An AI-based mobile battery swapping energy storage data control method

Abstract

The present invention discloses an artificial intelligence-based mobile battery swapping energy storage data control method, which specifically relates to the field of mobile battery swapping technology. It includes S1: Battery swapping data collection: Collect battery operation parameters and battery swapping station operation parameters; S2: Battery swapping data analysis: Obtain the analysis results of the mobile battery swapping operation status; S3: Mobile battery swapping demand analysis: Obtain the mobile battery swapping demand; S4: Energy storage strategy formulation: Formulate the energy storage strategy of the battery swapping station and control the energy storage device to perform energy storage operations; S5: Operation status monitoring of the energy storage device: Monitor the operation parameters of the energy storage device; S6: Operation status analysis of the energy storage device: Obtain the energy storage device status evaluation value; S7: Dynamically adjust the energy storage strategy: Dynamically adjust the energy storage strategy of the battery swapping station. Real-time monitor the energy supply demand of the battery swapping station, set the charging priorities of various types of batteries, and real-time monitor the operation status during the charging process of the energy storage device, effectively reducing the operation difficulty and time cost of the battery swapping station, reducing the overall operation efficiency, and improving the charging efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of mobile battery swapping, and more specifically, to an artificial intelligence-based mobile battery swapping energy storage data control method. Background Art

[0002] Under the general trend of global energy transformation, the electric vehicle market has shown explosive growth, and the problems of range anxiety and charging duration have become increasingly prominent. Energy storage systems can flexibly adjust resources and have the advantages of flexibility, scalability, environmental protection, and high efficiency. Mobile battery swapping refers to a service method in which a mobile battery swapping service vehicle provides a fast battery replacement service for electric vehicles. With the development of artificial intelligence technology, real-time monitoring, prediction, and optimization of energy storage systems can be achieved. With the development of smart grids and the energy Internet, the mobile battery swapping energy storage system, as an important part of the energy Internet, coordinates efficiently with the power grid, realizes the orderly charging and discharging of batteries through data control methods, optimizes the power grid load, and improves the operation efficiency and stability of the power grid.

[0003] However, when it is actually used, there are still some disadvantages. First, the resource allocation is uneven. When the energy storage priority of the existing mobile battery swapping technology is not divided, the battery swapping station may not be able to make reasonable resource allocation according to the status and requirements of the batteries. This may lead to some batteries in urgent need of charging not getting timely battery swapping services, while some batteries in good condition are prematurely replaced, resulting in resource waste. Without the division of energy storage priority, the operators of the battery swapping station may not be able to quickly and accurately judge which batteries need to be processed first, effectively reducing the operation difficulty and time cost of the battery swapping station and lowering the overall operation efficiency.

[0004] Second, the potential safety hazards are relatively high. The existing mobile battery swapping technology cannot real-time monitor the charging parameters, which means that abnormal situations during the battery charging process, such as overcharging, over-discharging, and abnormal temperature, cannot be detected in time. These abnormal situations may lead to a decline in battery performance, a shortening of battery life, and even cause battery failures or safety accidents. If real-time monitoring cannot be carried out, the charging process may not be dynamically adjusted according to the actual status of the battery, resulting in low charging efficiency. Summary of the Invention

[0005] In view of this, the embodiments of the present invention provide an artificial intelligence-based mobile battery swapping energy storage data control method, which effectively solves the problems of uneven resource allocation and relatively high potential safety hazards in the existing mobile battery swapping process by real-time monitoring the energy supply demand of the battery swapping station, setting the charging priorities of various types of batteries, and real-time monitoring the operating status during the charging process of the energy storage device.

