Energy storage robot, energy storage system and control method of energy storage robot

Abstract

The application discloses an energy storage robot, an energy storage system and a control method of the energy storage robot. The control method comprises the following steps: obtaining the moving energy consumption of the energy storage robot to multiple candidate areas; obtaining a photovoltaic target area, which is an area with the highest photovoltaic power generation net value in the multiple candidate areas within a preset stay duration; obtaining a wind power target area, which is an area with the highest wind power generation net value in the multiple candidate areas within the preset stay duration; and obtaining a power generation efficient area based on the photovoltaic target area and the wind power target area in the case that the area of the overlapping area of the photovoltaic target area and the wind power target area is less than or equal to a preset area threshold. Within the preset stay duration, the total power generation amount of the power generation efficient area is greater than the larger one of the total power generation amount of the photovoltaic target area and the total power generation amount of the wind power target area, and the total power generation amount is the sum of the photovoltaic power generation net value and the wind power generation net value within the preset stay duration minus the moving energy consumption of the energy storage robot moving to the power generation efficient area.

Description

Technical Field

[0001] This application relates to the field of energy storage technology, specifically to an energy storage robot, an energy storage system, and a control method for the energy storage robot. Background Technology

[0002] When users are outdoors and need electricity, they typically choose outdoor power supplies. However, the weight of outdoor power supplies increases with the amount of stored energy, making it impossible to simultaneously meet the needs of convenience and high capacity for outdoor power use. Therefore, energy storage robots with autonomous mobility can solve these problems to some extent. These robots can also carry both photovoltaic and wind power modules, improving their power generation efficiency.

[0003] However, the power generation strategies of wind turbines and photovoltaic modules can conflict, making it difficult for energy storage robots to balance both in order to improve power generation efficiency. Summary of the Invention

[0004] This application provides an energy storage robot, an energy storage system, and a control method for the energy storage robot.

[0005] This application provides a control method for an energy storage robot. The energy storage robot includes a body, a photovoltaic module mounted on the body, a wind turbine mounted on the body, a battery module mounted on the body, and a moving component mounted on the body. The photovoltaic module is configured to generate electrical energy through photoelectric conversion and output it to the battery module. The wind turbine is configured to generate electrical energy through wind power and output it to the battery module. The battery module is configured to be capable of charging or discharging. The moving component is configured to drive the body to move. The control method includes: acquiring the energy consumption Q_move of the energy storage robot moving to multiple candidate areas P; acquiring a photovoltaic target area A, wherein photovoltaic target area A is the area among the multiple candidate areas P where the photovoltaic module has the highest net photovoltaic power generation Q_light within a preset dwell time; acquiring a wind power target area B, wherein wind power target area B is the area among the multiple candidate areas P where the wind power module has the highest net wind power generation Q_wind within the preset dwell time; and, if the area of ​​the overlapping area of ​​photovoltaic target area A and wind power target area B is less than or equal to a preset area threshold, acquiring a high-efficiency power generation area C based on photovoltaic target area A and wind power target area B, wherein... Within the preset dwell time, the total power generation Q_final of the energy storage robot in the high-efficiency power generation zone C is greater than the larger of the total power generation Q_final in the photovoltaic target zone A and the total power generation Q_final in the wind power target zone B. The total power generation Q_final of the energy storage robot in the high-efficiency power generation zone C is the sum of the net photovoltaic power generation Q_light and the net wind power generation Q_wind within the preset dwell time, minus the energy consumption Q_move of the energy storage robot moving to the high-efficiency power generation zone C; and the movement component is controlled to move the body so that the energy storage robot can go to the high-efficiency power generation zone C.

[0006] In some embodiments, the control method further includes: if the area of ​​the overlapping region of the photovoltaic target area A and the wind power target area B is greater than the area threshold, the moving component drives the energy storage robot to the overlapping region.

[0007] In some implementations, obtaining the mobile energy consumption Q_move of the energy storage robot traveling to multiple candidate areas P in its environment includes: dividing the geographical area to which the energy storage robot will travel into multiple candidate areas P; obtaining the current position N of the energy storage robot; obtaining the travel path S from the current position N to each of the candidate areas P and the road conditions on the travel path S; and obtaining the mobile energy consumption Q_move for traveling to each of the candidate areas P based on a preset travel speed V and the road conditions.

[0008] In some implementations, dividing the geographical area to which the energy storage robot will travel into multiple candidate areas P includes: dividing the geographical area into multiple initial areas; obtaining the slope and obstacle coverage of each initial area based on contour line information and surface information; and selecting the initial areas that satisfy the condition that the slope is less than a preset slope and the obstacle coverage is less than a preset coverage as the candidate areas P.

