Charging pile energy collaborative management method and system based on energy time-sharing metering

Abstract

The invention relates to the technical field of electric vehicle charging, in particular to a charging pile energy collaborative management method and system based on energy time-sharing metering, and the method comprises the steps: obtaining vehicle parameters of a target electric vehicle, carrying out the battery power consumption prediction of the target electric vehicle, obtaining the predicted electric quantity, carrying out the charging judgment of the target electric vehicle, and obtaining a judgment result; if it is confirmed that the judgment result is charging, the optimal charging pile in the large charging station is obtained, the optimal charging pile is used for charging the target electric vehicle, a connected electric vehicle is obtained, a charging interface of the connected electric vehicle is recognized, the temperature of the charging interface is monitored, the charging rate of the optimal charging pile is adjusted, secondary charging is conducted on the target electric vehicle, and a full-electricity electric vehicle is obtained. And calculating the charging cost of the fully-charged electric vehicle, and completing the energy collaborative management of the charging pile based on the energy time-sharing measurement based on the charging cost. The intelligent level of the electric vehicle charging technology can be improved, and dynamic sensing and real-time adjustment of vehicle requirements are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle charging, and in particular to a charging pile energy collaborative management method, system, electronic device and computer-readable storage medium based on energy time-sharing metering. Background Art

[0002] Energy time-of-use metering is a power management strategy that divides time into different periods and sets corresponding electricity prices or energy allocation strategies. It can effectively alleviate the load pressure on the power grid, improve energy utilization efficiency, reduce users' charging costs, and promote the consumption of renewable energy. This makes energy time-of-use metering have important application value in modern power systems.

[0003] Most current charging technologies rely on fixed charging times and rates, lacking the ability to intelligently analyze and dynamically adjust real-time electricity prices, grid loads, and vehicle demand. In addition, during the charging process, the temperature of the charging port is not monitored in real time, making it impossible to warn and handle safety hazards such as overheating in a timely manner, increasing the risk of the charging process. Therefore, how to improve the intelligence level of electric vehicle charging technology and realize dynamic perception and real-time adjustment of vehicle demand is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a charging pile energy collaborative management method based on energy time-sharing metering, the main purpose of which is to improve the intelligence level of electric vehicle charging technology, realize dynamic perception and real-time adjustment of vehicle demand, and reduce excessive consumption of time and human resources.

[0005] To achieve the above-mentioned purpose, the present invention provides a charging pile energy collaborative management method based on energy time-sharing metering, comprising: receiving an energy collaborative management instruction of a charging pile, wherein the energy collaborative management instruction is initiated by an energy collaborative control terminal; receiving a vehicle charging request instruction of a target electric car, and obtaining vehicle parameters of the target electric car based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type; confirming the battery type according to the vehicle parameters, and calculating the predicted power of the target electric car using a pre-constructed battery power consumption formula and the battery type; making a charging judgment on the target electric car according to the predicted power and the battery power, and obtaining a judgment result, wherein the judgment result includes: charging and not charging; if it is confirmed that the judgment result is charging, obtaining a large-scale charging The optimal charging pile in the power station predicts the charging time of the target electric vehicle according to the optimal charging pile and the battery type to obtain the predicted charging time; according to the predicted charging time, the target electric vehicle is charged by using the optimal charging pile to obtain a connected electric vehicle, the charging interface of the connected electric vehicle is identified, the temperature of the charging interface is monitored to obtain a temperature data set, and a temperature warning level is obtained according to the temperature data set; the charging rate of the optimal charging pile is adjusted according to the temperature warning level to obtain an adjusted charging pile; the target electric vehicle is recharged based on the adjusted charging pile to obtain a fully charged electric vehicle, the charging cost of the fully charged electric vehicle is calculated using a pre-built charging billing formula, and the energy collaborative management of the charging piles based on energy time-sharing metering is completed based on the charging cost.

[0006] Optionally, the energy collaborative management instruction of the receiving charging pile includes: starting the energy collaborative management system pre-installed on the energy collaborative control terminal; after successfully starting the energy collaborative management system, indexing the average charging power and the longest charging time of the target tram that needs to perform the charging operation in the energy collaborative management system; after successfully obtaining the average charging power and the longest charging time, using the energy collaborative management system to locate the position of the target tram to obtain the real-time position; confirming that the target tram leaves the exit of the pre-constructed charging area after completing charging to obtain the exit position; generating the energy collaborative management instruction according to the average charging power, the longest charging time, the real-time position and the exit position.

[0007] Optionally, the method of determining the battery type according to the vehicle parameters and calculating the predicted power of the target electric vehicle using a pre-built battery power consumption formula and the battery type includes: determining the vehicle type according to the vehicle parameters, determining the battery type of the target electric vehicle according to the vehicle type, and calculating the predicted power of the target electric vehicle using the battery power consumption formula and the battery type, wherein the vehicle types include: pure electric vehicles and hybrid electric vehicles, the battery types include: lithium-ion batteries and nickel-metal hydride batteries, and the battery power consumption formula is as follows: ,in, represents the predicted power of the target tram, represents the battery power of the target tram, represents the battery capacity of the target electric vehicle, Indicates the aging factor of the battery type, Indicates the charging efficiency coefficient of the battery type, represents the energy consumption mode coefficient of the target vehicle, represents the environmental factor, Indicates the predicted travel time of the target train.

[0008] Optionally, the obtaining of the optimal charging pile in the large charging station includes: obtaining a healthy charging pile; obtaining free parking space data using a pre-built parking space detector and a preset detection frequency, wherein the free parking space data includes: free parking space density and free parking space type; constructing a blank matrix, importing the free parking space data into the blank matrix, and obtaining a filled matrix, wherein the filled matrix is ​​as follows: , where 𝐺 represents the padding matrix, represents the density of free parking spaces detected at the first second, Indicates the type of free parking space detected at the first second. represents the density of free parking spaces detected at the nth second, Indicates the type of vacant parking space detected at the nth second; the density weight is calculated based on the filling matrix, and the density weight is imported into the density weight matrix, where the density weight matrix is ​​as follows: ,in, represents the density weight matrix, represents the density weight detected at the 1st second, Represents the density weight detected at the nth second; the type weight is calculated based on the filling matrix, and the type weight is imported into the type weight matrix, where the type weight matrix is ​​as follows: ,in, represents the type weight matrix, Indicates the type weight detected at the 1st second, Represents the type weight detected at the nth second; extract row vectors from the filling matrix in turn, and use the extracted row vectors to extract the corresponding density weights and type weights from the density weight matrix and the type weight matrix to calculate the evaluation scores corresponding to the extracted row vectors, summarize the evaluation scores, and obtain multiple evaluation scores; sort the multiple evaluation scores in descending order to obtain multiple sorting scores, and extract a high-order score array from the multiple sorting scores, wherein the high-order score array includes: the three sorting scores with the largest sorting scores among the multiple sorting scores; perform the following operations on each high-order score in the high-order score array: obtain the target charging pile position according to the healthy charging pile and the high-order score, obtain the target tram position using the pre-built GPS module, and calculate the shortest moving path according to the target charging pile position and the target tram position; summarize the shortest moving paths to obtain multiple shortest moving paths, obtain the best path according to the multiple shortest moving paths, and obtain the optimal charging pile based on the best path.

[0009] Optionally, obtaining a healthy charging pile includes: obtaining a healthy charging pile using a pre-built charging pile health model; wherein the charging pile health model is as follows: ,in, Indicates the aging coefficient of the charging pile, Indicates the aging coefficient of components in the charging pile, Indicates the aging health index of the charging pile at a certain moment. Indicates the life of components in the charging pile. represents the load correction factor, represents the environmental correction factor, It represents a logarithmic function with the natural constant e as the base; if it is confirmed that the aging coefficient of the charging pile is less than or equal to 0.5, the charging pile is confirmed as a healthy charging pile.

