Charging pile charging control system and method

Abstract

The invention relates to the technical field of charging piles, and provides a charging pile charging control system and method, and the system comprises a data obtaining module, an intelligent charging scheduling module, and a charging control module. According to the method, the charging control objective function is established by acquiring the real-time state of the charging pile, the charging pile charging control model is constructed, the charging pile charging control model is iteratively calculated based on the use demand of each charging pile and the load of the power system by acquiring the charging pile control demand information, and the optimal charging strategy is continuously searched, so that the charging control accuracy is improved. And each charging pile is controlled to execute the corresponding charging action according to the charging strategy, and by considering the real-time sharing of the charging data, the networking of the charging piles and the collaboration degree, the efficient charging scheduling is realized, the availability and efficiency of the whole resource are improved, and the charging demand of the new energy automobile and the requirement of the electric power system are met at the same time.

Description

Technical Field

[0001] This invention relates to the field of charging pile technology, and in particular to a charging pile charging control system and method. Background Technology

[0002] With the urgent need for climate change and environmental protection, electric vehicles have become a sustainable mode of transportation to replace traditional internal combustion engine vehicles. The rapid growth of the electric vehicle market has led to a huge demand for charging infrastructure. However, current charging station systems face several challenges and problems, such as insufficient interaction between charging stations, low charging efficiency, energy waste, charging station congestion, and competition among charging stations. These issues affect the adoption of electric vehicles and user experience. Furthermore, many charging station systems lack intelligent power distribution control, resulting in the allocation of large amounts of electricity to some charging stations even when they are idle or in low-demand states, leading to energy waste.

[0003] In the field of electric vehicle charging pile control systems and methods, a series of problems exist that limit the efficiency and sustainability of electric vehicle charging infrastructure, reduce user experience, and pose challenges to energy utilization and power system stability.

[0004] (1) Charging station congestion and energy waste

[0005] Current charging station systems lack effective congestion management mechanisms, leading to overcrowding at charging stations during peak hours. Users experience long wait times, negatively impacting their charging experience and wasting their time. Furthermore, the independent operation of charging station systems results in inflexible resource allocation and fragmentation, leading to resource inefficiency. Some charging stations may be under heavy load for extended periods due to excessive competition, while others remain idle. This reduces overall resource availability and efficiency, and existing technologies do not provide sufficient solutions to reduce operator costs and ensure system sustainability.

[0006] (2) Lack of coordination and interaction among charging stations

[0007] Current charging pile systems are often operated independently, lacking effective coordination and interaction between charging piles and the ability to flexibly adjust charging strategies, resulting in inefficient resource utilization and competition among charging piles. Existing technologies do not provide effective energy management methods and cannot balance energy demand and supply, which may lead to instability in the power system. Existing technologies do not fully consider the synergistic effect between charging piles and the intelligence of energy allocation, which results in a large amount of electricity being allocated to some charging piles when they are in a low-demand or idle state, causing energy waste and impairing the overall resource utilization rate.

[0008] (3) Lack of intelligent control based on data and demand

[0009] The prior art does not provide an effective energy management method, which cannot balance energy demand and supply, and may lead to instability of the power system. The prior art does not fully utilize data analysis and intelligent decision-making to optimize charging services. The prior art generally adopts a static charging strategy, which cannot be intelligently adjusted according to actual demand and power system state, which means that even during low demand periods, charging piles may still be charging at the same rate, wasting power resources; conversely, during high demand periods, charging piles may not be able to meet the charging needs of users

[0010] In summary, the prior art has a series of problems in the charging control system and method of charging piles, which limits the efficiency and sustainability of electric vehicle charging infrastructure, reduces user experience, and poses challenges to energy utilization and the stability of the power system. Therefore, it is necessary to research and develop new charging control systems and methods for charging piles to address these problems and promote the development of the electric vehicle industry. SUMMARY

[0011] To solve the above-mentioned problems of the prior art, the present application provides a charging pile charging control system and method, aiming to solve the problem that the charging pile system in the prior art lacks congestion management mechanism, resulting in low overall resource availability and efficiency, and does not consider the synergy between charging piles and the intelligence of energy distribution, leading to instability of the power system and inability to balance energy demand and supply.

