Intelligent management system and method for balancing charging load of electric vehicle

Abstract

The invention discloses an intelligent management system and method for electric vehicle charging load balancing, and relates to the field of charging load balancing management, and the method comprises the steps: obtaining different types of electric vehicle battery pack type features, and building an electric vehicle battery pack type feature library; acquiring to-be-charged time of the to-be-charged electric vehicles, and establishing a to-be-charged time matrix of the to-be-charged electric vehicles in Setting the rated power of the area and the number of electric vehicle charging devices; determining a fuzzy comprehensive evaluation matrix of the to-be-charged electric vehicle battery pack state; establishing a fuzzy neural network comprehensive evaluation model of the battery pack state of the to-be-charged electric vehicle; a man-machine interaction platform is established and used for inquiring, reserving, displaying and updating queuing information and charging information of the electric vehicles. The method has the advantages that the operation condition of global electric vehicle charging is effectively evaluated, the optimal charging power and the optimal charging time are reasonably distributed to each charged electric vehicle, and therefore the charging efficiency is improved, and stable operation of a regional power grid is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of charging load balancing management, and in particular to an intelligent management system and method for charging load balancing of electric vehicles. Background Art

[0002] With the increase in the number and demand of electric vehicles, the requirements for the expansion capacity of the power grid in the area where electric vehicle charging devices are installed are getting higher and higher. If there is an imbalance in the number of electric vehicle charging devices and the charging power, the power grid will not be expanded in time and there will be deficiencies, resulting in power shortages, voltage instability and other problems, which will affect the normal electricity use of residents. Therefore, when setting up electric vehicle charging devices, it is necessary to reasonably plan the layout and number of charging piles according to the actual situation and electricity demand of the community. At the same time, it is necessary to consider the status of different types of electric vehicle battery packs. Optimizing the intelligent management of electric vehicle charging load balancing will be of great significance.

[0003] Existing electric vehicle charging device technology mainly focuses on charging electric vehicles at all times through a fixed charging power. However, this method lacks comprehensive analysis and evaluation of regional electricity demand and electric vehicle charging power, as well as comprehensive allocation of charging power for different types of electric vehicle battery packs to be charged, resulting in power shortages and voltage instability during peak hours, thereby affecting residents' normal electricity consumption. Summary of the invention

[0004] In order to solve the above technical problems, an intelligent management system and method for electric vehicle charging load balancing are provided. This technical solution solves the problem raised in the above background technology that there is a lack of comprehensive analysis and evaluation of regional electricity demand and electric vehicle charging power, as well as comprehensive allocation of charging power for different types of electric vehicle battery packs to be charged, which leads to power shortages and voltage instability during peak electricity consumption periods, thereby affecting the normal electricity consumption of residents.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: An intelligent management method for balancing charging load of electric vehicles, comprising: Based on big data, obtain the type characteristics of electric vehicle battery packs of different models and establish an electric vehicle battery pack type characteristic library; According to the ampere-hour integration method, the waiting time of electric vehicles to be charged is obtained, and a waiting time matrix of electric vehicles to be charged in the entire area is established; According to the actual demand of the area where the electric vehicle charging device is set, the rated power and the number of electric vehicle charging devices in the area are set; According to the fuzzy comprehensive evaluation algorithm, a fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged is determined; Based on the neural network algorithm and the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged, a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicle to be charged is established; Establish a human-computer interaction platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles.