[0010] Optionally, the shortest moving path is calculated according to the target charging pile position and the target tram position, including: constructing a global blank grid map based on the large charging station, inputting the charging pile position and the target tram position into the global blank grid map, and obtaining the target tram grid and the charging pile grid, wherein the target tram grid and the charging pile grid correspond to one grid respectively; obtaining the four sides of the target tram grid, and drawing a perpendicular line for each of the four sides to obtain four perpendicular lines, wherein the intersection of the side of the target tram grid and the perpendicular line is the foot of the perpendicular; performing the following operations on each of the four perpendicular lines: taking the perpendicular line and the foot of the perpendicular as a reference, generating a vector along the perpendicular line toward the direction of the outer layer of the pre-constructed target tram grid, and taking the direction of the vector as a reference, obtaining the next grid adjacent to the target tram grid to obtain a pending grid; calculating the Manhattan distance between the pending grid and the charging pile grid to obtain an initial distance; summarizing the initial distance, obtain an initial distance set, mark the calculated grids for each undetermined grid and the target tram grid corresponding to each initial distance in the initial distance set, and obtain a marked grid set; extract the initial distance with the smallest value from the initial distance set, and use the undetermined grid corresponding to the initial distance with the smallest value as the moving target to obtain the target moving grid, mark the moving path grid for the target moving grid, and obtain the target marked grid; obtain the target adjacent grid set based on the target moving grid, remove one or more marked grids in the marked grid set from the target adjacent grid set, and obtain the to-be-calculated grid set, use the to-be-calculated grid set as the initial distance set, return to the step of marking the calculated grids for each undetermined grid and the target tram grid corresponding to each initial distance in the initial distance set, until the Manhattan distance between the target moving grid and the charging pile grid is zero, and summarize the target marked grids to obtain the shortest moving path.

[0011] Optionally, the calculating the Manhattan distance between the grid to be determined and the charging pile grid includes: constructing a reference coordinate system, obtaining the coordinates of the grid to be determined and the grid coordinates of the charging pile based on the reference coordinate system; and calculating the Manhattan distance between the coordinates of the grid to be determined and the grid coordinates of the charging pile using a pre-constructed Manhattan distance formula, wherein the Manhattan distance formula is as follows: ,in, represents the Manhattan distance, ( represents the undetermined grid coordinates, ( represents the coordinates of the charging pile grid, Indicates the horizontal position of the pending grid coordinates or the charging pile grid coordinates. Indicates the vertical position of the pending grid coordinates or the charging pile grid coordinates.

[0012] Optionally, the method identifies the charging interface connected to the electric vehicle, monitors the temperature of the charging interface, obtains a temperature data set, performs temperature warning on the connected electric vehicle according to the temperature data set, and obtains a temperature warning level, wherein a monitoring frequency is preset, including: obtaining multiple historical detection time periods, extracting historical detection time periods in sequence from multiple historical detection time periods, and performing the following operations on the extracted historical detection time periods: detecting the charging interface based on the extracted historical detection time periods to obtain a historical charging data set, wherein the historical charging data set includes: historical charging time, historical charging rate, historical ambient temperature, historical battery type and historical interface temperature; deduplicating the historical charging data set to obtain a deduplicated data set, and normalizing the deduplicated data set to obtain a normalized data set; using the normalized data set to train a pre-constructed random forest model to obtain an initial random forest model; summarizing the initial random forest model to obtain multiple initial random forest models, wherein the initial random forest model corresponds one-to-one to the historical detection time period; monitoring the charging data of the charging interface using a preset monitoring frequency, wherein the charging data includes: charging time, charging rate, ambient temperature, battery type and interface temperature; and evaluating the charging data using multiple initial random forest models to obtain a temperature warning level.

[0013] Optionally, the use of a pre-built charging billing formula to calculate the charging cost of a fully charged electric vehicle includes: obtaining the charging cost on normal working days and the charging cost on holidays based on the energy collaborative management system; wherein the charging billing formula is as follows: , ,in, represents the charging cost for a normal working day, It represents the basic charging electricity price on a normal working day. Indicates the actual charging time of the target electric vehicle, represents the user loyalty discount, Indicates the charging cost during holidays. Indicates the basic charging unit price during holidays. Indicates holiday surcharges. Indicates the additional charge during peak hours. Indicates discounts during off-peak hours, represents the seasonal adjustment coefficient, The peak holiday season, Indicates peak period, Indicates off-peak hours.

[0014] To achieve the above-mentioned purpose, the present invention also provides a charging pile energy collaborative management system based on energy time-sharing metering, including: a vehicle parameter acquisition module, used to receive the energy collaborative management instruction of the charging pile, wherein the energy collaborative management instruction is initiated by the energy collaborative control terminal, and receives the vehicle charging request instruction of the target electric car, and obtains the pre-constructed vehicle parameters of the target electric car based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type; an electric car charging determination module, used to confirm the battery type according to the vehicle parameters, calculate the predicted power of the target electric car using the pre-constructed battery power consumption formula and the battery type, and perform charging determination on the target electric car according to the predicted power and the battery power to obtain a determination result, wherein the determination result includes: charging or not charging; a charging pile management module, used to determine if a charging pile is confirmed to be out of service. The judgment result is charging, and the optimal charging pile in the large charging station is obtained. The charging time of the target electric vehicle is predicted according to the optimal charging pile and the battery type to obtain the predicted charging time. According to the predicted charging time, the target electric vehicle is charged by using the optimal charging pile to obtain a connected electric vehicle, and the charging interface of the connected electric vehicle is identified. The temperature of the charging interface is monitored to obtain a temperature data set, and a temperature warning is performed on the connected electric vehicle according to the temperature data set to obtain a temperature warning level, wherein a monitoring frequency is preset, and the charging rate of the optimal charging pile is adjusted according to the temperature warning level to obtain an adjusted charging pile; a charging cost settlement module is used to perform secondary charging on the target electric vehicle based on the adjusted charging pile to obtain a fully charged electric vehicle, and the charging cost of the fully charged electric vehicle is calculated using a pre-built charging billing formula, and based on the charging cost, energy collaborative management of charging piles based on energy time-sharing metering is completed.

[0015] In order to solve the above problems, the present invention also provides an electronic device, which includes: a memory storing at least one instruction; a processor executing the instructions stored in the memory to implement the above-mentioned charging pile energy collaborative management method based on energy time-sharing metering.

[0016] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned charging pile energy collaborative management method based on energy time-sharing metering.

