A method and system for identifying adjustable charging and discharging capabilities and scheduling boundaries of electric vehicles

Abstract

The present invention relates to the technical field of electric vehicles, and discloses a method and system for identifying the adjustable charging and discharging capacity and scheduling boundaries of electric vehicles. The method determines the maximum controllable charging and discharging area of ​​a single electric vehicle through the charging and discharging history data of the electric vehicle to form an adjustable capacity polygon, and divides the adjustable capacity polygon into quadrants. The adjustable capacity and scheduling boundary data of the single electric vehicle in each quadrant are determined based on the quadrant division results and the current state of charge of the electric vehicle. Through quadrant data analysis, there is no need to rely on data quality and differences in the behavior patterns of single electric vehicles are ignored, thereby improving the accuracy of identifying the adjustable charging and discharging capacity of the electric vehicle and the reliability of the identification results of the adjustable capacity and scheduling boundaries.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicles, and in particular to a method and system for identifying the adjustable charging and discharging capability and scheduling boundaries of an electric vehicle. Background Art

[0002] Electric vehicles are a significant load in new power systems, capable of adjusting their load profiles through charge and discharge control. With the widespread adoption of electric vehicles in my country and the development of vehicle-grid interaction technologies, power grids, aggregators, and virtual power plants are building digital and intelligent information service platforms to integrate the flexible support capabilities of electric vehicles, guide the charging and discharging of electric vehicles to participate in vehicle-grid interaction, and facilitate various regulatory needs such as peak load regulation, frequency regulation, and the absorption of new energy sources.

[0003] Electric vehicles offer a certain degree of flexibility, and leveraging this flexibility to support grid dispatch has garnered widespread attention. Previous studies have proposed strategies for orderly charging and vehicle-grid interaction that meet the diverse charging needs of different vehicle owners, enabling this without impacting the vehicle's user experience. Furthermore, some studies have proposed methods to quantify the adjustable margin of electric vehicles, using multiple approaches, such as direct quantification of adjustable capacity and the energy feasible region. Some methods use Monte Carlo simulations of electric vehicle charging and driving behavior to derive energy trajectories, while others use load modeling combined with rolling corrections to derive load margins and thus obtain dispatch boundaries.

[0004] In summary, existing technologies for evaluating the adjustable charging and discharging capabilities and scheduling boundaries of electric vehicles rely heavily on data quality while ignoring the differences in the behavioral patterns of individual electric vehicles. This results in unreliable and poorly accurate identification of their adjustable capabilities and scheduling boundaries. Summary of the Invention

[0005] The present invention provides a method and system for identifying the adjustable charging and discharging capability and scheduling boundaries of electric vehicles, which solves the technical problem that the existing technology for evaluating the adjustable charging and discharging capability and scheduling boundaries of electric vehicles relies heavily on data quality while ignoring the differences in the behavior patterns of individual electric vehicles, resulting in unreliable and poorly accurate identification results of their adjustable capabilities and scheduling boundaries.

[0006] In view of this, a first aspect of the present invention provides a method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle, comprising the following steps:

[0007] Determining a maximum controllable charge and discharge region of a single electric vehicle based on charge and discharge history data of at least one electric vehicle, and mapping the maximum controllable charge and discharge region into a two-dimensional coordinate system to form an adjustable capacity polygon;

[0008] Dividing the adjustable capacity polygon into quadrants according to the state of charge of the electric vehicle;

[0009] According to the quadrant division results and the current state of charge of the electric vehicle, the adjustable capacity and scheduling boundary data of the single electric vehicle in each quadrant are determined.

[0010] Preferably, the charging and discharging history data includes state of charge, maximum state of charge, expected state of charge, minimum tolerable state of charge, grid-connected state of charge, minimum state of charge, grid-connected time, expected off-grid time, maximum charging power, maximum discharge power, earliest time the electric vehicle can leave, earliest suitable time to leave, earliest time to reach maximum state of charge, earliest time to stop discharging, charging start time, longest charging time, shortest charging time, longest idle time, actual state of charge when off-grid, and state of charge at different times.

