Electric vehicle charging behavior optimization method and device, equipment and storage medium

Abstract

The invention relates to the technical field of electric vehicle charging, in particular to an electric vehicle charging behavior optimization method and device, equipment and a storage medium, and the method comprises the steps: obtaining an optimization variable based on a user charging period, and building a target function based on the optimization variable; randomly generating an initial solution set based on the optimization variable, and calculating advantages and disadvantages of each initial solution in the initial solution set based on the objective function; iteratively generating an iterative solution set based on the advantages and disadvantages and the initial solution set; and in response to the iteration solution set reaching a preset iteration stop condition, obtaining a target charging period based on the iteration solution set. According to the invention, the peak-valley power supply pressure of the power distribution network and the charging cost and charging waiting time of users are reduced.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle charging, and in particular to a method, device, equipment and storage medium for optimizing electric vehicle charging behavior. Background Art

[0002] With the popularization of electric vehicles, a large number of electric vehicles have begun to appear in residential communities, which has also led to a surge in the demand for charging electric vehicles in residential communities.

[0003] Combined with historical charging statistics, it is found that users generally charge their electric vehicles from the evening to midnight. This period is generally the time after get off work when users have more free time and have ample time to charge their electric vehicles.

[0004] However, since users charge their electric vehicles mostly during this period, this will lead to a shortage of charging resources, a sharp increase in the peak-valley power supply pressure of the distribution network, and a significant increase in users' charging costs and waiting time. Summary of the invention

[0005] In order to reduce the peak-valley power supply pressure of the distribution network, as well as the charging cost and charging waiting time of users, the present application provides a method, device, equipment and storage medium for optimizing the charging behavior of an electric vehicle.

[0006] In a first aspect, the present application provides a method for optimizing charging behavior of an electric vehicle, comprising:

[0007] Obtaining optimization variables based on the user's charging period, and establishing an objective function based on the optimization variables;

[0008] Randomly generate an initial solution set based on the optimization variable, and calculate the quality of each initial solution in the initial solution set based on the objective function;

[0009] Iteratively generate an iterative solution set based on the merits and the initial solution set;

[0010] In response to the iterative solution set reaching a preset iteration stop condition, a target charging period is obtained based on the iterative solution set.

[0011] In a second aspect, the present application provides an electric vehicle charging behavior optimization device, comprising:

[0012] A function establishment module, used to obtain optimization variables based on the user's charging period, and establish an objective function based on the optimization variables;

[0013] A quality calculation module, used for randomly generating an initial solution set based on the optimization variable, and calculating the quality of each initial solution in the initial solution set based on the objective function;

[0014] A solution set iteration module, used for iteratively generating an iterative solution set based on the merits and the initial solution set;

[0015] The time period determination module is used to obtain a target charging time period based on the iterative solution set in response to the iterative solution set reaching a preset iteration stop condition.

[0016] In a third aspect, the present application provides a computer device, the computer device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the steps in the above method when executing the computer program.

[0017] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps in the above method when executed by a processor.

[0018] In a fifth aspect, the present application further provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0019] The above-mentioned electric vehicle charging behavior optimization method, device, equipment and storage medium obtain optimization variables based on the user's charging period, establish an objective function based on the optimization variables; randomly generate an initial solution set based on the optimization variables, calculate the quality of each initial solution in the initial solution set based on the objective function; iteratively generate an iterative solution set based on the quality and the initial solution set; in response to the iterative solution set reaching a preset iteration stop condition, obtain a target charging period based on the iterative solution set. Through the above implementation, the target charging period can be finally generated through the user's charging period, and the user has the highest probability of charging the electric vehicle within the target charging period, which can reduce the charging time and charging waiting time as much as possible, and the target charging period of each user can be evenly distributed through the objective function, so as to prevent users from charging in a concentrated manner during a certain period of time, thereby reducing the peak and valley power supply pressure of the distribution network.