[0006] Preferably, the method of obtaining the type characteristics of battery packs of different models of electric vehicles based on big data and establishing a type characteristic library of electric vehicle battery packs specifically includes: Based on big data, extract the type characteristics of battery packs of different models of electric vehicles, and determine the variation range of the type characteristics of battery packs of different models of electric vehicles according to the factory information of battery packs of different models of electric vehicles; The characteristics of different types of electric vehicle battery packs include: rated voltage, battery capacity, maximum input current, charging power, maximum input power and limited temperature; According to the variation range of the type characteristics of the battery packs of different models of electric vehicles, the type characteristic data of the battery packs of different models of electric vehicles are dimensionally eliminated and normalized according to the normalization formula; Classify and sort the normalized data of different types of electric vehicle battery pack types, and establish an electric vehicle battery pack type feature library; The normalization formula is: In the formula, is the normalized characteristic data of different types of electric vehicle battery packs. The original data of the battery pack type characteristics of different models of electric vehicles, is the mean of the original data of the battery pack characteristics of different models of electric vehicles, is the standard deviation of the original data of battery pack type characteristics of different models of electric vehicles.

[0007] Preferably, the obtaining of the charging time of the electric vehicles to be charged according to the ampere-hour integration method and establishing the charging time matrix of the electric vehicles to be charged in the entire area specifically includes: Setting a hardware reading device for the status of the battery pack of the electric vehicle to be charged to obtain the status information of the battery pack of the electric vehicle to be charged; According to the usage of the battery pack of the electric vehicle to be charged, the battery usage and remaining power of the battery pack of the electric vehicle to be charged are obtained; Obtaining the amount of charge to be charged of the battery pack of the electric vehicle to be charged according to the battery usage and remaining capacity of the battery pack of the electric vehicle to be charged; According to the ampere-hour integration method, combined with the charging capacity of the battery pack of the electric vehicle to be charged, the charging time of the battery pack of the electric vehicle to be charged is determined; According to the charging time of the battery packs of the electric vehicles to be charged, the charging time of the electric vehicles to be charged in the entire area is obtained, and a charging time matrix of the electric vehicles to be charged in the entire area is established; The ampere-hour integration method expression is: In the formula, For charging electric vehicle battery packs The state of charge at the moment, is the charge value of the initial state of charge, The rated capacity of the battery pack for charging electric vehicles, For charging electric vehicle batteries The charging current at the moment, Charging time for charging electric vehicle battery packs.

[0008] Preferably, the step of setting the rated power and the number of electric vehicle charging devices in the area according to the actual demand of the area where the electric vehicle charging devices are set specifically includes: According to the actual demand of the area where the electric vehicle charging device is set, the actual power consumption and power demand of the area in different time periods are obtained; According to the electricity consumption in the area where the electric vehicle charging device is to be installed, combined with the specifications and standards for installing the electric vehicle charging device, the rated power and number of electric vehicle charging devices in the area are set; In combination with the electricity consumption in the area where the electric vehicle charging device is to be installed, an initial value of the rated charging power is set for each electric vehicle charging device on average.

[0009] Based on big data, the maximum rated charging power threshold for electric vehicles is set in different time periods.

[0010] Preferably, the fuzzy comprehensive evaluation matrix for determining the state of the battery pack of the electric vehicle to be charged according to the fuzzy comprehensive evaluation algorithm specifically includes: According to the characteristics of battery packs of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the whole area, and the maximum rated charging power threshold of electric vehicles in different time periods, the fuzzy subsets and membership degrees of the fuzzy comprehensive evaluation algorithm are determined; According to the ordered-even representation method, the fuzzy subset of the battery state of the electric vehicle to be charged is determined Expression of According to the fuzzy subsets and membership degree of the fuzzy comprehensive evaluation algorithm, the fuzzy subsets of the battery pack status of the electric vehicle to be charged are determined. The membership function of Based on big data analysis, the important weights of different data in the characteristics of battery packs of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the entire region, and the maximum rated charging power threshold of electric vehicles in different time periods are set. ; Domain according to the state of the battery pack of the electric vehicle to be charged , Degree of affiliation and the importance weights of different data , establish a fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicle to be charged; The relationship between the fuzzy subset and the membership degree of the fuzzy comprehensive evaluation algorithm is: In the formula, It is a fuzzy subset of the battery pack status of electric vehicles to be charged, which refers to the collection of all data on the battery pack type characteristics of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the entire area, and the maximum rated charging power threshold of electric vehicles in different time periods. Fuzzy subset The domain of Fuzzy subset The membership function of Fuzzy subset The element value in for Fuzzy subset The degree of membership, that is, the degree of membership, is the interval range of the domain; The fuzzy subset of the battery state of the electric vehicle to be charged The expression is: In the formula, Fuzzy subset The element values, Fuzzy subset The Element value right degree of affiliation; The fuzzy subset of the battery state of the electric vehicle to be charged The membership function of is: In the formula, , It is a parameter for evaluating the critical value of the battery state of an electric vehicle to be charged.

