Split mobile charging robot operation regulation method and device, equipment and medium

Abstract

The application provides a split mobile charging robot operation regulation method and device, equipment and medium, relates to the new energy technology field, and the method comprises the following steps: determining the deviation value of an actual power compensation curve and a power grid planned power curve; determining the maximum incentive value of the power grid demand response according to the deviation value and the deviation threshold range; taking the target charging power curve corresponding to the maximum incentive value as the target charging strategy; taking the target power compensation curve corresponding to the maximum incentive value as the target power compensation strategy; sending a charging control instruction to the battery module to instruct the battery module to perform a charging operation on the vehicle to be charged according to the target charging strategy; sending a power compensation control instruction to the charging master station to instruct the charging master station to perform a power compensation operation on the battery module according to the target power compensation strategy; the charging resources are efficiently allocated, the system energy balance is maintained, and the power grid incentive income and the charging service reliability are improved.

Description

Technical Field

[0001] This application generally relates to the field of new energy technology, specifically to the operation control method, device, equipment and medium of a split-type mobile charging robot. Background Technology

[0002] With the increasing popularity of electric vehicles (EVs), charging demand is growing rapidly. The traditional fixed deployment of charging piles is insufficient to meet the dynamic and flexible charging needs. Modular Mobile Charging Robots (MMCRs), as a new type of charging facility, can provide efficient charging services for EVs in parking spaces through the flexible scheduling of mobile trailers and battery modules. However, current MMCR systems have two problems: first, the dynamic allocation efficiency of charging resources is low, failing to efficiently allocate charging resources according to user charging needs, resulting in long vehicle waiting times; second, the charging process is not coordinated with the grid's planned power output, easily causing grid load fluctuations exceeding thresholds and loss of demand response incentive benefits. Under the development trend of vehicle-grid interaction technology, it is urgent to solve the problem of coordinated optimization between charging resource scheduling and grid demand response. Summary of the Invention

[0003] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide a method, device, equipment and medium for the operation and control of a split-type mobile charging robot that can efficiently allocate charging resources, maintain system energy balance, and improve grid incentive benefits and charging service reliability.

[0004] In a first aspect, this application provides a method for controlling the operation of a split-type mobile charging robot. The split-type mobile charging robot includes: a charging master station, multiple battery modules, and multiple mobile trailers used in conjunction with each other; each battery module has a first interface and a second interface, the first interface being used to electrically connect with the charging master station to receive electrical energy from the charging master station; the second interface being used to charge a vehicle to be charged; and the mobile trailers being used to move the battery modules to the area where the vehicle to be charged is located.

[0005] The method includes the following steps:

[0006] Acquire the grid's planned power curve, charging demand data for all vehicles to be charged, and replenishment demand data for all battery modules; the charging demand data includes at least the target charging capacity of the vehicles to be charged; the replenishment demand data includes at least the current battery capacity and maximum replenishment power of the battery modules.

[0007] Based on the charging demand data and the replenishment demand data, the actual replenishment power curve is determined, and based on the actual replenishment power curve and the grid planned power curve, the deviation between the actual replenishment power curve and the grid planned power curve is determined.

[0008] Based on the deviation value and the deviation threshold range, the maximum incentive value for grid demand response is determined. The target charging power curve for charging all vehicles to be charged corresponding to the maximum incentive value is used as the target charging strategy. The target replenishment power curve for replenishing all battery modules by the charging master station corresponding to the maximum incentive value is used as the target replenishment strategy. The actual replenishment power curve is the total curve of the replenishment power of all battery modules changing over time.

[0009] According to the target charging strategy, a charging control command is sent to the battery module, which instructs the battery module to perform charging operations on the vehicle to be charged in accordance with the target charging strategy; according to the target replenishment strategy, a replenishment control command is sent to the charging master station, which instructs the charging master station to perform replenishment operations on the battery module in accordance with the target replenishment strategy.

[0010] According to the technical solution provided in this application, the maximum excitation value for power grid demand response is determined based on the deviation value and the deviation threshold range, specifically including the following steps:

[0011] If the deviation value falls within the target deviation threshold range of multiple deviation threshold ranges, then the excitation value corresponding to the target deviation threshold range shall be used as the maximum excitation value for the power grid demand response.

[0012] If the deviation value is not within any of the multiple deviation threshold ranges, then the preset excitation value will be used as the maximum excitation value for the power grid demand response.

[0013] According to the technical solution provided in this application, the deviation value between the actual power supply curve and the planned power grid curve is determined based on the actual power supply curve and the planned power grid curve, specifically including the following steps:

[0014] The timestamps of the planned power curve and the actual supplementary power curve are adjusted to the same sampling interval, and missing data points are filled based on linear interpolation.

[0015] Outliers in the actual power compensation curve after adjusting the sampling interval are removed, and the moving average method is used to smooth it to obtain the smoothed actual power compensation curve. Then, the effective time period is identified in the smoothed actual power compensation curve.

[0016] Within the effective time period, the absolute difference between the planned power of the power grid and the actual power replenishment after smoothing is integrated to obtain the absolute deviation integral, and the planned power of the power grid is integrated to obtain the planned power integral.

[0017] The quotient obtained by dividing the absolute deviation integral by the planned power integral is taken as the deviation value.

[0018] According to the technical solution provided in this application, the actual power replenishment curve is determined based on the charging demand data and the power replenishment demand data, specifically including the following steps:

[0019] Battery modules with a charge level lower than the target charging capacity and that are not currently in use are designated as battery modules to be recharged.

[0020] Based on the charging time and actual charging power of the battery module to be charged, the actual charging power curve is obtained by fitting.

[0021] According to the technical solution provided in this application, the method further includes updating the grid planned power curve according to the following steps:

[0022] Receive the power grid planned power curve sent by the power grid and record the corresponding reception time;

[0023] The receiving time is used as the starting time and timing is started. When the timing duration is equal to the power grid transmission interval, the power grid planned power curve transmitted by the power grid is received again and replaced with the previous power grid planned power curve. The receiving time is then recorded again.

[0024] Secondly, this application provides a split-type mobile charging robot operation control device, the device comprising:

[0025] The modular mobile charging robot includes: a charging master station, multiple battery modules, and multiple mobile trailers used in conjunction with each other; each battery module has a first interface and a second interface, the first interface being used to electrically connect with the charging master station to receive electrical energy from the charging master station; the second interface being used to charge the vehicle to be charged; and the mobile trailers being used to move the battery modules to the area where the vehicle to be charged is located.

[0026] The data acquisition module is used to acquire the grid planned power curve, the charging demand data of all vehicles to be charged, and the replenishment demand data of all battery modules; the charging demand data includes at least the target charging capacity of the vehicles to be charged; the replenishment demand data includes at least the current capacity and maximum replenishment power of the battery modules.

[0027] The data processing module is used to determine the actual power replenishment curve based on the charging demand data and the power replenishment demand data, and to determine the deviation between the actual power replenishment curve and the grid planned power curve based on the actual power replenishment curve and the grid planned power curve.