[0017] The present invention is to solve the problem described in the background technology. The present invention receives an energy collaborative management instruction of a charging pile, wherein the energy collaborative management instruction is initiated by an energy collaborative control terminal. By receiving the instruction initiated by the energy collaborative control terminal, the energy collaborative management system can respond to and adjust the charging strategy in real time, ensure that the charging process is coordinated with the overall energy management, and improve energy utilization efficiency. The vehicle charging request instruction of the target electric car is received, and the vehicle parameters of the target electric car are obtained based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type. The present invention can timely obtain the charging demand of the electric car, provide basic data for subsequent charging planning, ensure the timeliness and pertinence of the charging service, confirm the battery type according to the vehicle parameters, calculate the predicted power of the target electric car using the pre-constructed battery consumption formula and the battery type, and make a charging judgment on the target electric car according to the predicted power and the battery power to obtain a judgment result, wherein the judgment result includes: charging or not charging. The present invention can judge whether charging is needed by predicting the battery power consumption, avoid unnecessary charging operations, save energy and time, and ensure that the target electric car is charged when needed. If the determination result is confirmed to be charging, the optimal charging pile in the large charging station is obtained, and the charging time of the target electric vehicle is predicted according to the optimal charging pile and the battery type to obtain the predicted charging time. The present invention can improve the charging efficiency, reduce the charging time, and improve the user experience by selecting the optimal charging pile. According to the predicted charging time, the target electric vehicle is charged by using the optimal charging pile to obtain a connected electric vehicle, identify the charging interface of the connected electric vehicle, monitor the temperature of the charging interface, obtain a temperature data set, and perform a temperature warning on the connected electric vehicle according to the temperature data set to obtain a temperature warning level, wherein the monitoring frequency is preset. The present invention can timely discover overheating problems by real-time monitoring of the charging interface temperature to ensure the safety of the charging process, adjust the charging rate of the optimal charging pile according to the temperature warning level, and obtain an adjusted charging pile. Through the temperature warning, the system of the present invention can warn of potential safety problems in advance and take measures in time. The target electric vehicle is recharged based on the adjusted charging pile to obtain a fully charged electric vehicle, and the charging cost of the fully charged electric vehicle is calculated using a pre-constructed charging billing formula. Based on the charging cost, the energy collaborative management of charging piles based on energy time-sharing metering is completed. Therefore, the present invention can improve the intelligence level of electric vehicle charging technology, realize dynamic perception and real-time adjustment of vehicle demand, and reduce excessive consumption of time and human resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A schematic diagram of a flow chart of a charging pile energy collaborative management method based on energy time-sharing metering provided in one embodiment of the present invention; Figure 2 A functional module diagram of a charging pile energy collaborative management system based on energy time-sharing metering provided by an embodiment of the present invention; Figure 3A schematic diagram of the structure of an electronic device for implementing the charging pile energy collaborative management method based on energy time-sharing metering provided in one embodiment of the present invention.

[0019] Explanation of the reference numerals: 1. electronic device; 10. processor; 11. memory; 12. bus.

[0020] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0022] The embodiment of the present application provides a method for collaborative energy management of charging piles based on time-sharing energy metering. The execution subject of the method for collaborative energy management of charging piles based on time-sharing energy metering includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for collaborative energy management of charging piles based on time-sharing energy metering can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0023] Reference Figure 1 As shown, it is a flow chart of a charging pile energy collaborative management method based on energy time-sharing metering provided by an embodiment of the present invention. In this embodiment, the charging pile energy collaborative management method based on energy time-sharing metering includes: S1, receiving an energy collaborative management instruction of a charging pile, wherein the energy collaborative management instruction is initiated by an energy collaborative control terminal.

[0024] In detail, the energy collaborative management instruction of the receiving charging pile includes: starting the energy collaborative management system pre-installed on the energy collaborative control terminal; after successfully starting the energy collaborative management system, indexing the average charging power and the longest charging time of the target tram that needs to perform the charging operation in the energy collaborative management system; after successfully obtaining the average charging power and the longest charging time, using the energy collaborative management system to locate the position of the target tram to obtain the real-time position; confirming that the target tram leaves the exit of the pre-constructed charging area after completing charging to obtain the exit position; generating the energy collaborative management instruction according to the average charging power, the longest charging time, the real-time position and the exit position.

[0025] It is understood that the energy collaborative management system is an integrated system for coordinating and managing energy distribution and charging of multiple charging piles. Indexing refers to the operation of associating the GPS module of the target electric vehicle through the energy collaborative management system for positioning.

[0026] It should be explained that in the embodiment of the present invention, the energy collaborative control terminal is pre-installed in mobile devices such as mobile phones and tablets. For example, after Xiao Zhang traveled a long distance, the target tram driven by Xiao Zhang needed to be charged, so he came to a charging area including a large number of charging piles, and in the embodiment of the present invention, the charging area is characterized by a large area and a large number of charging piles, such as the charging area Xiao Zhang came to is 10 acres. Therefore, in order to find the optimal charging pile, Xiao Zhang opened the energy collaborative management system pre-installed on the mobile phone and entered the energy collaborative management instruction in the energy collaborative management system.

[0027] S2. Receive a vehicle charging request instruction of a target electric vehicle, and obtain vehicle parameters of the target electric vehicle based on the energy collaborative management instruction and the vehicle charging request instruction.

[0028] Specifically, the vehicle parameters include: vehicle type, battery power and battery type.

[0029] It should be explained that the vehicle charging request instruction in the embodiment of the present invention refers to an instruction issued by the target electric vehicle to request charging. The battery power refers to the power remaining in the current battery of the target electric vehicle.

[0030] For example, when Xiao Zhang is driving the target tram, the on-board system of the target tram automatically detects that the battery power drops below 30%. At this time, the navigation system of the target tram recognizes that there is a large charging station ahead, and the target tram automatically issues a charging request instruction.

[0031] S3. Determine the battery type according to the vehicle parameters, and calculate the predicted power of the target electric vehicle using the pre-built battery power consumption formula and battery type.

[0032] It should be explained that the step of confirming the battery type according to the vehicle type is as follows: if the target electric vehicle is a pure electric vehicle, the corresponding battery type is a lithium-ion battery; if the target electric vehicle is a hybrid electric vehicle, the corresponding battery type is a nickel-metal hydride battery.

[0033] In detail, the method of determining the battery type according to the vehicle parameters and calculating the predicted power of the target electric vehicle using the pre-built battery power consumption formula and the battery type includes: determining the vehicle type according to the vehicle parameters, determining the battery type of the target electric vehicle according to the vehicle type, and calculating the predicted power of the target electric vehicle using the battery power consumption formula and the battery type, wherein the vehicle types include: pure electric vehicles and hybrid electric vehicles, the battery types include: lithium-ion batteries and nickel-metal hydride batteries, and the battery power consumption formula is as follows: ,in, represents the predicted power of the target tram, represents the battery power of the target tram, represents the battery capacity of the target electric vehicle, Indicates the aging factor of the battery type, Indicates the charging efficiency coefficient of the battery type, represents the energy consumption mode coefficient of the target vehicle, represents the environmental factor, Indicates the predicted travel time of the target train.

[0034] It should be explained that a pure electric vehicle is a vehicle that relies entirely on battery power. A hybrid vehicle is a vehicle that combines an internal combustion engine and an electric motor, and can use both power sources simultaneously or separately. A lithium-ion battery is a battery that uses lithium ions to move between positive and negative electrodes to store and release energy. A nickel-metal hydride battery is a battery that uses nickel metal hydride as an electrode material.

[0035] It is understandable that the predicted power refers to the prediction of whether the battery power is sufficient to support driving to the destination. The battery power refers to the current remaining power of the target tram. The battery capacity refers to the total energy storage capacity of the battery pack of the target tram. The aging coefficient of the battery type refers to the coefficient of the performance degradation of the lithium-ion battery or the nickel-metal hydride battery during use. The charging efficiency coefficient refers to the coefficient of the efficiency of the lithium-ion battery or the nickel-metal hydride battery in converting electrical energy into internal energy of the battery during the charging process. The energy consumption mode coefficient refers to the coefficient of the energy consumption of the target tram under different vehicle types. The predicted driving time refers to the approximate time required for the target tram to travel to the destination.

[0036] It should be explained that the environmental factor refers to the coefficient of external environmental factors on battery consumption. For example, in cold weather, the chemical reaction rate of lithium-ion batteries will decrease, resulting in a decrease in the total available capacity of lithium-ion batteries and a decrease in the discharge rate. In a high temperature environment, the chemical reaction inside the lithium-ion battery is accelerated, causing the lithium-ion battery to overheat, making the lithium-ion battery more susceptible to aging and shortening the life of the lithium-ion battery.