[0009] In some embodiments, obtaining the photovoltaic target area A includes: obtaining the parking photovoltaic power generation Q_L1 of the energy storage robot when it stays in each of the candidate areas P within the preset dwell time; obtaining the moving photovoltaic power generation Q_L2 of the energy storage robot during its movement to the candidate area P based on the travel path S and the travel speed; obtaining the net photovoltaic power generation Q_light of the energy storage robot for each of the candidate areas P based on the moving energy consumption Q_move, the moving photovoltaic power generation Q_L2, and the parking photovoltaic power generation Q_L1; and determining the photovoltaic target area A based on multiple net photovoltaic power generation Q_light.

[0010] In some embodiments, obtaining the wind power target area B includes: obtaining the parking wind power generation Q_W1 of the energy storage robot when it stays in each of the candidate areas P within the preset dwell time; obtaining the moving wind power generation Q_W2 of the energy storage robot during its movement to the candidate area P based on the travel path S and the travel speed; obtaining the net wind power generation Q_wind of the energy storage robot for each of the candidate areas P based on the moving energy consumption Q_move, the moving wind power generation Q_W2, and the parking wind power generation Q_W1; and determining the wind power target area B based on multiple net wind power generation Q_wind values.

[0011] In some embodiments, obtaining the high-efficiency power generation zone C based on the photovoltaic target area A and the wind power target area B includes: obtaining the solar-wind potential ratio of the energy storage robot in each candidate area P, wherein the solar-wind potential ratio is the ratio of photovoltaic power generation to wind power generation within the preset dwell time; selecting a first area S1 in the candidate area P where the solar-wind potential ratio is within the preset ratio range; expanding the photovoltaic target area A, the wind power target area B, and the connecting path L between the photovoltaic target area A and the wind power target area B to obtain expanded areas S2; obtaining the intersection of the first area S1 and the expanded area S2 as a second area S3; and selecting the second area S3 with the largest total power generation Q_final as the high-efficiency power generation zone C.

[0012] In some embodiments, expanding the photovoltaic target area A, the wind power target area B, and the path between the photovoltaic target area A and the wind power target area B to obtain an expanded region S2 includes: expanding outwards from the photovoltaic target area A as a reference under a first condition to obtain a first expanded region S21; expanding outwards from the wind power target area B as a reference under a second condition to obtain a second expanded region S22; obtaining a connecting path L between the photovoltaic target area A and the wind power target area B; expanding outwards from the connecting path L as a reference under a third condition to obtain a third expanded region S23; and taking the union of the first expanded region S21, the second expanded region S22, and the third expanded region S23 as the expanded region S2.

[0013] This application also provides an energy storage robot. The energy storage robot includes a body, a photovoltaic module mounted on the body, a wind turbine mounted on the body, a battery module mounted on the body, a movement component mounted on the body, and a controller. The photovoltaic module is configured to generate electrical energy through photoelectric conversion and output it to the battery module. The wind turbine is configured to generate electrical energy through wind power and output it to the battery module. The battery module is configured to be capable of charging or discharging. The movement component is configured to move the body, and the controller is configured to execute the control method described in any of the above embodiments.

[0014] This application also provides an energy storage system. The energy storage system includes the energy storage robot and charging device described in any of the above embodiments, wherein the charging device is configured to provide electrical energy to the energy storage robot.

[0015] In the energy storage robot, energy storage system, and control method of this application, the controller can acquire the optimal positions of photovoltaic target area A and wind power target area B, i.e., the two independent power generation modes. When the area of ​​the overlapping region between photovoltaic target area A and wind power target area B is less than or equal to a preset area threshold, the controller acquires a high-efficiency power generation zone C based on photovoltaic target area A and wind power target area B, and controls the moving component to drive the robot body to move, thereby enabling the energy storage robot to move to the high-efficiency power generation zone C. Since the high-efficiency power generation zone C can take into account the power generation efficiency of both wind power components and photovoltaic components, the wind power components and photovoltaic components can work together to generate electricity after the energy storage robot reaches the high-efficiency power generation zone C, which can improve the power generation efficiency of the energy storage robot. Furthermore, the high-efficiency power generation zone C also considers the movement energy consumption Q_move of the energy storage robot, which can improve the accuracy of the control method.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, wherein:

[0018] Figure 1 This is a three-dimensional structural diagram of the energy storage robot in the retracted state according to some embodiments of this application;

[0019] Figure 2 This is a three-dimensional structural diagram of the energy storage robot in its deployed state according to some embodiments of this application;