[0020] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A flow chart of a method for optimizing charging behavior of an electric vehicle provided in an embodiment of the present application;

[0023] Figure 2 A flow chart of another method for optimizing charging behavior of an electric vehicle provided in an embodiment of the present application;

[0024] Figure 3 This is a schematic diagram of the structure of an electric vehicle charging behavior optimization device provided in an embodiment of the present application;

[0025] Figure 4 This is a schematic diagram of the structure of another electric vehicle charging behavior optimization device provided in an embodiment of the present application;

[0026] Figure 5 A schematic diagram of the structure of a computer device provided in an embodiment of the present application;

[0027] Figure 6 This is a diagram of the internal structure of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present disclosure more clear, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present disclosure and are not used to limit the present disclosure.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes 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 inherent to these processes, methods, products or equipment.

[0030] In this article, the term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the related objects before and after are in an "or" relationship.

[0031] Embodiment 1

[0032] Figure 1This is a flow chart of a method for optimizing charging behavior of an electric vehicle provided in Example 1 of the present application, refer to Figure 1 The method may be performed by a device for performing the method, and the device may be implemented by software and / or hardware. The method includes:

[0033] S110 , obtaining optimization variables based on the user's charging period, and establishing an objective function based on the optimization variables.

[0034] The user charging period is the period when the user charges the electric vehicle in the living community. In this implementation, 24 user charging periods are divided in a day. The duration of each user charging period is 1 hour. For example, 17:00-18:00 is a user charging period. The optimization variable is used to characterize whether the i-th user in the living community charges the electric vehicle in the t-th user charging period, and the optimization variable is recorded as x i,t ; The objective function f is established based on the optimization variables and is subsequently used to process data related to the optimization variables.

[0035] Specifically, taking the i-th user in the living community as an example, it is determined whether the user charges the electric vehicle in each of the 24 user charging periods in a day, and the judgment result, that is, the optimization variable, is obtained. If the i-th user charges the electric vehicle in the t-th user charging period, the optimization variable x i,t is recorded as 1, otherwise it is recorded as 0; further, according to the optimization variable x corresponding to each user in the living community, i,t Establish the objective function f.

[0036] S120, randomly generating an initial solution set based on the optimization variables, and calculating the quality of each initial solution in the initial solution set based on the objective function.

[0037] Among them, the initial solution set x i The initial solution x that corresponds to the 24 charging periods of the i-th user in the living community i,t , the initial solution x i,t The optimization variable x corresponding to each user charging period i,t , x i =[x i,1 , x i,2 , ..., x i,24 ]; the initial solution x i,t The input is processed into the objective function f, which outputs the corresponding result, i.e., the degree of excellence f(x i,t ), the superiority is used to characterize the superiority of the corresponding initial solution in determining the optimal charging period for users.

[0038] Specifically, taking the i-th user in the living community as an example, determine the optimization variable x corresponding to the 24 user charging periods corresponding to the user. i,1 , x i,2 , ..., x i,24 , collect the optimization variables corresponding to the charging periods of the above 24 users to obtain the initial solution set x corresponding to the user i ; Take an initial solution x in the initial solution set i,t For example, take the initial solution x i,t Input into the objective function f for processing, so as to obtain the initial solution x i,t The corresponding quality f(x i,t ); Calculate the goodness of each initial solution in the initial solution set as above.

[0039] S130, iteratively generating an iterative solution set based on the merits and the initial solution set.

[0040] Among them, based on the corresponding advantages and disadvantages of the initial solutions, the required several initial solutions can be determined from the initial solution set, and then the several initial solutions are further processed to obtain several variation solutions, that is, the first-generation iterative solution set, and the several variation solutions are used as the new initial solution set, and according to the new initial solution set, several new variation solutions are generated, that is, the second-generation iterative solution set; and so on, the iterative solution set is continuously obtained.

[0041] S140: In response to the iterative solution set reaching a preset iteration stop condition, obtaining a target charging period based on the iterative solution set.

[0042] Among them, an iteration stop condition is preset for the process of iteratively generating an iterative solution set. The iteration stop condition in this embodiment is that the number of generations of the generated iterative solution set reaches a preset algebraic threshold; when the iterative solution set reaches the preset iteration stop condition, the iterative solution set, that is, the target charging period, is output at this time; it should be noted that in the process of continuous iteration, the quality of the solutions in the iterative solution set is continuously improved, so that the solution in the iterative solution set finally output has the greatest possibility of being the best charging period for the user, that is, the target charging period.

