy storage capacity plan can be used as the charging plan.

[0050] Here, constraints refer to the conditions used to determine the total charging power, and various conditions are considered. For example, constraints may include conditions such as being below the capacity obtained by adding the rated outputs of the chargers to which the EV is to be connected, or being able to charge only while the EV is at the charging facility 102. Furthermore, the objective function described later, which aims to minimize the electricity purchase cost during charging, can itself be considered a constraint; constraints are not limited. In this embodiment, constraints refer to the conditions used to determine the total charging power.

[0051] Various constraints have been proposed in the past for creating charging plans for individual EVs, but in this embodiment, details of the constraints in the previous examples are omitted. Furthermore, the constraints described in this embodiment are not used for creating charging plans for individual EVs, but rather for creating charging plans for EV groups (total charging power at each time). Next, the processing of the total charging power determination unit 204 will be described.

[0052] First, the total charging power determination unit 204 merges device information such as charger device information managed by the charger information management unit 201 and device information of the charging battery installed in the EV (described later) for each group after being grouped by the grouping unit 203. Based on the merged device information, it sets constraints for an optimization problem to create a charging plan. In this embodiment, as an optimization problem, minimizing the electricity purchase cost during the plan creation period is described as an example using this as the objective function.

[0053] Therefore, the total charging power decision unit 204 creates a charging plan that minimizes the electricity purchase cost during the planning period. First, as part of the merging of equipment information, the merging of the current energy storage capacity of each EV is explained. The merging of the current energy storage capacity within a group is shown in the following equation (1).

[0054] [Number 1]

[0055]

[0056] EV_CUR_CAP total EV_CUR_CAP represents the current battery capacity of the merged EV. each (i) represents the current battery capacity of each EV. N represents the number of EVs in the group. EV_CUR_CAP total This is the sum of the current stored capacity of each EV connected to the charger of facility 102 at the time when the current stored capacity is determined. The current stored capacity of the merged EVs is 0 if no EVs are present at facility 102. The merging of the stored capacity of EVs within a group is shown in the following equation (2).

[0057] [Number 2]

[0058]

[0059] EV_CAP total (t) represents the battery capacity of the merged EV, EV_CAP each (i) represents the battery capacity of each EV, N represents the number of EVs in the group, IN_T represents the estimated arrival time of the EV, and OUT_T represents the estimated departure time of the EV. The sum of the battery capacities of the EVs residing in facility 102 of each group is the combined battery capacity of the EVs. The combined battery capacity of the EVs is determined for each time period.

[0060] The combined energy storage capacity is 0 when no EVs are present at facility 102. Here, it is assumed that the EVs present at facility 102 are connected to the charger of facility 102. Alternatively, IN_T can be set to the moment when the EV is connected to the charger, and OUT_T can be set to the moment when the EV's connector is disconnected. IN_T and OUT_T will be set to the same value in the following description. The information on the controllable range of the energy storage capacity of EVs within the group is combined as shown in the following equations (3) and (4).

[0061] [Number 3]

[0062]

[0063] [Number 4]

[0064]

[0065] Regarding equation (3), EV_CAP_MAX total (t) represents the maximum controllable range of the merged EV, EV_CAP each (i) represents the battery capacity of each EV, EV_SOC_MAX each (i) represents the proportion of the maximum controllable range of the energy storage capacity of each EV.

[0066] Regarding equation (4), EV_CAP_MIN total (t) represents the minimum controllable range of the merged EV, EV_CAP each (i) represents the energy storage capacity of each EV, EV_SOC_MIN each (i) represents the proportion of the minimum controllable range of the energy storage capacity of each EV.

[0067] N represents the number of EVs in the group, IN_T represents the estimated arrival time of the EVs, and OUT_T represents the estimated departure time of the EVs. The maximum and minimum values ​​of the combined controllable range are determined for each time period. The maximum and minimum values ​​of the combined controllable range are 0 when there are no EVs staying at facility 102. The combined rated output of the chargers connected to each EV in the group is shown in Equation (5) below. Here, the combined rated output is the maximum charging power of the EVs in the group as a whole.