[0012] In a first aspect of the present application, a charging pile charging control system is provided, comprising:

[0013] a data acquisition module configured to acquire charging pile state information and charging pile control demand information of each charging pile in a charging pile network at a target time;

[0014] an intelligent charging scheduling module configured to generate a charging control objective function based on the charging pile state information, construct a charging pile charging control model using the charging control objective function, and perform iterative calculation on the charging pile charging control model with the charging pile control demand information as the iterative convergence condition to obtain a charging control strategy at the target time;

[0015] a charging control module configured to control each charging pile to perform corresponding charging actions based on the charging control strategy.

[0016] Optionally, the data acquisition module specifically comprises:

[0017] The charging pile state information acquisition unit is configured to acquire charging pile state information collected by a data collector of each charging pile; wherein the charging pile state information comprises charging pile energy consumption information and charging pile coordination information;

[0018] The charging pile control demand acquisition unit is configured to acquire charging pile control demand information input by a control demand terminal; wherein the charging pile control demand information comprises charging demand information and / or power load demand information.

[0019] Optionally, the intelligent charging scheduling module specifically comprises:

[0020] The charging pile charging control model construction unit is configured to establish a charging control target function according to the charging pile energy consumption information and the charging pile coordination information of each charging pile, and to construct a charging pile charging control model by using the charging control target function;

[0021] The charging control strategy generation unit is configured to convert the charging demand information and / or the power load demand information into a model iteration convergence condition, and to perform iterative calculation on the charging pile charging control model by using the model iteration convergence condition, so as to obtain a charging control strategy at a target time.

[0022] Optionally, the charging pile energy consumption information comprises a charging pile energy consumption value, the charging pile energy consumption value being a function value of a charging rate of a charging pile with respect to charging time; and the charging pile coordination information comprises a charging pile coordination degree, the charging pile coordination degree being a coordination degree of a charging pile with respect to charging time.

[0023] The expression of the charging control target function is specifically as follows:

[0024]

[0025]

[0026] wherein f(X) is the charging control target function, N is the number of charging piles in the charging pile networking, E i is the charging pile energy consumption value of the i-th charging pile, λ is the coordination weight between the charging pile networking, S i (t) is the coordination degree of the charging pile i at time t, X i (t) is the charging rate of the charging pile i at time t, X j (t) is the charging rate of the charging pile j at time t, w ij is the weight between the charging pile i and the charging pile j, |X i (t)-X j(t) represents the difference of charging rate between charging piles i and j, a is the weight of real-time sharing data, ShareData i (t) is the real-time sharing data of other charging piles received by charging pile i at time t, and the real-time sharing data is the charging rate.

[0027] Optionally, the charging pile charging control model is configured as a charging pile charging control model based on a quantum particle optimization algorithm; wherein, in the charging pile charging control model based on the quantum particle optimization algorithm, the state updating rule of each quantum particle is defined as:

[0028] X i (t+1)=X i (t)+V i (t+1)

[0029] V i (t+1)=ωV i (t)+c1·r1·(P i (t)-X i (t))+c2·r2·(P g (t)-X i (t))

[0030] +c3·r3·Share(X i ,t)

[0031] Wherein, X i is the position state of each quantum particle at time t, represents the charging rate of N charging piles, V i (t) is the speed state of each quantum particle at time t, ω is the inertia weight, c1, c2, c3 are learning factors, r1, r2, r3 are random numbers, P i (t) is the individual optimal quantum bit of quantum particle i at time t, P g (t) is the global optimal quantum bit, Share(X i ,t) represents the coordination degree of charging pile i with surrounding charging piles, and represents the difference of charging rate.