[0037] S4. Determine whether to charge the target electric vehicle according to the predicted electric power and the battery power, and obtain a determination result, wherein the determination result includes: charging or not charging.

[0038] It should be explained that the charging determination described in the embodiment of the present invention refers to the operation of determining whether the target electric vehicle needs to be charged based on the relationship between the predicted power level and the battery power level.

[0039] For example, Xiao Zhang drives a pure electric car with a battery charge of 20. The battery consumption formula predicts that the predicted battery charge when the car reaches the destination is 30, and the preset battery threshold is 35. Since the predicted battery charge of 30 is lower than the set threshold of 35, it means that even if the estimated battery can provide 30 effective power, it is still not enough to support the vehicle to drive safely to the destination. Therefore, the judgment result is charging.

[0040] S5. If the determination result is confirmed to be charging, obtain the optimal charging pile in the large charging station.

[0041] In detail, the method of obtaining the optimal charging pile in the large charging station includes: obtaining a healthy charging pile; obtaining free parking space data using a pre-built parking space detector and a preset detection frequency, wherein the free parking space data includes: free parking space density and free parking space type; constructing a blank matrix, importing the free parking space data into the blank matrix, and obtaining a filled matrix, wherein the filled matrix is ​​as follows: , where 𝐺 represents the padding matrix, represents the density of free parking spaces detected at the first second, Indicates the type of free parking space detected at the first second. represents the density of free parking spaces detected at the nth second, Indicates the type of vacant parking space detected at the nth second; the density weight is calculated based on the filling matrix, and the density weight is imported into the density weight matrix, where the density weight matrix is ​​as follows: ,in, represents the density weight matrix, represents the density weight detected at the 1st second, Represents the density weight detected at the nth second; the type weight is calculated based on the filling matrix, and the type weight is imported into the type weight matrix, where the type weight matrix is ​​as follows: ,in, represents the type weight matrix, Indicates the type weight detected at the 1st second, Represents the type weight detected at the nth second; extract row vectors from the filling matrix in turn, and use the extracted row vectors to extract the corresponding density weights and type weights from the density weight matrix and the type weight matrix to calculate the evaluation scores corresponding to the extracted row vectors, summarize the evaluation scores, and obtain multiple evaluation scores; sort the multiple evaluation scores in descending order to obtain multiple sorting scores, and extract a high-order score array from the multiple sorting scores, wherein the high-order score array includes: the three sorting scores with the largest sorting scores among the multiple sorting scores; perform the following operations on each high-order score in the high-order score array: obtain the target charging pile position according to the healthy charging pile and the high-order score, obtain the target tram position using the pre-built GPS module, and calculate the shortest moving path according to the target charging pile position and the target tram position; summarize the shortest moving paths to obtain multiple shortest moving paths, obtain the best path according to the multiple shortest moving paths, and obtain the optimal charging pile based on the best path.

[0042] Importantly, the detection frequency refers to one detection per second. The GPS module refers to the module used by the target tram to receive the global positioning system signal. Extracting a row vector refers to the operation of extracting a row of elements from the padding matrix. The type weight refers to a weight corresponding to each type of free parking space in the padding matrix. The density weight refers to a weight corresponding to each density of free parking spaces in the padding matrix.

[0043] It should be explained that a blank matrix refers to a matrix in which no data is filled. A filled matrix refers to a matrix obtained after the free parking space data is imported into the blank matrix. The steps of calculating the free parking space density weight based on the filled matrix and calculating the free type density weight based on the filled matrix are: first calculate the information entropy value of all free parking space densities and all free parking space types, and use the information entropy value to calculate the weight of each free parking space density and each free parking space type, wherein the calculation formula of the information entropy is as follows: ,in, Indicates filling the first The information entropy value of a vector is represents the total number of columns filled in the matrix, represents the vector extracted from the padding matrix, Represents an index, Represents the logarithm with base e.

[0044] The weight calculation formula is as follows: ,in, Indicates filling the first The weight of a vector, represents the total number of columns filled in the matrix, Represents the sum of the information entropy values ​​of each column vector in the padded matrix.

[0045] It should be explained that the parking space detector refers to a geomagnetic parking space sensor. The geomagnetic parking space sensor described in the embodiment of the present invention is a prior art and will not be described in detail here. The density of free parking spaces refers to the number of free parking spaces per 100 square meters. The type of free parking spaces means that different types of cars have different charging places. For example, the parking space type for large buses is 1, and the parking space type for small electric vehicles is 2. Descending sorting refers to arranging the evaluation scores in order from the maximum value to the minimum value.

[0046] In detail, the obtaining of a healthy charging pile includes: obtaining a healthy charging pile using a pre-built charging pile health model; wherein the charging pile health model is as follows: ,in, Indicates the aging coefficient of the charging pile, Indicates the aging coefficient of components in the charging pile, Indicates the aging health index of the charging pile at a certain moment. Indicates the life of components in the charging pile. represents the load correction factor, represents the environmental correction factor, It represents a logarithmic function with the natural constant e as the base; if it is confirmed that the aging coefficient of the charging pile is less than or equal to 0.5, the charging pile is confirmed as a healthy charging pile.

[0047] It should be explained that the aging coefficient of the charging pile refers to the coefficient of a quantitative indicator of the health of the charging pile. The aging coefficient of the components refers to the coefficient of the aging degree of each component in the charging pile. The life of the components refers to the designed life of each component in the charging pile. The environmental correction coefficient refers to the coefficient of the influence of the environment in which the charging pile is located on the aging rate. The load correction coefficient refers to the coefficient of the influence of the operating state of the charging pile under different load conditions on the aging rate, among which the load correction coefficient is obtained by the following formula. ,in, represents the load correction factor, Indicates standard load, Indicates a test load.

[0048] For example, if the test load of the charging pile is measured to be 2% and the standard load is 2%, the load correction factor is calculated by the following formula: , where 1 represents the load correction factor.

[0049] Furthermore, the shortest moving path is calculated according to the target charging pile position and the target tram position, including: constructing a global blank grid map based on the large charging station, inputting the charging pile position and the target tram position into the global blank grid map, and obtaining the target tram grid and the charging pile grid, wherein the target tram grid and the charging pile grid correspond to one grid respectively; obtaining the four sides of the target tram grid, drawing a perpendicular line for each of the four sides, and obtaining four perpendicular lines, wherein the intersection of the side of the target tram grid and the perpendicular line is the foot of the perpendicular; performing the following operations on each of the four perpendicular lines: taking the perpendicular line and the foot of the perpendicular as a reference, generating a vector along the perpendicular line toward the direction of the outer layer of the pre-constructed target tram grid, and taking the direction of the vector as a reference, obtaining the next grid adjacent to the target tram grid, and obtaining a pending grid; calculating the Manhattan distance between the pending grid and the charging pile grid, and obtaining an initial distance; summarizing the initial distances; and summarizing the initial distances. distance, obtain an initial distance set, mark the calculated grids for each undetermined grid and the target tram grid corresponding to each initial distance in the initial distance set, and obtain a marked grid set; extract the initial distance with the smallest value from the initial distance set, and use the undetermined grid corresponding to the initial distance with the smallest value as the moving target to obtain the target moving grid, mark the moving path grid for the target moving grid, and obtain the target marked grid; obtain the target adjacent grid set based on the target moving grid, remove one or more marked grids in the marked grid set from the target adjacent grid set, and obtain the to-be-calculated grid set, use the to-be-calculated grid set as the initial distance set, return to the step of marking the calculated grids for each undetermined grid and the target tram grid corresponding to each initial distance in the initial distance set, until the Manhattan distance between the target moving grid and the charging pile grid is zero, and summarize the target marked grids to obtain the shortest moving path.