[0028] Based on the deviation value and the deviation threshold range, the maximum incentive value for grid demand response is determined. The target charging power curve for charging all vehicles to be charged corresponding to the maximum incentive value is used as the target charging strategy. The target replenishment power curve for replenishing all battery modules by the charging master station corresponding to the maximum incentive value is used as the target replenishment strategy. The actual replenishment power curve is the total curve of the replenishment power of all battery modules changing over time.

[0029] According to the target charging strategy, a charging control command is sent to the battery module, which instructs the battery module to perform charging operations on the vehicle to be charged in accordance with the target charging strategy; according to the target replenishment strategy, a replenishment control command is sent to the charging master station, which instructs the charging master station to perform replenishment operations on the battery module in accordance with the target replenishment strategy.

[0030] According to the technical solution provided in this application, the split-type mobile charging robot further includes: a spare trailer that is communicatively connected to the data processing module;

[0031] The data processing module is used to generate a backup control command when the mobile trailer fails; the backup control command is used to control the standby trailer to take over the task of the failed trailer.

[0032] According to the technical solution provided in this application, the data processing module is further used for:

[0033] If the deviation value falls within the target deviation threshold range of multiple deviation threshold ranges, then the excitation value corresponding to the target deviation threshold range shall be used as the maximum excitation value for the power grid demand response.

[0034] If the deviation value is not within any of the multiple deviation threshold ranges, then the preset excitation value will be used as the maximum excitation value for the power grid demand response.

[0035] According to the technical solution provided in this application, the data processing module is further used for:

[0036] The timestamps of the planned power curve and the actual supplementary power curve are adjusted to the same sampling interval, and missing data points are filled based on linear interpolation.

[0037] Outliers in the actual power compensation curve after adjusting the sampling interval are removed, and the moving average method is used to smooth it to obtain the smoothed actual power compensation curve. Then, the effective time period is identified in the smoothed actual power compensation curve.

[0038] Within the effective time period, the absolute difference between the planned power of the power grid and the actual power replenishment after smoothing is integrated to obtain the absolute deviation integral, and the planned power of the power grid is integrated to obtain the planned power integral.

[0039] The quotient obtained by dividing the absolute deviation integral by the planned power integral is taken as the deviation value.

[0040] According to the technical solution provided in this application, the data processing module is further used for:

[0041] Battery modules with a charge level lower than the target charging capacity and that are not currently in use are designated as battery modules to be recharged.

[0042] Based on the charging time and actual charging power of the battery module to be charged, the actual charging power curve is obtained by fitting.

[0043] According to the technical solution provided in this application, the data processing module is further used for:

[0044] Receive the power grid planned power curve sent by the power grid and record the corresponding reception time;

[0045] The receiving time is used as the starting time and timing is started. When the timing duration is equal to the power grid transmission interval, the power grid planned power curve transmitted by the power grid is received again and replaced with the previous power grid planned power curve. The receiving time is then recorded again.

[0046] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the split-type mobile charging robot operation control method described above.

[0047] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method for controlling the operation of a split-type mobile charging robot.

[0048] As can be seen from the above technical solution, this application has at least the following beneficial effects:

[0049] This application provides a method for controlling the operation of a split-type mobile charging robot, comprising the following steps: acquiring the grid's planned power curve, charging demand data for all vehicles to be charged, and replenishment demand data for all battery modules; the charging demand data includes at least the target charging capacity of the vehicles to be charged; the replenishment demand data includes at least the current battery capacity and maximum replenishment power of the battery modules; determining the actual replenishment power curve based on the charging demand data and the replenishment demand data, and determining the deviation value between the actual replenishment power curve and the grid's planned power curve based on the actual replenishment power curve and the grid's planned power curve; and determining the maximum excitation value for the grid demand response based on the deviation value and the deviation threshold range. The target charging power curve for charging all vehicles to be charged, corresponding to the maximum incentive value, is used as the target charging strategy. The target replenishment power curve for replenishing all battery modules, corresponding to the maximum incentive value, is used as the target replenishment power curve. The actual replenishment power curve is the total curve of the replenishment power of all battery modules changing over time. According to the target charging strategy, charging control commands are sent to the battery modules to instruct them to charge the vehicles to be charged according to the target charging strategy. According to the target replenishment strategy, replenishment control commands are sent to the charging master station to instruct it to replenish the battery modules according to the target replenishment strategy.

[0050] Traditional fixed charging piles are unable to meet the dynamic and flexible charging needs of electric vehicles. Furthermore, in the context of vehicle-grid interaction, there is a lack of a coordinated optimization mechanism for the scheduling of charging resources and the response of grid demand, resulting in large fluctuations in grid load and low efficiency in the allocation of charging resources. This application divides the scheduling of vehicles waiting to be charged and the replenishment of battery modules into two sub-problems that can be coordinated and optimized. Specifically, based on the split structure (coordinated operation of the charging master station, battery modules, and mobile trailers), it integrates and optimizes the grid planned power curve, vehicle charging demand, and battery module replenishment demand. Specifically, based on the charging demand data and replenishment demand data, the actual replenishment power curve is determined. Then, based on the actual replenishment power curve and the grid planned power curve, the deviation value between the actual replenishment power curve and the grid planned power curve is determined. Based on the deviation value and the deviation threshold range, the maximum incentive value of the grid demand response is determined. The target charging power curve corresponding to the maximum incentive value for charging all vehicles waiting to be charged is used as the target charging strategy. The target replenishment power curve corresponding to the maximum incentive value for the charging master station to replenish all battery modules is used as the target replenishment strategy. This achieves efficient coordination and adaptation between the charging system and the grid, effectively avoids the impact of disorderly charging on the grid, and ensures the stability of grid operation. Secondly, by acquiring demand data such as the target charging capacity of vehicles waiting to be charged, and combining this with the battery module's charging capability, targeted target charging strategies and charging strategies are generated. This ensures that each vehicle can reach its target charging capacity within the planned time, significantly improving the reliability and timeliness of the charging service. Simultaneously, the charging station's charging process for the battery module and the charging process from the battery module to the vehicle are independently yet coordinated. Utilizing the battery module as an energy buffer, the energy distribution between grid power supply, battery storage, and vehicle charging is flexibly balanced. This not only improves the resource utilization rate of the battery module but also allows for the rational scheduling of charging periods based on grid load conditions, reducing charging costs and overall improving the operational efficiency and economy of the mobile charging system. Attached Figure Description

[0051] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0052] Figure 1 A schematic diagram of the operation control device for a split-type mobile charging robot.

[0053] Figure 2 This is a structural diagram of a split-type mobile charging robot.

[0054] Figure 3 This is a schematic diagram of the control unit.

[0055] Figure 4 A flowchart illustrating the operation control method for a split-type mobile charging robot.

[0056] Figure 5 A flowchart for determining the deviation between the actual power supply curve and the planned power curve of the power grid.

[0057] Figure 6 This is an example diagram showing the planned power curve and the actual supplementary power curve of the power grid.

[0058] Figure 7 This is a schematic diagram of the electronic device.