[0011] Preferably, the fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicle to be charged is established based on the neural network algorithm and in combination with the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged, and specifically includes: According to the fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicle to be charged, the data in the matrix is ​​normalized; The normalized fuzzy comprehensive evaluation matrix data of the battery pack state of the electric vehicle to be charged is used as the input matrix of the neural network algorithm model; Based on big data, combined with the fuzzy comprehensive evaluation matrix data of the battery pack status of the electric vehicle to be charged, the training sample set and the target sample set are established; A fuzzy neural network comprehensive evaluation model for the battery pack status of electric vehicles to be charged is established, and the optimal charging power and charging time of the electric vehicles to be charged are screened out by combining the training sample set and the target sample set.

[0012] Preferably, the establishment of a human-computer interaction platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles specifically includes: Establish a human-computer interaction platform to build a hardware environment for the operation of a fuzzy neural network comprehensive evaluation model for the battery pack status of electric vehicles to be charged; Based on the human-computer interaction platform, it is convenient for users to obtain the status of the electric vehicle charging device and the charging status of the electric vehicle through the display interface; Based on the human-computer interaction platform, it is used to update and optimize the parameters and sample data of the fuzzy neural network comprehensive evaluation model of the battery pack status of the electric vehicle to be charged; Based on the human-computer interaction platform, it is used to receive and feedback user experience and feedback to ensure good interoperability of electric vehicle charging.

[0013] Furthermore, the present invention proposes an intelligent management system for balancing charging loads of electric vehicles, which is used to implement the intelligent management method for balancing charging loads of electric vehicles as described above, including: A feature extraction module, which is used to obtain the type features of battery packs of different models of electric vehicles based on big data, and to establish a type feature library of battery packs of electric vehicles; A hardware setting module, the hardware setting module is used to set the rated power and the number of electric vehicle charging devices in the area according to the actual demand of the area where the electric vehicle charging device is set; The optimal charging module is used to obtain the waiting time of the electric vehicles to be charged according to the ampere-hour integration method, and establish the waiting time matrix of the electric vehicles to be charged in the whole area; determine the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicles to be charged according to the fuzzy comprehensive evaluation algorithm; based on the neural network algorithm, combined with the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicles to be charged, establish a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicles to be charged; The interactive platform module is used to establish a human-computer interactive platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles.

[0014] Preferably, the optimal charging module includes: A global charging time unit, which is used to obtain the waiting time of the electric vehicles to be charged according to the ampere-hour integration method, and establish a waiting time matrix of the electric vehicles to be charged in the whole area; A state evaluation unit, the state evaluation unit is used to determine a fuzzy comprehensive evaluation matrix of the state of the battery pack of the electric vehicle to be charged according to a fuzzy comprehensive evaluation algorithm; The optimal charging unit is used to establish a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicle to be charged based on a neural network algorithm and in combination with a fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged.