[0068] [Number 5]

[0069]

[0070] EV_OUT_MAX total (t) represents the maximum charging power for the EVs after merging (the maximum charging power for all EVs within the group), EV_OUT_MAXeach (i) represents the maximum charging power of each EV (the rated output of the charger connected to each EV), N represents the number of EVs in the group, IN_T represents the estimated arrival time of the EV, and OUT_T represents the estimated departure time of the EV.

[0071] The combined maximum charging power for EVs refers to the sum of the maximum charging power of each EV within the group. The combined maximum charging power for EVs is determined for each time period. The combined maximum charging power is 0 when no EVs are present at facility 102. The combined charging efficiency of the chargers connected to the EVs within the group is shown in the following equation (6).

[0072] [Number 6]

[0073]

[0074] EV_EFF total (t) represents the charging efficiency of the merged EV, EV_EFF each (i) represents the charging efficiency of each EV within the group, EV_CAP each (i) represents the battery capacity of each EV in the group, N represents the number of EVs in the group, IN_T represents the expected arrival time of the EV, and OUT_T represents the expected departure time of the EV. The combined charging efficiency is determined for each time. In the case of no EVs staying at facility 102, the denominator of equation (6) is 0, so the charging efficiency is 0.

[0075] Next, the constraints used to determine the total charging power for the grouping, i.e., the total charging power, and the objective function of the optimization problem will be explained. In this embodiment, the total charging power is determined in a way that minimizes the electricity cost. In this embodiment, the objective function is set to minimize the electricity cost, but it is not limited to this; it can also be a function that improves the electricity efficiency. The objective function of the optimization problem that minimizes the electricity cost is shown in the following equation (7).

[0076] [Number 7]

[0077]

[0078] Equation (7) is the objective function that minimizes the sum of the results obtained by multiplying the electricity Rec(t) at each time point by the electricity unit price unit(t) at each time point. The electricity unit price is determined by the power company, etc. By solving the optimization problem with equations (8) to (12) as constraints and equation (7) as the objective function, the total charging power determination unit 204 can calculate the total charging power at each time point that minimizes the electricity cost. Next, the constraints will be explained. Here, the electricity at the electricity receiving point point is as shown in equation (8) below.

[0079] [Number 8]

[0080] Rec(t) = EV_OUT total (t)

[0081] ……(8)

[0082] Equation (8) represents the constraint formula (constraint condition) of the supply and demand balance constraint. The power Rec(t) at the receiving power point and the charging power of the EV group within the group, i.e., the total charging power EV_OUT, are considered together. total (t) are equal.

[0083] In this embodiment, it is assumed that electricity is purchased from a single power receiving point, so the power at the power receiving point is equal to the total charging power. However, considering various scenarios such as situations where no charging power is incurred due to home power generation or the existence of multiple power receiving points, equation (8) is not limited. Next, the upper and lower limits of the power at the power receiving point are constrained as shown in equation (9).

[0084] [Number 9]

[0085] Rec_Min≤Rec(t)≤Rec_Max

[0086] ……(9)

[0087] Equation (9) represents the upper and lower limits of the power received at the power receiving point, where Rec(t) represents the power received at the power receiving point, Rec_Min represents the lower limit of the power received at the power receiving point, and Rec_Max represents the upper limit of the power received at the power receiving point. Next, the upper and lower limits of the total charging power are constrained as shown in Equation (10).

[0088] [Number 10]

[0089] 0≤EV_OUT total (t)≤EV_OUT_MAX total (t) ......(10)

[0091] Equation (10) represents the upper and lower limits of the total charging power. EV_OUT total(t) represents the total charging power of each EV group, i.e., the total charging power, EV_OUT_MAX. total (t) represents the upper limit of the total charging power, i.e., the maximum charging power. The upper limit, i.e., the maximum charging power, refers to the maximum charging power of the EV after merging, as described in equation (5). The lower limit of the total charging power is 0. Next, the upper and lower limits of the controllable range of the combined storage capacity of the EVs in the group are constrained by equation (11).