[0011] Preferably, the step of determining the maximum controllable charge and discharge area of ​​a single electric vehicle based on the charge and discharge history data of at least one electric vehicle, and mapping the maximum controllable charge and discharge area in a two-dimensional coordinate system to form an adjustable capacity polygon specifically includes:

[0012] Determining, based on charging and discharging history data of at least one electric vehicle, each boundary segment of a maximum controllable charging and discharging region of a single electric vehicle and an inequality for characterizing each boundary segment;

[0013] Defining a closed area of ​​the maximum controllable charge and discharge area according to each boundary line segment and an inequality used to characterize each boundary line segment, and mapping the defined closed area into a two-dimensional coordinate system to form an adjustable capacity polygon;

[0014] Wherein, the maximum controllable charge and discharge range is:

[0015]

[0016] Where, represents the set of maximum controllable charge and discharge regions, is the rated capacity of the electric vehicle, 、 are the horizontal and vertical coordinate values, respectively. 、 are the maximum discharge power and the maximum charging power respectively, The charging state of the grid is The time of joining the network, 、 are the lowest state of charge and the highest state of charge, respectively. is the desired state of charge, The expected off-grid time;

[0017] Constructing the constraint conditions of the maximum controllable charge and discharge area, wherein the constraint conditions include charge limit constraint, normal distribution constraint of expected state of charge, charging power limit constraint, discharging power limit constraint and charge and discharge power mutual exclusion constraint.

[0018] Preferably, the step of dividing the adjustable capacity polygon into quadrants according to the state of charge of the electric vehicle specifically includes:

[0019] Construct the linear equations of the electric vehicle at different states of charge. The three linear equations are:

[0020]

[0021] Where, is the charging start time;

[0022] The three straight line equations are mapped into a two-dimensional coordinate system and covered on the adjustable capacity polygon, and the adjustable capacity polygon is divided into quadrants.

[0023] Preferably, the step of determining the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant according to the quadrant division result and the current state of charge of the electric vehicle specifically includes:

[0024] Determining the boundary points of the feasible region of each quadrant according to the quadrant division result, wherein each boundary point is a boundary state of charge of the electric vehicle;

[0025] The adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant are determined based on the boundary points of the feasible domain of each quadrant and the current state of charge of the electric vehicle. The scheduling boundary data includes the continuous discharge time of the electric vehicle at the maximum discharge power at different times, the continuous charging time at the maximum charging power, the maximum discharge capacity and the maximum chargeable power.

[0026] Preferably, the step of determining the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant according to the quadrant division result and the current state of charge of the electric vehicle further includes:

[0027] The sampling moments of the electric vehicle are rolled within a preset time period, and the adjustable capacity and scheduling boundary data of the electric vehicle within the preset time period are determined according to the charge states corresponding to all sampling moments of the electric vehicle within the preset time period and the quadrant division results.

[0028] In a second aspect, the present invention further provides a system for identifying the adjustable charging and discharging capability and scheduling boundaries of electric vehicles, comprising:

[0029] A graphic mapping module, configured to determine a maximum controllable charge and discharge area of ​​a single electric vehicle based on charge and discharge history data of at least one electric vehicle, and map the maximum controllable charge and discharge area into a two-dimensional coordinate system to form an adjustable capacity polygon;

[0030] A quadrant division module, configured to divide the adjustable capacity polygon into quadrants according to the state of charge of the electric vehicle;

[0031] The quadrant identification module is used to determine the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant based on the quadrant division results and the current state of charge of the electric vehicle.

[0032] In a third aspect, the present invention further provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for identifying the adjustable charging and discharging capability and scheduling boundaries of electric vehicles.

[0033] In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the above-mentioned electric vehicle charging and discharging adjustable capability and scheduling boundary identification method.

[0034] In a fifth aspect, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the above-mentioned electric vehicle charging and discharging adjustable capability and scheduling boundary identification method.

[0035] It can be seen from the above technical solutions that the present invention has the following advantages:

[0036] The present invention determines the maximum controllable charging and discharging area of ​​a single electric vehicle through the charging and discharging history data of the electric vehicle to form an adjustable capacity polygon, and divides the adjustable capacity polygon into quadrants. According to the quadrant division result and the current charge state of the electric vehicle, the adjustable capacity and scheduling boundary data of the single electric vehicle in each quadrant are determined. Through quadrant data analysis, there is no need to rely on data quality and the differences in the behavior patterns of single electric vehicles are ignored, thereby improving the accuracy of identifying the adjustable capacity of electric vehicle charging and discharging and the reliability of the identification results of the adjustable capacity and scheduling boundaries. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 A flow chart of a method for identifying the adjustable charging and discharging capability and scheduling boundaries of an electric vehicle provided in an embodiment of the present invention;

[0038] Figure 2A flowchart of step S1 provided in an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of the structure of an adjustable capacity polygon provided by an embodiment of the present invention;

[0040] Figure 4 A schematic diagram of the quadrant division result provided by an embodiment of the present invention;

[0041] Figure 5 A schematic diagram of the first quadrant area provided in an embodiment of the present invention;

[0042] Figure 6 A schematic diagram of the second quadrant area provided by an embodiment of the present invention;

[0043] Figure 7 A schematic diagram of the third quadrant area provided by an embodiment of the present invention;

[0044] Figure 8 A schematic diagram of the fourth quadrant region provided by an embodiment of the present invention;

[0045] Figure 9 A schematic diagram of the fifth quadrant area provided by an embodiment of the present invention;

[0046] Figure 10 A schematic diagram of the sixth quadrant area provided by an embodiment of the present invention;

[0047] Figure 11 A schematic diagram of the structure of a system for identifying the adjustable charging and discharging capability and scheduling boundaries of electric vehicles provided by an embodiment of the present invention;

[0048] Figure 12 A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0050] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products or apparatuses.