[0032] Optionally, the charging control strategy generation unit specifically comprises:

[0033] An iteration convergence condition generation subunit;

[0034] Wherein, the iteration convergence condition generation subunit is configured to determine a model iteration convergence condition based on the charging demand information and / or the power load demand information.

[0035] The charging demand information includes a charging rate demand value of each charging pile, the power load demand information includes an upper limit value of the charging rate of each charging pile, the iterative convergence condition is a fitness convergence interval determined according to the charging rate demand value and / or the upper limit value of the charging rate, and the fitness convergence interval is a value interval of a target function.

[0036] Optionally, the charging control strategy generation unit specifically includes:

[0037] The charging pile charging control model iterative calculation subunit;

[0038] The charging pile charging control model iterative calculation subunit is configured to perform fitness iterative calculation on the charging pile charging control model based on the quantum particle optimization algorithm by using the fitness convergence interval until the model converges.

[0039] The fitness iterative calculation specifically includes:

[0040] Initializing a quantum particle position X i (0) and a velocity V i (0), calculating a target function f(X(0)) as fitness according to the initialized position X i (0), recording an individual optimal position P i (0) and a global optimal position P g (0);

[0041] Entering iteration, updating the quantum particle position and the velocity to obtain X i (t+1) and V i (t+1), calculating fitness f(X(t+1)), judging whether f(X(t+1)) is the current individual optimal position and the global optimal position, if yes, updating the individual optimal position P i (t+1) and the global optimal position P g (t+1), repeating the iteration process until the calculated fitness falls into the fitness convergence interval, and stopping the iteration process.

[0042] Optionally, the charging control strategy generation unit specifically includes:

[0043] The charging control strategy generation subunit;

[0044] The charging control strategy generation subunit is configured to determine the charging control strategy according to a charging rate of each charging pile at time t corresponding to the current quantum particle position X i (0) when the model converges.

[0045] The charging control strategy is a target charging rate and a target charging time of each charging pile in a target time period.

[0046] Optionally, the charging control module specifically comprises:

[0047] a charging control signal generation unit configured to generate, according to the charging control strategy, a charging control signal for controlling each charging pile to perform a charging action of a target charging rate and a target charging time within a target time period;

[0048] a charging control signal sending unit configured to send the charging control signal to the charging piles so as to make each charging pile perform the charging action of the target charging rate and the target charging time.

[0049] In a second aspect, the present application provides a charging pile charging control method, comprising:

[0050] S1: obtaining charging pile state information and charging pile control demand information of each charging pile in a charging pile network at a target time;

[0051] S2: generating a charging control target function according to the charging pile state information, constructing a charging pile charging control model by using the charging control target function, and performing iterative calculation on the charging pile charging control model with the charging pile control demand information as the iterative convergence condition to obtain a charging control strategy at the target time;

[0052] S3: controlling each charging pile to perform a corresponding charging action based on the charging control strategy.

[0053] The present application has the beneficial effects that: a charging pile charging control system and method are provided, a charging control target function is established by obtaining real-time state of charging piles, a charging pile charging control model is constructed therefrom, control demand information of the charging piles is obtained, the charging pile charging control model is iteratively calculated based on use demand of each charging pile and load of a power system, an optimal charging strategy is constantly searched for, and each charging pile is controlled to perform a corresponding charging action according to the charging strategy, real-time sharing of charging data, networking of charging piles and coordination degree are considered to realize efficient charging scheduling, improve availability and efficiency of overall resources, and meet charging demand of new energy vehicles and requirements of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 FIG. 1 is a structural schematic diagram of a charging pile charging control system provided by the present application;

[0055] Figure 2 FIG. 2 is a flowchart of a charging pile charging control method provided by the present application.

[0056] Reference signs:

[0057] 10 - data acquisition module; 20 - intelligent charging scheduling module; 30 - charging control module. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

[0059] Embodiment 1

[0060] Referring to Figure 1 , Figure 1 A structural schematic diagram of a charging pile charging control system provided by the embodiments of the present application.