[0050] It should be explained that the global blank grid map refers to a blank grid map drawn according to the area of ​​large charging stations. The target tram grid refers to a grid occupied by the target tram in the global blank grid map. The charging pile grid refers to a grid occupied by the charging pile in the global blank grid map. The benchmark is a standard for a specific direction used for reference. The target tram grid outer layer refers to the outside of the target tram grid. A vector refers to the direction of the next grid adjacent to the target tram grid, extending from a perpendicular line of an edge of the target tram grid, along this perpendicular line. The pending grid refers to the grid that is temporarily determined as the next possible move of the target tram during the calculation process. Manhattan distance refers to the sum of the horizontal and vertical distances from the points of the pending grid to the points of the charging pile grid in a two-dimensional grid.

[0051] It is understandable that the initial distance refers to the distance between the pending grid and the charging pile grid. The initial distance set refers to the set of all pending grids and their corresponding initial distances. The identified grid set refers to the set in which each grid and its corresponding initial distance are identified and recorded in the initial distance set. The target moving grid refers to the grid with the smallest initial distance in the identified grid set, which is selected as the next moving target. The target identified grid refers to the grid that is identified in the moving path of the target moving grid after being selected as the target moving grid. The target adjacent grid set refers to the set composed of all target moving grids. The grid set to be calculated refers to the set of all remaining grids after the identified grids are removed from the target adjacent grid set.

[0052] Importantly, the step of marking each corresponding grid in the initial distance set and the target tram grid refers to the operation of filling each corresponding grid in the initial distance set and the target tram grid with red. The step of marking the target moving grid refers to the operation of filling the target moving grid with green.

[0053] Importantly, the calculation of the Manhattan distance between the grid to be determined and the charging pile grid includes: constructing a reference coordinate system, obtaining the coordinates of the grid to be determined and the grid coordinates of the charging pile based on the reference coordinate system; and calculating the Manhattan distance between the coordinates of the grid to be determined and the grid coordinates of the charging pile using a pre-constructed Manhattan distance formula, wherein the Manhattan distance formula is as follows: ,in, represents the Manhattan distance, ( represents the undetermined grid coordinates, ( represents the coordinates of the charging pile grid, Indicates the horizontal position of the pending grid coordinates or the charging pile grid coordinates. Indicates the vertical position of the pending grid coordinates or the charging pile grid coordinates.

[0054] It should be explained that the reference coordinate system described in the embodiment of the present invention refers to a two-dimensional reference coordinate system, with the upper left corner of the global blank grid map as the origin of the two-dimensional reference coordinate system, the horizontal right is the X axis, and the horizontal left is the Y axis. Manhattan distance refers to the distance calculated using the Manhattan distance formula.

[0055] S6. Predicting the charging time of the target electric vehicle according to the optimal charging pile and battery type to obtain a predicted charging time.

[0056] Importantly, the prediction of charging time for the target electric vehicle according to the optimal charging pile and battery type to obtain the predicted charging time includes: obtaining the maximum power and charging efficiency of the optimal charging pile; calculating the required charging amount according to the battery capacity and battery power, and calculating the actual charging power according to the maximum power of the optimal charging pile and the charging efficiency of the optimal charging pile; calculating the initial charging time according to the required charging amount and the actual charging power, and calculating the later charging time according to the initial charging time; obtaining the predicted charging time based on the initial charging time and the later charging time, wherein the calculation formula for the predicted charging time is: ,in, Indicates the predicted charging time. represents the maximum power of the optimal charging pile, represents the charging efficiency of the optimal charging pile, Indicates the initial stage.

[0057] It should be explained that the initial stage refers to the stage from 0% to 80% for lithium-ion batteries.

[0058] S7. Based on the predicted charging time, the target electric vehicle is charged using the optimal charging pile to obtain a connected electric vehicle, the charging interface of the connected electric vehicle is identified, the temperature of the charging interface is monitored to obtain a temperature data set, and a temperature warning is performed on the connected electric vehicle based on the temperature data set to obtain a temperature warning level, wherein a monitoring frequency is preset.

[0059] It is understood that a connected electric car refers to an electric car that is being charged using an optimal charging station.

[0060] In detail, the method identifies the charging interface connected to the electric vehicle, monitors the temperature of the charging interface, obtains a temperature data set, performs a temperature warning on the connected electric vehicle according to the temperature data set, and obtains a temperature warning level, wherein a monitoring frequency is preset, including: obtaining multiple historical detection time periods, extracting historical detection time periods in sequence from multiple historical detection time periods, and performing the following operations on the extracted historical detection time periods: detecting the charging interface based on the extracted historical detection time periods to obtain a historical charging data set, wherein the historical charging data set includes: historical charging time, historical charging rate, historical ambient temperature, historical battery type and historical interface temperature; deduplicating the historical charging data set to obtain a deduplicated data set, and normalizing the deduplicated data set to obtain a normalized data set; using the normalized data set to train a pre-constructed random forest model to obtain an initial random forest model; summarizing the initial random forest model to obtain multiple initial random forest models, wherein the initial random forest model corresponds one-to-one to the historical detection time period; monitoring the charging data of the charging interface using a preset monitoring frequency, wherein the charging data includes: charging time, charging rate, ambient temperature, battery type and interface temperature; and evaluating the charging data using multiple initial random forest models to obtain a temperature warning level.

[0061] It should be explained that multiple historical detection time periods refer to time periods in which the charging interface was detected once every minute in the past. The step of deduplicating the historical charging data set is: removing duplicate data in the historical charging data set. The monitoring frequency refers to monitoring once a minute. The historical charging time, historical charging rate, historical ambient temperature, historical battery type and historical interface temperature respectively refer to the charging time, charging rate, ambient temperature, battery type and interface temperature obtained in the past time period. The random forest model refers to a model used to analyze and predict the temperature of the charging interface. The random forest model is a prior art and will not be described here. The deduplicated data set refers to a set of historical charging data sets detected in different historical detection time periods after removing duplicate charging data.

[0062] S8. Adjust the charging rate of the optimal charging pile according to the temperature warning level to obtain an adjusted charging pile.

[0063] Specifically, the temperature warning levels include: level one warning, level two warning and level three warning.

[0064] It should be explained that the adjustment described in the embodiment of the present invention refers to the operation of adjusting the charging power of the optimal charging pile according to the temperature warning level. The adjusted charging pile refers to the charging pile after the optimal charging pile has been adjusted. The first-level warning means that the charging interface temperature is in the preset first temperature range, indicating that the charging interface temperature is normal and no measures are required. The second-level warning means that the charging interface temperature is in the preset second temperature range, indicating that the charging interface temperature is slightly high, and the charging power level of the optimal charging pile is reduced. The third-level warning means that the charging interface temperature is in the preset third temperature range, indicating that the charging interface temperature is too high, and charging is stopped immediately and the level of the interface is checked.

[0065] Exemplarily, the preset first temperature range is [20, 40], the preset second temperature range is [41, 50], and the preset third temperature range is [51, 60]. Xiao Zhang detects that the charging interface temperature is 42, and issues a second-level warning to reduce the charging power of the optimal charging pile.

[0066] S9. Based on the adjustment of the charging pile, the target electric vehicle is recharged to obtain a fully charged electric vehicle. The charging cost of the fully charged electric vehicle is calculated using a pre-built charging billing formula. Based on the charging cost, the energy collaborative management of the charging pile based on energy time-sharing metering is completed.

