An intelligent charging method, a charging device and a computer storage medium

Abstract

The application discloses an intelligent charging method, a charging device and a computer storage medium, and relates to the technical field of charging. The intelligent charging method is applied to the charging device, and comprises the following steps: a charging-related Boolean variable is constructed according to a charging time at a charging starting time and an estimated plug-out time; a constraint condition based on a preset optimization tool is constructed according to the charging-related Boolean variable; a target function based on the preset optimization tool is constructed according to a charging cost of a station; an optimal solution of the charging-related Boolean variable is determined based on the preset optimization tool according to the constraint condition and the target function; and charging power is configured and issued according to the optimal solution of the charging-related Boolean variable. Therefore, the target of cost minimization is achieved when the minimum charging power is defined, and the charging demands of different users are met under multiple constraint conditions.

Description

[0001] The present application relates to the technical field of charging, in particular to an intelligent charging method, a charging device and a computer storage medium.

[0002] At present, electric vehicle technology is developing rapidly, and how to reasonably and safely use charging piles as energy supply equipment for electric vehicles has become the focus of researchers. Charging piles have developed from traditional offline maintenance mode to current online cloud platform remote control mode.

[0003] The existing charging pile has a single intelligent charging algorithm for minimizing charging cost. Basically, load balancing is first performed for power, and then charging time segments n are calculated according to user charging capacity and issued power in time scheduling. The charging is performed in the n time segments with the cheapest charging price, and charging is not performed in other time segments. However, when the electric vehicle starts charging, if the owner requires the minimum charging power to be non-zero, the above algorithm cannot achieve the goal of cost minimization.

[0004] Embodiments of the present application aim to provide an intelligent charging method, a charging device and a computer storage medium, and aim to solve the problem that the existing charging pile charging cost minimization intelligent charging algorithm is single and cannot achieve the goal of cost minimization when the minimum charging power is defined.

[0005] To solve the above technical problems, a first aspect of the present application provides an intelligent charging method, which is applied to a charging device, and the intelligent charging method comprises:

[0006] A charging-related Boolean variable is constructed according to the charging time from the charging start time to the estimated gun pulling time;

[0007] A constraint condition based on a preset optimization tool is constructed according to the charging-related Boolean variable;

[0008] A target function based on a preset optimization tool is constructed according to the charging cost of the station;

[0009] An optimal solution of the charging-related Boolean variable is determined based on a preset optimization tool according to the constraint condition and the target function;

[0010] The optimal solution of the charging-related Boolean variable is configured to issue the charging power.

[0011] Optionally, the charging-related Boolean variable is constructed according to the charging time from the charging start time to the estimated gun pulling time, comprising:

[0012] ​​​The charging time is divided into n time segments with preset time intervals, where n is an integer greater than or equal to 1, within the charging time from the charging start time to the estimated plug-out time;

[0013] A plurality of charging-related Boolean variables are constructed in each time segment, where the Boolean variables include a charging power Boolean variable, a charging or not Boolean variable, a charging amount Boolean variable, and an end charging time Boolean variable.

[0014] Optionally, the constraint condition based on the preset optimization tool is constructed according to the charging-related Boolean variables, including:

[0015] The constraint condition that the user-defined power is required to be reached before the estimated plug-out time is constructed based on the preset optimization tool;

[0016] The constraint condition of the end charging time Boolean variable is constructed based on the preset optimization tool;

[0017] The constraint condition that the total charging cost of the user cannot exceed the total fast charging cost of the user is constructed based on the preset optimization tool.

[0018] Optionally, the constraint condition that the user-defined power is required to be reached before the estimated plug-out time is constructed based on the preset optimization tool, including:

[0019] The constraint condition that the user-defined power is required to be reached before the estimated plug-out time is determined according to the charging-related Boolean variables;

[0020] The constraint condition that the user-defined power is required to be reached before the estimated plug-out time is converted into a constraint condition based on the preset optimization tool.

[0021] Optionally, the constraint condition of the end charging time Boolean variable is constructed based on the preset optimization tool, including:

[0022] When a certain time segment is within the range of the time segment of the charging end, charging is performed in the time segment, otherwise, no charging is performed in the time segment.