[0051] Existing technologies for evaluating the adjustable charging and discharging capabilities and scheduling boundaries of electric vehicles rely heavily on data quality while ignoring the differences in the behavioral patterns of individual electric vehicles. This results in unreliable and poorly accurate identification of their adjustable capabilities and scheduling boundaries.

[0052] In view of this, embodiments of the present invention provide a method for identifying the charge and discharge adjustability and scheduling boundaries of an electric vehicle. Embodiments of the present invention are applicable to identifying the charge and discharge adjustability and scheduling boundaries of an electric vehicle. The method can be performed by an electric vehicle charge and discharge adjustability and scheduling boundary identification device, which can be implemented in hardware and / or software and can be configured in a computer device.

[0053] See also Figure 1 , Figure 1 The present invention illustrates a method for identifying the adjustable charging and discharging capability and scheduling boundaries of an electric vehicle provided by an embodiment of the present invention.

[0054] The present invention provides a method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle, which includes the following steps S1 to S3:

[0055] Step S1: determining a maximum controllable charge and discharge area of ​​a single electric vehicle based on charge and discharge history data of at least one electric vehicle, and mapping the maximum controllable charge and discharge area into a two-dimensional coordinate system to form an adjustable capacity polygon.

[0056] Among them, the charge and discharge history data includes the state of charge , maximum state of charge , desired state of charge , minimum tolerable state of charge , Grid charge status , minimum state of charge , Network access time Expected off-grid time , Maximum charging power , Maximum discharge power , the earliest time an electric car can leave , the earliest suitable time to leave , the earliest time to reach maximum state of charge , Earliest stop discharge time , Charging start time , Maximum charging time , shortest charging time , longest idle time , Actual state of charge when off-grid and the state of charge at different times .

[0057] Among them, the state of charge of electric vehicles Refers to the battery's state of charge, which describes the ratio of the current capacity of an electric vehicle battery to its rated capacity. The expression is:

[0058]

[0059] Where, 、 They are the current power and rated power of the electric vehicle respectively.

[0060] Maximum state of charge Indicates the maximum power required to ensure the health of electric vehicle batteries and driving needs.

[0061] If an electric vehicle is connected to the power grid, The electric vehicle reaches the desired state of charge when the power is discharged. The time when the maximum state of charge is reached at the earliest for:

[0062]

[0063] Minimum state of charge Indicates the minimum power required to ensure the health of electric vehicle batteries and driving needs.

[0064] If an electric vehicle is connected to the power grid, The electric vehicle reaches the lowest state of charge when the power is discharged. The earliest time to stop discharging for:

[0065]

[0066] Grid charge state It refers to the state of charge of an electric vehicle when it is connected to the power grid.

[0067] Desired state of charge It refers to the state of charge that the owner hopes to achieve when picking up the car and driving away.

[0068] If the electric vehicle enters the delayed charging state at this time, it will not enter the delayed charging state until the earliest mandatory charging time is reached. , then it is necessary to force Otherwise, electric vehicles cannot be charged Time to reach The earliest charging start time corresponding to the forced charging curve fed is reached for:

[0069]

[0070] In order to achieve the desired state of charge, electric vehicles The longest charging time required, i.e. the longest charging time for:

[0071]

[0072] In order to achieve the desired state of charge, electric vehicles The shortest charging time required, i.e. the shortest charging time for:

[0073]

[0074] Electric vehicles reach the desired state of charge The earliest suitable time to leave for

[0075]

[0076] Minimum tolerable state of charge It refers to the lower limit of the electric vehicle's state of charge that the owner can tolerate, and is the lowest state of charge that the owner can accept when the expected state of charge cannot be achieved.

[0077] If an electric vehicle is connected to the power grid, If the electric vehicle is charged with the power of The time is the earliest time the electric car can leave :

[0078]

[0079] After the electric vehicle is connected to the grid, if it maintains neither charging nor discharging for the longest time, that is, the longest idle time for:

[0080] .