[0020] Figure 3 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0021] Figure 4 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0022] Figure 5 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0023] Figure 6 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0024] Figure 7 This is a schematic diagram of a control method for an energy storage robot according to some embodiments of this application;

[0025] Figure 8 This is a schematic diagram of a control method for an energy storage robot according to some embodiments of this application;

[0026] Figure 9 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0027] Figure 10 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0028] Figure 11 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0029] Figure 12 This is a flowchart illustrating the control method of an energy storage robot according to some embodiments of this application;

[0030] Figure 13 This is a schematic diagram of a control method for an energy storage robot according to some embodiments of this application;

[0031] Figure 14This is a schematic diagram of a control method for an energy storage robot according to some embodiments of this application;

[0032] Figure 15 This is a schematic diagram of the energy storage system according to some embodiments of this application.

[0033] The reference numerals in the detailed embodiments are as follows:

[0034] Energy storage system 1000; charging device 300; energy storage robot 100; body 10; battery module 30; moving component 50; controller 70; wind power component 80; blade component 81; first telescopic structure 83; photovoltaic module 90; photovoltaic element 91; second telescopic structure 93. Detailed Implementation

[0035] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0036] In the description of this application, it should be understood that the terms "center", "length", "upper", "lower", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0038] In this application, unless otherwise expressly specified and limited, the terms "installation," "connection," "joining," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0039] In this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0040] When users are outdoors and need electricity, they typically choose outdoor power supplies. However, the weight of outdoor power supplies increases with the amount of stored energy, making it impossible to simultaneously meet the needs of convenience and large capacity for outdoor power use. Therefore, energy storage robots with autonomous mobility can solve these problems to some extent. Energy storage robots can simultaneously carry both photovoltaic and wind power modules, improving their power generation efficiency. However, the power generation strategies of wind and photovoltaic modules can conflict, making it difficult for energy storage robots to simultaneously utilize both to improve power generation efficiency. To address this issue, this application provides an energy storage robot 100 (… Figure 1 and Figure 2 As shown), energy storage system 1000 ( Figure 15 (as shown) and the control method of the energy storage robot ( Figures 3 to 6 ,and Figures 9 to 12 (As shown).

[0041] Please see Figure 1 and Figure 2The energy storage robot 100 according to this application includes a body 10, a battery module 30 disposed on the body 10, a moving component 50 disposed on the body 10, a controller 70 disposed on the body 10, a wind turbine component 80 disposed on the body 10, and a photovoltaic module 90 disposed on the body. The battery module 30 is configured to be able to charge or discharge. The moving component 50 is configured to drive the body 10 to move. The wind turbine component 80 is configured to generate electrical energy through wind power to output to the battery module 30. The photovoltaic module 90 is configured to generate electrical energy through photoelectric conversion to output electrical energy to the battery module 30.

[0042] The energy storage robot 100 is a power distribution device integrating energy storage, autonomous mobility, and intelligent control functions. The energy storage robot 100 can autonomously move to a target location based on the user's power demand and provide regular or temporary power supply. The energy storage robot 100 can be used, but is not limited to, in scenarios such as outdoor camping, dynamic energy management, emergency disaster relief, and microgrid support to address the power needs of areas without a power grid or with unstable power. The energy of the energy storage robot 100 can be provided by rechargeable battery modules 30, non-rechargeable battery modules 30, or charging structures (such as wind turbine modules 80 and photovoltaic modules 90) installed within the energy storage robot 100, ensuring that the energy storage robot 100 has sufficient stored energy.

[0043] The fuselage 10 is a structure for mounting other components. The fuselage 10 of this application is used to mount the battery module 30, the moving component 50, the controller 70, the wind turbine component 80, and the photovoltaic component 90. The cross-sectional shape of the fuselage 10 can be, but is not limited to, circular, elliptical, rectangular, or other polygonal shapes, and the material of the fuselage 10 can be plastic or metal. When the fuselage 10 is made of plastic, it has good insulation performance, low cost, and light weight. When the fuselage 10 is made of metal, it has high strength, good wear resistance, and a long service life.

[0044] Battery module 30 is the core module of the energy storage robot 100, used for storing and releasing electrical energy. Depending on the different application scenarios of the energy storage robot 100, the energy storage robot 100 has different capacities, meaning the battery module 30 has different capacities. For example, in small household or commercial energy storage robots 100, the capacity of battery module 30 is typically from several kilowatt-hours to tens of kilowatt-hours. In industrial energy storage robots 100, the capacity of battery module 30 is typically from tens of kilowatt-hours to hundreds of kilowatt-hours. Battery module 30 is housed within the body 10 and can be electrically connected to other functional components. Battery module 30 can be a rechargeable battery module or a non-rechargeable battery module. When battery module 30 is a rechargeable battery module, the energy storage robot 100 can charge battery module 300 (such as a charging pile) to replenish its electrical energy. In the case where battery module 30 is a non-rechargeable battery module, the energy storage robot 100 can replace the battery in battery module 30 via charging device 300 to replenish its power. Charging device 300 is a device that provides power to devices with energy storage functions. For example, charging device 300 can provide power to new energy vehicles, energy storage robot 100, or other energy storage devices. This application uses a charging pile as an example to illustrate this. The charging device 300 provides power to the energy storage robot 100 by charging battery module 30 in the energy storage robot 100, and also by replacing (swapping) battery module 30 in the energy storage robot 100.