[0006] To achieve the above object, the present invention provides the following technical solution: A mobile battery swapping energy storage data control method based on artificial intelligence, including a battery monitoring terminal, a swapping station operation monitoring terminal, an energy storage device monitoring terminal, a control center, and an energy storage device. The battery monitoring terminal, the swapping station operation monitoring terminal, the energy storage device monitoring terminal, and the energy storage device should be set on the swapping station, and specifically include the following steps:

[0007] S1: Battery swapping data collection: Real-time collect battery operation parameters and swapping station operation parameters through the battery monitoring terminal and the swapping station operation monitoring terminal, and transmit the collected data to the control center;

[0008] S2: Battery swapping data analysis: The control center analyzes the battery operation parameters and swapping station operation parameters collected in real time to obtain the analysis result of the mobile battery swapping operation status;

[0009] S3: Mobile battery swapping demand analysis: The control center conducts mobile battery swapping demand analysis based on the analysis result of the mobile battery swapping operation status to obtain the mobile battery swapping demand;

[0010] S4: Energy storage strategy formulation: The control center formulates the energy storage strategy of the swapping station according to the mobile battery swapping demand, and controls the energy storage device to perform energy storage operations;

[0011] S5: Energy storage device operation status monitoring: Real-time monitor the operation parameters of the energy storage device through the energy storage device monitoring terminal, and transmit the collected data to the control center;

[0012] S6: Energy storage device operation status analysis: The control center analyzes the operation parameters of the energy storage device collected in real time to obtain the energy storage device status evaluation value;

[0013] S7: Dynamically adjust the energy storage strategy: The control center dynamically adjusts the energy storage strategy of the swapping station according to the energy storage device status evaluation value.

[0014] The technical effects and advantages of the present invention:

[0015] 1. The present invention real-time collects battery operation parameters and swapping station operation parameters through the battery monitoring terminal and the swapping station operation monitoring terminal, and analyzes the battery operation parameters and swapping station operation parameters through the control center to obtain the total amount of electricity to be supplemented within a single time interval and the priority weight coefficients corresponding to each type of battery, and sets the energy supply demand of the swapping station within a single time interval and the charging priorities of each type of battery accordingly. The swapping station can reasonably allocate resources according to the status and demand of the battery, avoid resource waste, and the operation personnel of the swapping station can quickly and accurately judge which batteries need to be processed first, reducing the operation difficulty and time cost, and improving the overall operation efficiency;

[0016] The present invention monitors the operating parameters of the energy storage device in real time through the energy storage device monitoring terminal, analyzes the data of the operating parameters of the energy storage device through the control center, obtains the state evaluation value of the energy storage device, establishes the state evaluation standard value of the energy storage device, compares the state evaluation value of the energy storage device with the state evaluation standard value of the energy storage device, determines the real-time operating state of the energy storage device, helps to timely detect abnormal situations during the battery charging process, reduces the possibility of safety accidents, and the charging process can be dynamically adjusted according to the actual state of the battery, improving the charging efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of the method steps of the present invention.

[0018] Figure 2 It is a schematic diagram of the overall structure of the present invention.

[0019] Figure 3 It is a schematic diagram of the data analysis steps for battery swapping of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] As shown in the Figure 1 accompanying drawings, a mobile battery swapping energy storage data control method based on artificial intelligence includes a battery monitoring terminal, a swapping station operation monitoring terminal, an energy storage device monitoring terminal, a control center, and an energy storage device. The battery monitoring terminal, the swapping station operation monitoring terminal, the energy storage device monitoring terminal, and the energy storage device should be arranged on the swapping station.

[0022] Specifically, in this embodiment, the battery monitoring terminal is used to monitor and collect the battery operating parameters in real time, including the battery power, charging speed, charging efficiency, and the number of batteries. Real-time collection of the battery operating parameters helps to ensure that the swapping station has sufficient power reserves to maintain normal battery swapping services. Over-discharging or long-term idling will damage the battery. By monitoring the battery power, the swapping station can charge the battery in time when the battery power is close to exhaustion, avoiding over-discharging. Monitoring the charging speed helps to improve the battery swapping efficiency, and the swapping station can formulate different battery swapping strategies according to the power and charging requirements of different batteries.