[0023] Optionally, the constraint condition that the total charging cost of the user cannot exceed the total fast charging cost of the user is constructed based on the preset optimization tool, including:

[0024] The total fast charging cost of the user is determined;

[0025] The constraint condition that the total charging cost of the user cannot exceed the total fast charging cost of the user is constructed based on the preset optimization tool.

[0026] Optionally, the objective function based on the preset optimization tool is constructed according to the charging cost of the site, including:

[0027] determining a charging cost of the station;

[0028] constructing a target function based on a preset optimization tool according to the charging cost of the station.

[0029] Optionally, the intelligent charging method further comprises: smoothing the power mutation point.

[0030] Correspondingly, a second aspect of the present application provides a charging device, comprising: a memory, a processor and a computer program stored in the memory and running on the processor, and the computer program implements the steps of the intelligent charging method of the first aspect of the present application when executed by the processor.

[0031] Correspondingly, a fifth aspect of the present application provides a computer storage medium, and the computer storage medium stores a program of an intelligent charging method, and the program of the intelligent charging method implements the steps of the intelligent charging method of the first aspect of the present application when executed by a processor.

[0032] Compared with the prior art, the embodiments of the present application provide an intelligent charging method, a charging device and a computer storage medium, the intelligent charging method comprising: constructing a charging-related Boolean variable according to a charging time from a charging start time to an estimated plug-out time; constructing a constraint condition based on a preset optimization tool according to the charging-related Boolean variable; constructing a target function based on the preset optimization tool according to a charging cost of the station; determining an optimal solution of the charging-related Boolean variable based on the preset optimization tool according to the constraint condition and the target function; and configuring and issuing a charging power according to the optimal solution of the charging-related Boolean variable. Thus, the charging pile charging cost minimization charging algorithm is optimized from the perspective of minimizing the cost of the station, the goal of cost minimization is achieved when the minimum charging power is defined, and the charging needs of different users are met under multiple constraint conditions. Thus, the problem that the existing charging pile charging cost minimization intelligent charging algorithm is relatively single and cannot achieve the goal of cost minimization when the minimum charging power is defined is solved. BRIEF DESCRIPTION OF DRAWINGS

[0033] One or more embodiments are illustrated by way of example in the drawings in which like reference numerals indicate like elements, and in which: the drawings are not necessarily to scale, as the emphasis instead is placed upon illustrating the embodiments, and

[0034] Figure 1 is a flowchart of an intelligent charging method provided by the present application;

[0035] Figure 2 is a flowchart of step S1 in an intelligent charging method provided by the present application;

[0036] Figure 3 is a flowchart of step S2 in the intelligent charging method provided by the present application;

[0037] Figure 4 is a flowchart of step S3 in the intelligent charging method provided by the present application;

[0038] Figure 5 is another flowchart of the intelligent charging method provided by the present application;

[0039] Figure 6 is a structural schematic diagram of the charging device provided by the present application.

DETAILED DESCRIPTION

[0040] For the purpose of facilitating the understanding of the present application, the present application will be described in more detail below in combination with the drawings and specific embodiments. It should be noted that when an element is described as being "fixed to" another element, it can be directly on the other element or one or more intermediate elements can be present therebetween. When an element is described as being "electrically connected to" another element, it can be directly connected to the other element or one or more intermediate elements can be present therebetween. The terms "upper", "lower", "inner", "outer", "bottom", etc. used in the present specification indicate the orientation or positional relationship shown in the drawings and are only used for the purpose of facilitating the description of the present application and simplifying the description, and thus cannot be understood as indicating or implying that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus cannot be understood as limiting the present application. In addition, the terms "first", "second", "third", etc. are only used for the purpose of description and cannot be understood as indicating or implying relative importance.

[0041] Unless otherwise defined, all technical and scientific terms used in the present specification have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not used to limit the present application. The term "and / or" used in the present specification includes any and all combinations of one or more related listed items.

[0042] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0043] In one embodiment, as shown in Figure 1 The present application provides an intelligent charging method applied to a charging device, which comprises the following steps:

[0044] S1, constructing a charging-related Boolean variable according to the charging time at the charging start time and the estimated plug-out time;

[0045] S2. Construct constraints based on preset optimization tools according to the charging-related Boolean variables;

[0046] S3. Construct an objective function based on preset optimization tools according to the charging cost of the station;

[0047] S4. Based on the constraint and the objective function, determine the optimal solution for the charging-related Boolean variables using a preset optimization tool;

[0048] S5. Configure and distribute the charging power based on the optimal solution of the charging-related Boolean variables.