[0015] Compared with the prior art, the present invention has the following beneficial effects: By obtaining the characteristics of different types of electric vehicle battery packs, the characteristics of different types of electric vehicle battery packs are normalized, and by obtaining the status information of the battery packs of the electric vehicles to be charged, the charging time of the electric vehicles to be charged is calculated based on the ampere-hour integration method, so as to determine the charging time of the electric vehicles to be charged in the whole area. Secondly, according to the characteristics of different types of electric vehicle battery packs, the charging time matrix of the electric vehicles to be charged in the whole area and the data of the maximum rated charging power threshold of the electric vehicles to be charged in different time periods, the battery pack status of the electric vehicles to be charged is fuzzy comprehensively evaluated, and a fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicles to be charged is established. Finally, based on the neural network algorithm, combined with the fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicles to be charged, a fuzzy neural network comprehensive evaluation model of the battery pack status of the electric vehicles to be charged is established, and the optimal charging power and charging time of the electric vehicles to be charged are screened out, so as to effectively evaluate the operation of the charging of electric vehicles in the whole area, and reasonably allocate the optimal charging power and charging time to each charging electric vehicle, so as to improve the charging efficiency, ensure the stable operation of the regional power grid, and avoid power shortage and voltage instability during peak power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 This is a flow chart of an intelligent management method for balancing charging load of electric vehicles according to the present invention; Figure 2 According to the ampere-hour integration method of the present invention, the waiting time of the electric vehicles to be charged is obtained, and a matrix flow chart of the waiting time of the electric vehicles to be charged in the whole area is established; Figure 3 The present invention is a fuzzy comprehensive evaluation matrix flow chart for determining the state of a battery pack of an electric vehicle to be charged according to a fuzzy comprehensive evaluation algorithm. DETAILED DESCRIPTION

[0017] The following description is used to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are only examples, and those skilled in the art may think of other obvious variations.

[0018] Reference Figure 1As shown, an intelligent management method for balancing charging load of electric vehicles includes: Based on big data, obtain the type characteristics of electric vehicle battery packs of different models and establish an electric vehicle battery pack type characteristic library; According to the ampere-hour integration method, the waiting time of electric vehicles to be charged is obtained, and a waiting time matrix of electric vehicles to be charged in the entire area is established; According to the actual demand of the area where the electric vehicle charging device is set, the rated power and the number of electric vehicle charging devices in the area are set; According to the fuzzy comprehensive evaluation algorithm, a fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged is determined; Based on the neural network algorithm and the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged, a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicle to be charged is established; Establish a human-computer interaction platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles.

[0019] It can be explained that this scheme obtains the type characteristics of battery packs of electric vehicles of different models, normalizes the type characteristics of battery packs of electric vehicles of different models, obtains the status information of the battery packs of electric vehicles to be charged, and calculates the waiting time of the electric vehicles to be charged based on the ampere-hour integration method, thereby determining the waiting time of electric vehicles to be charged in the entire area. Secondly, through the fuzzy comprehensive evaluation algorithm, according to the type characteristics of battery packs of electric vehicles of different models, the waiting time matrix of electric vehicles to be charged in the entire area and the data of the maximum rated charging power threshold of electric vehicles charging in different time periods, the state of the battery packs of the electric vehicles to be charged is fuzzy comprehensively evaluated, and a fuzzy comprehensive evaluation matrix of the state of the battery packs of the electric vehicles to be charged is established. Finally, based on the neural network algorithm, combined with the fuzzy comprehensive evaluation matrix of the state of the battery packs of the electric vehicles to be charged, a fuzzy neural network comprehensive evaluation model of the state of the battery packs of the electric vehicles to be charged is established, and the optimal charging power and charging time of the electric vehicles to be charged are screened out, thereby effectively evaluating the operation of electric vehicle charging in the entire area, and reasonably allocating the optimal charging power and charging time to each charging electric vehicle.