[0023] The operation monitoring terminal of the battery swapping station is used to monitor the operation parameters of the battery swapping equipment in real time, such as the battery swapping time, battery swapping frequency, and the number of battery swapping vehicles for different models of batteries. By establishing remote monitoring, it remotely monitors and collects data of the battery swapping station, and real-time collects the operation parameters of the battery swapping station. In addition, the operation monitoring terminal of the battery swapping station can collect some data that cannot be directly collected by sensors through manual detection and input methods, such as vehicle fault information, battery appearance inspection, etc., as a supplement to the operation parameters of the battery swapping station, providing more comprehensive operation information of the battery swapping station.

[0024] The energy storage device monitoring terminal is used to monitor the operation parameters of the energy storage device in the battery swapping station in real time, such as the voltage, current, power, and frequency of the energy storage device. By real-time monitoring the performance parameters of the energy storage device, it helps to timely discover potential safety hazards such as equipment overheating, overcharging, and over-discharging, facilitating timely measures to avoid accidents. Monitoring the performance parameters of the energy storage device is of great significance for ensuring the safe operation of the equipment, optimizing the system performance, extending the equipment life, supporting decision-making, and enhancing the user experience.

[0025] The control center analyzes the data monitored and collected by the above-mentioned battery monitoring terminal, operation monitoring terminal of the battery swapping station, and energy storage device monitoring terminal, and controls the energy storage device to perform relevant operations according to the analysis results.

[0026] The energy storage device refers to a device that can store electrical energy and release electrical energy when needed. The energy storage device is usually used to store electrical energy obtained from the power grid and renewable resources, and provide electrical energy when an electric vehicle needs to swap batteries. The energy storage device specifically includes chemical energy storage devices and physical energy storage devices. Chemical energy storage devices include lithium-ion batteries and nickel-metal hydride batteries, and physical energy storage devices include supercapacitors and flywheel batteries.

[0027] For the connection methods of the above-mentioned battery monitoring terminal, operation monitoring terminal of the battery swapping station, energy storage device monitoring terminal, control center, and energy storage device, refer to Figure 2 .

[0028] The specific implementation manner of the present invention includes the following steps:

[0029] S1: Battery swapping data collection: The battery operation parameters and the operation parameters of the battery swapping station are real-time collected through the battery monitoring terminal and the operation monitoring terminal of the battery swapping station, and the collected data is transmitted to the control center.

[0030] Further, the battery operation parameters real-time collected through the battery monitoring terminal include the remaining power Q, charging speed v, number of batteries n, and capacity C of each battery in the battery swapping station, and the operation parameters of the battery swapping station real-time collected through the operation monitoring terminal of the battery swapping station specifically include the battery swapping time t.

[0031] It should be specifically noted in this embodiment that the battery monitoring terminal in the above steps specifically refers to the battery management system (BMS) of the battery swapping station. The charging power, battery power, estimated full charge time, and the number of batteries are monitored in real time through the battery management system. The battery management system accurately calculates the remaining battery power through complex algorithms and sensor networks, and monitors the charging speed of the battery based on the data provided by the battery management system. The operation monitoring terminal of the battery swapping station in the above steps monitors each link in the battery swapping process in real time through the monitoring system of the battery swapping station, including steps such as battery disassembly, installation of new batteries, and vehicle status monitoring. Through the time recording function of the monitoring system, the battery swapping time required for each type of battery can be accurately measured.

[0032] S2: Battery swapping data analysis: The control center analyzes the real-time collected battery operation parameters and battery swapping station operation parameters to obtain the analysis result of the mobile battery swapping operation status.

[0033] Furthermore, the steps of battery swapping data analysis are as follows:

[0034] A1: Set a time interval. The number of batteries in the battery swapping station within a single time interval is monitored in real time through the battery monitoring terminal, and the battery types are classified and marked as a, b, and c respectively. The corresponding number n of each type of battery is counted.

[0035] It should be specifically noted in this embodiment that the time interval can be one day, one week, or one month. By monitoring the number of batteries used for mobile battery swapping in the battery swapping station per unit time length, the power consumption of the battery swapping station in one day can be better analyzed. It should be noted that the single time interval selected in this embodiment should be able to represent the average level of the battery swapping station.