[0049] In this embodiment, a smart charging method is provided and applied to a charging device. This smart charging method includes: constructing charging-related Boolean variables based on the charging time (charging start time and estimated gun disconnection time); constructing constraints based on the charging-related Boolean variables using a preset optimization tool; constructing an objective function based on the charging cost of the charging station using the preset optimization tool; determining the optimal solution for the charging-related Boolean variables based on the constraints and the objective function using the preset optimization tool; and configuring and distributing the charging power based on the optimal solution for the charging-related Boolean variables. This optimizes the charging cost minimization algorithm for charging piles from the perspective of minimizing the main cost of the charging station, achieving the goal of cost minimization when a custom minimum charging power is defined, and meeting the charging needs of different users under multiple constraints. This solves the problem that existing smart charging algorithms for minimizing charging costs are relatively simple and cannot achieve the goal of cost minimization when a custom minimum charging power is defined.

[0050] For example, the charging equipment includes charging piles, but is not limited to charging piles. It can be understood that the charging equipment is a supporting device that can charge electric vehicles.

[0051] In one embodiment, such as Figure 2 As shown, in step S1, charging-related Boolean variables are constructed based on the charging time at the charging start time and the estimated gun removal time, including:

[0052] S11. During the charging time, which includes the charging start time and the estimated gun removal time, the charging time is divided into n time segments with a preset time interval, where n is an integer greater than or equal to 1.

[0053] For example, if the charging start time is 0:00 and the estimated time to unplug the charging gun is 10:00, then the charging time is from 0:00 to 10:00. The preset time interval is 5 minutes, that is, the charging time is divided into 120 (n=120) time segments with a time interval of 5 minutes. It should be noted that in the subsequent embodiments of the present invention, the above examples are used to illustrate the technical solution of the present invention, but it is not limited to the above examples.

[0054] S12, constructing a plurality of charging-related Boolean variables in each time segment, wherein the Boolean variables include a charging power Boolean variable xt, a charging or not Boolean variable lt, a charging amount Boolean variable yt, and an end charging time Boolean variable z. Wherein:

[0055] The charging power Boolean variable xt represents the charging power in the time segment t. When the charging power Boolean variable xt = 1, it represents that the charging is carried out at the highest charging power in the time segment t. When the charging power Boolean variable xt = 0, it represents that the charging is carried out at the lowest charging power in the time segment t. Wherein, integer t <= n.

[0056] The charging or not Boolean variable lt represents whether the charging is carried out in the time segment t. When the charging or not Boolean variable lt = 1, it represents that the charging is carried out in the time segment t. When the charging or not Boolean variable lt = 0, it represents that the charging is not carried out in the time segment t.

[0057] The charging amount Boolean variable yt represents the charging amount in the time segment t. The charging amount Boolean variable yt is the product of the charging power Boolean variable xt and the charging or not Boolean variable lt, that is: yt = xt * lt.

[0058] The end charging time Boolean variable z represents the time segment when the charging ends. z is an integer, and the value range is 0 to n.

[0059] For example, the charging start time is 0 o'clock, the estimated gun pulling time is 10 o'clock, the charging time is 0 o'clock to 10 o'clock, and the preset time interval is 5 minutes, that is, the charging time is divided into 120 time segments with 5 minutes as the time interval. Then a plurality of charging-related Boolean variables are constructed in each time segment, as shown below:

[0060] X = [x0, x1, x2, x3, …, x120]

[0061] Y = [y0, y1, y2, y3, …, y120]

[0062] L = [l0, l1, l2, l3, …, l120]

[0063] In this embodiment, for the charging problem of user-defined minimum charging power, a plurality of charging-related Boolean variables including charging power Boolean variable, charging or not Boolean variable, charging amount Boolean variable and end charging time Boolean variable are constructed in each time segment to optimize the charging pile charging cost minimization charging algorithm, to achieve the goal of cost minimization when the minimum charging power is customized, and to meet the charging needs of different users under multiple constraint conditions.