[0045] The moving component 50 is a component in the energy storage robot 100 used to drive the body 10 to move. The moving component 50 is disposed on the body 10, typically at the bottom. The moving component 50 may include a drive element (not shown) and an actuator. The drive element is a power-providing component, such as a drive motor, internal combustion engine, or pneumatic motor. The actuator is a component for direct movement, such as tracks or wheels. The drive element is directly connected to the actuator and transmits power directly to the actuator to drive its movement, thereby enabling the moving component 50 to drive the body 10 to move. The movement of the body 10 driven by the moving component 50 can be, but is not limited to, translation, rotation, or a combination of translation and rotation. Furthermore, the moving component 50 may also include a transmission element that connects the drive element and the actuator. That is, the drive element is indirectly connected to the actuator through the transmission element, and the drive element directly transmits power to the transmission element, which then transmits it to the actuator to move, thereby enabling the moving component 50 to drive the body 10 to move.

[0046] The controller 70 is a device in the energy storage robot 100 used to receive signals, process signals, and issue control commands. The controller 70 includes a circuit board and a control chip mounted on the circuit board. The controller 70 is electrically connected to components such as the battery module 30, the moving component 50, and the photovoltaic module 90 to control the energy storage robot 100 (including starting and stopping the moving component 50, acquiring the status of the energy storage robot 100, and switching the operating mode of the energy storage robot 100). In some embodiments, the controller 70 is wired to the battery module 30, the moving component 50, and the photovoltaic module 90. Wired connections provide higher reliability of electrical connections between components, and the controller 70 provides more stable and rapid control over these components. In other embodiments, the controller 70 is wirelessly connected to the battery module 30, the positioning module 30, and the moving component 50. Compared to wired connections, wireless connections eliminate the need for electrical connectors and save space.

[0047] The wind turbine assembly 80 is a component in the energy storage robot 100 that generates electrical energy using wind power and outputs it to the battery module 30. The wind turbine assembly 80 includes a blade assembly 81 and a first telescopic structure 83. The blade assembly 81 includes blades and a generator, which are connected. The blades can rotate using wind power, driving the generator to produce electrical energy. The first telescopic structure 83 is connected to the body 10 and can receive control commands from the controller 70. In response to the control commands, the first telescopic structure 83 moves the blade assembly 81 relative to the body 10, causing the wind turbine assembly 80 to be in a retracted or extended state. In the retracted state, as shown... Figure 1 As shown, the blade assembly 81 is fitted to or housed within the body 10, reducing the space occupied by the energy storage robot 100. In the deployed state, as... Figure 2 As shown, the first telescopic structure 83 causes the blade assembly 81 to protrude relative to the fuselage 10 and extend outward. The electrical energy generated by the wind turbine assembly 80 can be transmitted to the battery module 30 for storage, providing supplementary power to the energy storage robot 100.

[0048] The photovoltaic module 90 includes a photovoltaic element 91 and a second telescopic structure 93, with the photovoltaic element 91 disposed on the second telescopic structure 93. The photovoltaic element 91 is a solar energy conversion device that generates electrical energy through photoelectric conversion. The photovoltaic element 91 can be different types of solar energy conversion devices such as monocrystalline silicon, polycrystalline silicon, or thin-film solar cells. Users can select photovoltaic elements 91 with different efficiencies and sizes according to their needs. The second telescopic structure 93 is connected to the body 10 and can receive control commands from the controller 70. Responding to the control commands, the second telescopic structure 93 moves the photovoltaic element 91 relative to the body 10 to change the light-receiving area of ​​the photovoltaic element 91, thereby placing the photovoltaic module 90 in a retracted or extended state. In the retracted state, such as... Figure 1 As shown, the second telescopic structure 93 responds to control commands, causing the photovoltaic element 91 to fit against or be housed within the body 10, reducing the space occupied by the energy storage robot 100. In the extended state, as... Figure 2 As shown, the second telescopic structure 93 causes the photovoltaic element 91 to protrude relative to the body 10 and extend outward. The light-receiving area of ​​the photovoltaic element 91 in the retracted state is smaller than the light-receiving area of ​​the photovoltaic element 91 in the unfolded state.