[0061] As Figure 1 shown, a charging pile charging control system comprises: a data acquisition module 10 configured to acquire charging pile state information and charging pile control demand information of each charging pile in a charging pile network at a target time; an intelligent charging scheduling module 20 configured to generate a charging control target function according to the charging pile state information, construct a charging pile charging control model using the charging control target function, and perform iterative calculation on the charging pile charging control model with the charging pile control demand information as the iterative convergence condition to obtain a charging control strategy at the target time; and a charging control module 30 configured to control each charging pile to perform a corresponding charging action based on the charging control strategy.

[0062] It needs to be explained that the lack of congestion management mechanism in the prior art charging pile system leads to low overall resource availability and efficiency, without considering the synergy and intelligence of energy distribution among charging piles, leading to unstable power system and the problem of unbalanced energy demand and supply. Therefore, in order to solve the above problems, the embodiment proposes a charging pile charging control system, which obtains charging pile state information and charging pile control demand information of each charging pile in the charging pile networking at the target time, then generates a charging control target function using the charging pile state information and constructs a charging pile charging control model, and uses the charging pile control demand information as the iteration convergence condition to iteratively calculate the charging pile charging control model, obtains the charging control strategy at the target time, and finally controls each charging pile to execute the corresponding charging action according to the charging strategy. Thus, by obtaining the real-time state of the charging pile to establish the charging control target function, the charging pile charging control model is constructed, and by obtaining the charging pile control demand information, the charging pile charging control model is iteratively calculated based on the use demand of each charging pile and the power system load, the best charging strategy is constantly sought, and each charging pile is controlled to execute the corresponding charging action according to the charging strategy. By considering real-time sharing of charging data, charging pile networking and coordination degree, efficient charging scheduling is realized, the availability and efficiency of the overall resources are improved, and the charging demand of new energy vehicles and the requirements of the power system are met.

[0063] In a preferred embodiment, the data acquisition module specifically includes: a charging pile state information acquisition unit configured to acquire charging pile state information collected by a data collector of each charging pile; wherein the charging pile state information includes charging pile energy consumption information and charging pile coordination information; a charging pile control demand acquisition unit configured to acquire charging pile control demand information input by a control demand terminal; wherein the charging pile control demand information includes charging demand information and / or power load demand information.

[0064] On this basis, the intelligent charging scheduling module specifically includes: a charging pile charging control model construction unit configured to establish a charging control target function according to the charging pile energy consumption information and the charging pile coordination information of each charging pile, and construct a charging pile charging control model using the charging control target function; a charging control strategy generation unit configured to convert the charging demand information and / or the power load demand information into a model iteration convergence condition, and iteratively calculate the charging pile charging control model using the model iteration convergence condition to obtain a charging control strategy at the target time.

[0065] In this embodiment, the charging control target function is established by acquiring the charging pile state information collected by the data collector of each charging pile; wherein the charging pile state information includes charging pile energy consumption information and charging pile cooperation information, that is, the energy consumption information of each charging pile and the cooperation degree between the charging piles in the charging pile networking are considered when establishing the charging control target function, so that when the charging control target function is used for charging strategy analysis, the problems of unstable power system, energy waste and damaged resource utilization caused by lack of cooperation and interaction between charging piles can be avoided. At the same time, the charging pile control demand information input by the control demand terminal is acquired; wherein the charging pile control demand information includes the charging demand information input by the charging user on the charging demand sending device (such as the user's smart phone) and / or the power load demand information input by the power system user on the load demand sending device (such as the load management device of the power system), that is, after the charging pile charging control model is constructed by using the target function, the model iteration convergence condition converted by using the charging demand information and / or the power load demand information is used to iteratively calculate the charging pile charging control model to obtain the charging control strategy, which can consider real-time sharing of charging data, charging pile networking and cooperation degree to realize efficient charging scheduling, improve the availability and efficiency of overall resources, and realize more intelligent charging scheduling strategy by combining real-time sharing of charging data and charging pile networking, improve the overall performance of the charging system, while meeting the charging demand of new energy vehicles and the requirements of the power system, avoiding the problems of charging pile congestion, energy waste and inability to adapt to different demands caused by static charging pile control strategy.