[0064] In one embodiment, such as Figure 3 As shown, in step S2, constraints based on a preset optimization tool are constructed according to the charging-related Boolean variables; including:

[0065] S21. Based on preset optimization tools, construct constraints that require the user-defined battery level to be reached before the estimated gun-drawing time. Specifically, this includes:

[0066] S211. Determine the constraint condition that the user-defined power level must be reached before the estimated gun removal time based on the Boolean variables related to charging.

[0067] For example, the charging start time is 0:00, the estimated time to unplug the charging gun is 10:00, the charging time is from 0:00 to 10:00, and the preset time interval is 5 minutes. That is, the charging time is divided into 120 time segments with a time interval of 5 minutes.

[0068] Let the maximum charging power be p_max and the minimum charging power be p_min. The user-defined charge energy must be reached before the estimated time of unplugging the charging gun. The corresponding constraint is determined as shown in the following equation (1):

[0069]

[0070] S212. The constraint that requires the user-defined battery level to be reached before the estimated gun-drawing time is transformed into a constraint based on a preset optimization tool.

[0071] For example, the default optimization tool is or-tools.

[0072] In equation (1) above, since the minimum high charging power p_min, the maximum charging power p_max, and the user-defined energy level required to be reached before the estimated unplugging time are kept to a maximum of 2 decimal places, the integer nonlinear optimization format required by the preset optimization tool (or-tools) is as shown in equation (2) below:

[0073]

[0074] Further, the above equation (2) is transformed into the integer linear optimization format required by the preset optimization tool or-tools, as shown in equation (3) below:

[0075]

[0076] For the Boolean variable yt of charge amount, add the following constraints:

[0077] yt = xt * lt(for t = 0 to 120)

[0078] The corresponding constraints for the preset optimization tool or-tools are:

[0079] model.AddMultiplicationEquality(yt, [xt, lt]), (for t = 0 to 120).

[0080] S22, constructing a constraint condition of the end charging time Boolean variable z based on a preset optimization tool.

[0081] Specifically, the constraint condition of the end charging time Boolean variable z is that when a certain time slice t is within the range of the end charging time slice z, charging is performed in the time slice t, otherwise, charging is not performed in the time slice t. That is, when the integer t <= the integer z, the charging and not charging Boolean variable lt = 1, otherwise, the charging and not charging Boolean variable lt = 0.

[0082] That is, the constraint condition of the end charging time Boolean variable z is as follows:

[0083] Lt == 1 if t <= z else lt == 0 (for t = 0 to 120)

[0084] The corresponding preset optimization tool or-tools constraint is as follows:

[0085] model.Add(t > y).OnlyEnforceIf(lt.Not())

[0086] model.Add(t <= y).OnlyEnforceIf(lt)

[0087] S23, constructing a constraint condition that the total charging cost of the user cannot exceed the total fast charging cost of the user based on a preset optimization tool. Including:

[0088] S231, determining the total fast charging cost cost of the user, and the total charging cost of the user cannot exceed the total fast charging cost of the user.

[0089] Assuming that the user charging electricity quantity fee unit price is energy_price, as follows:

[0090] energy_price = {“0-9 points”: 0.12, “9-15 points”: 1.56, “15-17”: 0.56, “17-19”: 0.78, “19-20”: 1.25, “20-24”: 1.15}

[0091] The above user charging electricity price per unit is converted to a 0:00 to 24:00 range with a preset time interval of 5 minutes to form a price dictionary energy_price_dict, and the user charging electricity price of the corresponding time segment t is energy_price_dict[t].

[0092] Assuming that the user charging duration fee is time_price, as follows:

[0093] time_price = {“charging 0-10 minutes”, 0.67, “charging 10-30 minutes”, 1.15, “charging 30-120 minutes”: 0.75, ……}

[0094] Compared with the different user charging duration fee, the conversion of this charging format needs to generate a corresponding charging duration fee dictionary time_price_dict based on the charging starting point as 0, with a time interval of 5 minutes, and the charging duration fee per unit of the corresponding time segment t is time_price_dict[t].