[0067] Importantly, secondary charging refers to the operation of charging after the first charge is completed, when the battery power is not fully charged, using the charging pile after the charging rate is adjusted. A fully charged electric car refers to an electric car whose battery power has reached 100% after charging.

[0068] In detail, the method of calculating the charging cost of a fully charged electric vehicle using a pre-built charging billing formula includes: obtaining the charging cost on normal working days and the charging cost on holidays based on the energy collaborative management system; wherein the charging billing formula is as follows: , ,in, represents the charging cost for a normal working day, It represents the basic charging electricity price on a normal working day. Indicates the actual charging time of the target electric vehicle, represents the user loyalty discount, Indicates the charging cost during holidays. Indicates the basic charging unit price during holidays. Indicates holiday surcharges. Indicates the additional charge during peak hours. Indicates discounts during off-peak hours, represents the seasonal adjustment coefficient, The peak holiday season, Indicates peak period, Indicates off-peak hours.

[0069] It should be explained that normal working days refer to working days from Monday to Friday, excluding statutory holidays and rest days. The basic charging electricity price refers to the price of charging fees on normal working days. The actual charging time refers to the time taken by the target electric vehicle from the beginning to the end of charging. The user loyalty discount refers to the discount given to users for long-term use of charging services. Holiday surcharges refer to additional fees charged in addition to the electricity price during statutory holidays or special holidays. Peak period surcharges refer to additional charges for electricity resources during periods of greater demand. Off-peak period discounts refer to discounts provided for charging during off-peak periods when electricity prices are lower. The seasonal adjustment factor refers to the adjustment factor based on seasonal changes.

[0070] For example, in winter, due to the increased demand for heating, electricity demand is relatively high, and the seasonal adjustment coefficient is set to 1.1. In spring and summer, the temperature is suitable, electricity demand is relatively stable, and the seasonal adjustment coefficient is set to 1.0. In summer, due to the use of refrigeration equipment such as air conditioners, electricity demand increases significantly, and the seasonal adjustment coefficient is set to 1.2.

[0071] The present invention is to solve the problem described in the background technology. The present invention receives an energy collaborative management instruction of a charging pile, wherein the energy collaborative management instruction is initiated by an energy collaborative control terminal. By receiving the instruction initiated by the energy collaborative control terminal, the energy collaborative management system can respond to and adjust the charging strategy in real time, ensure that the charging process is coordinated with the overall energy management, and improve energy utilization efficiency. The vehicle charging request instruction of the target electric car is received, and the vehicle parameters of the target electric car are obtained based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type. The present invention can timely obtain the charging demand of the electric car, provide basic data for subsequent charging planning, ensure the timeliness and pertinence of the charging service, confirm the battery type according to the vehicle parameters, calculate the predicted power of the target electric car using the pre-constructed battery consumption formula and the battery type, and make a charging judgment on the target electric car according to the predicted power and the battery power to obtain a judgment result, wherein the judgment result includes: charging or not charging. The present invention can judge whether charging is needed by predicting the battery power consumption, avoid unnecessary charging operations, save energy and time, and ensure that the target electric car is charged when needed. If the determination result is confirmed to be charging, the optimal charging pile in the large charging station is obtained, and the charging time of the target electric vehicle is predicted according to the optimal charging pile and the battery type to obtain the predicted charging time. The present invention can improve the charging efficiency, reduce the charging time, and improve the user experience by selecting the optimal charging pile. According to the predicted charging time, the target electric vehicle is charged by using the optimal charging pile to obtain a connected electric vehicle, identify the charging interface of the connected electric vehicle, monitor the temperature of the charging interface, obtain a temperature data set, and perform a temperature warning on the connected electric vehicle according to the temperature data set to obtain a temperature warning level, wherein the monitoring frequency is preset. The present invention can timely discover overheating problems by real-time monitoring of the charging interface temperature to ensure the safety of the charging process, adjust the charging rate of the optimal charging pile according to the temperature warning level, and obtain an adjusted charging pile. Through the temperature warning, the system of the present invention can warn of potential safety problems in advance and take measures in time. The target electric vehicle is recharged based on the adjusted charging pile to obtain a fully charged electric vehicle, and the charging cost of the fully charged electric vehicle is calculated using a pre-constructed charging billing formula. Based on the charging cost, the energy collaborative management of charging piles based on energy time-sharing metering is completed. Therefore, the present invention can improve the intelligence level of electric vehicle charging technology, realize dynamic perception and real-time adjustment of vehicle demand, and reduce excessive consumption of time and human resources.

[0072] like Figure 2 , which is a functional module diagram of a charging pile energy collaborative management system based on energy time-sharing metering provided by an embodiment of the present invention.

[0073] The charging pile energy collaborative management system 100 based on energy time-sharing metering of the present invention can be installed in an electronic device. According to the functions implemented, the charging pile energy collaborative management system 100 based on energy time-sharing metering can include a vehicle parameter acquisition module 101, a tram charging determination module 102, a charging pile management module 103 and a charging fee settlement module 104. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and is stored in the memory of the electronic device; the vehicle parameter acquisition module 101 is used to receive the energy collaborative management instruction of the charging pile, wherein the energy collaborative management instruction is initiated by the energy collaborative control terminal, and the vehicle charging request instruction of the target electric vehicle is received, and the vehicle parameters of the target electric vehicle are obtained based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type; the electric vehicle charging determination module 102 is used to confirm the battery type according to the vehicle parameters, calculate the predicted power of the target electric vehicle using the pre-constructed battery power consumption formula and the battery type, and make a charging determination on the target electric vehicle according to the predicted power and the battery power to obtain a determination result, wherein the determination result includes: charging or not charging; The charging pile management module 103 is used to obtain the optimal charging pile in the large charging station if it is confirmed that the judgment result is charging, predict the charging time of the target electric car according to the optimal charging pile and the battery type, and obtain the predicted charging time. According to the predicted charging time, the target electric car is charged by using the optimal charging pile to obtain a connected electric car, identify the charging interface of the connected electric car, monitor the temperature of the charging interface, and obtain a temperature data set. According to the temperature data set, a temperature warning is performed on the connected electric car to obtain a temperature warning level, wherein a monitoring frequency is preset, and the charging rate of the optimal charging pile is adjusted according to the temperature warning level to obtain an adjusted charging pile; the charging fee settlement module 104 is used to perform secondary charging on the target electric car based on the adjusted charging pile to obtain a fully charged electric car, calculate the charging fee of the fully charged electric car by using a pre-constructed charging billing formula, and complete the charging pile energy collaborative management based on energy time-sharing metering based on the charging fee.

[0074] In detail, each module in the charging pile energy collaborative management system 100 based on energy time-sharing metering in the embodiment of the present invention is used in the same manner as above. Figure 1 The technical means are the same as the charging pile energy collaborative management method based on energy time-sharing metering described in, and can produce the same technical effects, so they will not be repeated here.

[0075] like Figure 3 , which is a schematic diagram of the structure of an electronic device for implementing a charging pile energy collaborative management method based on energy time-sharing metering provided by an embodiment of the present invention.

[0076] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a charging pile energy collaborative management method program based on energy time-sharing metering.

[0077] Among them, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the charging pile energy collaborative management method program based on energy time-sharing metering, but also can be used to temporarily store data that has been output or is to be output.

[0078] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (such as a charging pile energy collaborative management method program based on energy time-sharing metering, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device 1 and process data.