[0081] Specifically, if Figure 2 As shown, Figure 2The process of step S1 is shown in FIG. Step S1 specifically includes:

[0082] Step S101: determining each boundary segment of a maximum controllable charge and discharge area of ​​a single electric vehicle and an inequality for characterizing each boundary segment based on charge and discharge history data of at least one electric vehicle.

[0083] The maximum controllable charge and discharge region of a single electric vehicle can be defined (quantified) in a two-dimensional coordinate system using inequalities or equations. The feasible region can be defined by listing the conditions that must be satisfied by points on each boundary segment.

[0084] Step S102 : defining a closed area of ​​the maximum controllable charge and discharge area according to each boundary line segment and an inequality for characterizing each boundary line segment, and mapping the defined closed area into a two-dimensional coordinate system to form an adjustable capacity polygon.

[0085] like Figure 3 As shown, Figure 3 The structure of the adjustable capacity polygon is shown. , G 、F 、 D 、C 、B The maximum controllable charging and discharging area of ​​a single electric vehicle can be described by listing the inequalities corresponding to each line segment:

[0086] Line segment AG:

[0087] From A to G The slope of the straight line is Since the slope is known, for all points (x, y) on the right side of segment AG, we have:

[0088]

[0089] Line segment GF:

[0090] GF is parallel to the horizontal axis, so for all points (x, y) above the GF segment, we have:

[0091]

[0092] Line segment FD:

[0093] Similarly, from F to D The slope of the straight line is For all points (x, y) on the left side of the FD segment, we have:

[0094]

[0095] Line segment DC:

[0096] DC is parallel to the y-axis, so for all points (x, y) to the left of DC, we have:

[0097]

[0098] Line segment CB:

[0099] From C(17,14) to B(11,14), this line segment is also parallel to the x-axis, so for all points (x, y) below the CB line segment, we have:

[0100]

[0101] Line segment BA:

[0102] From B to A , the slope of the straight line For all points (x, y) on the right side of the BA segment, we have:

[0103]

[0104] In summary, the maximum controllable charge and discharge range is:

[0105]

[0106] Where, represents the set of maximum controllable charge and discharge regions, is the rated capacity of the electric vehicle, 、 are the horizontal and vertical coordinate values, respectively. 、 are the maximum discharge power and the maximum charging power respectively, The charging state of the grid is The time of joining the network, 、 are the lowest state of charge and the highest state of charge, respectively. is the desired state of charge, The expected off-grid time.

[0107] Step S103: Constructing constraints for the maximum controllable charge and discharge area, the constraints including charge limit constraint, normal distribution constraint of expected state of charge, charge power limit constraint, discharge power limit constraint, and charge and discharge power mutual exclusion constraint.

[0108] Among them, the maximum controllable charging and discharging area must meet the following constraints:

[0109] 1) The charge limit constraint is expressed as:

[0110]

[0111] 2) The normal distribution constraint of the expected state of charge is expressed as:

[0112] Desired state of charge Obey the following normal distribution:

[0113]

[0114] Where δ is the standard deviation and μ is the expected value. The standard deviation δ = 0.179 and the expected value μ = 0.466.

[0115] 3) Charging power limit constraints

[0116]

[0117] Where, is the charging power of the electric vehicle at time t, and Indicates the upper and lower limits of the charging power of the electric vehicle charging load.

[0118] 4) Discharge power limit constraints

[0119]

[0120] Where, is the discharge power of the electric vehicle at time t, and Indicates the upper and lower limits of the electric vehicle charging load discharge power.

[0121] 5) Mutual exclusion constraints on charge and discharge power

[0122]

[0123] By adjusting the terminal voltage and current of electric vehicle batteries according to their state of charge, the power battery can achieve variable charge and discharge power. Power regulation during electric vehicle grid access will facilitate energy management and two-way interaction with the grid. However, with current technology, variable power may affect battery health.

[0124] Assuming that electric vehicle batteries The charging and discharging is done at constant power during the time, and the self-discharge of the power battery is ignored in the short term. The capacity model for the SOC in the charging and discharging mode of a single electric vehicle is established as follows:

[0125]

[0126] Where, Indicates the time difference, Indicates that at t+ The charge state at the moment, It represents the charging and discharging switch state factor of the electric vehicle at time t in the charging and discharging mode. 1 indicates entering the charging state, 0 indicates disconnecting from the power grid, and -1 indicates entering the discharging state.

[0127] Step S2: Divide the adjustable capacity polygon into quadrants according to the state of charge of the electric vehicle.

[0128] Specifically, step S2 includes:

[0129] Step S201: Construct linear equations of the electric vehicle at different states of charge. The three linear equations are:

[0130]

[0131] Where, is the charging start time;

[0132] Step S202: Map the three straight line equations into a two-dimensional coordinate system, overlay the three straight line equations onto the adjustable capacity polygon, and divide the adjustable capacity polygon into quadrants.