[0095] Then the corresponding user charging total cost is P1:

[0096]

[0097] The corresponding user charging duration fee is P2:

[0098]

[0099] The corresponding user charging total cost is P3:

[0100] P3 = P1 + P2 (6)

[0101] Then the constraint condition that the user charging total cost cannot exceed the user fast charging total cost is:

[0102] charge_cost = P3 (7)

[0103] charge_cost <= cost (8)

[0104] S232, based on a preset optimization tool, construct the constraint condition that the user charging total cost cannot exceed the user fast charging total cost.

[0105] After the user charging total cost charge_cost and the user fast charging total cost cost are converted to integers, the corresponding preset optimization tool or-tools constraint condition is:

[0106] Model.add(charge_cost <= cost)

[0107] In one embodiment, such as Figure 4 As shown, in step S3, an objective function based on a preset optimization tool is constructed according to the charging cost of the charging station; including:

[0108] S31. Determine the charging costs of the charging stations.

[0109] The main cost electricity price of the power station is set as price, and billing is carried out in stages according to different time segments. The corresponding billing dictionary after conversion is price_dict, and the charging cost of the corresponding power station is shown in the following formula (9):

[0110] Station_cost=prict_dict[t]*(p_max*yt+(lt-yt)*p_min)*5 / 60 (9)

[0111] S32. Construct an objective function based on a preset optimization tool according to the charging cost of the station.

[0112] Once the charging cost of a charging station is determined, and this charging cost is used as the objective function, the charging cost of the charging station can be minimized.

[0113] Converting equation (9) to integer form yields equation (10), as shown below:

[0114] Station cost =100*prict_dict[t]*(100*p_max*yt+(lt-yt)*p_min)*100)*5 / 60 (10)

[0115] The objective function based on the preset optimization tool or-tools is constructed as follows:

[0116] Model.Minimize(Station_cost)

[0117] In this embodiment, by constructing constraints based on preset optimization tools, such as requiring the user-defined battery level to be reached before the estimated charging time, constructing constraints on the Boolean variable z for the end charging time, and constructing constraints that the user's total charging cost cannot exceed the user's total fast charging cost, multiple constraints can be imposed on charging-related Boolean variables. This optimizes the charging pile charging cost minimization algorithm, achieving the goal of cost minimization when a minimum charging power is defined. Under multiple constraints, it meets the charging needs of different users. Furthermore, it is compatible with two different billing methods: segmented billing based on time period and segmented billing based on charging duration, thus satisfying the charging needs of different users.

[0118] In one embodiment, in step S4, the optimal solution of the charging-related Boolean variables is determined based on a preset optimization tool according to the constraint conditions and the objective function.

[0119] Specifically, the optimal solution of the charging-related Boolean variables is obtained by solving the above-mentioned constraint conditions and objective functions using a preset optimization tool or-tools, that is, the optimal solution of the charging-related Boolean variables X, Y and L is obtained by solving using the preset optimization tool or-tools.

[0120] Specifically, the optimal solution of the charging-related Boolean variables is obtained by solving using cp_model.CpSolver in the preset optimization tool or-tools, as shown below:

[0121] status = solver.Solve(model)

[0122] In one embodiment, in step S5, the charging power is configured and delivered according to the optimal solution of the charging-related Boolean variables.

[0123] Specifically, the charging power is configured and delivered according to the optimal solution of the charging-related Boolean variables. The preliminary configuration table Result0 of the charging power delivery is as follows:

[0124] Result0 = {0: p_max, 5: p_min, 10: p_min 15: p_max, 20: p_max, 25: p_max, 30: p_max, 35: p_max, 40: p_max, …, 100: p_max, 105: 0, 110: 0, 115: 0, 120: 0}

[0125] In the above formula, the data format is key:Value, Key is the time segment, and value is the configured delivery power corresponding to the time segment.

[0126] In the above formula, the last time segment of the highest charging power delivery is 100, because the key=110 develops the delivery power to 0, then n=110 represents the user's estimated charging end time segment, the user inserts the gun at 0 o'clock to start charging, and the estimated end charging time is 9:50 (that is, the end charging time Boolean variable z is 9:50).

[0127] In one embodiment, as shown in Figure 5 the intelligent charging method further comprises: S6, smoothing the power mutation point.