[0049] Furthermore, in the unfolded state, the second telescopic structure 93 can also change the area of ​​the photovoltaic element 91 protruding relative to the body 10. That is, in the unfolded state, part of the photovoltaic element 91 can be housed within the body 10, while another part extends outward from the body 10, and the ratio between the two can be controlled by the second telescopic structure 93. It is understood that the portion of the photovoltaic element 91 protruding relative to the body 10 can perform photoelectric conversion under illumination. Therefore, the area of ​​the portion extending outward from the body 10 is also the light-receiving area of ​​the photovoltaic element 91, which can characterize the photoelectric conversion efficiency of the energy storage robot 100. It should be noted that the photovoltaic element 91, which is attached to or housed within the body 10, can also perform photoelectric conversion in certain scenarios. For example, a photovoltaic element 91 attached to the outer surface of the body 10 can receive illumination and perform photoelectric conversion; further for example, a photovoltaic element 91 housed within the body 10 can receive light penetrating the body 10 and perform photoelectric conversion. That is, regardless of the state of the photovoltaic element 91, this application does not limit the photoelectric conversion process of the photovoltaic element 91.

[0050] For example, when the energy storage robot 100 moves to an area with good lighting, open surroundings, and low wind speed, the energy storage robot 100 can be fixed in one place, and the photovoltaic element 91 can extend entirely from the body 20 to maximize photoelectric conversion. For example, when the energy storage robot 100 moves in a confined area, the photovoltaic element 91 can be entirely housed within the body 20 to prevent it from being scratched by branches or other objects during movement. For example, when the energy storage robot 100 follows the user, part of the photovoltaic element 91 can be housed within the body 20, while the other part extends outward from the body 20, thus avoiding scratches from branches or other objects during movement while still enabling photoelectric conversion.

[0051] Please see Figure 2 , Figure 3 and Figure 13 This application provides a control method for an energy storage robot. The control method includes:

[0052] 01: Obtain the energy consumption Q_move of the energy storage robot 100 moving to multiple candidate areas P;

[0053] 03: Obtain photovoltaic target area A. Photovoltaic target area A is the area with the highest net photovoltaic power generation value Q_light among multiple candidate areas P for photovoltaic modules 90 within a preset dwell time.

[0054] 05: Obtain wind target area B. Wind target area B is the area with the highest net wind power generation value Q_wind among multiple candidate areas P for wind turbine components 80 within a preset dwell time.

[0055] 071: When the area of ​​the overlapping region between photovoltaic target area A and wind power target area B is less than or equal to a preset area threshold, a high-efficiency power generation zone C is obtained based on photovoltaic target area A and wind power target area B. Within a preset dwell time, the total power generation Q_final of the energy storage robot 100 in high-efficiency power generation zone C is greater than the larger of the total power generation Q_final in photovoltaic target area A and the total power generation Q_final in wind power target area B. The total power generation Q_final of the energy storage robot 100 in high-efficiency power generation zone C is the sum of the net photovoltaic power generation Q_light and the net wind power generation Q_wind within the preset dwell time, minus the energy consumption Q_move of the energy storage robot 100 moving to high-efficiency power generation zone C; and

[0056] 09: Control the moving component 50 to move the body 10 so that the energy storage robot 100 can move to the high-efficiency power generation zone C.

[0057] Correspondingly, controller 70 is used to execute methods 01, 03, 05, and 071. More specifically, controller 70 is configured to: acquire the energy consumption Q_move of the energy storage robot 100 moving to multiple candidate areas P; acquire photovoltaic target area A, which is the area in multiple candidate areas P where the photovoltaic module 90 has the highest net photovoltaic power generation value Q_light within a preset dwell time; acquire wind power target area B, which is the area in multiple candidate areas P where the wind power module 80 has the highest net wind power generation value Q_wind within a preset dwell time; if the area of ​​the overlapping area of ​​photovoltaic target area A and wind power target area B is less than or equal to a preset area threshold, acquire a high-efficiency power generation area C based on photovoltaic target area A and wind power target area B. Within a preset dwell time, the total power generation Q_final of the energy storage robot 100 in the high-efficiency power generation zone C is greater than the larger of the total power generation Q_final in the photovoltaic target zone A and the total power generation Q_final in the wind target zone B. The total power generation Q_final of the energy storage robot 100 in the high-efficiency power generation zone C is the sum of the net photovoltaic power generation Q_light and the net wind power generation Q_wind within the preset dwell time, minus the moving energy consumption Q_move of the energy storage robot 100 moving to the high-efficiency power generation zone C, and the movement of the body 10 driven by the control moving component 50, so that the energy storage robot 100 moves to the high-efficiency power generation zone C.