[0043] Specifically, when it is determined that the number of iterations corresponding to the iterative solution set is a preset iterative algebra threshold, the solution in the iterative solution set is used as the target charging period.

[0044] It should be noted that, in this embodiment, optimization variables are obtained based on the user's charging period, and an objective function is established based on the optimization variables; an initial solution set is randomly generated based on the optimization variables, and the quality of each initial solution in the initial solution set is calculated based on the objective function; an iterative solution set is iteratively generated based on the quality and the initial solution set; in response to the iterative solution set reaching a preset iteration stop condition, a target charging period is obtained based on the iterative solution set. Through the above implementation, the target charging period can be finally generated through the user's charging period, and the user has the highest probability of charging the electric vehicle within the target charging period, which can reduce the charging time and charging waiting time as much as possible, and the target charging period of each user can be evenly distributed through the objective function, so as to prevent users from charging in a concentrated manner during a certain period of time, thereby reducing the peak and valley power supply pressure of the distribution network.

[0045] Embodiment 2

[0046] A method for optimizing charging behavior of an electric vehicle is provided in the second embodiment of the present application. The method optimizes the "establishing an objective function based on the optimization variables" in the first embodiment. It should be noted that for the parts not described in detail in the present embodiment, reference may be made to the descriptions of other embodiments. The method includes:

[0047] S211. Obtain optimization variables based on the user's charging period.

[0048] S212: Establishing a load variance function, a load peak-valley difference function, and a charging cost function based on the optimization variables.

[0049] Among them, the load variance function is used to reflect the fluctuation of the overall electric load of the community. The smaller the load variance, the better. The formula of the load variance function is as follows: T is 24, corresponding to 24 user charging periods; t is the t-th user charging period; TL t is the total charging load of the living quarters during the charging period of the tth user; is the average of the 24 total charging loads corresponding to the charging periods of 24 users in the residential area, Among them, TL t =OL t +∑ i x i,t E,OL t is the total charging load of the t-th user in the residential area during the charging period, excluding the charging of electric vehicles; x i,t It is used to indicate whether the i-th user in the living community charges during the t-th user charging period; E is the average hourly charging amount, and E is a preset fixed value.

[0050] Among them, the load peak-valley difference function is used to reflect the difference between the charging loads corresponding to the two user charging periods with the highest and lowest electric vehicle charging loads in a day. The smaller the load peak-valley difference, the better. The formula of the load peak-valley difference function is as follows:

[0051] Among them, the charging cost function is used to reflect the electricity cost corresponding to the total charging load of electric vehicles in the living community within a day. The lower the charging cost, the better. The formula of the charging cost function is as follows:∑ t∈T ∑ i Ex i,t P t , where P t The unit price of electricity corresponding to each user's charging period; for example, during the day, the peak unit price is adopted from 8:00 to 21:00, and the peak unit price is 0.5783 yuan / kWh, and the valley unit price is adopted for the rest of the time, and the valley unit price is 0.3783 yuan / kWh.

[0052] S213: Establish constraint conditions based on the optimization variables.

[0053] Among them, the constraints are used to constrain the process of the objective function processing the initial solution; the constraints are established based on the optimization variables.

[0054] S214. Obtain an objective function based on the load variance function, the load peak-valley difference function, the charging cost function and the constraint conditions.

[0055] Among them, the function body of the objective function f is the sum of the load variance function, the load peak-valley difference function, and the charging cost function, that is, The constraints of the objective function are also the constraints established based on the optimization variables mentioned above.

[0056] S220, randomly generating an initial solution set based on the optimization variables, and calculating the quality of each initial solution in the initial solution set based on the objective function.

[0057] S230, iteratively generating an iterative solution set based on the merits and the initial solution set.

[0058] S240: In response to the iterative solution set reaching a preset iteration stop condition, obtaining a target charging period based on the iterative solution set.

[0059] Embodiment 3

[0060] A method for optimizing charging behavior of an electric vehicle is provided in the third embodiment of the present application. The method optimizes the "establishing constraint conditions based on the optimization variables" in the second embodiment. It should be noted that for the parts not described in detail in the present embodiment, reference may be made to the descriptions of other embodiments. The method includes:

[0061] S311. Obtain optimization variables based on the user's charging period.