[0133] It should be noted that when the state of charge at time t is known, the maximum discharge capacity at time t can be calculated , the amount of charge required to reach the expected amount of charge , Maximum rechargeable capacity (Continuous discharge time at maximum discharge capacity , Minimum remaining charging time ), the latest time T to start charging, so as to characterize the adjustable capacity area of ​​​​the electric vehicle at any time after it is connected to the grid.

[0134] Since there are many situations for the state of charge of electric vehicles at time t, the electric vehicle adjustable capacity polygon is divided into six quadrants, such as Figure 4 As shown, Figure 4 The quadrant division result is shown, which is divided into six quadrants by straight lines mf, nc, and vd.

[0135] Since the coordinates F are known The slope of the mf line is , then let the equation of the mf line be:

[0136]

[0137] Since the coordinates C are known The slope of the line nc is , then let the equation of the nc line be:

[0138]

[0139] Since the coordinates D The slope of the line vd is , then let the equation of the vd line be:

[0140]

[0141] By dividing the adjustable capacity polygon by the straight lines mf, nc, and vd, six quadrants can be obtained. The specific quadrants are as follows:

[0142] The first quadrant area is Figure 5 The shaded area in the first quadrant is the pentagonal mbvus, which can be expressed as the intersection of the following inequalities:

[0143]

[0144] In the formula, the set Indicates the first quadrant area.

[0145] The second quadrant area is Figure 6 The shaded area in the second quadrant is the pentagon amsng, which can be expressed as the intersection of the following inequalities:

[0146]

[0147] In the formula, the set Indicates the second quadrant area.

[0148] The third quadrant area is Figure 7 The shaded area in the third quadrant is the quadrilateral sudf, which can be expressed as the intersection of the following inequalities:

[0149]

[0150] In the formula, the set Indicates the third quadrant area.

[0151] The fourth quadrant area is Figure 8 The shaded area in the fourth quadrant is the triangle nsf, which can be expressed as the intersection of the following inequalities:

[0152]

[0153] In the formula, the set Indicates the fourth quadrant area.

[0154] The fifth quadrant area is Figure 9The shaded area in the figure, the fifth quadrant area is the triangle vcu, which can be expressed as the intersection of the following inequalities:

[0155]

[0156] In the formula, the set Indicates the fifth quadrant area.

[0157] The sixth quadrant area is Figure 10 The shaded area in the figure, the sixth quadrant area is triangle ucd. This area can be expressed as the intersection of the following inequalities:

[0158]

[0159] In the formula, the set Indicates the sixth quadrant area.

[0160] Step S3: determining the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant according to the quadrant division result and the current state of charge of the electric vehicle.

[0161] Specifically, step S3 includes:

[0162] Step S301: Determine the boundary points of the feasible region of each quadrant according to the quadrant division result, wherein each boundary point is a boundary state of charge of the electric vehicle.

[0163] The boundary points of the feasible domain of each quadrant refer to the coordinate information of the vertices of the feasible domain of each quadrant, and each boundary point is the boundary charge state of the electric vehicle.

[0164] Taking the first quadrant as an example, assuming that at time t, the state of charge is at point n, and the coordinates are , the point is in the first quadrant. Then the feasible region at time t is the pentagon nmcds.

[0165] Requires the maximum discharge state of charge , then we need to find out the maximum discharge power of the electric vehicle at point n. Discharge to the coordinate of point s. Then find the difference between the vertical coordinates of point n and point s, which is the maximum discharge capacity of the electric vehicle at time t. .

[0166] Since the coordinates of the known point The slope of the line ns is , then let the equation of the ns line be:

[0167]

[0168] Since the coordinates of the known point The slope of the fd line is , then let the equation of the fd line be:

[0169]

[0170] By combining the equations of the ns line and the fd line, we can get the coordinates of point s:

[0171]

[0172] Step S302: determining the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant according to the boundary points of the feasible region of each quadrant and the current state of charge of the electric vehicle.

[0173] It should be noted that the electric vehicle adjustability is used to evaluate the controllable range of electric vehicles, which is determined by the scheduling boundary of individual vehicles.

[0174] The scheduling boundary data includes the continuous discharge time of the electric vehicle at the maximum discharge power at different times, the continuous charging time at the maximum charging power, the maximum dischargeable capacity and the maximum chargeable power.

[0175] The dispatch boundary data of individual electric vehicles reflects the scalability of individual electric vehicles. A larger dispatch boundary data indicates a stronger scalability, while a smaller dispatch boundary data indicates a weaker scalability. Therefore, evaluating or comparing the dispatch boundary data of individual electric vehicles can reflect the scalability of individual electric vehicles.