[0058] Specifically, in method 01, the candidate region P refers to a potential location that the energy storage robot 100 may move to. There are multiple candidate regions P, and the controller 70 selects one from these as the target location. The target location refers to the location that the energy storage robot 100, selected by the controller 70, will travel to from its current location N. In this application, the target location is one of the overlapping area of ​​the photovoltaic target area A and the wind power target area B, or the high-efficiency power generation area C.

[0059] The current position N is the position of the energy storage robot 100 at the current moment. The movement energy consumption Q_move refers to the energy consumed by the energy storage robot 100 to move from the current position N to any candidate region P.

[0060] In method 03, the preset dwell time refers to the expected dwell time of the energy storage robot 100 in the candidate area P. The photovoltaic target area A is the candidate area P in which the controller 70 obtains the highest net power generation value of the photovoltaic module 90 within the preset dwell time, without considering the power generation of the wind turbine module 80. The photovoltaic target area A is the area where the photovoltaic power generation efficiency of the energy storage robot 100 is the highest.

[0061] In method 05, the wind power target area B is the candidate area P where the controller 70 obtains the highest net power generation value of the wind turbine module 80 within a preset dwell time, without considering the power generation of the photovoltaic module 90. The wind power target area B is the area where the energy storage robot 100 has the highest wind power generation efficiency.

[0062] In the method of 071, the preset area threshold is a threshold pre-set by the controller 70, which is used to determine the degree of overlap between the photovoltaic target area A and the wind power target area B. For example, the preset area threshold in this application is 0. If the area of ​​the overlapping region between the photovoltaic target area A and the wind power target area B is less than or equal to 0, it indicates that the photovoltaic target area A and the wind power target area B do not overlap. That is, if there is no candidate area P among the candidate areas that simultaneously satisfies the conditions of highest photovoltaic power generation efficiency and highest wind power generation efficiency, the controller 70 needs to obtain a new candidate area P based on the photovoltaic target area A and the wind power target area B, namely, a high-efficiency power generation area C. The high-efficiency power generation area C is defined as the area where, within a preset dwell time, the total power generation Q_final of the energy storage robot 100 is greater than the larger of the total power generation Q_final when only in the photovoltaic target area A and the total power generation Q_final when only in the wind power target area B. The total power generation Q_final is obtained as follows: within a preset dwell time, the sum of the net photovoltaic power generation Q_light and the net wind power generation Q_wind in this area is subtracted from the energy consumption Q_move of the energy storage robot 100 moving to the high-efficiency power generation zone C, i.e., Q_final = Q_light + Q_wind - Q_move. Thus, the total power generation Q_final not only considers the power generation efficiency of the wind turbine module 80 and the photovoltaic module 90, but also removes the energy consumption generated during the movement to the high-efficiency power generation zone C, resulting in a more accurate total power generation Q_final, which helps improve the accuracy of the control method. In the method of 09, the moving component 50 drives the body 10 to move, so that the energy storage robot 100 moves to the high-efficiency power generation zone C.

[0063] In the control method of this application, the controller 70 can acquire the photovoltaic target area A and the wind power target area B, i.e., the optimal positions under two independent power generation modes. When the area of ​​the overlapping region between the photovoltaic target area A and the wind power target area B is less than or equal to a preset area threshold, the controller acquires a high-efficiency power generation zone C based on the photovoltaic target area A and the wind power target area B, and controls the moving component 50 to move the body 10, thereby enabling the energy storage robot 100 to move to the high-efficiency power generation zone C. Since the high-efficiency power generation zone C can take into account the power generation efficiency of both the wind power component 80 and the photovoltaic component 90, the wind power component 80 and the photovoltaic component 90 can work together to generate electricity after the energy storage robot 100 reaches the high-efficiency power generation zone C, thereby improving the power generation efficiency of the energy storage robot 100. Furthermore, the high-efficiency power generation zone C also considers the movement energy consumption Q_move of the energy storage robot 100, which can improve the accuracy of the control method.

[0064] Please see Figure 2 , Figure 4 and Figure 13 In some implementations, the control method further includes:

[0065] 073: If the area of ​​the overlapping region between photovoltaic target area A and wind power target area B is greater than the area threshold, the mobile component 50 drives the energy storage robot 100 to the overlapping region.

[0066] Correspondingly, controller 70 is used to execute the method in 073. More specifically, controller 70 is configured such that, if the area of ​​the overlapping region between photovoltaic target area A and wind power target area B is greater than an area threshold, the moving component 50 drives the energy storage robot 100 to the overlapping region.