[0036] It should be specifically noted in this embodiment that the types of batteries configured in the battery swapping station depend on the types and brands of electric vehicles served by the battery swapping station. The types of batteries in the battery swapping station can be classified according to the voltage platform, battery type, and battery capacity. Specifically, according to the voltage platform, they can be divided into 400V platform batteries and 800V platform batteries. The capacities of 400V platform batteries include 70 / 75 degrees, 100 degrees, and 150 degrees, which are applicable to electric vehicles with high voltage platforms, such as the NT1 / 2 series of NIO; the capacities of 800V platform batteries include 60 degrees, 85 degrees, 102 degrees, and 120 degrees, which are applicable to electric vehicles with high voltage platforms, such as the LeDao series of NIO. Specifically, according to the battery type, they can be divided into lithium batteries and lead-acid batteries. Lithium batteries are the types adopted by most battery swapping stations and have the advantages of high energy density and short charging time. Specifically, they include ternary lithium batteries, ternary hybrid batteries, and lithium iron phosphate batteries; lead-acid batteries are traditional electric vehicle batteries and are used less. Specifically, according to the battery capacity, they can be divided into standard batteries, lightweight batteries, and large-capacity batteries. Standard batteries are suitable for small electric vehicles to meet daily use needs; lightweight batteries are suitable for small transportation tools such as lightweight electric vehicles and folding electric vehicles to meet short-distance travel needs, and are suitable for large electric vehicles and commercial electric vehicles to meet long-distance travel and heavy-load transportation needs.

[0037] A2: Collect the remaining power Q corresponding to any battery within this time interval through the battery monitoring terminal i , collect the charging speed v corresponding to each type of battery, and obtain the capacity C of each type of battery through the battery monitoring terminal;

[0038] It should be specifically noted in this embodiment that any battery within this time interval includes all the battery quantities for mobile battery swapping stored in the battery swapping station. The storage quantity of each type of battery should be related to the vehicle type served by the battery swapping station. Set the batteries stored in the battery swapping station according to the required quantity of each type of battery by the battery swapping station. Each type of battery has a rated charging speed.

[0039] A3: Process and analyze the real-time collected battery swapping data to obtain the total power Q that needs to be supplemented within a single time interval of the battery swapping station s ;

[0040] Furthermore, by calculating the difference between the capacity of each type of battery and the remaining power corresponding to any battery, obtain the power that needs to be supplemented for any battery, including Q ai 、Q bi and Q ci Three types, accumulate the power that needs to be supplemented for any battery and use the formula Calculate the total power Q required for a single time interval s , C a 、C b and C crespectively represent the capacities corresponding to three types of batteries a, b, and c, and n a , n b and n c respectively represent the quantities of three types of batteries a, b, and c;

[0041] It should be specifically noted in this embodiment that by real-time monitoring the remaining power of any battery to be replaced within a single time interval, and using the total capacity of each type of battery to be replaced, the power that the battery swapping station needs to supplement for the battery within this time interval is calculated, providing strong data support for the selection of the charging method in the subsequent steps.

[0042] A4: Through the battery monitoring terminal, real-time monitor the replacement time t required for each type of battery within a single time interval, and process these replacement data to obtain the priority weight coefficient ρ of each type of battery.

[0043] Furthermore, by dividing the difference between the rated capacity C of any battery in a single time interval and the remaining power Qi by the charging speed v corresponding to each type of battery, the charging time t required for a single battery is obtained 1 , and after accumulating the charging times required for the same type of batteries within a single time interval and dividing by the quantity n of this type of battery, the average charging time of this type of battery is obtained. After accumulating the replacement times t required for each type of battery that are real-time collected by the battery monitoring terminal within a single time interval and dividing by the quantity n of this type of battery, the average replacement time of this type of battery is obtained. Add the average charging time and the average replacement time of each type of battery according to a fixed ratio and use the formula to obtain the weighted time t of each type of battery w , μ 1 and μ 2 are respectively the proportionality coefficients of the average charging time and the average replacement time. Calculate the weighted times t wa , t wb and t wc corresponding to three types of batteries a, b, and respectively, and calculate the quantity ratio of each type of battery. Use the formula to calculate the priority weight coefficient ρ corresponding to each type of battery k , where the subscript k represents the type of battery, including a, b, and c, and σ 1 and σ 2 respectively represent the proportionality coefficients of the weighted time and the quantity of each type of battery.