[0079] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0080] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0081] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0082] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0083] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0084] The program of the charging pile energy collaborative management method based on energy time-sharing metering stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: receiving the energy collaborative management instruction of the charging pile, wherein the energy collaborative management instruction is initiated by the energy collaborative control terminal; receiving the vehicle charging request instruction of the target electric car, and obtaining the vehicle parameters of the target electric car based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type; confirming the battery type according to the vehicle parameters, and calculating the predicted power of the target electric car using the pre-constructed battery power consumption formula and the battery type; making a charging judgment on the target electric car according to the predicted power and battery power, and obtaining a judgment result, wherein the judgment result includes: charging or not charging; if it is confirmed The judgment result is charging, the optimal charging pile in the large charging station is obtained, and the charging time of the target electric vehicle is predicted according to the optimal charging pile and the battery type to obtain the predicted charging time; according to the predicted charging time, the target electric vehicle is charged by using the optimal charging pile to obtain a connected electric vehicle, the charging interface of the connected electric vehicle is identified, the temperature of the charging interface is monitored to obtain a temperature data set, and a temperature warning level is obtained according to the temperature data set; the charging rate of the optimal charging pile is adjusted according to the temperature warning level to obtain an adjusted charging pile; the target electric vehicle is recharged based on the adjusted charging pile to obtain a fully charged electric vehicle, the charging cost of the fully charged electric vehicle is calculated by using a pre-constructed charging billing formula, and the energy collaborative management of charging piles based on energy time-sharing metering is completed based on the charging cost.

[0085] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0086] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0087] The present invention also provides a computer-readable storage medium, wherein the computer program is stored in the computer program. When the computer program is executed by the processor of the electronic device, the computer program can achieve: receiving the energy collaborative management instruction of the charging pile, wherein the energy collaborative management instruction is initiated by the energy collaborative control terminal; receiving the vehicle charging request instruction of the target electric car, and obtaining the vehicle parameters of the target electric car based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type; confirming the battery type according to the vehicle parameters, and calculating the predicted power of the target electric car using the pre-constructed battery power consumption formula and the battery type; making a charging judgment on the target electric car according to the predicted power and the battery power, and obtaining a judgment result, wherein the judgment result includes: charging or not charging; if the judgment result is confirmed, The result is charging, obtaining the optimal charging pile in a large charging station, predicting the charging time of the target electric vehicle according to the optimal charging pile and the battery type, and obtaining the predicted charging time; according to the predicted charging time, charging the target electric vehicle with the optimal charging pile to obtain a connected electric vehicle, identifying the charging interface of the connected electric vehicle, monitoring the temperature of the charging interface, and obtaining a temperature data set; performing a temperature warning on the connected electric vehicle according to the temperature data set, and obtaining a temperature warning level, wherein a monitoring frequency is preset; adjusting the charging rate of the optimal charging pile according to the temperature warning level, and obtaining an adjusted charging pile; performing a secondary charging on the target electric vehicle based on the adjusted charging pile to obtain a fully charged electric vehicle, calculating the charging cost of the fully charged electric vehicle using a pre-built charging billing formula, and completing the energy collaborative management of charging piles based on energy time-sharing metering based on the charging cost.

[0088] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only illustrative, and actual implementation may have other division methods.

[0089] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0090] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0091] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0092] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A charging pile energy collaborative management method based on energy time-sharing metering, characterized in that: The method comprises: receiving an energy collaborative management instruction of a charging pile, wherein the energy collaborative management instruction is initiated by an energy collaborative control terminal; receiving a vehicle charging request instruction of a target electric car, and obtaining vehicle parameters of the target electric car based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type; confirming the battery type according to the vehicle parameters, and calculating the predicted power of the target electric car using a pre-constructed battery power consumption formula and the battery type; making a charging determination for the target electric car according to the predicted power and the battery power, and obtaining a determination result, wherein the determination result includes: charging and not charging; if the determination result is confirmed to be charging, obtaining the optimal charging pile in a large charging station, and calculating the predicted power of the target electric car according to the optimal power consumption formula and the battery type; The charging time of the target electric vehicle is predicted based on the charging pile and battery type to obtain the predicted charging time; based on the predicted charging time, the target electric vehicle is charged with the optimal charging pile to obtain a connected electric vehicle, the charging interface of the connected electric vehicle is identified, the temperature of the charging interface is monitored to obtain a temperature data set, and a temperature warning level is obtained based on the temperature data set; the charging rate of the optimal charging pile is adjusted according to the temperature warning level to obtain an adjusted charging pile; the target electric vehicle is recharged based on the adjusted charging pile to obtain a fully charged electric vehicle, the charging cost of the fully charged electric vehicle is calculated using a pre-built charging billing formula, and the energy collaborative management of charging piles based on energy time-sharing metering is completed based on the charging cost.

2. The charging pile energy collaborative management method based on energy time-sharing metering according to claim 1 is characterized in that: The energy collaborative management instruction of the receiving charging pile includes: starting the energy collaborative management system pre-installed on the energy collaborative control terminal; after successfully starting the energy collaborative management system, indexing the average charging power and the longest charging time of the target electric vehicle that needs to perform the charging operation in the energy collaborative management system; after successfully obtaining the average charging power and the longest charging time, using the energy collaborative management system to locate the position of the target electric vehicle to obtain the real-time position; confirming that the target electric vehicle leaves the exit of the pre-constructed charging area after completing charging to obtain the exit position; generating the energy collaborative management instruction according to the average charging power, the longest charging time, the real-time position and the exit position.

3. The charging pile energy collaborative management method based on energy time-sharing metering as claimed in claim 2 is characterized in that: The method of determining the battery type according to the vehicle parameters and calculating the predicted power of the target electric vehicle using the pre-built battery power consumption formula and the battery type includes: determining the vehicle type according to the vehicle parameters, determining the battery type of the target electric vehicle according to the vehicle type, and calculating the predicted power of the target electric vehicle using the battery power consumption formula and the battery type, wherein the vehicle types include: pure electric vehicles and hybrid electric vehicles, the battery types include: lithium-ion batteries and nickel-hydrogen batteries, and the battery power consumption formula is as follows: ,in, represents the predicted power of the target tram, represents the battery power of the target tram, represents the battery capacity of the target electric vehicle, Indicates the aging factor of the battery type, Indicates the charging efficiency coefficient of the battery type, represents the energy consumption mode coefficient of the target vehicle, represents the environmental factor, Indicates the predicted travel time of the target train.

4. The charging pile energy collaborative management method based on energy time-sharing metering as claimed in claim 3 is characterized in that: The method of obtaining the optimal charging pile in the large charging station includes: obtaining a healthy charging pile; obtaining free parking space data using a pre-built parking space detector and a preset detection frequency, wherein the free parking space data includes: free parking space density and free parking space type; constructing a blank matrix, importing the free parking space data into the blank matrix, and obtaining a filled matrix, wherein the filled matrix is ​​as follows: , where 𝐺 represents the padding matrix, represents the density of free parking spaces detected at the first second, Indicates the type of free parking space detected at the first second. represents the density of free parking spaces detected at the nth second, Indicates the type of vacant parking space detected at the nth second; the density weight is calculated based on the filling matrix, and the density weight is imported into the density weight matrix, where the density weight matrix is ​​as follows: ,in, represents the density weight matrix, represents the density weight detected at the 1st second, Represents the density weight detected at the nth second; the type weight is calculated based on the filling matrix, and the type weight is imported into the type weight matrix, where the type weight matrix is ​​as follows: ,in, represents the type weight matrix, Indicates the type weight detected at the 1st second, Represents the type weight detected at the nth second; extract row vectors from the filling matrix in turn, and use the extracted row vectors to extract the corresponding density weights and type weights from the density weight matrix and the type weight matrix to calculate the evaluation scores corresponding to the extracted row vectors, summarize the evaluation scores, and obtain multiple evaluation scores; sort the multiple evaluation scores in descending order to obtain multiple sorting scores, and extract a high-order score array from the multiple sorting scores, wherein the high-order score array includes: the three sorting scores with the largest sorting scores among the multiple sorting scores; perform the following operations on each high-order score in the high-order score array: obtain the target charging pile position according to the healthy charging pile and the high-order score, obtain the target tram position using the pre-built GPS module, and calculate the shortest moving path according to the target charging pile position and the target tram position; summarize the shortest moving paths to obtain multiple shortest moving paths, obtain the best path according to the multiple shortest moving paths, and obtain the optimal charging pile based on the best path.