[0067] The overlapping area of ​​the photovoltaic target area A and the wind power target area B is greater than the area threshold, indicating that the overlapping area is the area with the highest net value of photovoltaic power generation Q_light and the highest net value of wind power generation Q_wind. The energy storage robot 100 goes directly to the overlapping area, which simplifies the decision-making process of the controller 70 and reduces the computational complexity of the controller 70.

[0068] Please see Figure 2 , Figure 5 and Figure 13 In some implementations, 01: Obtaining the energy consumption Q_move of the energy storage robot 100 moving to multiple candidate areas P in its environment includes:

[0069] 011: Divide the geographical area that the energy storage robot 100 will travel to into multiple candidate areas P;

[0070] 013: Obtain the current position N of the energy storage robot 100;

[0071] 015: Obtain the travel path S from the current location N to each candidate region P and the road conditions along the travel path S; and

[0072] 017: Based on the preset travel speed V and road conditions, obtain the travel energy consumption Q_move for traveling to each candidate area P.

[0073] Correspondingly, controller 70 is used to execute the methods in 011, 013, 015, and 017. More specifically, controller 70 is configured to: divide the geographical area to which energy storage robot 100 is to be traveled into multiple candidate areas P; obtain the current position N of energy storage robot 100; obtain the travel path S from the current position N to each candidate area P and the road conditions on the travel path S; and obtain the mobile energy consumption Q_move for traveling to each candidate area P based on a preset travel speed V and road conditions.

[0074] In the 011 method, the geographical area refers to the area that the energy storage robot 100 may need to move within, as pre-defined by the controller 70. Figure 13The white-framed area represents the location of the energy storage robot 100. Geographic region acquisition can be based on various methods, such as the controller 70 expanding outwards from the current position N of the energy storage robot 100, or pre-setting a region based on user needs. The geographic region is used to limit the potential working range of the energy storage robot 100 to a manageable area, thereby reducing the complexity of subsequent calculations by the controller 70. Partitioning refers to dividing the geographic region into multiple smaller, independent sub-regions; these sub-regions are candidate regions P. The candidate regions P discretize the geographic region, enabling the controller 70 to analyze each candidate region P.

[0075] In the 013 method, the current position N is as described above. Obtaining the current position N is the basis for the controller 70 to obtain the mobile energy consumption Q_move.

[0076] In the method of 015, the travel path S refers to the route taken by the energy storage robot 100 from its current position N to a candidate region P. The controller 70 can obtain the travel path S through a path planning algorithm. Path planning algorithms include, but are not limited to, genetic algorithms, simulated annealing algorithms, ant colony algorithms, Dijkstra's algorithm, or A* algorithms, etc., and are not limited here. The road conditions in this application include three types: uphill, downhill, and flat road. In some embodiments, road conditions may also include information such as road surface roughness. It is understood that the energy consumption Q_move of the energy storage robot 100 is different under different road conditions. For example, uphill usually consumes more energy consumption Q_move than flat or downhill. Distinguishing between different road conditions can improve the accuracy of the obtained energy consumption Q_move.

[0077] In the method of 017, the preset travel speed V represents the average travel speed assumed by the controller 70 when acquiring the mobile energy consumption Q_move. The preset travel speed V can be a fixed value or a range that is dynamically adjusted according to road conditions, and is not limited in this application. After the controller 70 acquires the travel path S, the controller 70 divides the distance of the travel path S into three categories according to the differences in road conditions: downhill distance Dt1, uphill distance Dt2, and flat road distance Dt3, in order to better analyze the specific movement and energy consumption of the energy storage robot 100 on the travel path S.

[0078] The downhill distance Dt1 represents the distance the energy storage robot 100 travels downhill. During this downhill journey, the robot's gravity does work, resulting in less energy consumption per unit time for the battery module 30 at the same speed V. The uphill distance Dt2 represents the distance the robot travels uphill. During this uphill journey, the robot overcomes gravity, resulting in more energy consumption per unit time for the battery module 30 at the same speed V. The flat road distance Dt3 represents the distance the robot travels on flat ground. During this flat road journey, the robot's center of gravity remains constant, and the energy consumption per unit time for the battery module 30 remains unchanged at the same speed V. The controller 70 then obtains the energy consumption per unit distance for the three road conditions. The energy consumption per unit distance is pre-calibrated or obtained experimentally: q1 for downhill, q2 for uphill, and q3 for flat road. For each road condition, the controller 70 obtains the energy consumption for different road conditions based on the preset travel speed V, the distance of the road condition (downhill distance Dt1, uphill distance Dt2, or flat road distance Dt3), and the energy consumption per unit distance. The controller then adds up the energy consumption for the three road conditions to obtain the movement energy consumption Q_move for each candidate region P. That is, Q_move = Q{Dt1, q1, V} + Q{Dt2, q2, V} + Q{Dt3, q3, V}.