[0092] [Number 11]

[0093] EV_CAP_MIN total (t)≤EV_PL_CAP total (t)≤EV_CAP_MAX totat (t) ......(11)

[0095] EV_PL_CAP total (t) represents the combined energy storage capacity of the EVs within the group. EV_CAP_MIN total (t) represents the minimum controllable range of the combined storage capacity, EV_CAP_MAX total (t) represents the maximum controllable range of the combined storage capacity. Here, EV_CAP_MAX total (t) is the maximum controllable range of the merged EV as described in equation (3). EV_CAP_MIN total (t) is the minimum controllable range of the merged EV as described in equation (4).

[0096] The combined energy storage capacity is a value determined for each specific time period. That is, it represents the energy storage capacity state at each time period, from which EV_PL_CAP is calculated. total (t), thus enabling the calculation of the charging plan for the energy storage capacity. Next, the constraint formula (constraint condition) representing the state change of the combined energy storage capacity (charging plan) of the EVs in the group is shown as in equation (12).

[0097] [Number 12]

[0098] EV_PL_CAP total (t)=EV_PL_CAP total (t-1)+EV_EFF total (t)×EV_OUT total (t)

[0099] ……(12)

[0100] EV_PL_CAP total(t) represents the combined energy storage capacity of EVs within the group, EV_EFF total (t) represents the charging efficiency of the combined EV calculated using equation (6), EV_OUT total (t) represents the total charging power of the EV group within the group, calculated using equation (8). Equation (12) means that the energy storage capacity at a certain time t is obtained by multiplying the total charging power at time t by the charging efficiency of the merged EVs at time t, and then adding the energy storage capacity at the previous time (t-1).

[0101] As described above, by using an optimization solver to solve the optimization problem, the total charging power determination unit 204 can determine the charging power of the EV group for each group at each time, i.e., the total charging power. Here, the charging power of the EV group in each group refers to the total charging power of all chargers connected to the EVs after the grouping.

[0102] Furthermore, the total charging power determination unit 204 can create a charging plan for the battery capacity of each EV group by determining the total charging power for each time period. In this embodiment, the charging plan is planned based on the battery capacity, but it can also be created based on the charging power; the method of creating the plan is not limited.

[0103] Next, details of the individual charging power determination unit 205 will be explained. The individual charging power determination unit 205 determines the individual charging power of each EV based on the total charging power of each group determined by the total charging power determination unit 204. Since the total charging power determined by the total charging power determination unit 204 is determined for each time period, the individual charging power determination unit 205 can determine the individual charging power for each time period based on the total charging power.

[0104] More specifically, the individual charging power determination unit 205 allocates total charging power to each EV based on the ratio of the difference between the EV's current energy storage capacity and the energy storage capacity within the group, and determines the individual charging power for each time period. Here, energy storage capacity refers to the current energy storage capacity at each time period, but it is not limited to this. It can also be allocated based on the energy storage capacity of the EV when it arrives at facility 102, etc., and the timing is not limited.

[0105] Figure 6 This is an example diagram showing device information for a rechargeable battery used in an EV. Figure 6 In this example, the battery's storage capacity and charge level are managed. More specifically, the battery's device information is managed, including the EV name, storage capacity, and current charge level. However, it is not limited to this information; it can also manage information such as charging efficiency, and is not limited to this. Figure 6 Examples. In Figure 6In the example, we can see that vehicle 1 has a battery capacity of 30.0 kWh and a current battery capacity of 15.0 kWh; vehicle 2 has a battery capacity of 30.0 kWh and a current battery capacity of 10.0 kWh; and vehicle 3 has a battery capacity of 30.0 kWh and a current battery capacity of 12.0 kWh. The current battery capacity is updated in real time according to the current situation.