[0066] Specifically, the charging pile energy consumption information includes a charging pile energy consumption value, and the charging pile energy consumption value is a function value of the charging rate of the charging pile with respect to the charging time; the charging pile cooperation information includes a charging pile cooperation degree, and the charging pile cooperation degree is a cooperation degree of the charging pile with respect to the charging time; wherein the expression of the charging control target function is specifically:

[0067]

[0068]

[0069] wherein f(X) is the charging control target function, N is the number of charging piles in the charging pile networking, E i is the charging pile energy consumption value of the i-th charging pile, λ is the cooperation weight between the charging pile networking, S i (t) is the cooperation degree of the charging pile i at time t, X i (t) is the charging rate of the charging pile i at time t, X j (t) is the charging rate of the charging pile j at time t, w ij is the weight between the charging pile i and the charging pile j, |X i (t)-Xj (t) represents the difference between the charging rates of charging piles i and j, and a is the weight of real-time sharing data, ShareData i (t) is the real-time sharing data of other charging piles received by charging pile i at time t, and the real-time sharing data is the charging rate.

[0070] Specifically, the charging pile charging control model is configured as a charging pile charging control model based on a quantum particle optimization algorithm; wherein the state updating rule of each quantum particle in the charging pile charging control model based on the quantum particle optimization algorithm is defined as:

[0071] X i (t+1)=X i (t)+V i (t+1)

[0072] V i (t+1)=ωV i (t)+c1·r1·(P i (t)-X i (t))+c2·r2·(P g (t)-X i (t))

[0073] +c3·r3·Share(X i ,t)

[0074] Wherein, X i is the position state of each quantum particle at time t, represents the charging rate of N charging piles, V i (t) is the speed state of each quantum particle at time t, ω is the inertia weight, c1, c2, c3 are learning factors, r1, r2, r3 are random numbers, P i (t) is the individual optimal quantum bit of quantum particle i at time t, P g (t) is the global optimal quantum bit, Share(X i ,t) represents the degree of cooperation of charging pile i with surrounding charging piles, and represents the difference between the charging rates.

[0075] In this embodiment, the charging control objective function is established by considering the charging pile energy consumption value of each charging pile (i.e. a function of charging rate and charging time) and the degree of cooperation between each charging pile and the surrounding charging piles; wherein the weight between charging piles i and j can be determined according to the distance and voltage loss between the charging piles, or can be copied through experience, thereby representing the degree of cooperation between each charging pile and the surrounding charging piles. At the same time, the charging pile charging control model based on quantum particle optimization algorithm is used to realize the generation of dynamic charging pile charging control strategy based on real-time state data and demand information of the charging pile, and the position and speed of the quantum particle are updated multiple times by using the quantum particle optimization algorithm, and the charging strategy is gradually optimized to minimize the total energy consumption or meet the demand of the power system. In this way, the charging scheduling combined with real-time sharing of charging data and charging pile networking is realized, the overall performance of the charging system is improved, and the charging demand of new energy vehicles and the requirements of the power system can also be met at the same time.

[0076] In a preferred embodiment, the charging control strategy generation unit specifically comprises: an iterative convergence condition generation subunit; wherein the iterative convergence condition generation subunit is configured to determine a model iterative convergence condition based on the charging demand information and / or power load demand information; wherein the charging demand information comprises a charging rate demand value of each charging pile, and the power load demand information comprises a charging rate upper limit value of each charging pile; the iterative convergence condition is a fitness convergence interval determined according to the charging rate demand value and / or the charging rate upper limit value, and the fitness convergence interval is a value interval of the objective function.