[0059] Numbered in the diagram: 1. Charging master station; 2. Battery module; 3. Mobile trailer; 4. Backup trailer; 5. Data acquisition module; 6. Data processing module; 70. Power grid; 71. Control unit; 72. Split-type mobile charging robot; 73. Vehicle to be charged; 74. Parking space; 500. Electronic equipment; 501. CPU; 502. ROM; 503. RAM; 504. Bus; 505. I / O interface; 506. Input section; 507. Output section; 508. Storage section; 509. Communication section; 510. Driver; 511. Removable media. Detailed Implementation

[0060] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0061] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0062] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0063] Vehicle-to-Grid (V2G) refers to a technological model that enables bidirectional energy flow and information exchange between electric vehicles (EVs) and the power grid. In this application, V2G primarily focuses on the bidirectional constraints and collaborative optimization between vehicle charging resources and the power grid. Its role is to both help the power grid operate stably and tap into peak-shaving value, while enabling vehicles to optimize operations and ensure user experience. Through iterative processes, it continuously promotes more efficient vehicle-grid collaboration.

[0064] With the rapid annual growth in the number of electric vehicles, the power capacity and coverage density of traditional fixed charging stations are no longer sufficient to meet the demand. For example, during peak hours in large parking lots, the waiting time for a single charging station can exceed one hour, and the fixed deployment model leads to insufficient utilization of charging facilities in remote areas, resulting in significant resource waste. Furthermore, user charging demand exhibits significant spatiotemporal randomness; for instance, charging demand is concentrated during the day in commercial parks and peaks in residential areas at night. Traditional charging stations cannot dynamically adjust their location and power allocation according to real-time demand, leading to a supply-demand mismatch.

[0065] Furthermore, traditional fixed charging stations require pre-buried cables and power distribution facilities, resulting in long construction periods (typically 3-6 months) and high renovation costs. This makes them unsuitable for adapting to parking lot layout adjustments or temporary charging needs (such as during exhibitions and sporting events). Existing fixed charging stations are mostly unidirectional, lacking real-time power interaction capabilities with the power grid. When the regional charging load exceeds the distribution network capacity (such as during peak summer electricity consumption), voltage fluctuations or power supply reliability issues can easily occur, and they cannot participate in grid demand response to obtain incentive benefits.

[0066] Existing MMCR systems lack dynamic optimization mechanisms for resource allocation within user time windows. For example, when multiple electric vehicles arrive simultaneously, there may be a clustering of battery modules and mobile trailers, causing charging delays for some vehicles; or, due to the lack of consideration for remaining battery module charge, high-energy-consuming trailers may frequently travel to and from the charging station for recharging, reducing overall system efficiency. Currently, traditional MMCR recharging often uses timed charging or threshold-triggered modes, without combining grid power constraints with user charging plans for coordinated optimization. For example, when the grid requires a reduction in charging power during a certain period, if recharging is still performed according to a fixed strategy, the power interaction between the charging station and the grid may exceed the allowable deviation; for example, if the deviation exceeds 20%, the incentive benefits may be reset to zero.

[0067] In view of this, this application provides a method for the operation and control of a split-type mobile charging robot, which divides the scheduling of vehicles to be charged and the replenishment of battery modules into two sub-problems that can be coordinated and optimized. Specifically, based on the split structure (cooperative work of the charging master station, battery modules and mobile trailers), the planned power curve of the power grid, the vehicle charging demand and the battery module replenishment demand are integrated and optimized. Specifically, based on the charging demand data and the replenishment demand data, the actual replenishment power curve is determined. Then, based on the actual replenishment power curve and the planned power curve of the power grid, the deviation value between the actual replenishment power curve and the planned power curve of the power grid is determined. Based on the deviation value and the deviation threshold range, the maximum incentive value of the power grid demand response is determined. The target charging power curve corresponding to the maximum incentive value for charging all vehicles to be charged is used as the target charging strategy. The target replenishment power curve corresponding to the maximum incentive value for replenishing all battery modules of the charging master station is used as the target replenishment strategy. This achieves efficient coordination and adaptation between the charging system and the power grid, effectively avoids the impact of disorderly charging on the power grid, and ensures the stability of the power grid operation. Secondly, by acquiring demand data such as the target charging capacity of vehicles waiting to be charged, and combining this with the battery module's charging capability, targeted target charging strategies and charging strategies are generated. This ensures that each vehicle can reach its target charging capacity within the planned time, significantly improving the reliability and timeliness of the charging service. Simultaneously, the charging station's charging process for the battery module and the charging process from the battery module to the vehicle are independently yet coordinated. Utilizing the battery module as an energy buffer, the energy distribution between grid power supply, battery storage, and vehicle charging is flexibly balanced. This not only improves the resource utilization rate of the battery module but also allows for the rational scheduling of charging periods based on grid load conditions, reducing charging costs and overall improving the operational efficiency and economy of the mobile charging system.

[0068] Among them, such as Figure 1 As shown, this application provides a split-type mobile charging robot operation control device, including:

[0069] like Figure 2 As shown, the split-type mobile charging robot 72 includes: a charging master station 1, multiple battery modules 2, and multiple mobile trailers 3 used in conjunction; the battery modules 2 have a first interface and a second interface, the first interface is used to electrically connect with the charging master station 1 to receive electrical energy from the charging master station 1; the second interface is used to charge the vehicle to be charged; the mobile trailers 3 are used to move the battery modules 2 to the area where the vehicle to be charged is located; the power grid 70 is used to send the power grid planned power curve to the charging master station 1;

[0070] like Figure 3 As shown, the control unit 71 includes:

[0071] Data acquisition module 5 is used to acquire the grid planned power curve, the charging demand data of all vehicles 73 to be charged, and the replenishment demand data of all battery modules 2. The charging demand data includes at least the target charging capacity of the vehicles 73 to be charged. The replenishment demand data includes at least the current capacity and maximum replenishment power of the battery modules 2. The vehicles 73 to be charged refer to electric vehicles parked at parking space 74 that need to be charged.

[0072] Data processing module 6 is used to determine the actual power replenishment curve based on charging demand data and power replenishment demand data, and to determine the deviation between the actual power replenishment curve and the grid planned power curve based on the actual power replenishment curve and the grid planned power curve.

[0073] Based on the deviation value and the deviation threshold range, the maximum incentive value for grid demand response is determined. The target charging power curve for charging all vehicles 73 to be charged corresponding to the maximum incentive value is used as the target charging strategy. The target replenishment power curve for replenishing all battery modules 2 by charging station 1 corresponding to the maximum incentive value is used as the target replenishment strategy. The actual replenishment power curve is the total curve of the replenishment power of all battery modules 2 changing over time.

[0074] According to the target charging strategy, a charging control command is sent to the battery module 2, which is used to instruct the battery module 2 to perform charging operation on the vehicle 73 to be charged in accordance with the target charging strategy; according to the target replenishment strategy, a replenishment control command is sent to the charging master station 1, which is used to instruct the charging master station 1 to perform replenishment operation on the battery module 2 in accordance with the target replenishment strategy.

[0075] Furthermore, the split-type mobile charging robot 72 also includes: a backup trailer 4 that is communicatively connected to the data processing module 6;

[0076] The data processing module 6 is used to generate a backup control command when the mobile trailer 3 fails; the backup control command is used to control the standby trailer 4 to take over the task of the faulty trailer (i.e., the malfunctioning mobile trailer 3).