[0020] Reference Figure 2 As shown, the method of obtaining the waiting time of the electric vehicles to be charged according to the ampere-hour integration method and establishing the waiting time matrix of the electric vehicles to be charged in the whole area specifically includes: Setting a hardware reading device for the status of the battery pack of the electric vehicle to be charged to obtain the status information of the battery pack of the electric vehicle to be charged; According to the usage of the battery pack of the electric vehicle to be charged, obtaining the battery usage and remaining power of the battery pack of the electric vehicle to be charged; According to the battery usage and remaining power of the battery pack of the electric vehicle to be charged, the amount of power to be charged of the battery pack of the electric vehicle to be charged is obtained; According to the ampere-hour integration method, combined with the charging capacity of the battery pack of the electric vehicle to be charged, the charging time of the battery pack of the electric vehicle to be charged is determined; According to the charging time of the battery packs of the electric vehicles to be charged, the charging time of the electric vehicles to be charged in the entire area is obtained, and a charging time matrix of the electric vehicles to be charged in the entire area is established; The ampere-hour integration method expression is: In the formula, For charging electric vehicle battery packs The state of charge at the moment, is the charge value of the initial state of charge, The rated capacity of the battery pack for charging electric vehicles, For charging electric vehicle batteries The charging current at the moment, Charging time for charging electric vehicle battery packs.

[0021] It can be explained that when considering the load balancing of electric vehicle charging, it is necessary to start from the overall situation and comprehensively consider the charging conditions of all charging electric vehicles and regional electricity consumption. Under the condition of ensuring the stability of regional electricity consumption, reasonable allocation and priority allocation of reasonable charging power are carried out to complete the charging arrangements of electric vehicles in the whole region in an efficient and distributed manner. This solution reads the status information of the battery pack of the electric vehicle to be charged, and determines the waiting time of the battery pack of the electric vehicle to be charged according to the ampere-hour integration method and the waiting power of the battery pack of the electric vehicle to be charged, thereby determining the comprehensive evaluation data on the waiting time.

[0022] Reference Figure 3 As shown, the fuzzy comprehensive evaluation matrix for determining the state of the battery pack of the electric vehicle to be charged according to the fuzzy comprehensive evaluation algorithm specifically includes: According to the characteristics of battery packs of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the whole area, and the maximum rated charging power threshold of electric vehicles in different time periods, the fuzzy subsets and membership degrees of the fuzzy comprehensive evaluation algorithm are determined; According to the ordered-even representation method, the fuzzy subset of the battery state of the electric vehicle to be charged is determined Expression of According to the fuzzy subsets and membership degree of the fuzzy comprehensive evaluation algorithm, the fuzzy subsets of the battery pack status of the electric vehicle to be charged are determined. The membership function of Based on big data analysis, the important weights of different data in the characteristics of battery packs of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the entire region, and the maximum rated charging power threshold of electric vehicles in different time periods are set. ; Domain according to the state of the battery pack of the electric vehicle to be charged , Degree of affiliation and the importance weights of different data , establish a fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicle to be charged; The relationship between the fuzzy subset and the membership degree of the fuzzy comprehensive evaluation algorithm is: In the formula, It is a fuzzy subset of the battery pack status of electric vehicles to be charged, which refers to the collection of all data on the battery pack type characteristics of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the entire area, and the maximum rated charging power threshold of electric vehicles in different time periods. Fuzzy subset The domain of Fuzzy subset The membership function of Fuzzy subset The element value in for Fuzzy subset The degree of membership, that is, the degree of membership, is the interval range of the domain; The fuzzy subset of the battery state of the electric vehicle to be charged The expression is: In the formula, Fuzzy subset The element values, Fuzzy subset The Element value right degree of affiliation; The fuzzy subset of the battery state of the electric vehicle to be charged The membership function of is: In the formula, , It is a parameter for evaluating the critical value of the battery state of an electric vehicle to be charged.

[0023] It can be explained that conventional neural network models require a large amount of training sample data and target sample data support, and for the uncertain types of electric vehicle charging battery packs, conventional neural network models are prone to overfitting, resulting in inaccurate model judgment. Therefore, this scheme adds a fuzzy comprehensive evaluation algorithm, and sets and limits the data on the type characteristics of different types of electric vehicle battery packs, the charging time matrix of electric vehicles to be charged in the entire area, and the maximum rated charging power threshold of electric vehicles in different time periods in the early stage, so as to expand the comprehensive evaluation range of the data, improve the robustness and adaptability of the model, thereby effectively improving the acceptability and adaptability of different types of electric vehicle charging battery packs and reducing the overfitting phenomenon.