[0077] Further, the charging control strategy generation unit specifically comprises: a charging pile charging control model iterative calculation subunit; wherein the charging pile charging control model iterative calculation subunit is configured to perform fitness iterative calculation on the charging pile charging control model based on the quantum particle optimization algorithm by using the fitness convergence interval until the model converges; wherein the fitness iterative calculation specifically comprises: initializing the position X i (0) and the speed V i (0) of the quantum particle, calculating the objective function f(X(0)) as the fitness according to the initialized position X i (0), recording the individual optimal position P i (0) and the global optimal position P g (0); entering iteration, updating the position and speed of the quantum particle to obtain X i (t+1) and V i (t+1), calculating the fitness f(X(t+1)), judging whether f(X(t+1)) is the current individual optimal position and the global optimal position, and if so, updating the individual optimal position P i (t+1) and the global optimal position P g(t+1), repeat the iteration process until the calculated fitness falls into the fitness convergence interval, then stop the iteration process.

[0078] In this embodiment, the charging rate demand value and charging rate upper limit value of each charging pile are first extracted from the charging demand information and / or power load demand information. The value of the objective function is calculated based on the charging rate range determined in this process to obtain the fitness convergence interval. The charging pile charging control model is iteratively calculated using this fitness convergence interval until the calculated fitness falls into the fitness convergence interval, at which point the iteration process stops. Thus, this embodiment evaluates the performance of the charging strategy based on the objective function and then updates the velocity and position of the particles. By iterating and updating the position and velocity of the particles multiple times, the charging strategy is gradually optimized to minimize the total energy consumption or meet the power system requirements. This achieves charging scheduling that combines real-time sharing of charging data and charging pile networking, improves the overall performance of the charging system, and can simultaneously meet the charging needs of new energy vehicles and the requirements of the power system.

[0079] In a preferred embodiment, the charging control strategy generation unit specifically includes: a charging control strategy generation subunit; wherein, the charging control strategy generation subunit is configured to generate the current quantum particle position X at model convergence. i (0) The charging rate of each charging pile at time t is used to determine the charging control strategy; wherein, the charging control strategy is the target charging rate and target charging time of each charging pile within the target time period.

[0080] In a preferred embodiment, the charging control module specifically includes: a charging control signal generation unit configured to generate a charging control signal that controls each charging pile to perform a charging action with a target charging rate and a target charging time within a target time period, according to the charging control strategy; and a charging control signal sending unit configured to send the charging control signal to the charging piles so that each charging pile performs a charging action with a target charging rate and a target charging time.

[0081] In this embodiment, the current quantum particle position X at the time of model convergence is used... i (0) The charging rate of each charging pile at time t is used as the charging control strategy to control the charging action of each charging pile in the charging pile network. Taking into account the status, charging demand and load limit of each charging pile in the charging pile network, the charging control strategy is generated in this way. It can quickly respond to changing demands and conditions, ensure the efficiency and reliability of the charging process, so as to meet the user demand and the load of the power system to the greatest extent, and achieve the effects of optimizing resource utilization, improving user experience, improving power system stability and reducing costs.

[0082] A second aspect of the present invention provides a charging control method for a charging pile, comprising:

[0083] Reference Figure 2 , Figure 2 This is a flowchart illustrating a charging control method for a charging pile provided in an embodiment of the present invention.

[0084] like Figure 2 As shown, a charging control method for a charging pile includes the following steps:

[0085] S1: Obtain the charging pile status information and charging pile control requirement information of each charging pile in the charging pile network at the target time;

[0086] S2: Based on the charging pile status information, generate a charging control objective function, construct a charging pile charging control model using the charging control objective function, and iteratively calculate the charging pile charging control model using the charging pile control demand information as the iterative convergence condition to obtain the charging control strategy at the target time.

[0087] S3: Based on the charging control strategy, control each charging pile to perform the corresponding charging action.