[0077] It should be noted that the split-type mobile charging robot 72 includes a charging master station 1, multiple battery modules 2, multiple mobile trailers 3 and a spare trailer 4, forming a split-type architecture for centralized power supply and mobile service. The charging master station 1 is connected to the power grid and provides centralized charging for the battery modules 2. It can be electrically connected to the first interface of the battery modules 2 through cables to replenish their power.

[0078] Here, the charging master station 1 has two types of demand response information receiving sources: one is that the information receiving module of the charging master station 1 of MMCR is interconnected with the new power grid load management system platform through the 4G / 5G wireless private network, and the message interaction is once every 3 minutes, and the demand response instructions of the large power grid or distribution network with regulation plans are interacted; the other is that the information receiving module of the charging master station 1 of MMCR is interconnected with the new generation of intelligent centralized concentrator of the substation through local communication methods such as high-speed power line communication (HPLC) / micro-power wireless / WIFI, and the message interaction is once every 200 milliseconds, and the emergency demand response instructions related to the safety of the local distribution network are interacted. When there is a conflict between the two types of demand response information sources, the local information source is prioritized.

[0079] The battery module 2 has a first interface and a second interface. The first interface is electrically connected to the charging master station 1 and is used to receive electric energy and store it in the battery module 2; the second interface is used to supply power to the vehicle 73 to be charged; the mobile trailer 3 can realize the position transfer and charging service of the battery module 2.

[0080] The spare trailer 4 is used as a fault redundancy unit to ensure the reliability of the device, that is: when the mobile trailer 3 fails, the spare trailer 4 can receive the备案 control instruction issued by the data processing module 6 to take over the tasks of the faulty trailer, such as transporting the battery module 2, charging operation, etc., to avoid service interruption.

[0081] The number of the above-mentioned charging master stations 1 is, for example, one, the number of battery modules 2 is, for example, n, the number of mobile trailers 3 is, for example, m, here, 1 < m < n; the number of spare trailers 4 is, for example, one, and the service range of this device is, for example, h parking spaces. Here, n, m, and h are all natural numbers.

[0082] Here, the type of the data acquisition module 5 is, for example, a detection sensor, and the type of the data processing module 6 is, for example, an embedded module based on an ARM (Advanced RISC Machine) processor.

[0083] According to the technical solution provided by this application, the data processing module 6 is further used for:

[0084] If the deviation value is within the target deviation threshold range among multiple deviation threshold ranges, then use the incentive value corresponding to the target deviation threshold range as the maximum incentive value for grid demand response;

[0085] If the deviation value is not within any of the multiple deviation threshold ranges, then use the preset incentive value as the maximum incentive value for grid demand response.

[0086] According to the technical solution provided by this application, the data processing module 6 is further used for:

[0087] The timestamps of the planned power curve and the actual supplementary power curve are adjusted to the same sampling interval, and missing data points are filled based on linear interpolation.

[0088] Outliers in the actual power compensation curve after adjusting the sampling interval are removed, and the moving average method is used to smooth it to obtain the smoothed actual power compensation curve. Then, the effective time period is identified in the smoothed actual power compensation curve.

[0089] Within the effective time period, the absolute difference between the planned power of the power grid and the actual power replenishment after smoothing is integrated to obtain the absolute deviation integral, and the planned power of the power grid is integrated to obtain the planned power integral.

[0090] The quotient obtained by dividing the absolute deviation integral by the planned power integral is taken as the deviation value.

[0091] According to the technical solution provided in this application, the data processing module 6 is also used for:

[0092] Battery modules with a charge level lower than the target charge level and not currently in use will be designated as battery modules to be recharged.

[0093] Based on the charging time and actual charging power of the battery module to be charged, the actual charging power curve is obtained by fitting.

[0094] According to the technical solution provided in this application, the data processing module 6 is also used for:

[0095] Receive the power grid planned power curve sent by the power grid and record the corresponding reception time;

[0096] The receiving time is used as the starting time and the timing is recorded. When the timing duration is equal to the power grid transmission interval, the power grid planned power curve transmitted by the power grid is received again and replaced with the previous power grid planned power curve. The receiving time is then recorded again.

[0097] To make the operation and control method of the split-type mobile charging robot provided in this application embodiment clearer and easier to understand, the method is described below with reference to the accompanying drawings. Figure 4 As shown in the figure, this is a flowchart of the operation control method for a split-type mobile charging robot provided in an embodiment of this application. The method includes the following steps:

[0098] S10. Obtain the grid planned power curve, charging demand data for all vehicles to be charged, and replenishment demand data for all battery modules; the charging demand data shall include at least the target charging capacity of the vehicles to be charged; the replenishment demand data shall include at least the current capacity and maximum replenishment power of the battery modules.

[0099] The charging station obtains the grid's planned power curve from the power grid. Vehicles awaiting charging refer to electric vehicles parked in parking spaces that have not yet completed charging. Charging demand data and supplementary charging demand data can be collected by data acquisition module 5.

[0100] In some examples, the target charging capacity = target battery capacity - remaining capacity; the target battery capacity is set by the user or is the maximum capacity of the battery module. The remaining battery capacity (SOC) can be obtained through the communication protocol of the on-board diagnostic system interface or the charging interface (such as ISO15118). For example, after the mobile trailer is connected to the vehicle to be charged, the data acquisition module sends a query command to the vehicle, receives and parses the remaining capacity data.

[0101] The current battery level of the battery module can be obtained in real time by the data acquisition module, and the maximum charging power of the battery module is obtained from the factory specifications of the battery module.

[0102] S20. Based on the charging demand data and the replenishment demand data, determine the actual replenishment power curve, and based on the actual replenishment power curve and the grid planned power curve, determine the deviation between the actual replenishment power curve and the grid planned power curve.

[0103] The data processing module determines the actual power replenishment curve based on charging demand data and power replenishment demand data, specifically including the following steps:

[0104] Battery modules with a charge level lower than the target charge level and not currently in use will be designated as battery modules to be recharged.

[0105] Based on the charging time and actual charging power of the battery module to be charged, the actual charging power curve is obtained by fitting.

[0106] The battery module's charge level refers to the current charge level of the battery module in the charging demand data. If the current charge level of the battery module is less than the target charging capacity and it is not currently occupied, it means that the battery module cannot meet the charging needs of any vehicle waiting to be charged and needs to be charged through the charging station. This type of battery module is a battery module waiting to be charged. Conversely, it is a target battery module. Here, "not occupied" means that the battery module is in an idle state and is not currently charging, waiting to be charged, or in transit.

[0107] The charging time for a battery module to be charged refers to the time period during which the battery module can receive charging from the charging station. The actual charging power of the battery module to be charged is calculated by taking the maximum charging power of the battery module as the upper limit, combined with the charging time and the target charging amount.

[0108] The initial replenishment power is calculated as: Target charging capacity / Replenishment time. If the initial replenishment power is less than or equal to the maximum replenishment power of the battery module, then the initial replenishment power is used as the actual replenishment power. If the initial replenishment power is greater than the maximum replenishment power of the battery module, then the maximum replenishment power of the battery module is used as the actual replenishment power. A time-segment overlay method can be used, dividing the replenishment time window into multiple equal-length sampling periods, consistent with the sampling interval of the grid planned power curve. The sum of the actual replenishment power of all battery modules to be replenished within each sampling period is calculated as the total actual replenishment power for that period. Finally, a continuous curve is obtained by fitting the coordinates of the sampling periods and the total power, and this curve is used as the actual replenishment power curve.