[0062] S312: Establish a load variance function, a load peak-valley difference function, and a charging cost function based on the optimization variables.

[0063] S313A. Establish phase constraints, power consumption upper limit constraints, charging time constraints, overall power consumption demand constraints and optimization variable constraints based on the optimization variables.

[0064] The distribution network used to supply power to electric vehicles involves three voltage phases: phase A, phase B, and phase C. The phase constraint requires that the number of electric vehicles charged in each phase of the charging pile during the same user charging period shall not exceed 2. The phase constraint is: ∑ j∈G X j,t ≤2,G={A,B,C}; where X j,t is the number of electric vehicles charged by the charging pile using the jth phase during the tth user charging period, X j,t Based on the optimization variable x i,t Sure.

[0065] The upper limit constraint requires that the total load of electric vehicles in the residential area during each user's charging period does not exceed the preset maximum load threshold MAXEL. t ; The upper limit of electricity consumption is: E∑ i x i,t ≤MAXEL t .

[0066] The charging time constraint requires the user not to charge the electric vehicle within a preset time range, which is 08:00-17:00 on weekdays and 10:00-15:00 on weekends; and the time range is recorded as M; the charging time constraint is: x i,t =0 or ∑ t x i,t =0, t∈M.

[0067] Among them, the overall electricity demand constraint requires that the total load of electric vehicle charging in a day should not be less than the minimum load threshold EL i ; The overall power demand constraint is: ∑ t x i,t E≥EL i .

[0068] The optimization variable constraint requires a specific user charging period for charging; the optimization variable constraint is: t∈[0,23]; illustratively, if t=0, it means that the user starts charging the electric vehicle from 0 o'clock.

[0069] S313B, obtaining constraint conditions based on the phase constraint, the power consumption upper limit constraint, the charging time constraint, the overall power consumption demand constraint and the optimization variable constraint.

[0070] Among them, the constraints include: phase constraints, power consumption upper limit constraints, charging time constraints, overall power demand constraints and optimization variable constraints.

[0071] S314. Obtain an objective function based on the load variance function, the load peak-valley difference function, the charging cost function and the constraint condition.

[0072] S320, randomly generating an initial solution set based on the optimization variables, and calculating the quality of each initial solution in the initial solution set based on the objective function.

[0073] S330, iteratively generating an iterative solution set based on the merits and the initial solution set.

[0074] S340: In response to the iterative solution set reaching a preset iteration stop condition, obtaining a target charging period based on the iterative solution set.

[0075] Embodiment 4

[0076] A method for optimizing charging behavior of an electric vehicle is provided in a fourth embodiment of the present application. The method optimizes the "iterative solution set is generated based on the merits and the initial solution set" in the first embodiment. It should be noted that for the parts not described in detail in this embodiment, reference may be made to the descriptions of other embodiments. The method includes:

[0077] S410: Obtain optimization variables based on the user's charging period, and establish an objective function based on the optimization variables.

[0078] S420, randomly generating an initial solution set based on the optimization variables, and calculating the quality of each initial solution in the initial solution set based on the objective function.

[0079] S431. Determine corresponding initial solutions from the initial solution set based on the merits as target solutions.

[0080] Among them, the merits corresponding to each initial solution in the initial solution set are generally different. The larger the merits, the more the corresponding initial solution is the desired initial solution set. In this embodiment, the target solution can be a combination of multiple initial solutions determined from the initial solution set according to the size of the merits, and the number of target solutions is at least two.

[0081] Specifically, the magnitude of the merit corresponding to each initial solution in the initial solution set is determined, at least two merits with the highest numerical ranking are determined, and then the initial solution corresponding to each merit is determined, and the initial solutions are combined to obtain the target solution.

[0082] S432: Process the target solution based on the exchange probability of each of the initial solutions in the target solution to obtain an exchange solution.