[0106] With a total charging power of 30kW and the energy storage capacity of each EV being [missing information] Figure 6 In the case of allocating power based on the difference between the battery capacity and the current battery level, the individual charging power determination unit 205 first calculates the difference between the battery capacity and the current battery level of each EV, standardizes it so that the sum is 1.0, and then allocates the total charging power using the standardized value. As a result, the calculated power is approximately 8.5kW for vehicle 1, 11.3kW for vehicle 2, and approximately 10.2kW for vehicle 3.

[0107] Here, the individual charging power determination unit 205 allocates the total charging power based on the difference between the storage capacity and the current storage capacity. However, it is not limited to this. It can also allocate the total charging power by standardizing the storage capacity to make the total of 1.0. As long as the method of allocation is based on the proportion of the device information of the charging battery installed in the EV, the method is not limited.

[0108] Furthermore, when allocating total charging power to each EV, the individual charging power determination unit 205 can also allocate the total charging power starting from the EVs with higher priority and determine the individual charging power for each time period. Here, the individual charging power determination unit 205 determines the priority of the EVs based on the device information of the charging batteries installed in the EVs. For example, the individual charging power determination unit 205 can also calculate the difference between the stored energy and the stored capacity, and determine the priority by starting with the EVs with the larger difference.

[0109] Additionally, the individual charging power determination unit 205 can calculate the remaining time from the current moment to the expected departure time for each EV, and determine the priority by giving higher priority to EVs with less remaining time. Furthermore, the individual charging power determination unit 205 can also determine the priority by giving higher priority to EVs with more required energy storage at departure based on the required energy storage at departure time.

[0110] Additionally, the individual charging power determination unit 205 can calculate the difference between the stored capacity and the required stored capacity at departure (required stored capacity at departure - stored capacity) for each EV, and determine the priority of EVs with a larger difference. Furthermore, the individual charging power determination unit 205 can also calculate the value obtained by dividing the difference between the stored capacity and the required stored capacity at departure (required stored capacity at departure - stored capacity) by the remaining time from the current time to the expected departure time of each EV, and determine the priority of EVs with a larger value. Several methods for determining priority have been described here, but the method is not limited to these; the user can also arbitrarily determine the priority.

[0111] The individual charging power determination unit 205 allocates total charging power starting with the highest-priority EVs based on the determined priority. However, in this case, it allocates as much individual charging power as possible to the highest-priority EVs. Alternatively, the individual charging power determination unit 205 may allocate the total charging power proportionally based on benchmark values ​​such as battery capacity, and then multiply the allocation by a larger coefficient (weighting) for the highest-priority EVs. Priority is information used to determine which EVs should be allocated total charging power with priority; the method of allocation is not limited as long as priority is given.

[0112] Next, details of the instruction value notification unit 206 will be explained. The instruction value notification unit 206 notifies the controllers (HEMS, FEMS, BEMS) that control the chargers of each facility 102, or the EV user, of the instruction value based on the individual charging power determination control value for each EV at each time period determined by the individual charging power determination unit 205. Furthermore, the notification can also be sent to dedicated applications capable of controlling EV charging, such as the charging control devices of each facility 102. As a method of notification to the EV user, the notification can also be sent to the EV user's portable terminal.

[0113] Figure 7 This is a flowchart illustrating the processing of the charging power management device 101. Regarding the processing of this embodiment, along with... Figure 7 The flowchart is explained below.

[0114] First, in the charger information acquisition step S101, the charger information management unit 201 acquires and manages the charger's device information. More specifically, the charger information management unit 201 acquires and manages the rated output of the charger maintained by the various facilities 102 to which the EV is to be connected. The charger's device information can be managed by the charger information management unit 201, or it can be obtained from external databases, etc., and is not limited to these methods.