[0109] like Figure 5 As shown, the deviation between the actual power supply curve and the planned power grid curve is determined based on the actual power supply curve and the planned power grid curve. This process includes the following steps:

[0110] S201. Adjust the timestamps of the planned power curve and the actual supplementary power curve of the power grid to the same sampling interval, and fill in the missing data points based on the linear interpolation method.

[0111] The planned power curve of the power grid (e.g., at 1-minute intervals) and the actual supplementary power curve (e.g., at 3-minute intervals) have different time resolutions and need to be unified to the same time base. Here, the planned power curve of the power grid and the actual supplementary power curve are unified to the same sampling interval, and missing points are filled by linear interpolation to ensure that all curves are completely aligned in the time dimension, providing a basis for subsequent difference calculation.

[0112] Example: If the actual power replenishment is 200kW at 19:00 and 220kW at 19:03, adjust the sampling interval to 1 minute, and the period from 19:00 to 19:03 to be 3 minutes. Calculate the power increase per minute using the following formula:

[0113] ;

[0114] For example, the actual power supply at 19:01 was 206.67kW.

[0115] In addition, missing points in the curve (such as data loss due to communication interruption) are also supplemented using linear interpolation to ensure the continuity of the curve.

[0116] S202. Remove outliers from the actual power compensation curve after adjusting the sampling interval, and smooth it using the moving average method to obtain the smoothed actual power compensation curve. Then, identify the effective time period in the smoothed actual power compensation curve.

[0117] Here, statistical methods (such as the 3σ principle) can be used to identify outliers in the actual power compensation curve (such as instantaneous jumps exceeding 3 times the standard deviation), and the curve can be replaced by interpolation of adjacent points to obtain a smoothed actual power compensation curve.

[0118] The rule for identifying outliers is as follows: if the value exceeds the sum of the mean and three times the standard deviation, it is considered an outlier; otherwise, it is considered a normal point. Example: The actual power compensation curve has a mean of 280kW and a standard deviation of 5kW. If a point has a power of 300kW (>280 + 3 × 5 = 295kW), it is considered an outlier and replaced with an interpolated value from an adjacent point.

[0119] Apply a sliding window (such as a 5-minute window) to the curve after removing outliers to calculate the average value and eliminate high-frequency fluctuations.

[0120] Among them, the start and end times of power replenishment can be determined by the power threshold, i.e. whether it is a valid time point. For example, if the actual power replenishment is greater than the power threshold, it is considered to have entered a valid power replenishment state and is marked as a valid time point. If the actual power replenishment at multiple consecutive time points (such as 3) is greater than the power threshold, then this period is judged to be a valid time period.

[0121] Example: The actual power replenishment at 19:00, 19:01, and 19:02 are 60kW, 55kW, and 52kW respectively. The power threshold is 50kW. If the power is less than this threshold, it is considered invalid; otherwise, it is valid. By judgment, we can see that 60kW, 55kW, and 52kW are all greater than the power threshold, so the corresponding time points are valid time points, that is, 19:00-19:02 is the valid time period. If the actual power replenishment at 19:03 is 45kW, which is less than the power threshold, it is considered invalid.

[0122] S203. Within the effective time period, integrate the absolute difference between the planned power of the power grid and the actual power replenishment after smoothing to obtain the absolute deviation integral, and integrate the planned power of the power grid to obtain the planned power integral.

[0123] Within the effective time period, the absolute difference between the planned power of the power grid and the actual power replenishment after smoothing is integrated, and the formula is as follows:

[0124] ;

[0125] Among them, the absolute deviation integral represents the accumulated amount of power deviation during charging and recharging. This is the power grid planned power function, which can be represented by the power grid planned power curve. This is the actual power compensation function, which can be represented by the actual power compensation curve. , Let be the actual power replenishment power of the i-th battery module at each time point. At the starting time, It's the end time. The number of battery modules. This represents the total number of battery modules.

[0126] Planned power integral: The integral of the planned power of the power grid over the same time period is given by the following formula:

[0127] ;

[0128] Among them, the planned power integral represents the total expected power supply of the power grid.

[0129] S204. The quotient obtained by dividing the absolute deviation integral by the planned power integral is taken as the deviation value.

[0130] Here, the deviation value reflects the discrepancy between the actual supplementary power and the planned power from the grid. The deviation value is defined as the ratio of the integral of the absolute deviation to the integral of the planned power, expressed as a percentage:

[0131] ,

[0132] Right now: ;

[0133] in, This is the deviation value. This is the power grid planned power function, which can be represented by the power grid planned power curve. This is the actual power compensation function, which can be represented by the actual power compensation curve. , Let be the actual power replenishment power of the i-th battery module at each time point. At the starting time, It's the end time.

[0134] S30. Based on the deviation value and the deviation threshold range, determine the maximum incentive value for grid demand response. Use the target charging power curve corresponding to the maximum incentive value for charging all vehicles to be charged as the target charging strategy. Use the target replenishment power curve corresponding to the maximum incentive value for replenishing all battery modules as the target replenishment strategy. The actual replenishment power curve is the total curve of the replenishment power of all battery modules changing over time.

[0135] The determination of the maximum incentive value for grid demand response, based on the deviation value and the deviation threshold range, specifically includes the following steps:

[0136] If the deviation value falls within the target deviation threshold range among multiple deviation threshold ranges, then the excitation value corresponding to the target deviation threshold range will be used as the maximum excitation value for the power grid demand response.

[0137] If the deviation value is not within any of the multiple deviation threshold ranges, then the preset excitation value will be used as the maximum excitation value for the power grid demand response.

[0138] The calculated deviation value is compared with each deviation threshold range to determine its corresponding deviation threshold range. If a target deviation threshold range exists, the incentive value corresponding to the target deviation threshold range is used as the maximum incentive value for grid demand response. If the deviation value exceeds all preset deviation threshold ranges, there is no incentive value matching the corresponding deviation threshold range, and the preset incentive value is used as the maximum incentive value. Here, the preset incentive value is, for example, 0.

[0139] For example, the deviation threshold range is ≤10%, and the corresponding incentive value is 100%; the deviation threshold range is 10% < deviation ≤20%, and the corresponding incentive value is 80%; the deviation threshold range is >20%, and the corresponding incentive value is 0. The maximum incentive value refers to the highest return corresponding to the deviation threshold range in which the calculated deviation value is located.

[0140] The maximum excitation value corresponds to the optimal power curve combination, that is, the target charging power curve and the target replenishment power curve must be able to match the deviation value with the maximum excitation, and also meet the user's charging needs and equipment constraints.

[0141] S40. According to the target charging strategy, send a charging control command to the battery module. The charging control command is used to instruct the battery module to perform charging operations on the vehicle to be charged in accordance with the target charging strategy. According to the target replenishment strategy, send a replenishment control command to the charging master station. The replenishment control command is used to instruct the charging master station to perform replenishment operations on the battery module in accordance with the target replenishment strategy.