[0024] Further, based on the same inventive concept as the above-mentioned intelligent management method for electric vehicle charging load balancing, this solution proposes an intelligent management system for electric vehicle charging load balancing, including: A feature extraction module, which is used to obtain the type features of battery packs of different models of electric vehicles based on big data, and to establish a type feature library of battery packs of electric vehicles; A hardware setting module, the hardware setting module is used to set the rated power and the number of electric vehicle charging devices in the area according to the actual demand of the area where the electric vehicle charging device is set; The optimal charging module is used to obtain the waiting time of the electric vehicles to be charged according to the ampere-hour integration method, and establish the waiting time matrix of the electric vehicles to be charged in the whole area; determine the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicles to be charged according to the fuzzy comprehensive evaluation algorithm; based on the neural network algorithm, combined with the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicles to be charged, establish a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicles to be charged; An interactive platform module, which is used to establish a human-computer interactive platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles; The optimal charging module includes: A global charging time unit, which is used to obtain the waiting time of the electric vehicles to be charged according to the ampere-hour integration method, and establish a waiting time matrix of the electric vehicles to be charged in the whole area; A state evaluation unit, the state evaluation unit is used to determine a fuzzy comprehensive evaluation matrix of the state of the battery pack of the electric vehicle to be charged according to a fuzzy comprehensive evaluation algorithm; The optimal charging unit is used to establish a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicle to be charged based on a neural network algorithm and in combination with a fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged.

[0025] To sum up, the advantages of the present invention are: effectively evaluating the operating conditions of electric vehicle charging in the entire region, reasonably allocating the optimal charging power and charging time to each charging electric vehicle, thereby improving the charging efficiency, ensuring the stable operation of the regional power grid, and avoiding power supply shortages, voltage instability and other problems during peak power consumption periods.

[0026] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. An intelligent management method for balancing charging load of electric vehicles, characterized in that: include: Based on big data, obtain the type characteristics of electric vehicle battery packs of different models and establish an electric vehicle battery pack type characteristic library; According to the ampere-hour integration method, the waiting time of electric vehicles to be charged is obtained, and a waiting time matrix of electric vehicles to be charged in the entire area is established; According to the actual demand of the area where the electric vehicle charging device is set, the rated power and the number of electric vehicle charging devices in the area are set; According to the fuzzy comprehensive evaluation algorithm, a fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged is determined; Based on the neural network algorithm and the fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicle to be charged, a fuzzy neural network comprehensive evaluation model of the battery pack status of the electric vehicle to be charged is established to screen out the optimal charging power and charging time of the electric vehicle to be charged; Establish a human-computer interaction platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles.

2. The intelligent management method for balancing charging load of electric vehicles according to claim 1, characterized in that: The method of obtaining the type characteristics of battery packs of different models of electric vehicles based on big data and establishing a type characteristic library of electric vehicle battery packs specifically includes: Based on big data, extract the type characteristics of battery packs of different models of electric vehicles, and determine the variation range of the type characteristics of battery packs of different models of electric vehicles according to the factory information of battery packs of different models of electric vehicles; The characteristics of different types of electric vehicle battery packs include: rated voltage, battery capacity, maximum input current, charging power, maximum input power and limited temperature; According to the variation range of the type characteristics of the battery packs of different models of electric vehicles, the type characteristic data of the battery packs of different models of electric vehicles are dimensionally eliminated and normalized according to the normalization formula; Classify and sort the normalized data of different types of electric vehicle battery pack types, and establish an electric vehicle battery pack type feature library; The normalization formula is: In the formula, is the normalized characteristic data of different types of electric vehicle battery packs. The original data of the battery pack type characteristics of different models of electric vehicles, is the mean of the original data of the battery pack characteristics of different models of electric vehicles, is the standard deviation of the original data of battery pack type characteristics of different models of electric vehicles.