[0088] In this embodiment, a charging control objective function is established by acquiring the real-time status of the charging piles, thereby constructing a charging pile charging control model. Then, by acquiring the charging pile control demand information, the charging pile charging control model is iteratively calculated based on the usage demand of each charging pile and the power system load to continuously find the optimal charging strategy. Each charging pile is then controlled to execute the corresponding charging action according to the charging strategy. By considering real-time sharing of charging data, charging pile networking, and coordination, efficient charging scheduling is achieved, improving the overall availability and efficiency of resources, while meeting the charging needs of new energy vehicles and the requirements of the power system.

[0089] The specific implementation method of the charging pile charging control method in this application is basically the same as the embodiments of the charging pile charging control system described above, and will not be repeated here.

[0090] In the description of the embodiments of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "center," "top," "bottom," "top," "bottom," "inner," "outer," "inner side," and "outer side," etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the accompanying drawings and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. "Inner side" refers to the interior or enclosed area or space. "Outer perimeter" refers to the area surrounding a specific component or specific area.

[0091] In the description of embodiments of the present invention, the terms "first," "second," "third," and "fourth" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," "third," or "fourth" may explicitly or implicitly include one or more of that feature. In the description of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0092] In the description of the embodiments of the present invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," "joining," and "assembly" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0093] In the description of embodiments of the present invention, specific features, structures, materials or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0094] In the description of the embodiments of the present invention, it should be understood that "-" and "~" represent a range between two numerical values, and this range includes the endpoints. For example, "AB" represents a range greater than or equal to A and less than or equal to B. "A~B" represents a range greater than or equal to A and less than or equal to B.

[0095] In the description of embodiments of the present invention, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0096] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A charging pile charging control system, characterized in that, include: The data acquisition module is configured to acquire the charging pile status information and charging pile control requirement information of each charging pile in the charging pile network at a target time. The intelligent charging scheduling module is configured to generate a charging control objective function based on the charging pile status information, construct a charging pile charging control model using the charging control objective function, and iteratively calculate the charging pile charging control model using the charging pile control demand information as the iterative convergence condition to obtain the charging control strategy at the target time. A charging control module is configured to control each charging pile to perform a corresponding charging action based on the charging control strategy.

2. The charging pile charging control system according to claim 1, characterized in that, The data acquisition module specifically includes: A charging pile status information acquisition unit is configured to acquire charging pile status information collected by the data acquisition device of each charging pile; wherein, the charging pile status information includes charging pile energy consumption information and charging pile coordination information; A charging pile control demand acquisition unit is configured to acquire charging pile control demand information input by a control demand terminal; wherein the charging pile control demand information includes charging demand information and / or power load demand information.

3. The charging pile charging control system according to claim 2, characterized in that, The intelligent charging scheduling module specifically includes: A charging pile charging control model building unit is configured to establish a charging control objective function based on the charging pile energy consumption information and the charging pile coordination information of each charging pile, and to build a charging pile charging control model using the charging control objective function. A charging control strategy generation unit is configured to convert the charging demand information and / or the power load demand information into model iteration convergence conditions, and use the model iteration convergence conditions to iteratively calculate the charging control model of the charging pile to obtain the charging control strategy at the target time.

4. The charging pile charging control system according to claim 3, characterized in that, The charging pile energy consumption information includes the charging pile energy consumption value, which is a function of the charging rate of the charging pile with respect to the charging time; the charging pile coordination information includes the charging pile coordination degree, which is the coordination degree of the charging pile with respect to the charging time. The expression for the charging control objective function is specifically as follows: Where f(X) is the charging control objective function, N is the number of charging piles in the charging pile network, and E i S is the energy consumption value of the i-th charging pile, λ is the coordination weight between charging piles in the network, and S is the energy consumption value of the i-th charging pile. i (t) represents the coordination degree of charging pile i at time t, X i (t) is the charging rate of charging pile i at time t, X j (t) is the charging rate of charging pile j at time t, w ij It is the weight between charging piles i and j, |X i (t)-X j (t) represents the difference in charging rate between charging piles i and j, where α is the weight of the real-time shared data. i (t) is the real-time shared data received by charging pile i from other charging piles at time t, and the real-time shared data is the charging rate.