[0142] The charging control command must include at least the target vehicle number, charging power, charging duration, and stop conditions. For example, charging vehicle 74-3 with a charging power of 20kW and a charging duration of 1 hour will stop upon completion. The replenishment control command must include at least the target module number, replenishment power, replenishment duration, and stop conditions. For example, replenishing battery module BM003 with a replenishment power of 90kW and a replenishment duration of 30 minutes will stop upon completion.

[0143] Furthermore, this application updates the grid planned power curve according to the following steps:

[0144] Receive the power grid planned power curve sent by the power grid and record the corresponding reception time;

[0145] The receiving time is used as the starting time and the timing is recorded. When the timing duration is equal to the power grid transmission interval, the power grid planned power curve transmitted by the power grid is received again and replaced with the previous power grid planned power curve. The receiving time is then recorded again.

[0146] The charging station receives the planned power curve sent by the power grid through a 4G / 5G wireless private network or a local communication interface such as HPLC, and records the receiving time as the starting point for timing.

[0147] An internal timer is started for real-time monitoring. When the timing duration equals the power grid's set transmission interval, the curve receiving process is triggered, which means receiving the power grid's planned power curve again, overwriting and replacing the power grid's planned power curve already stored in memory, and re-recording the current receiving time. This receiving time serves as the starting point for a new timing cycle and enters the next timing cycle.

[0148] For example, if the planned power output during the peak load period (19:00-20:00) is reduced from 300kW to 280kW, the update mechanism can trigger curve replacement within 3 minutes to avoid exceeding the power limit.

[0149] If not updated in time, the actual power will still be executed according to the original grid planned power (300kW), while the grid planned power to be updated is 280kW. The deviation value may increase from 10% to 25%, and the corresponding deviation threshold range is >20%, which will cause the maximum incentive benefit (maximum incentive value) to drop from 80% to 0. Here, the maximum incentive benefit (maximum incentive value) is related to the magnitude of its corresponding deviation value. If the deviation value does not have a corresponding target deviation threshold range, it is directly recorded as 0. If the deviation value has a corresponding target deviation threshold range, the incentive value corresponding to the target deviation threshold range can be used as the maximum incentive value.

[0150] When two types of communication links simultaneously issue different planned power curves, the local communication priority principle shall be followed. For example, if 4G / 5G issues a planned power of 300kW and HPLC issues an emergency command of 280kW, the system shall prioritize the 280kW curve and record the conflict log.

[0151] Furthermore, after each curve update, the device automatically uses the received grid planned power curve as a benchmark to recalculate the deviation value of the actual power curve. If the update time is within the effective power replenishment period, the system will regenerate the actual charging power curve and the actual power replenishment curve (step S30) to ensure that the deviation is controlled within the threshold.

[0152] After receiving the grid planned power curve, the power optimization module returns to step S20 based on the latest grid planned power curve to adjust the power allocation of each module.

[0153] For example, if the grid's planned power increases from 300kW to 350kW, the power replenishment optimization module can increase the power replenishment to 350kW, thereby improving the battery module's power replenishment efficiency.

[0154] If the required updated grid planned power curve is not received within the specified interval, the device can trigger a communication fault alarm. At the same time, it will continue to use the original grid planned power curve and execute according to the preset strategy (such as reducing the supplementary power by 10%) to avoid deviation and loss of control.

[0155] Through a timed update mechanism, the MMCR system can respond to changes in power grid dispatch commands in real time, meeting the timeliness requirements of demand response. Furthermore, dual communication links and a priority strategy ensure the accuracy and timeliness of command reception in complex power grid environments, enhancing system robustness.

[0156] Furthermore, this application also includes the following steps:

[0157] When a mobile trailer fails, a backup control command is generated; the backup control command is used to control a standby trailer to take over the task of the failed trailer.

[0158] It should be noted that the mobile trailer's motor current, drive voltage, position feedback, and other parameters are monitored in real time by sensors. The system is considered to be malfunctioning when the following conditions occur: the motor drive current exceeds 150% of the rated value for more than 10 seconds; the position deviation exceeds the preset path by more than 50 centimeters and cannot be corrected; or the signal of key sensors (such as encoders and tilt sensors) is interrupted for more than 2 seconds.

[0159] If the mobile trailer fails to perform any action within 1 minute of receiving the moving command, or if the power output remains at 0 for more than 30 seconds during charging, it is deemed to be malfunctioning.

[0160] After the data processing module detects a malfunction signal from the mobile trailer, it immediately generates a backup control command. This command contains the following key information: the faulty trailer number (e.g., MT005) and the current task information (target parking space, battery module number); the backup trailer number (e.g., MT004) and the specific requirements for taking over the task (movement path, charging power parameters); and the task switching time window (e.g., requiring the backup trailer to take over within 2 minutes).

[0161] The backup trailer (MT006) is normally parked near the main charging station and is in a standby state with sufficient power (SOC≥90%) and the system ready (all sensors are normal).

[0162] After receiving the filing control command, the backup trailer parses the current task location (e.g., parking space 74-3) and battery module information (e.g., BM002) of the faulty trailer.

[0163] Based on the parking lot map, plan the optimal route from the current location to the location of the breakdown tow truck (avoiding obstacles), and start the drive motor to move to the target parking space.

[0164] Upon arrival at the parking space, the device connects to the battery module BM002 via the second interface and simultaneously reads the charging parameters stored in the faulty trailer (such as remaining charging capacity and charging time).

[0165] Continue charging the electric vehicle according to the original mission parameters of the disabled tow truck, ensuring a seamless charging process.

[0166] Here, before the malfunctioning trailer fails, it stores the charging data (such as 15kWh already charged and 5kWh remaining) in the non-volatile memory of the battery module; after the standby trailer reads this data, it automatically sets the charging target to 5kWh to ensure that the user's charging needs are not affected.

[0167] After the standby trailer takes over the task, the data processing module sends a power-off command to the faulty trailer to cut off its power supply and charging circuit; and generates a fault code (such as GZ-MT002) based on the failure characteristics (such as motor overcurrent), and uploads it to the operation and maintenance platform via 4G / 5G, along with the operation log of the 10 minutes before the failure.

[0168] In addition, when the mobile trailer malfunctions, the data processing module analyzes the cause of the malfunction (such as motor failure or sensor failure) to provide a basis for generating backup control commands; if it is determined to be a temporary software failure (such as program freeze), the device will first try to restart the malfunctioning trailer, and if the restart fails, it will trigger the backup trailer to take over.

[0169] When the standby trailer takes over the task, the data processing module recalculates the actual power replenishment to ensure that the actual power replenishment curve does not exceed the grid's planned power curve.

[0170] Traditional fixed charging piles will directly interrupt charging when they encounter a fault. However, the backup trailer takeover mechanism in this application is a redundant design scheme. When the mobile trailer fails, the backup trailer can quickly take over the charging service. It is suitable for high-traffic scenarios (such as airport parking lots) to ensure that the charging service is not interrupted during peak hours.

[0171] The specific optimization process for applying the solution in this application to the following scenarios is as follows:

[0172] Assumptions: 50 parking spaces, 10 battery modules, 2 mobile trailers, 500kW maximum power of the distribution station, 100kW maximum charging power of a single battery module, and 50kWh total rated capacity (energy) of a single battery module.