[0044] It should be specifically noted in this embodiment that the proportionality coefficients μ 1 and μ 2The sum should be 1. The acquisition of this proportional coefficient requires specific analysis of the importance of the charging time and battery swapping time of the battery in the battery swapping station and is set according to the requirements of the battery swapping station. The proportional coefficients σ of the weighted time and quantity of each type of battery in the above steps 1 and σ 2 The sum should be 1. This proportional coefficient should be set by the battery swapping station according to historical battery swapping data.

[0045] It should be specifically noted in this embodiment that in the above steps, by calculating the average charging time and average battery swapping time of different types of batteries, the weighted time required by the battery as a whole is reflected, and the processing priority of each type of battery in the battery swapping station is specifically analyzed by analyzing the weighted time and quantity ratio required by different batteries.

[0046] S3: Mobile battery swapping demand analysis: The control center performs mobile battery swapping demand analysis based on the analysis result of the mobile battery swapping operation status to obtain the mobile battery swapping demand.

[0047] Furthermore, the mobile battery swapping demand analysis includes the following steps:

[0048] The control center sets the energy supply demand within a single time interval of the battery swapping station according to the total amount of electricity to be supplemented obtained from the battery swapping data analysis, and sets the energy supply index. According to the priority weight coefficients of each type of battery obtained from the battery swapping data analysis, the charging priority of each type of battery in the battery swapping station is determined.

[0049] Even further, the energy supply index should be divided into three levels, including the small demand power supply standard, the medium demand power supply standard, and the large demand power supply standard, which are respectively marked as Q 1 、Q 2 and Q 3 , where Q 1 =1 / 3Q 3 ,Q 2 =2 / 3Q 3 .

[0050] It should be specifically noted in this embodiment that to avoid a large amount of power loss in the power supply station, the energy supply demand set by the battery swapping station should be the sum of the total amount of electricity to be supplemented within a single time interval and the floating power value of the battery swapping station. When determining the charging priority according to the weight coefficients of each type of battery, it should be noted that the larger the priority weight coefficient of each type, it indicates that the charging and battery swapping time required for this type of battery is longer or the number of batteries is larger, and the charging priority of this type of battery should be higher. For example, ρ a <ρ b <ρ c, it shows that the priority weight coefficient of type-a batteries is less than that of type-b batteries, and the priority weight coefficient of type-b batteries is less than that of type-c batteries. Correspondingly, the charging priority of type-c batteries should be higher than that of type-b batteries, and the charging priority of type-b batteries should be higher than that of type-a batteries.

[0051] S4: Energy storage strategy formulation: The control center formulates the energy storage strategy of the swapping station according to the mobile swapping demand and controls the energy storage device to perform energy storage operations.

[0052] Furthermore, the formulation of the energy storage strategy requires the control center to compare the energy supply demand obtained from the analysis of the mobile swapping demand with the functional indicators, judge the energy supply level required by the swapping station, select the charging method according to the energy supply level, and determine the charging order of each type of battery according to the charging priority of each type of battery obtained from the analysis of the mobile swapping demand.

[0053] In this embodiment, it should be specifically noted that when the energy supply demand is less than the small-demand power supply standard, it indicates that the swapping station belongs to the small-demand power supply and needs to provide less power. The fine charging method can be used to charge the battery. This method can give priority to solar charging and energy storage battery charging to supply power to the battery, and use a small current for trickle charging to avoid overcharging and battery damage; when the energy supply demand is greater than the small-demand power supply standard and less than the medium-demand power supply standard, it indicates that the swapping station belongs to the medium-demand power supply and needs to provide moderate power. The conventional charging method can be used to charge the battery. This method is suitable for AC conventional charging from the power grid, with a moderate charging speed and less damage to the battery; when the functional demand is greater than the medium-demand power supply standard and less than the high-demand power supply standard, the fast charging method can be used to charge the battery. This method usually selects DC fast charging from the power grid and can replenish a large amount of electric energy to the battery in a short time.