[0176] Specifically, in the first quadrant, according to the boundary point of the feasible region where the first quadrant is located, the continuous discharge time of the electric vehicle at the maximum discharge power at time t can be determined by calculating according to step S301. Calculation method in each case, at state of charge Less than desired state of charge When the two are equal, it is 0, that is, no discharge, in the charged state Greater than desired state of charge When the discharge time is equal to the maximum discharge power of the electric vehicle at time t, the discharge time is determined. for:

[0177]

[0178] Similarly, according to step S301, the continuous discharge time of the electric vehicle at the maximum discharge power at time t can be determined. Calculation method in each case, at state of charge Less than desired state of charge When the two are equal, it is 0, that is, no discharge, in the charged state Greater than desired state of charge When the discharge is continued until the two are equal, that is, the maximum discharge capacity of the electric vehicle at time t for:

[0179]

[0180] The maximum chargeable capacity of the electric vehicle at time t for:

[0181]

[0182] because Always less than or equal to ,but Always greater than or equal to zero.

[0183] when When the following happens:

[0184] The amount of electricity that the electric vehicle should be charged to reach the expected amount of electricity at time t for:

[0185]

[0186] Continuous charging time of electric vehicle at maximum charging power at time t for:

[0187]

[0188] According to the above formula and the graph, it can be seen that if the discharge time is to be increased, the total discharge capacity must be reduced, and vice versa.

[0189] In the second quadrant, assuming that at time t, the state of charge is at point n, the coordinates are , located in the second quadrant. Then the feasible region at time t is the hexagon nmcdefs. In this case, the electric vehicle is discharged to the lowest state of charge , then the maximum discharge capacity of the electric vehicle at time t is for:

[0190]

[0191] The amount of electricity that the electric vehicle should be charged to reach the expected amount of electricity at time t for:

[0192]

[0193] Continuous discharge time of the electric vehicle at maximum discharge power at time t for:

[0194]

[0195] Continuous charging time of electric vehicle at maximum charging power at time t for:

[0196]

[0197] The maximum chargeable capacity of the electric vehicle at time t for:

[0198]

[0199] In the third quadrant, assuming that at time t, the state of charge is at point n, the coordinates are , located in the third quadrant. Then the feasible region at time t is the quadrilateral nmds.

[0200] Continuous discharge time of the electric vehicle at maximum discharge power at time t for:

[0201]

[0202] The maximum discharge capacity of the electric vehicle at time t for:

[0203]

[0204] The maximum chargeable capacity of the electric vehicle at time t for:

[0205]

[0206] when When the following happens:

[0207] The amount of electricity that the electric vehicle should be charged to reach the expected amount of electricity at time t for:

[0208]

[0209] Continuous charging time of electric vehicle at maximum charging power at time t for:

[0210]

[0211] In the fourth quadrant, assuming that at time t, the state of charge is at point n, the coordinates are , located in the fourth quadrant. Then the feasible region at time t is the pentagon nmdfs.

[0212] Continuous discharge time of the electric vehicle at maximum discharge power at time t for:

[0213]

[0214] Maximum discharge capacity of electric vehicle at time t for:

[0215]

[0216] The maximum chargeable capacity of the electric vehicle at time t for:

[0217]

[0218] Continuous charging time of electric vehicle at maximum charging power at time t for:

[0219]

[0220] The amount of electricity that the electric vehicle should be charged to reach the expected amount of electricity at time t for:

[0221]

[0222] In the fifth quadrant, assuming that at time t, the state of charge is at point n, the coordinates are , located in the fifth quadrant. Then the feasible region at time t is the quadrilateral nmcs.

[0223] Continuous discharge time of the electric vehicle at maximum discharge power at time t for:

[0224]

[0225] Maximum discharge capacity of electric vehicle at time t for:

[0226]

[0227] The maximum chargeable capacity of the electric vehicle at time t for:

[0228]

[0229] Continuous charging time of electric vehicle at maximum charging power at time t for:

[0230]

[0231] In the sixth quadrant, assuming that at time t, the state of charge is at point n, the coordinates are , located in the sixth quadrant. Then the feasible region at time t is the triangle nms.

[0232] Continuous discharge time of the electric vehicle at maximum discharge power at time t for:

[0233]

[0234] Maximum discharge capacity of electric vehicle at time t for:

[0235]

[0236] The maximum chargeable capacity of the electric vehicle at time t for:

[0237]

[0238] Continuous charging time of electric vehicle at maximum charging power at time t for:

[0239]

[0240] In a specific embodiment, step S3 further includes:

[0241] Step S4: rolling the sampling moments of the electric vehicle within the preset time period, and determining the adjustable capacity and scheduling boundary data of the electric vehicle within the preset time period according to the charge states corresponding to all sampling moments of the electric vehicle within the preset time period and the quadrant division results.