[0173] Setting: Grid planned power curve The curve between 19:00 and 20:00 is smooth, with a maximum power of 300kW and a fluctuation range of ±20kW. Specifically, it can be expressed as:

[0174] ;

[0175] Where t is time (hours). .

[0176] Assuming there are 50 parking spaces, each with 74 users, the user demand is as follows:

[0177] The target charging capacity is 20kWh (assuming all users have the same needs).

[0178] Arrival times at parking spaces are evenly distributed between 18:00 and 19:00; departure times are evenly distributed between 20:00 and 21:00.

[0179] Here, sensors deployed in the parking space (such as Radio Frequency Identification (RFID) readers, infrared sensors, or cameras) can monitor vehicle parking actions in real time, triggering timestamp recording to capture the arrival time of the vehicle waiting to be charged. For example, when a vehicle enters the parking space and blocks the infrared beam, the sensor sends a signal to the data acquisition module, recording the current time as the arrival time of the vehicle. The departure time can be input by the user through the vehicle's central control screen.

[0180] The initial charge of each battery module is evenly distributed between 10-30 kWh.

[0181] At 19:00, the target charging capacity of vehicle 74-1 is 20kWh, and its target departure time is 19:30; the target charging capacity of vehicle 74-2 is 15kWh, and its target departure time is 19:45; the target charging capacity of vehicle 74-3 is 25kWh, and its target departure time is 20:00. Vehicles in parking spaces 74-1, 74-2, and 74-3 arrive simultaneously. The system scheduling logic is as follows:

[0182] Mobile trailer MT001 (10m away from E1, carrying module BM003, SOC=50%) serves vehicle 74-1.

[0183] Mobile trailer MT002 (8m away from parking space 74-3, carrying module BM005, SOC=60%) serves the vehicle in parking space 74-3;

[0184] After E2 completes the vehicle task at parking space 74-1 at 19:30, it takes over the service. At this time, the remaining power of battery module BM003 is 25kWh, which is ≥15kWh and meets the requirements.

[0185] Here, the mobile trailer not only moves the corresponding battery modules to the required location, but it also has a standard charging gun plug to charge the vehicles waiting to be charged. Therefore, only after the mobile trailer has completed the charging service at the current parking space can it be determined whether the mobile trailer needs to carry the corresponding battery modules to the next parking space to continue the service.

[0186] Under this scheduling, all vehicles complete charging within the target time and ensure that the power replenishment of the battery modules is controlled within the grid's planned power of 300kW.

[0187] The actual power supply curve shows an average power of 280kW between 19:00 and 20:00, with a fluctuation range of ±15kW. Figure 6 As shown, Figure 6 This is an example graph showing the planned power curve and the actual supplementary power curve of the power grid, used to visually demonstrate the power regulation effect of this solution in power grid demand response. Figure 6 The horizontal axis represents the time dimension, covering a one-hour period from 19:00 to 20:00, accurate to the minute (e.g., 19:00, 19:15, etc.). Figure 6 The vertical axis represents the power dimension, with units of kW, showing the numerical changes between the grid's planned power and the actual power executed by the MMCR.

[0188] Figure 6 Curve 1 in the figure is the power plan curve issued by the power grid. Theoretically, the MMCR system is required to follow this curve to interact with the power grid. Figure 6 Curve 2 in the figure is the actual power compensation curve of MMCR under the regulation of this application, which reflects the system's execution of grid commands.

[0189] Here, the planned power curve of the power grid is in the form of a sinusoidal fluctuation, and its expression is:

[0190] ;

[0191] in, The reference power is, for example, 300kW, with ±20kW as the fluctuation range, reflecting the power grid's dynamic power regulation requirements for MMCR (such as peak shaving and valley filling, load balancing).

[0192] From 19:00 to 20:00, the planned power of the power grid gradually increased from 280.68kW to about 282.68kW, showing periodic fluctuations, which is in line with the smooth control requirements of the power grid in response to non-urgent demands.

[0193] The power range of the actual supplementary power curve of MMCR is as follows: the actual supplementary power fluctuates between 267.68kW and 297.34kW, with an average power of about 280kW. The fluctuation range (±15kW) is less than the ±20kW range allowed by the power grid.

[0194] 19:00-19:15 time period:

[0195] The planned power of the power grid gradually increased from 280.68kW to 282.10kW, while the actual power replenishment fluctuated between 267.68kW and 294.06kW. At some times (such as 19:01 and 19:15), the actual power replenishment was higher than the planned power of the power grid. This was due to the dynamic scheduling of user charging demand and battery module power replenishment, but the overall deviation was still within a controllable range.

[0196] 19:15-19:30 period:

[0197] The planned power of the power grid continued to rise to around 282.55kW. The actual power replenishment was gradually brought closer to the planned power grid curve through optimization and adjustment (such as reducing the power replenishment of some battery modules). For example, at 19:20, the actual power replenishment was 267.21kW, which is about 5% different from the planned power of 281.21kW, reflecting the system's rapid response to the power grid command.

[0198] 19:30-20:00 period:

[0199] The planned power of the power grid continued to fluctuate and rise to 282.68kW, while the actual power supply was between 268.22kW and 297.34kW. For example, at 19:50, the actual power supply of 274.55kW deviated from the planned power of 282.55kW by about 2.8%, and the corresponding maximum incentive value was 100%.

[0200] At 19:15, the planned power output of the power grid suddenly dropped from 300kW to 240kW (fluctuation of -20%). The system response process is as follows:

[0201] At 19:16, the planned power of the power grid after the sudden drop is received, and the power at each time point is updated using linear interpolation.

[0202] At 19:17, the actual power supply was reduced from 280kW to 230kW (the power of each module was reduced by 18%).

[0203] At 19:20, a new actual power compensation curve was generated, and the final deviation value increased from 2.8% to 4.2%, corresponding to a maximum excitation value of 100%.

[0204] During this process, the standby trailer MT004 was in hot standby mode and no takeover command was triggered.

[0205] The calculated deviation value d = 10% corresponds to a target deviation threshold range of ≤ 10%, so the maximum excitation value is 100%.

[0206] When the planned power of the power grid fluctuates around 19:15 (e.g., from 281.06kW to 282.10kW), the actual power supply is quickly adjusted between 19:16 and 19:19 (from 294.06kW to 288.15kW), with a response delay of ≤1 minute, demonstrating the system's real-time adaptability to power grid commands.

[0207] The fluctuation of the actual power replenishment curve conforms to the battery module charging and replenishment constraints, and no power mutation or abnormal jump occurs, verifying the feasibility of the control strategy under hardware constraints.

[0208] This application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a split-type mobile charging robot operation control method as described in the above embodiments.

[0209] Among them, such as Figure 7 As shown, the electronic device 500 includes a CPU 501, which can perform various appropriate actions and processes according to a program stored in ROM 502 or a program loaded from storage section 508 into RAM 503.

[0210] RAM 503 also stores various programs and data required for system operation. CPU 501, ROM 502, and RAM 503 are interconnected via bus 504. I / O interface 505 is also connected to bus 504.

[0211] The following components are connected to I / O interface 505: an input section 506 including a keyboard, mouse, etc.; an output section 507 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 508 including a hard disk, etc.; and a communication section 509 including a network interface card such as a LAN card, modem, etc. The communication section 509 performs communication processing via a network such as the Internet. A drive 510 is also connected to I / O interface 505 as needed. A removable medium 511, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 510 as needed so that computer programs read from it can be installed into storage section 508 as needed.