[0054] In this embodiment, it should be specifically noted that when various batteries need to be charged simultaneously, the control center will set the charging order of the batteries according to the charging priority of each type of battery. For example, when the charging priority of type-c batteries is higher than that of type-b batteries, and the charging priority of type-b batteries is higher than that of type-a batteries, type-c batteries should be charged first. When there are spare charging devices, type-b batteries will be charged, and so on. Finally, type-a batteries will be charged.

[0055] S5: Monitoring the operating state of the energy storage device: The operating parameters of the energy storage device are monitored in real time through the energy storage device monitoring terminal, and the collected data is transmitted to the control center.

[0056] Furthermore, the operating parameters of the energy storage device specifically include the real-time voltage U and real-time current I during the charging process of the energy storage device.

[0057] Specifically in this embodiment, if the real-time voltage during the charging process of the energy storage device is too high, it may exceed the rated voltage of the energy storage device, resulting in device damage or performance degradation, and even leading to safety accidents. The voltage level also affects the charging efficiency. At an appropriate voltage, the energy storage device can receive and store electrical energy more effectively. If the voltage is too low, it may cause a slow charging speed and affect the charging efficiency; while too high a voltage may cause unnecessary energy loss and device heating. The real-time current during the charging process of the energy storage device directly determines the charging rate of the energy storage device. At the same voltage, the larger the current, the faster the charging speed. However, too high a current may cause problems such as device overheating and battery polarization, thus affecting the charging efficiency and device life.

[0058] S6: Analysis of the operating state of the energy storage device: The control center analyzes the operating parameters of the energy storage device collected in real time to obtain the state evaluation value of the energy storage device.

[0059] Furthermore, the calculation steps of the state evaluation value of the energy storage device are as follows:

[0060] B1: Set a monitoring interval for the charging process of the energy storage device and divide it into individual sub-time regions at equal time lengths, marked as 1, 2, …, j, …, m;

[0061] Specifically in this embodiment, the establishment of the monitoring interval for the charging process of the energy storage device needs to include at least the entire process of charging one battery. The charging processes of each battery are parallel according to the number of idle charging devices. A monitoring interval for the charging process of an energy storage device can be 5 hours, and the sub-time regions are divided according to a time length of 5 minutes.

[0062] B2: Collect the real-time voltage U in any sub-time region through the energy storage device monitoring terminal j , and calculate the average voltage through the real-time voltage in any sub-time region. Calculate the difference between the real-time voltage in any sub-time region and the average voltage, accumulate the squares of the differences between the real-time voltage in any sub-time region and the average voltage, divide by the number of m - 1 sub-time regions, and take the square root to obtain the voltage fluctuation value of the user in the monitoring time region. Use the same method to calculate the current fluctuation value, add the voltage fluctuation value and the current fluctuation value according to a fixed proportional coefficient, and use the formula to calculate the state evaluation value D of the energy storage device, where ω 1 and ω 2 respectively represent the proportional coefficients of the voltage fluctuation value and the current fluctuation value.

[0063] Specifically in this embodiment, the proportional coefficients ω 1 and ω 2The sum should be 1. It is necessary to analyze the state of the energy storage device during a large number of charging processes through the battery swapping station, and set the corresponding proportional coefficient according to the analysis results.

[0064] S7: Dynamically adjust the energy storage strategy: The control center dynamically adjusts the energy storage strategy of the battery swapping station according to the evaluation value of the energy storage device state.

[0065] Furthermore, set the evaluation standard value D of the energy storage device state 标 , compare the evaluation value of the energy storage device state with the evaluation standard value of the energy storage device state, judge the real-time operation state of the energy storage device, and dynamically adjust the energy storage strategy according to the real-time operation state of the energy storage device.