[0083] Among them, each target solution contains multiple initial solutions, and the number of initial solutions in each target solution is the same; each initial solution has a one-to-one corresponding exchange probability; assuming that there are two target solutions, respectively recorded as the first target solution and the second target solution, and the first target solution is [x 11 , x 12 , x 13 ], the second objective solution is [x 21 , x 22 , x 23 ]; the initial solution in the first target solution corresponds to the initial solution in the second target solution in position; the initial solution x in the first group of corresponding positions in the first target solution and the second target solution 11 With x 21 Take the initial solution x as an example. 11 With x 21 Is there at least one initial solution whose exchange probability is greater than the preset first probability threshold? If so, exchange x 11 With x 21 The exchange solution is the target solution after the initial solution position is exchanged.

[0084] Specifically, a group of initial solutions at positions corresponding to two target solutions is obtained, and it is determined whether there is at least one initial solution in the group of initial solutions whose exchange probability is greater than a preset first probability threshold. If so, the positions of two initial solutions in the group of initial solutions are exchanged to obtain an exchange solution.

[0085] S433: Process the exchange solution based on the change probability of each of the initial solutions in the exchange solution to obtain a changed solution.

[0086] Among them, each initial solution in the exchange solution has its corresponding change probability. If the change probability of the initial solution in the exchange solution is greater than the preset second probability threshold, the initial solution is transformed into another preset initial solution, and the changed solution is the exchange solution that completes the transformation of the initial solution.

[0087] Specifically, it is determined whether the change probability of the initial solution in the exchange solution is greater than a preset second probability threshold. If so, the initial solution is transformed into another preset initial solution to obtain the exchange solution.

[0088] S434. In response to the changed solutions not reaching a preset iteration stop condition, each of the changed solutions is used as a new initial solution set, and a new changed solution is generated based on the new initial solution set, and the new changed solution is used as an iterative solution set.

[0089] Among them, the changed solution is also the first-generation iterative solution set, and the new changed solution is the second-generation iterative solution set.

[0090] S440: In response to the iterative solution set reaching a preset iteration stop condition, obtain a target charging period based on the iterative solution set.

[0091] Embodiment 5

[0092] A method for optimizing charging behavior of an electric vehicle is provided in a fifth embodiment of the present application. The method optimizes the "determining corresponding initial solutions from the initial solution set as target solutions based on the merits" in the fourth embodiment; it should be noted that for the parts not described in detail in this embodiment, reference may be made to the descriptions of other embodiments. The method includes:

[0093] S510: Obtain optimization variables based on the user's charging period, and establish an objective function based on the optimization variables.

[0094] S520: randomly generate an initial solution set based on the optimization variables, and calculate the quality of each initial solution in the initial solution set based on the objective function.

[0095] S531A. Calculate the selection probability corresponding to the merit based on the merit.

[0096] Among them, the selection probability p i The calculation formula is: Among them, f is the objective function, f(x i,t ) is the degree of quality.

[0097] Specifically, x i,t Substitute into the selection probability p i The calculation formula is used to calculate the selection probability p i .

[0098] S531B, determining corresponding initial solutions from the initial solution set based on the selection probability as target solutions.

[0099] Among them, each initial solution in the initial solution set has its corresponding selection probability p i , arrange the selection probabilities p in order from large to small i Then select the first preset number of choices with probability p i The corresponding initial solutions are combined to obtain the target solution.

[0100] S532: Process the target solution based on the exchange probability of each of the initial solutions in the target solution to obtain an exchange solution.

[0101] S533: Process the exchange solution based on the change probability of each of the initial solutions in the exchange solution to obtain a changed solution.

[0102] S534. In response to the changed solutions not reaching a preset iteration stop condition, each of the changed solutions is used as a new initial solution set, and a new changed solution is generated based on the new initial solution set, and the new changed solution is used as an iterative solution set.

[0103] S540: In response to the iterative solution set reaching a preset iteration stop condition, obtain a target charging period based on the iterative solution set.

[0104] Embodiment 6

[0105] Figure 2 A flow chart of an electric vehicle charging behavior optimization method provided in Example 6 of this application, refer to Figure 2 The method may be performed by a device for performing the method, and the device may be implemented by software and / or hardware. The method includes:

[0106] S610: Obtain optimization variables based on the user's charging period, and establish an objective function based on the optimization variables.

[0107] S620: randomly generate an initial solution set based on the optimization variables, and calculate the quality of each initial solution in the initial solution set based on the objective function.

[0108] S630: Iteratively generate an iterative solution set based on the merits and the initial solution set.