[0115] Next, in the driving plan acquisition step S102, the driving plan management unit 202 acquires the EV's driving plan. More specifically, the driving plan management unit 202 acquires the EV's estimated arrival time at facility 102, estimated departure time from facility 102, estimated arrival time at the facility, and the required battery power at departure. The driving plan can be managed by the driving plan management unit 202, or it can be acquired from an external database, etc., and is not limited to this step.

[0116] Next, in the grouping and classification step S103, the grouping unit 203 groups multiple EVs into an EV group based on classification rules and driving plans. Here, as described previously, the grouping unit 203 can also group using classification rules that do not utilize driving plans. The objects of grouping are essentially EVs and are not limited. For example, control can also be performed by grouping according to driving plans.

[0117] Next, in the device information merging step S104, the total charging power determination unit 204 merges device information such as the charger's device information and the device information of the battery installed in the EV. The total charging power determination unit 204 merges device information related to the charging equipment of the EV and facility 102, such as the current charge level, charge capacity, and charging efficiency from the battery information of the EV and the rated output information from the charger's device information, to determine the overall current charge level, charge capacity, controllable range, maximum charging power, and charging efficiency of the group. The merging of each piece of information is as described in the aforementioned total charging power determination unit 204.

[0118] Next, in the constraint setting step S105, the total charging power determination unit 204 sets constraints for the optimization problem of optimizing (determining) the total charging power of each group based on the information merged in the device information merging step S104. The total charging power determination unit 204 sets constraints such as the upper and lower limits of the total charging power, the upper and lower limits of the controllable range of the planned value of the merged EV storage capacity, and the constraint formula for the state change of the total planned value.

[0119] Furthermore, the content regarding the constraints is as described in the total charging power determination unit 204. Next, in the objective function setting step S106, the total charging power determination unit 204 sets the objective function for the optimization problem of optimizing (determining) the charging plan for each group. Furthermore, the content regarding the objective function is as described in the total charging power determination unit 204.

[0120] Next, in the total charging power determination step S107, the total charging power determination unit 204 uses an optimization solver to solve the optimization problem set in the device information merging step S104, the constraint setting step S105, and the objective function setting step S106. Thus, the total charging power determination unit 204 determines the charging power, i.e., the total charging power, for each EV group at each time, based on the constraints.

[0121] Next, in the charging plan creation step S108, the total charging power determination unit 204 creates a charging plan for each group based on the total charging power determined in the total charging power determination step S107. The charging plan can be a plan for the total charging power at each time or a plan for the stored capacity at each time, and is not limited thereto.

[0122] Next, in the instruction value notification step S109, the individual charging power determination unit 205 allocates the total charging power determined by the total charging power determination unit 204 to each EV to determine the individual charging power. Furthermore, the instruction value notification unit 206, based on the individual charging power determination control value determined by the individual charging power determination unit 205, notifies the controllers (HEMS, FEMS, BEMS) that control the chargers of each facility 102 or the EV user of the instruction value. Moreover, the method for determining the individual charging power for each EV is as described in the individual charging power determination unit 205.

[0123] Next, in update determination step S110, the update determination unit determines whether it is time for a charging plan update. If it is determined to be time for an update, the process returns to grouping and classification step S103 and repeats. The charging plan update can be at any interval, or it can be determined by being triggered by a driving plan update. The update determination unit is not shown in the figure.

[0124] Next, in the system termination determination step S111, the termination determination unit determines whether to terminate the system. If the system is not determined to be terminated, the process returns to the charging plan creation step S108 and repeats. If the system is determined to be terminated, the process ends. The termination of the process can also be determined by inputting a termination command from the user, or it can be determined when a specific time is reached; the method of determination is not limited. The termination determination unit is not shown in the figure.

[0125] Figure 8 This is a hardware structure diagram showing the structure of the charging power management device 101. The charging power management device 101 includes an input interface 301, a CPU (Central Processing Unit) 302, a storage device 303, and an output interface 304. Hereafter, the interfaces will be referred to as IF.