[0128] Specifically, the smoothing of the power mutation point includes reducing the number of incremental mutation occurrences, and smoothing by combining the currently reported real-time power.

[0129] For example, according to the initial configuration table Result0 of the issued charging power, the power mutation points are key=0 and key=15, so the high and low power charging sequence in the time slice of the initial configuration table Result0 of the issued charging power is sorted from low to high according to the user charging electricity price, and then sorted from low to high according to the cost price of the charging station, so as to reduce the number of power mutations without affecting the charging cost of the charging station and the total cost of user charging. The improved configuration table Result1 of the issued charging power after smoothing the power mutation points is as follows:

[0130] Result1={0:p_max,5:p_max,10:p_max 15:p_max,20:p_max,25:p_max,30:p_max,35:p_max,40:p_max,……,99:p_min,100:p_min,105:0,110:0,115:0,120:0}

[0131] At this time, the incremental power mutation point is only key=0, and the value p_max of the power mutation point is smoothed by 5-minute average increment.

[0132] In this embodiment, for the power mutation problem, the current reported real-time power is combined for smoothing processing, which can reduce the number of incremental mutations and does not affect the charging cost of the charging station and the total cost of user charging.

[0133] Based on the same concept, in one embodiment, the present application also provides a charging device, as shown in Figure 6 The charging device 900 includes a memory 902, a processor 901, and one or more computer programs stored in the memory 902 and executable on the processor 901, the memory 902 and the processor 901 are coupled together through a bus system 903, and the one or more computer programs are executed by the processor 901 to implement the following steps of an intelligent charging method provided by an embodiment of the present application:

[0134] S1, constructing a charging-related Boolean variable according to the charging time at the charging start time and the estimated gun pulling time;

[0135] S2, constructing a constraint condition based on a preset optimization tool according to the charging-related Boolean variable;

[0136] S3, constructing a target function based on a preset optimization tool according to the charging cost of the charging station;

[0137] S4, determining the optimal solution of the charging-related Boolean variable based on a preset optimization tool according to the constraint condition and the objective function;

[0138] S5, configuring and delivering the charging power according to the optimal solution of the charging-related Boolean variable.

[0139] The method disclosed by the embodiments of the present application can be applied to or implemented by the processor 901. The processor 901 can be an integrated circuit chip with a signal processing capability. In the implementation process, each step of the above method can be completed by an integrated logic circuit or an instruction in a software form of the processor 901. The processor 901 can be a general-purpose processor, a DSP, or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc. The processor 901 can implement or execute the disclosed methods, steps and logic block diagrams in the embodiments of the present application. The general-purpose processor can be a microprocessor or any conventional processor, etc. In combination with the steps of the method disclosed in the embodiments of the present application, the hardware decoding processor can be directly embodied to complete the execution, or the hardware and software modules in the decoding processor can be combined to complete the execution. The software module can be located in a storage medium, and the storage medium is located in the memory 902. The processor 901 reads the information in the memory 902 and combines the hardware to complete the steps of the above method.

[0140] It can be understood that the memory 902 of the embodiment of the present application can be a volatile memory or a non-volatile memory, and can also include both volatile and non-volatile memories. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), a ferromagnetic random access memory (FRAM), a flash memory or other memory technology, a compact disk read-only memory (CD-ROM), a digital versatile disk (DVD) or other optical disk storage, a magnetic cassette, a magnetic tape, a magnetic disk storage or other magnetic storage device; the volatile memory can be a random access memory (RAM), and many forms of RAM are available by way of example but not limitation, for example, static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), direct memory bus random access memory (DRRAM).The memory described in the embodiments of the present application is intended to include, but not limited to, these and any other suitable types of memory.

[0141] It should be noted that the charging device embodiments and the method embodiments described above belong to the same concept, the specific implementation process is detailed in the method embodiments, and the technical features in the method embodiments are all applicable to the charging device embodiments, which will not be repeated here.