[0109] S640: In response to the iterative solution set reaching a preset iteration stop condition, obtain a target charging period based on the iterative solution set.

[0110] S650: Generate a predicted charging time based on the acquired historical charging time data of the user.

[0111] Among them, the historical charging time data includes the user's historical charging time period, historical charging amount and other data; by counting the proportion of the user's historical charging time period in the historical charging time data in the 24 user charging time periods, the user charging time period corresponding to the largest proportion of the charging prediction time is obtained.

[0112] Specifically, the proportions of the historical charging time periods of the users in the historical charging time data in the 24 user charging time periods are counted, and the user charging time period corresponding to the largest proportion is used as the charging prediction time.

[0113] S660: Generate charging suggestion information based on the predicted charging time and the target charging period.

[0114] Among them, if the charging prediction time is consistent with the target charging period, the generated charging suggestion information includes a charging reminder, which asks the user to charge the electric vehicle during the target charging period; if the charging prediction time is inconsistent with the target charging period, the charging suggestion information includes a recommended charging period and a reason for the suggestion. The recommended charging period is the target charging period, and the reason for the suggestion is that the charging cost and charging waiting time can be reduced as much as possible.

[0115] Specifically, determine whether the predicted charging time is consistent with the target charging period; if consistent, further generate charging suggestion information based on the charging reminder generated by the target charging period; if inconsistent, further generate charging suggestion information based on the charging suggestion period and suggestion reason generated by the target charging period.

[0116] S670: Send the charging suggestion information to the user.

[0117] Among them, the methods of sending charging suggestion information to users include but are not limited to emails, APP notifications, messages, etc.

[0118] It should be noted that, when the target charging period is obtained, the charging suggestion information is sent to the user in a timely manner, so that the user can timely understand the user charging period that can minimize the charging cost and charging waiting time, so as to assist the user in making charging decisions.

[0119] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0120] Embodiment 7

[0121] Based on the same inventive concept, this embodiment also provides an electric vehicle charging behavior optimization device for implementing the electric vehicle charging behavior optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more electric vehicle charging behavior optimization device embodiments provided below can refer to the limitations of the electric vehicle charging behavior optimization method above, and will not be repeated here.

[0122] In this embodiment, Figure 3 As shown, a device for optimizing charging behavior of an electric vehicle is provided, comprising:

[0123] A function establishment module, used to obtain optimization variables based on the user's charging period, and establish an objective function based on the optimization variables;

[0124] A quality calculation module, used for randomly generating an initial solution set based on the optimization variable, and calculating the quality of each initial solution in the initial solution set based on the objective function;

[0125] A solution set iteration module, used for iteratively generating an iterative solution set based on the merits and the initial solution set;

[0126] The time period determination module is used to obtain a target charging time period based on the iterative solution set in response to the iterative solution set reaching a preset iteration stop condition.

[0127] Embodiment 8

[0128] Based on the same inventive concept, this embodiment also provides an electric vehicle charging behavior optimization device for implementing the electric vehicle charging behavior optimization method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more electric vehicle charging behavior optimization device embodiments provided below can refer to the limitations of the electric vehicle charging behavior optimization method above, and will not be repeated here.

[0129] In this embodiment, Figure 4 As shown, a device for optimizing charging behavior of an electric vehicle is provided, comprising:

[0130] A function establishment module, used to obtain optimization variables based on the user's charging period, and establish an objective function based on the optimization variables;

[0131] A quality calculation module, used for randomly generating an initial solution set based on the optimization variable, and calculating the quality of each initial solution in the initial solution set based on the objective function;

[0132] A solution set iteration module, used for iteratively generating an iterative solution set based on the merits and the initial solution set;

[0133] The time period determination module is used to obtain a target charging time period based on the iterative solution set in response to the iterative solution set reaching a preset iteration stop condition.

[0134] A time generation module, used to generate a charging prediction time based on the acquired historical charging time data of the user;

[0135] A suggestion generating module, configured to generate charging suggestion information based on the predicted charging time and the target charging period;

[0136] An information sending module is used to send the charging suggestion information to the user.