3. The intelligent management method for charging load balancing of electric vehicles according to claim 2, characterized in that: The method of obtaining the charging time of the electric vehicles to be charged according to the ampere-hour integration method and establishing the charging time matrix of the electric vehicles to be charged in the entire area specifically includes: Setting a hardware reading device for the status of the battery pack of the electric vehicle to be charged to obtain the status information of the battery pack of the electric vehicle to be charged; According to the usage of the battery pack of the electric vehicle to be charged, the battery usage and remaining power of the battery pack of the electric vehicle to be charged are obtained; Obtaining the amount of charge to be charged of the battery pack of the electric vehicle to be charged according to the battery usage and remaining capacity of the battery pack of the electric vehicle to be charged; According to the ampere-hour integration method, combined with the charging capacity of the battery pack of the electric vehicle to be charged, the charging time of the battery pack of the electric vehicle to be charged is determined; According to the charging time of the battery packs of the electric vehicles to be charged, the charging time of the electric vehicles to be charged in the entire area is obtained, and a charging time matrix of the electric vehicles to be charged in the entire area is established; The ampere-hour integration method expression is: In the formula, For charging electric vehicle battery packs The state of charge at the moment, is the charge value of the initial state of charge, The rated capacity of the battery pack for charging electric vehicles, For charging electric vehicle batteries The charging current at the moment, Charging time for charging electric vehicle battery packs.

4. The intelligent management method for charging load balancing of electric vehicles according to claim 3 is characterized in that: The setting of the rated power and the number of electric vehicle charging devices in the area according to the actual demand of the area where the electric vehicle charging device is set specifically includes: According to the actual demand of the area where the electric vehicle charging device is set, the actual power consumption and power demand of the area in different time periods are obtained; According to the electricity consumption in the area where the electric vehicle charging device is to be installed, combined with the specifications and standards for installing the electric vehicle charging device, the rated power and number of electric vehicle charging devices in the area are set; In combination with the electricity consumption in the area where the electric vehicle charging device is to be installed, an initial value of the rated charging power is set for each electric vehicle charging device on average; Based on big data, the maximum rated charging power threshold for electric vehicles is set in different time periods.

5. The intelligent management method for charging load balancing of electric vehicles according to claim 4, characterized in that: The fuzzy comprehensive evaluation matrix for determining the state of the battery pack of the electric vehicle to be charged according to the fuzzy comprehensive evaluation algorithm specifically includes: According to the characteristics of battery packs of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the whole area, and the maximum rated charging power threshold of electric vehicles in different time periods, the fuzzy subsets and membership degrees of the fuzzy comprehensive evaluation algorithm are determined; According to the ordered-even representation method, the fuzzy subset of the battery state of the electric vehicle to be charged is determined Expression of According to the fuzzy subsets and membership degree of the fuzzy comprehensive evaluation algorithm, the fuzzy subsets of the battery pack status of the electric vehicle to be charged are determined. The membership function of Based on big data analysis, the important weights of different data in the characteristics of battery packs of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the entire region, and the maximum rated charging power threshold of electric vehicles in different time periods are set. ; Domain according to the state of the battery pack of the electric vehicle to be charged , Degree of affiliation and the importance weights of different data , establish a fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicle to be charged; The relationship between the fuzzy subset and the membership degree of the fuzzy comprehensive evaluation algorithm is: In the formula, It is a fuzzy subset of the battery pack status of electric vehicles to be charged, which refers to the collection of all data on the battery pack type characteristics of different models of electric vehicles, the waiting time matrix of electric vehicles to be charged in the entire area, and the maximum rated charging power threshold of electric vehicles in different time periods. Fuzzy subset The domain of Fuzzy subset The membership function of Fuzzy subset The element value in for Fuzzy subset The degree of membership, that is, the degree of membership, is the interval range of the domain; The fuzzy subset of the battery state of the electric vehicle to be charged The expression is: In the formula, Fuzzy subset The element values, Fuzzy subset The Element value right degree of affiliation; The fuzzy subset of the battery state of the electric vehicle to be charged The membership function of is: In the formula, , It is a parameter for evaluating the critical value of the battery state of an electric vehicle to be charged.