[0066] It should be specifically explained in this embodiment that when the evaluation value of the energy storage device state is less than the evaluation standard value of the energy storage device state, it means that the energy storage device is in a normal operation state and does not need to be adjusted; when the evaluation value of the energy storage device state is greater than the evaluation standard value of the energy storage device state, it means that the energy storage device is in an abnormal operation state, and the battery being charged and the energy storage device should be immediately checked for abnormalities.

[0067] Secondly: In the attached drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. Other structures can refer to the general design. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other; Finally: The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A mobile battery replacement energy storage data control method based on artificial intelligence, characterized in that: It includes a battery monitoring terminal, a battery swap station operation monitoring terminal, an energy storage device monitoring terminal, a control center and an energy storage device. The battery monitoring terminal, the battery swap station operation monitoring terminal, the energy storage device monitoring terminal and the energy storage device should be set up at the battery swap station, and specifically includes the following steps: S1: Battery swap data collection: The battery operating parameters and battery swap station operating parameters are collected in real time through the battery monitoring terminal and the battery swap station operation monitoring terminal, and the collected data is transmitted to the control center; S2: Battery swap data analysis: The control center analyzes the real-time collected battery operating parameters and battery swap station operating parameters to obtain the mobile battery swap operating status analysis results; S3: Mobile battery swap demand analysis: The control center performs mobile battery swap demand analysis based on the mobile battery swap operation status analysis results to obtain the mobile battery swap demand; S4: Energy storage strategy formulation: The control center formulates the energy storage strategy of the battery swap station according to the mobile battery swap demand, and controls the energy storage device to perform energy storage operations; S5: Energy storage device operation status monitoring: monitor the operation parameters of the energy storage device in real time through the energy storage device monitoring terminal, and transmit the collected data to the control center; S6: Energy storage device operating status analysis: The control center analyzes the energy storage device operating parameters collected in real time to obtain the energy storage device status evaluation value; S7: Dynamically adjust the energy storage strategy: The control center dynamically adjusts the energy storage strategy of the battery swap station according to the status evaluation value of the energy storage device.

2. According to the artificial intelligence-based mobile battery exchange energy storage data control method of claim 1, it is characterized by: The battery operating parameters collected in real time by the battery monitoring terminal include the remaining power Q, charging speed v, number of batteries n and capacity C of each battery in the battery swap station. The battery swap station operating parameters collected in real time by the battery swap station operation monitoring terminal specifically include the battery swap time t.

3. According to the artificial intelligence-based mobile battery replacement energy storage data control method of claim 1, it is characterized by: The steps for battery swap data analysis are as follows: A1: Set a time interval, monitor the number of batteries in the battery swap station in a single time interval in real time through the battery monitoring terminal, and classify the battery types, marking them as a, b and c, and count the number n of each type of battery; A2: Use the battery monitoring terminal to collect the remaining power Q of any battery within the time interval i , collect the charging speed v corresponding to each type of battery, and obtain the capacity C of each type of battery through the battery monitoring terminal; A3: Process and analyze the real-time collected battery swap data to obtain the total amount of electricity required to be replenished within a single time interval of the battery swap station Q s ; A4: The battery monitoring terminal is used to monitor the battery replacement time t required for each type of battery within a single time interval in real time, and the battery replacement data are processed to obtain the priority weight coefficient ρ of each type of battery.

4. According to claim 3, a method for controlling mobile battery replacement energy storage data based on artificial intelligence is characterized in that: The total amount of electricity required to be replenished within the single time interval is Q s It is obtained by calculating the difference between the capacity of each type of battery and the remaining power corresponding to any battery, including Q ai , Q bi and Q ci Three types, accumulate the amount of electricity that needs to be replenished for any battery and use the formula Calculate the total amount of electricity required for a single time interval Q s , C a , C b and C c Respectively represent the capacities of three types of batteries: a, b, and c, n a 、n b and n c Respectively represent the number of three types of batteries: a, b and c.