[0137] Each module in the above-mentioned electric vehicle charging behavior optimization device can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0138] It should be noted that, in this embodiment, optimization variables are obtained based on the user's charging period, and an objective function is established based on the optimization variables; an initial solution set is randomly generated based on the optimization variables, and the quality of each initial solution in the initial solution set is calculated based on the objective function; an iterative solution set is iteratively generated based on the quality and the initial solution set; in response to the iterative solution set reaching a preset iteration stop condition, a target charging period is obtained based on the iterative solution set. Through the above implementation, the target charging period can be finally generated through the user's charging period, and the user has the highest probability of charging the electric vehicle within the target charging period, which can reduce the charging time and charging waiting time as much as possible, and the target charging period of each user can be evenly distributed through the objective function, so as to prevent users from charging in a concentrated manner during a certain period of time, thereby reducing the peak and valley power supply pressure of the distribution network.

[0139] Embodiment 9

[0140] In this embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the charging behavior of an electric vehicle is implemented.

[0141] Those skilled in the art will understand that Figure 5 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0142] Embodiment 10

[0143] In this embodiment, a computer readable storage medium is provided. Figure 6 As shown, a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0144] Embodiment 11

[0145] In this embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0147] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided by the present disclosure can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided by the present disclosure may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited thereto. The processor involved in each embodiment provided by the present disclosure may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited thereto.

[0148] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] The above-described embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the attached claims.

Claims

1. A method for optimizing charging behavior of electric vehicles, characterized in that: include: Obtaining optimization variables based on the user's charging period, and establishing an objective function based on the optimization variables; Randomly generate an initial solution set based on the optimization variable, and calculate the quality of each initial solution in the initial solution set based on the objective function; Iteratively generate an iterative solution set based on the merits and the initial solution set; In response to the iterative solution set reaching a preset iteration stop condition, a target charging period is obtained based on the iterative solution set.

2. The method according to claim 1, characterized in that The establishing of the objective function based on the optimization variables comprises: Based on the optimization variables, a load variance function, a load peak-valley difference function and a charging cost function are respectively established; Establishing constraint conditions based on the optimization variables; An objective function is obtained based on the load variance function, the load peak-valley difference function, the charging cost function and the constraint condition.

3. The method according to claim 2, characterized in that The establishing of constraint conditions based on the optimization variables comprises: Based on the optimization variables, phase constraints, power consumption upper limit constraints, charging time constraints, overall power demand constraints and optimization variable constraints are respectively established; Constraint conditions are obtained based on the phase constraint, the power consumption upper limit constraint, the charging time constraint, the overall power consumption demand constraint, and the optimization variable constraint.

4. The method according to claim 1, characterized in that The iteratively generating an iterative solution set based on the merits and the initial solution set includes: Based on the merits, corresponding initial solutions are determined from the initial solution set as target solutions respectively; Processing the target solution based on the exchange probability of each of the initial solutions in the target solution to obtain an exchange solution; Processing the exchange solution based on the change probability of each of the initial solutions in the exchange solution to obtain a change solution; In response to the variation solutions not reaching a preset iteration stop condition, each variation solution is used as a new initial solution set, and a new variation solution is generated based on the new initial solution set, and the new variation solution is used as an iteration solution set.

5. The method according to claim 4, characterized in that The determining, based on the merits, of corresponding initial solutions from the initial solution set as target solutions respectively comprises: Calculating the selection probability corresponding to the merit based on the merit; Based on the selection probability, corresponding initial solutions are determined from the initial solution set as target solutions.

6. The method according to claim 1, characterized in that Also includes: Generate a charging prediction time based on the acquired historical charging time data of the user; generating charging suggestion information based on the predicted charging time and the target charging period; The charging suggestion information is sent to the user.

7. An electric vehicle charging behavior optimization device, characterized in that: The device comprises: A function establishment module, used to obtain optimization variables based on the user's charging period, and establish an objective function based on the optimization variables; A quality calculation module, used for randomly generating an initial solution set based on the optimization variable, and calculating the quality of each initial solution in the initial solution set based on the objective function; A solution set iteration module, used for iteratively generating an iterative solution set based on the merits and the initial solution set; The time period determination module is used to obtain a target charging time period based on the iterative solution set in response to the iterative solution set reaching a preset iteration stop condition.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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