[0212] Specifically, according to embodiments of this application, the above reference flow Figure 4 The described process can be implemented as a computer software program.

[0213] For example, this application includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via communication section 509, and / or installed from removable medium 511. When the computer program is executed by CPU 501, it performs the functions defined in the system of this application.

[0214] It should be noted that the computer-readable medium shown in this application may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM (random access memory), ROM (read-only memory), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0215] In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in connection with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can transmit, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wireline, optical fiber, RF, etc., or any suitable combination thereof.

[0216] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment—or portion of code—containing one or more executable instructions for implementing a specified logical function.

[0217] It should also be noted that in some alternative implementations, the functions marked in the boxes may occur in a different order than those shown in the accompanying drawings. For example, two consecutively indicated boxes may actually be executed substantially in parallel, or sometimes in reverse order, depending on the functions involved. It should also be noted that each box in a block diagram or flowchart, and combinations of boxes in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified functions or operations, or using a combination of dedicated hardware and computer instructions.

[0218] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself. The described units or modules can also be located in a processor.

[0219] This application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to implement the split-type mobile charging robot operation control method as described in the above embodiments.

[0220] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for operating and regulating a split mobile charging robot, characterized in that, The split mobile charging robot comprises a charging master station, a plurality of battery modules and a plurality of mobile trailers used in cooperation; the battery module has a first interface and a second interface, the first interface is used for electrical connection with the charging master station to receive electrical energy of the charging master station, and the second interface is used for charging a vehicle to be charged; the mobile trailer is used for moving the battery module to an area where the vehicle to be charged is located; The method comprises the following steps: obtaining a power grid planned power curve, charging demand data of all vehicles to be charged, and power supplement demand data of all battery modules; the charging demand data at least comprises a target charging power of the vehicle to be charged; the power supplement demand data at least comprises a current power of the battery module and a maximum power supplement power; determining an actual power supplement power curve according to the charging demand data and the power supplement demand data, and determining a deviation value of the actual power supplement power curve and the power grid planned power curve according to the actual power supplement power curve and the power grid planned power curve; determining a maximum incentive value of a power grid demand response according to the deviation value and a deviation threshold range, taking a target charging power curve corresponding to the maximum incentive value as a target charging strategy, and taking a target power supplement power curve corresponding to the maximum incentive value as a target power supplement strategy; the actual power supplement power curve is a total curve of power supplement powers of all battery modules changing over time; sending a charging control instruction to the battery module according to the target charging strategy, the charging control instruction being used for instructing the battery module to perform a charging operation on the vehicle to be charged according to the target charging strategy; and sending a power supplement control instruction to the charging master station according to the target power supplement strategy, the power supplement control instruction being used for instructing the charging master station to perform a power supplement operation on the battery module according to the target power supplement strategy.

2. The method of claim 1, wherein the method further comprises: The method comprises the following steps: if the deviation value is in a target deviation threshold range in a plurality of deviation threshold ranges, taking an incentive value corresponding to the target deviation threshold range as the maximum incentive value of the power grid demand response; if the deviation value is not in any one of the plurality of deviation threshold ranges, taking a preset incentive value as the maximum incentive value of the power grid demand response.

3. The method of claim 1, wherein the method further comprises: The method comprises the following steps: adjusting time stamps of the power grid planned power curve and the actual power supplement power curve to a same sampling interval, and filling missing data points based on a linear interpolation method; removing an abnormal value in the actual power supplement power curve after the adjustment of the sampling interval, performing smoothing processing by using a sliding average method to obtain a smoothed actual power supplement power curve, and identifying an effective time period in the smoothed actual power supplement power curve; Integrate absolute difference value between grid planned power and smoothed actual power supply power in the effective time period to obtain absolute deviation integral, and integrate the grid planned power to obtain planned power integral; Divide the absolute deviation integral and the planned power integral to obtain a quotient as a deviation value.

4. The method of claim 1, wherein the method further comprises: According to the charging demand data and the power supply demand data, an actual power supply power curve is determined, specifically including the following steps: A battery module with an electric quantity less than the target charging electric quantity and not occupied is taken as a battery module to be supplied power; An actual power supply power curve is fitted according to the power supply time and the actual power supply power of the battery module to be supplied power.

5. The method of claim 1, wherein the method further comprises: The method further includes updating the grid planned power curve according to the following steps: A grid planned power curve issued by a grid is received and a corresponding receiving time is recorded; The receiving time is taken as a starting time and timing is performed, when the timing length is equal to the grid issuing interval length, a grid planned power curve issued by the grid again is received and the previous grid planned power curve is replaced, and the receiving time is recorded again.

6. A split mobile charging robot operation regulating device, characterized by, The device includes: A split mobile charging robot, which includes a charging master station, a plurality of battery modules and a plurality of mobile trailers used in cooperation; the battery module has a first interface and a second interface, the first interface is used for electrical connection with the charging master station to receive electric energy of the charging master station; the second interface is used for charging a vehicle to be charged; the mobile trailer is used to move the battery module to an area where the vehicle to be charged is located; A data acquisition module, which is used to acquire a grid planned power curve, charging demand data of all vehicles to be charged and power supply demand data of all battery modules; the charging demand data at least includes a target charging electric quantity of the vehicle to be charged; the power supply demand data at least includes a current electric quantity and a maximum power supply power of the battery module; A data processing module, which is used to determine an actual power supply power curve according to the charging demand data and the power supply demand data, and determine a deviation value of the actual power supply power curve and the grid planned power curve according to the actual power supply power curve and the grid planned power curve; According to the deviation value and a deviation threshold range, a maximum incentive value of grid demand response is determined, a target charging power curve corresponding to the maximum incentive value is taken as a target charging strategy, and a target power supply power curve corresponding to the maximum incentive value is taken as a target power supply strategy; the actual power supply power curve is a total curve of power supply power of all battery modules changing with time; According to the target charging strategy, a charging control instruction is sent to the battery module, the charging control instruction is used to instruct the battery module to perform a charging operation on the vehicle to be charged according to the target charging strategy; according to the target power supply strategy, a power supply control instruction is sent to the charging master station, the power supply control instruction is used to instruct the charging master station to perform a power supply operation on the battery module according to the target power supply strategy.

7. The split mobile charging robot operation regulating device according to claim 6, wherein, The split mobile charging robot further includes a standby trailer in communication connection with the data processing module; The data processing module is configured to generate a record control instruction when the mobile trailer fails; and the record control instruction is configured to control the standby trailer to take over the task of the failed trailer.

8. The split mobile charging robot operation regulating device according to claim 6, wherein, The data processing module is further configured to: If the deviation value is in a target deviation threshold range among the plurality of deviation threshold ranges, a corresponding incentive value of the target deviation threshold range is taken as a maximum incentive value of the grid demand response; If the deviation value is not in any one of the plurality of deviation threshold ranges, a preset incentive value is taken as the maximum incentive value of the grid demand response.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the split mobile charging robot operation regulation method according to any one of claims 1 to 5.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the steps of the split mobile charging robot operation regulation method according to any one of claims 1 to 5.

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