[0242] After obtaining the adjustable capacity and scheduling boundary of the electric vehicle charging and discharging at a certain moment, the time is rolled over and the calculation of step S3 is repeated to obtain the adjustable capacity and scheduling boundary of the electric vehicle charging and discharging for the entire process.

[0243] It should be noted that the present invention determines the maximum controllable charging and discharging area of ​​a single electric vehicle through the charging and discharging history data of the electric vehicle to form an adjustable capacity polygon, and divides the adjustable capacity polygon into quadrants. According to the quadrant division results and the current charge state of the electric vehicle, the adjustable capacity and scheduling boundary data of the single electric vehicle in each quadrant are determined. Through quadrant data analysis, there is no need to rely on data quality and the differences in the behavior patterns of single electric vehicles are ignored, thereby improving the accuracy of identifying the adjustable charging and discharging capacity of electric vehicles and the reliability of the identification results of the adjustable capacity and scheduling boundaries.

[0244] The above is a detailed description of an embodiment of an electric vehicle charging and discharging adjustable capability and scheduling boundary identification method provided by the present invention. The following is a detailed description of an embodiment of an electric vehicle charging and discharging adjustable capability and scheduling boundary identification system provided by the present invention.

[0245] like Figure 11 As shown, Figure 11 The structure of an electric vehicle charging and discharging adjustable capability and scheduling boundary identification system provided by the present invention is illustrated. The electric vehicle charging and discharging adjustable capability and scheduling boundary identification system provided by the present invention includes:

[0246] A graphic mapping module 100 is used to determine the maximum controllable charge and discharge area of ​​a single electric vehicle based on the charge and discharge history data of at least one electric vehicle, and map the maximum controllable charge and discharge area in a two-dimensional coordinate system to form an adjustable capacity polygon;

[0247] A quadrant division module 200 is used to divide the adjustable capacity polygon into quadrants according to the state of charge of the electric vehicle;

[0248] The quadrant identification module 300 is used to determine the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant according to the quadrant division result and the current state of charge of the electric vehicle.

[0249] like Figure 12 As shown, the present invention provides an electronic device 10, including a memory 20 and a processor 30. The memory 20 stores a computer program. When the computer program is executed by the processor 30, the processor 30 executes the steps of the above-mentioned electric vehicle charging and discharging adjustable capability and scheduling boundary identification method.

[0250] The present invention also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed, the above-mentioned electric vehicle charging and discharging adjustable capability and scheduling boundary identification method are implemented.

[0251] The present invention also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the above-mentioned method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle.

[0252] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, electronic devices, computer storage media, and computer program products can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0253] In several embodiments provided by the present invention, it is understood that each box in the flow chart or block diagram can represent a module, program segment or part of code, and the module, program segment or part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved.

[0254] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, electronic devices, computer storage media, computer program products and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0255] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0256] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0257] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the method described in each embodiment of the present invention via a computer device (which can be a personal computer, server, or network device, etc.). The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0258] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle, characterized in that: The following steps are involved: Determine the maximum controllable charge and discharge area of ​​a single electric vehicle based on the charge and discharge history data of at least one electric vehicle, and map the maximum controllable charge and discharge area into a two-dimensional coordinate system to form an adjustable capacity polygon, including: Determining, based on charging and discharging history data of at least one electric vehicle, each boundary segment of a maximum controllable charging and discharging region of a single electric vehicle and an inequality for characterizing each boundary segment; Defining a closed area of ​​the maximum controllable charge and discharge area according to each boundary line segment and an inequality used to characterize each boundary line segment, and mapping the defined closed area into a two-dimensional coordinate system to form an adjustable capacity polygon; Wherein, the maximum controllable charge and discharge range is: ； Where, represents the set of maximum controllable charge and discharge regions, is the rated capacity of the electric vehicle, 、 are the horizontal and vertical coordinate values, respectively. 、 are the maximum discharge power and the maximum charging power respectively, The charging state of the grid is The time of joining the network, 、 are the lowest state of charge and the highest state of charge, respectively. is the desired state of charge, The expected off-grid time; Constructing constraints for the maximum controllable charge and discharge region, the constraints including a charge limit constraint, a normal distribution constraint of a desired state of charge, a charge power limit constraint, a discharge power limit constraint, and a charge and discharge power mutual exclusion constraint; The adjustable capacity polygon is divided into quadrants according to the state of charge of the electric vehicle, including: Construct the linear equations of the electric vehicle at different states of charge. The three linear equations are: ； ； ； Where, is the charging start time; Mapping the three straight line equations into a two-dimensional coordinate system, overlaying the system on the adjustable capacity polygon, and dividing the adjustable capacity polygon into quadrants; According to the quadrant division results and the current state of charge of the electric vehicle, the adjustable capacity and scheduling boundary data of the single electric vehicle in each quadrant are determined.