6. The intelligent management method for charging load balancing of electric vehicles according to claim 5, characterized in that: The method based on the neural network algorithm and the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged is used to establish a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicle to be charged, and screen out the optimal charging power and charging time of the electric vehicle to be charged, which specifically includes: According to the fuzzy comprehensive evaluation matrix of the battery pack status of the electric vehicle to be charged, the data in the matrix is ​​normalized; The normalized fuzzy comprehensive evaluation matrix data of the battery pack state of the electric vehicle to be charged is used as the input matrix of the neural network algorithm model; Based on big data, combined with the fuzzy comprehensive evaluation matrix data of the battery pack status of the electric vehicle to be charged, the training sample set and the target sample set are established; A fuzzy neural network comprehensive evaluation model for the battery pack status of electric vehicles to be charged is established, and the optimal charging power and charging time of the electric vehicles to be charged are screened out by combining the training sample set and the target sample set.

7. The intelligent management method for electric vehicle charging load balancing according to claim 6, characterized in that: The establishment of a human-computer interaction platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles specifically includes: Establish a human-computer interaction platform to build a hardware environment for the operation of a fuzzy neural network comprehensive evaluation model for the battery pack status of electric vehicles to be charged; Based on the human-computer interaction platform, it is convenient for users to obtain the status of the electric vehicle charging device and the charging status of the electric vehicle through the display interface; Based on the human-computer interaction platform, it is used to update and optimize the parameters and sample data of the fuzzy neural network comprehensive evaluation model of the battery pack status of the electric vehicle to be charged; Based on the human-computer interaction platform, it is used to receive and feedback user experience and feedback to ensure good interoperability of electric vehicle charging.

8. An intelligent management system for electric vehicle charging load balancing, characterized in that: The intelligent management method for realizing the electric vehicle charging load balancing according to any one of claims 1 to 7 comprises: A feature extraction module, which is used to obtain the type features of battery packs of different models of electric vehicles based on big data, and to establish a type feature library of battery packs of electric vehicles; A hardware setting module, the hardware setting module is used to set the rated power and the number of electric vehicle charging devices in the area according to the actual demand of the area where the electric vehicle charging device is set; The optimal charging module is used to obtain the waiting time of the electric vehicles to be charged according to the ampere-hour integration method, and establish the waiting time matrix of the electric vehicles to be charged in the whole area; determine the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicles to be charged according to the fuzzy comprehensive evaluation algorithm; based on the neural network algorithm, combined with the fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicles to be charged, establish a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicles to be charged; The interactive platform module is used to establish a human-computer interactive platform for querying, booking, displaying and updating the queuing information and charging information of electric vehicles.

9. The intelligent management system for charging load balancing of electric vehicles according to claim 8, characterized in that: The optimal charging module comprises: A global charging time unit, which is used to obtain the waiting time of the electric vehicles to be charged according to the ampere-hour integration method, and establish a waiting time matrix of the electric vehicles to be charged in the whole area; A state evaluation unit, the state evaluation unit is used to determine a fuzzy comprehensive evaluation matrix of the state of the battery pack of the electric vehicle to be charged according to a fuzzy comprehensive evaluation algorithm; The optimal charging unit is used to establish a fuzzy neural network comprehensive evaluation model of the battery pack state of the electric vehicle to be charged based on a neural network algorithm and in combination with a fuzzy comprehensive evaluation matrix of the battery pack state of the electric vehicle to be charged.

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