[0142] In addition, in the exemplary embodiments, the embodiments of the present application also provide a computer storage medium, specifically a computer readable storage medium, for example, a storage medium including a memory 902 storing a computer program, and one or more programs of an intelligent charging method are stored on the computer storage medium, and the one or more programs of the intelligent charging method are executed by the processor 901 to implement the following steps of an intelligent charging method provided by the embodiments of the present application:

[0143] S1, constructing a charging-related Boolean variable according to the charging time at the charging start time and the estimated plug-out time;

[0144] S2, constructing a constraint condition based on a preset optimization tool according to the charging-related Boolean variable;

[0145] S3, constructing an objective function based on a preset optimization tool according to the charging cost of the site;

[0146] S4, determining an optimal solution of the charging-related Boolean variable based on the preset optimization tool according to the constraint condition and the objective function;

[0147] S5, configuring and issuing a charging power according to the optimal solution of the charging-related Boolean variable.

[0148] It should be noted that the intelligent charging method program on the computer readable storage medium described above and the method embodiments belong to the same concept, the specific implementation process is detailed in the method embodiments, and the technical features in the method embodiments are all applicable to the embodiments of the computer readable storage medium, which will not be repeated here.

[0149] It should be noted that in this paper, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or includes elements inherent to such process, method, article or device. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of another identical element in the process, method, article or device including the element.

[0150] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not limited to them; under the idea of the present application, the technical features of the above examples or different examples can also be combined, the steps can be implemented in any order, and there are many other changes of different aspects of the present application as described above, which are not provided in details for simplicity; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A smart charging method, characterized in that, The intelligent charging method is applied to a charging device, and the intelligent charging method includes: Based on the charging time between the charging start time and the estimated unplugging time, charging-related Boolean variables are constructed, including: dividing the charging time between the charging start time and the estimated unplugging time into n time segments with a preset time interval, where n is an integer greater than or equal to 1; constructing multiple charging-related Boolean variables in each time segment, wherein the Boolean variables include charging power Boolean variable, charging status Boolean variable, charging amount Boolean variable, and charging end time Boolean variable; Based on the charging-related Boolean variables, constraints are constructed using preset optimization tools, including: constructing constraints based on preset optimization tools to require the user-defined battery level to be reached before the estimated unplugging time; constructing constraints based on preset optimization tools for the Boolean variable of the charging end time; and constructing constraints based on preset optimization tools to ensure that the user's total charging cost does not exceed the user's total fast charging cost. Construct an objective function based on a preset optimization tool according to the charging cost of the charging station; Based on the constraints and the objective function, the optimal solution for the charging-related Boolean variables is determined using a preset optimization tool; Based on the optimal solution of the charging-related Boolean variables, the charging power is configured and distributed.

2. The intelligent charging method according to claim 1, characterized in that, The constraint condition, constructed based on a preset optimization tool, requiring the user-defined battery level to be reached before the estimated gun-drawing time includes: The user-defined power level constraint is determined based on the charging-related Boolean variables to be reached before the estimated gun-unplugging time. The constraint of requiring the user-defined battery level to be reached before the estimated gun-drawing time is transformed into a constraint based on a preset optimization tool.

3. The intelligent charging method according to claim 1, characterized in that, The constraints on the Boolean variable of the end charging time constructed based on the preset optimization tool include: Charging occurs within a time segment when a certain time segment falls within the range of the time segment where charging ends; otherwise, charging does not occur within the time segment.

4. The intelligent charging method according to claim 1, characterized in that, The constraint that the total cost of user charging cannot exceed the total cost of user fast charging, constructed based on a preset optimization tool, includes: Determine the user's total fast charging cost; Based on a preset optimization tool, a constraint is constructed to ensure that the total cost of user charging cannot exceed the total cost of user fast charging.

5. The intelligent charging method according to claim 1, characterized in that, The construction of the objective function based on the charging cost of the charging station using a preset optimization tool includes: Determine the charging costs at the charging stations; An objective function based on a preset optimization tool is constructed according to the charging cost of the aforementioned charging station.

6. The intelligent charging method according to claim 1, characterized in that, The intelligent charging method also includes smoothing out power abrupt change points.

7. A charging device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and running on the processor, wherein the computer program, when executed by the processor, implements the steps of the smart charging method as described in any one of claims 1 to 6.

8. A computer storage medium, characterized in that, The computer storage medium stores a program for a smart charging method, which, when executed by a processor, implements the steps of the smart charging method as described in any one of claims 1 to 6.

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