5. According to claim 3, a method for controlling mobile battery storage data based on artificial intelligence is characterized in that: The charging time t1 required for a single battery is obtained by dividing the difference between the rated capacity C and the remaining power Qi of any battery in a single time interval by the charging speed v corresponding to each type of battery. The charging time required for batteries of the same type in a single time interval is accumulated and divided by the number of batteries of this type n to obtain the average charging time of batteries of this type. The battery replacement time t required for each type of battery collected in real time by the battery monitoring terminal in a single time interval is accumulated and divided by the number of batteries of this type n to obtain the average battery replacement time of batteries of this type. The average charging time and the average battery replacement time of each type of battery are added in a fixed ratio using the formula Get the weight time t of each type of battery w , μ1 and μ2 are the proportional coefficients of the average charging time and the average battery replacement time, respectively, and the weighted time t corresponding to a, b and the three types of batteries are calculated respectively wa ,t wb and t wc , and calculate the proportion of each type of battery, using the formula Calculate the priority weight coefficient ρ corresponding to each type of battery k , the subscript k represents the type of battery, including a, b and c, σ1 and σ2 represent the proportional coefficients of the weight time and quantity of each type of battery, respectively.

6. According to the artificial intelligence-based mobile battery replacement energy storage data control method of claim 1, it is characterized by: The mobile battery swap demand analysis includes the following steps: The control center sets the energy supply demand in a single time interval of the battery swap station according to the total amount of electricity required to be replenished in a single time interval obtained from the battery swap data analysis, and sets the energy supply index. The charging priority of each type of battery in the battery swap station is determined according to the priority weight coefficient of each type of battery obtained from the battery swap data analysis; Energy supply indicators should be divided into three levels, including small demand power supply standard, medium demand power supply standard and large demand power supply standard, marked as Q1, Q2 and Q3 respectively, where Q1=1 / 3Q3, Q2=2 / 3Q3.

7. According to claim 6, a method for controlling mobile battery storage data based on artificial intelligence is characterized in that: The formulation of the energy storage strategy requires the control center to compare the energy supply demand and functional indicators obtained from the mobile battery swap demand analysis, determine the energy supply level required by the battery swap station, select the charging method according to the energy supply level, and determine the order of charging each type of battery according to the charging priority of each type of battery obtained from the mobile battery swap demand analysis.

8. According to the artificial intelligence-based mobile battery replacement energy storage data control method of claim 1, it is characterized by: The operating parameters of the energy storage device specifically include the real-time voltage U and the real-time current I during the charging process of the energy storage device.

9. According to the artificial intelligence-based mobile battery replacement energy storage data control method of claim 1, it is characterized by: The calculation steps of the energy storage device state evaluation value are as follows: B1: Set a monitoring interval for the charging process of an energy storage device and divide it into individual sub-time zones of equal time length, marked as 1, 2, ..., j, ..., m; B2: The real-time voltage U in any sub-time zone is obtained through the energy storage device monitoring terminal. j , and calculate the voltage mean through the real-time voltage in any sub-time zone, calculate the difference between the real-time voltage and the voltage mean in any sub-time zone, add up the squares of the difference between the real-time voltage and the voltage mean in any sub-time zone, divide by the number of m-1 sub-time zones and take the square root, and get the voltage fluctuation value of the user in the monitoring time zone, and use the same method to calculate the current fluctuation value, add the voltage fluctuation value and the current fluctuation value according to a fixed proportional coefficient and use the formula The energy storage device state evaluation value D is calculated, and ω1 and ω2 represent the proportionality coefficients of the voltage fluctuation value and the current fluctuation value respectively.

10. The method for controlling mobile battery replacement energy storage data based on artificial intelligence according to claim 1 is characterized in that: The energy storage strategy is set by constructing the energy storage device state evaluation standard value D 标 , compare the energy storage device status evaluation value with the energy storage device status evaluation standard value, determine the real-time operating status of the energy storage device, and dynamically adjust the energy storage strategy according to the real-time operating status of the energy storage device.

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