5. The charging pile charging control system according to claim 4, characterized in that, The charging pile charging control model is configured as a charging pile charging control model based on the quantum particle optimization algorithm; wherein, in the charging pile charging control model based on the quantum particle optimization algorithm, the state update rule for each quantum particle is defined as: X i (t+1)=X i (t)+V i (t+1) V i (t+1)=ωV i (t)+c1·r1·(P i (t)-X i (t))+c2·r2·(P g (t)-X i (t))+c3·r3·Share(X i ,t) Among them, X i Let V be the position and state of each quantum particle at time t, representing the charging rate of N charging stations. i (t) represents the velocity state of each quantum particle at time t, ω is the inertial weight, c1, c2, and c3 are learning factors, r1, r2, and r3 are random numbers, and P i P(t) is the individual optimal qubit of quantum particle i at time t. g (t) is the globally optimal qubit, Share(X) i ,t) represents the coordination degree between charging pile i and surrounding charging piles, and represents the difference in charging rate.

6. The charging pile charging control system according to claim 5, characterized in that, The charging control strategy generation unit specifically includes: Iterative convergence conditions generate sub-units; The iterative convergence condition generation subunit is configured to determine the model iterative convergence condition based on the charging demand information and / or power load demand information. The charging demand information includes the charging rate demand value for each charging pile, the power load demand information includes the upper limit value of the charging rate for each charging pile, and the iterative convergence condition is the fitness convergence interval determined based on the charging rate demand value and / or the upper limit value of the charging rate, wherein the fitness convergence interval is the value range of the objective function.

7. The charging pile charging control system according to claim 6, characterized in that, The charging control strategy generation unit specifically includes: Iterative calculation subunit for charging pile charging control model; The charging pile charging control model iterative calculation subunit is configured to use the fitness convergence interval to perform fitness iterative calculation on the charging pile charging control model based on quantum particle optimization algorithm until the model converges. The fitness iteration calculation specifically includes: Initialize the position X of the quantum particle i (0) and velocity V i (0), based on the initial position X i (0) Calculate the objective function f(X(0)) as the fitness and record the individual's optimal position P. i (0) and the global optimal position P g (0); Entering the iteration, the position and velocity of the quantum particle are updated to obtain X. i (t+1) and V i (t+1), calculate the fitness f(X(t+1)), determine whether f(X(t+1)) is the current individual's optimal position and global left and right positions, if so, update the individual's optimal position P. i (t+1) and global left and right positions P g (t+1), repeat the iteration process until the calculated fitness falls into the fitness convergence interval, then stop the iteration process.

8. The charging pile charging control system according to claim 7, characterized in that, The charging control strategy generation unit specifically includes: The charging control strategy generation subunit; The charging control strategy generation subunit is configured to determine the charging control strategy based on the charging rate of each charging pile at time t corresponding to the current quantum particle position Xi(0) when the model converges. The charging control strategy specifies the target charging rate and target charging time for each charging pile within a target time period.

9. The charging pile charging control system according to claim 8, characterized in that, The charging control module specifically includes: A charging control signal generation unit is configured to generate a charging control signal according to the charging control strategy, which controls each charging pile to perform a charging action with a target charging rate and a target charging time within a target time period. A charging control signal transmitting unit is configured to transmit the charging control signal to the charging pile so that each charging pile performs a charging action with a target charging rate and a target charging time.

10. A charging control method for a charging pile, characterized in that, include: S1: Obtain the charging pile status information and charging pile control requirement information of each charging pile in the charging pile network at the target time; S2: Based on the charging pile status information, generate a charging control objective function, construct a charging pile charging control model using the charging control objective function, and iteratively calculate the charging pile charging control model using the charging pile control demand information as the iterative convergence condition to obtain the charging control strategy at the target time. S3: Based on the charging control strategy, control each charging pile to perform the corresponding charging action.

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