5. The charging pile energy collaborative management method based on energy time-sharing metering as claimed in claim 4 is characterized in that: The obtaining of a healthy charging pile includes: obtaining a healthy charging pile using a pre-built charging pile health model; wherein the charging pile health model is as follows: ,in, Indicates the aging coefficient of the charging pile, Indicates the aging coefficient of components in the charging pile, Indicates the aging health index of the charging pile at a certain moment. Indicates the life of components in the charging pile. represents the load correction factor, represents the environmental correction factor, It represents a logarithmic function with the natural constant e as the base; if it is confirmed that the aging coefficient of the charging pile is less than or equal to 0.5, the charging pile is confirmed as a healthy charging pile.

6. The charging pile energy collaborative management method based on energy time-sharing metering as claimed in claim 5 is characterized in that: The method for calculating the shortest moving path according to the target charging pile position and the target tram position includes: constructing a global blank grid map based on a large charging station, inputting the charging pile position and the target tram position into the global blank grid map, obtaining a target tram grid and a charging pile grid, wherein the target tram grid and the charging pile grid correspond to one grid respectively; obtaining four sides of the target tram grid, drawing a perpendicular line for each of the four sides, obtaining four perpendicular lines, wherein the intersection of the side of the target tram grid and the perpendicular line is the foot of the perpendicular; performing the following operations on each of the four perpendicular lines: taking the perpendicular line and the foot of the perpendicular as a reference, generating a vector along the perpendicular line toward the direction of the outer layer of the pre-constructed target tram grid, taking the direction of the vector as a reference, obtaining the next grid adjacent to the target tram grid, and obtaining a pending grid; calculating the Manhattan distance between the pending grid and the charging pile grid to obtain an initial distance; summarizing the initial distance , obtain an initial distance set, mark the calculated grids for each undetermined grid and the target tram grid corresponding to each initial distance in the initial distance set, and obtain a marked grid set; extract the initial distance with the smallest value from the initial distance set, and use the undetermined grid corresponding to the initial distance with the smallest value as the moving target to obtain the target moving grid, mark the moving path grid for the target moving grid, and obtain the target marked grid; obtain the target adjacent grid set based on the target moving grid, remove one or more marked grids in the marked grid set from the target adjacent grid set, and obtain the to-be-calculated grid set, use the to-be-calculated grid set as the initial distance set, return to the step of marking the calculated grids for each undetermined grid and the target tram grid corresponding to each initial distance in the initial distance set, until the Manhattan distance between the target moving grid and the charging pile grid is zero, and summarize the target marked grids to obtain the shortest moving path.

7. The charging pile energy collaborative management method based on energy time-sharing metering according to claim 6 is characterized in that: The Manhattan distance between the undetermined grid and the charging pile grid is calculated, including: constructing a reference coordinate system, obtaining the coordinates of the undetermined grid and the charging pile grid based on the reference coordinate system; and calculating the Manhattan distance between the coordinates of the undetermined grid and the charging pile grid using a pre-constructed Manhattan distance formula, wherein the Manhattan distance formula is as follows: ,in, represents the Manhattan distance, ( represents the undetermined grid coordinates, ( represents the coordinates of the charging pile grid, Indicates the horizontal position of the pending grid coordinates or the charging pile grid coordinates. Indicates the vertical position of the pending grid coordinates or the charging pile grid coordinates.

8. The charging pile energy collaborative management method based on energy time-sharing metering as claimed in claim 7 is characterized in that: The method comprises the steps of: identifying a charging interface connected to a tram, monitoring the temperature of the charging interface, obtaining a temperature data set, performing a temperature warning on the connected tram according to the temperature data set, and obtaining a temperature warning level, wherein a monitoring frequency is preset, including: obtaining multiple historical detection time periods, extracting historical detection time periods in sequence from multiple historical detection time periods, and performing the following operations on the extracted historical detection time periods: detecting the charging interface based on the extracted historical detection time periods to obtain a historical charging data set, wherein the historical charging data set includes: historical charging time, historical charging rate, historical ambient temperature, historical battery type, and historical interface temperature; deduplicating the historical charging data set to obtain a deduplicated data set, and normalizing the deduplicated data set to obtain a normalized data set; training a pre-constructed random forest model using the normalized data set to obtain an initial random forest model; summarizing the initial random forest model to obtain multiple initial random forest models, wherein the initial random forest model corresponds one to one to the historical detection time periods; monitoring the charging data of the charging interface using a preset monitoring frequency, wherein the charging data includes: charging time, charging rate, ambient temperature, battery type, and interface temperature; and evaluating the charging data using multiple initial random forest models to obtain a temperature warning level.

9. The charging pile energy collaborative management method based on energy time-sharing metering according to claim 8 is characterized in that: The method of calculating the charging cost of a fully charged electric vehicle by using a pre-built charging billing formula includes: obtaining the charging cost on normal working days and the charging cost on holidays based on the energy collaborative management system; wherein the charging billing formula is as follows: , ,in, represents the charging cost for a normal working day, It represents the basic charging electricity price on a normal working day. Indicates the actual charging time of the target electric vehicle, represents the user loyalty discount, Indicates the charging cost during holidays. Indicates the basic charging unit price during holidays. Indicates holiday surcharges. Indicates the additional charge during peak hours. Indicates discounts during off-peak hours, represents the seasonal adjustment coefficient, The peak holiday season, Indicates peak period, Indicates off-peak hours.

10. A charging pile energy collaborative management system based on energy time-sharing metering, characterized in that: The system includes: a vehicle parameter acquisition module, which is used to receive the energy collaborative management instruction of the charging pile, wherein the energy collaborative management instruction is initiated by the energy collaborative control terminal, receives the vehicle charging request instruction of the target electric car, and obtains the pre-constructed vehicle parameters of the target electric car based on the energy collaborative management instruction and the vehicle charging request instruction, wherein the vehicle parameters include: vehicle type, battery power and battery type; an electric car charging determination module, which is used to confirm the battery type according to the vehicle parameters, calculate the predicted power of the target electric car using the pre-constructed battery power consumption formula and the battery type, and make a charging determination on the target electric car according to the predicted power and the battery power to obtain a determination result, wherein the determination result includes: charging and not charging; a charging pile management module, which is used to obtain the parameters in the large charging station if the determination result is confirmed to be charging. An optimal charging pile predicts the charging time of a target electric vehicle according to the optimal charging pile and the battery type to obtain a predicted charging time, charges the target electric vehicle using the optimal charging pile according to the predicted charging time to obtain a connected electric vehicle, identifies the charging interface of the connected electric vehicle, monitors the temperature of the charging interface to obtain a temperature data set, performs a temperature warning on the connected electric vehicle according to the temperature data set to obtain a temperature warning level, wherein a monitoring frequency is preset, and the charging rate of the optimal charging pile is adjusted according to the temperature warning level to obtain an adjusted charging pile; a charging fee settlement module is used to perform secondary charging on the target electric vehicle based on the adjusted charging pile to obtain a fully charged electric vehicle, calculate the charging fee of the fully charged electric vehicle using a pre-built charging billing formula, and complete the energy collaborative management of charging piles based on energy time-sharing metering based on the charging fee.

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