[0126] Information stored in the charger information management unit 201, driving plan management unit 202, etc., is obtained by inputting IF 301. The obtained data is stored in storage device 303, and the functions of grouping unit 203, total charging power determination unit 204, individual charging power determination unit 205, etc., are realized by the CPU 302 executing a program.

[0127] Information such as charger device information, battery device information, and driving plan can also be obtained externally via input IF301. The command value notification unit 206 outputs a command value from output IF304 based on the charging plan generated by the separate charging power determination unit 205. Alternatively, the charging plan itself can be output from output IF304, allowing an external device to issue the command value.

[0128] In addition, IF refers to wired ports such as cable ports, USB ports, direct connection ports, and wireless network ports. Storage device 303 includes storage media such as HDDs, SSDs, and flash memory. The processing of these various processing units is handled by computing devices such as computers.

[0129] As described above, the charging power management device 101 groups EVs according to classification rules, provides constraints and objective functions for the grouped EVs and defines an optimization problem to determine the total charging power, and determines the individual charging power based on the total charging power.

[0130] This construction reduces constraints and other conditions compared to defining optimization problems for each EV, thus minimizing the increase in computational resources required to solve optimization problems associated with charging schedule creation.

Claims

1. A charging power management device, comprising: The grouping department groups multiple electric vehicles scheduled for charging according to classification rules; The total charging power determination unit, based on the battery information and charger information of the electric vehicles within the group, sets constraint conditions to constrain the charging of the merged electric vehicles after combining the electric vehicles within the group, and determines the charging power, i.e., the total charging power, for the merged electric vehicles at each time according to the constraint conditions; and The individual charging power determination unit allocates the total charging power to the electric vehicles within the group that are merged into the combined electric vehicles, and determines the individual charging power for each electric vehicle at each time. When the driving plan of the electric vehicle is expressed using vectors, the grouping unit groups electric vehicles whose Euclidean distance between the vectors is below a threshold into the same group.

2. The charging power management device as described in claim 1, characterized in that, The grouping unit groups the electric vehicles according to the classification rules based on the driving plan of the electric vehicles.

3. The charging power management device as described in claim 2, characterized in that, The driving plan includes information on facilities where the electric vehicle is expected to be charged. The classification rule is a rule that groups electric vehicles with the same facilities into the same group.

4. The charging power management device as described in claim 2, characterized in that, The driving plan includes information on at least one of the following: the estimated arrival time of the electric vehicle at the intended charging facility, the estimated departure time from the facility, and the necessary battery power at departure. The classification rule is the rule for grouping the electric vehicles according to the driving plan.

5. The charging power management device according to any one of claims 1 to 4, characterized in that, The constraint condition is that the capacity obtained by adding the rated outputs of the chargers to be connected to the electric vehicle is below the limit.

6. The charging power management device according to any one of claims 1 to 4, characterized in that, The constraint is that the cost of purchasing electricity is minimized.

7. The charging power management device according to any one of claims 1 to 4, characterized in that, The individual charging power determination unit allocates the total charging power based on a standardized value of the difference between the battery capacity of the electric vehicle and the current charge capacity, and determines the individual charging power for each time period.

8. The charging power management device according to any one of claims 1 to 4, characterized in that, The individual charging power determination unit determines the priority of each electric vehicle based on the remaining time before departure, and allocates the total charging power starting from the electric vehicle with the highest priority, thereby determining the individual charging power for each time period.

9. A charging power management method, comprising: The steps for grouping multiple electric vehicles scheduled for charging according to classification rules; Based on the battery information and charger information of the electric vehicles within the group, constraints are set to constrain the charging of the merged electric vehicles after combining the electric vehicles within the group. Based on these constraints, a step is taken to determine the charging power (i.e., the total charging power) of the merged electric vehicles at each time point; and... The steps of distributing the total charging power to the electric vehicles within the group that are merged into the merged electric vehicles, and determining the individual charging power for each electric vehicle at each time period. When a driving plan for an electric vehicle is expressed using vectors, electric vehicles whose Euclidean distance between the vectors is below a threshold are grouped into the same group.

Images