2. The method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle according to claim 1, characterized in that: The charge and discharge history data includes state of charge, maximum state of charge, expected state of charge, minimum tolerable state of charge, grid-connected state of charge, minimum state of charge, grid-connected time, expected off-grid time, maximum charging power, maximum discharge power, earliest time the electric vehicle can leave, earliest suitable time to leave, earliest time to reach maximum state of charge, earliest time to stop discharging, charging start time, longest charging time, shortest charging time, longest idle time, actual state of charge when off-grid, and state of charge at different times.

3. The method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle according to claim 1, characterized in that: The step of determining the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant according to the quadrant division result and the current state of charge of the electric vehicle specifically includes: Determining the boundary points of the feasible region of each quadrant according to the quadrant division result, wherein each boundary point is a boundary state of charge of the electric vehicle; The adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant are determined based on the boundary points of the feasible domain of each quadrant and the current state of charge of the electric vehicle. The scheduling boundary data includes the continuous discharge time of the electric vehicle at the maximum discharge power at different times, the continuous charging time at the maximum charging power, the maximum discharge capacity and the maximum chargeable power.

4. The method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle according to claim 1, characterized in that: The step of determining the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant according to the quadrant division result and the current state of charge of the electric vehicle further includes: The sampling moments of the electric vehicle are rolled within a preset time period, and the adjustable capacity and scheduling boundary data of the electric vehicle within the preset time period are determined according to the charge states corresponding to all sampling moments of the electric vehicle within the preset time period and the quadrant division results.

5. A system for identifying the adjustable charging and discharging capability and scheduling boundaries of electric vehicles, characterized in that: include: A graphic mapping module, configured to determine a maximum controllable charge and discharge area of ​​a single electric vehicle based on charge and discharge history data of at least one electric vehicle, and map the maximum controllable charge and discharge area into a two-dimensional coordinate system to form an adjustable capacity polygon; Determine the maximum controllable charge and discharge area of ​​a single electric vehicle based on the charge and discharge history data of at least one electric vehicle, and map the maximum controllable charge and discharge area into a two-dimensional coordinate system to form an adjustable capacity polygon, including: Determining, based on charging and discharging history data of at least one electric vehicle, each boundary segment of a maximum controllable charging and discharging region of a single electric vehicle and an inequality for characterizing each boundary segment; Defining a closed area of ​​the maximum controllable charge and discharge area according to each boundary line segment and an inequality used to characterize each boundary line segment, and mapping the defined closed area into a two-dimensional coordinate system to form an adjustable capacity polygon; Wherein, the maximum controllable charge and discharge range is: ； Where, represents the set of maximum controllable charge and discharge regions, is the rated capacity of the electric vehicle, 、 are the horizontal and vertical coordinate values, respectively. 、 are the maximum discharge power and the maximum charging power respectively, The charging state of the grid is The time of joining the network, 、 are the lowest state of charge and the highest state of charge, respectively. is the desired state of charge, The expected off-grid time; Constructing constraints for the maximum controllable charge and discharge region, the constraints including a charge limit constraint, a normal distribution constraint of a desired state of charge, a charge power limit constraint, a discharge power limit constraint, and a charge and discharge power mutual exclusion constraint; A quadrant division module, configured to divide the adjustable capacity polygon into quadrants according to the state of charge of the electric vehicle; The adjustable capacity polygon is divided into quadrants according to the state of charge of the electric vehicle, including: Construct the linear equations of the electric vehicle at different states of charge. The three linear equations are: ； ； ； Where, is the charging start time; Mapping the three straight line equations into a two-dimensional coordinate system, overlaying the system on the adjustable capacity polygon, and dividing the adjustable capacity polygon into quadrants; The quadrant identification module is used to determine the adjustable capacity and scheduling boundary data of a single electric vehicle in each quadrant based on the quadrant division results and the current state of charge of the electric vehicle.

6. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, the electric vehicle charging and discharging adjustable capability and scheduling boundary identification method as described in any one of claims 1 to 4 is implemented.

8. A computer program product, characterized in that The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, wherein the computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer is caused to execute the method for identifying the adjustable charging and discharging capability and scheduling boundary of an electric vehicle as described in any one of claims 1 to 4.

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