[0079] Please see Figure 2 , Figure 6 , Figure 7 and Figure 8 In some implementations, 011: the geographical area to which the energy storage robot 100 will travel is divided into multiple candidate areas P, including:

[0080] 0111: Divide the geographical region into multiple initial regions;

[0081] 0113: Obtain the slope and obstacle coverage of each initial area based on contour line information and surface information; and

[0082] 0115: Obtain an initial region P that meets the requirements of a slope less than a preset slope and an obstacle coverage rate less than a preset coverage rate.

[0083] Correspondingly, controller 70 is used to execute the methods in 0111, 0113, and 0115. More specifically, controller 70 is configured to: divide the geographic region into multiple initial regions; obtain the slope and obstacle coverage of each initial region based on contour line information and surface information; and obtain initial regions that satisfy the conditions of a slope less than a preset slope and obstacle coverage less than a preset coverage as candidate regions P.

[0084] In the 0111 method, the initial region refers to smaller, adjacent sub-regions within the geographic area to which the energy storage robot 100 will travel, divided according to predetermined rules (e.g., grid partitioning, geographic feature-based segmentation, etc.). The initial region characterizes all potential locations where the energy storage robot 100 may move and remain, serving as the basis for subsequent screening of candidate regions P.

[0085] In the 0113 method, contour line information represents the topographic relief of a geographic area, reflecting its slope. Surface information represents various characteristics of the ground cover in the geographic area, such as vegetation type, water distribution, building distribution, and road network. Surface information can be derived from satellite imagery, remote sensing data, or a Geographic Information System (GIS) database. Controller 70 can obtain the slope of each initial area based on the contour line information. Slope represents the degree of surface tilt. Controller 70 can also obtain the obstacle coverage rate of each initial area based on the surface information. Obstacle coverage rate represents the proportion of the initial area occupied by impassable or difficult-to-pass obstacles (such as dense vegetation, water bodies, steep rocks, building ruins, etc.).

[0086] In the 0115 method, initial areas with a slope greater than or equal to a preset slope may cause the energy storage robot 100 to have difficulty moving, increase its movement energy consumption Q_move, and even cause it to overturn. Areas with obstacle coverage greater than or equal to a preset coverage rate may prevent the robot from passing through. Initial areas with a slope greater than or equal to a preset slope are non-candidate areas F. The controller 70 obtains initial areas that satisfy both a slope less than a preset slope and an obstacle coverage rate less than a preset coverage rate as candidate areas P. This can directly eliminate areas unsuitable for the energy storage robot 100 to move or stay in (i.e., initial areas with a slope greater than or equal to a preset slope and areas with an obstacle coverage rate greater than or equal to a preset coverage rate), reducing the number of candidate areas P, thereby reducing the computational burden on the controller 70 and improving the computational efficiency and real-time performance of the control method.

[0087] If the controller 70 acquires and calculates information in the form of coordinate points, the number of coordinate points to be processed within the geographical area would be too large, placing an extremely high computational burden on the controller 70. Instead, the controller 70 divides the geographical area into a finite number of initial regions (P+F) and further filters out candidate regions P that meet the criteria. The controller 70 does not need to acquire the mobile energy consumption Q_move or total power generation Q_final for each coordinate point. Coordinate points within a candidate region P can be treated as a single entity. For example, the data of the center coordinate point of a candidate region P can represent the data of all coordinate points within that region. This reduces the computational burden on the controller 70, lowers computational complexity, and improves the real-time performance of the control method.

[0088] Please see Figure 2 , Figure 9 and Figure 13 In some implementations, 03: Obtaining photovoltaic target area A includes:

[0089] 031: Obtain the parking photovoltaic power generation Q_L1 of the energy storage robot 100 when it stays in each candidate area P within the preset dwell time;

[0090] 033: Based on the travel path S and travel speed, obtain the mobile photovoltaic power generation Q_L2 of the energy storage robot 100 during its movement to the candidate area P;

[0091] 035: Based on mobile energy consumption Q_move, mobile photovoltaic power generation Q_L2, and parking photovoltaic power generation Q_L1, obtain the net photovoltaic power generation value Q_light for each candidate region P corresponding to the energy storage robot 100; and

[0092] 037: Based on multiple net photovoltaic power generation values ​​Q_light, determine the photovoltaic target area A.

[0093] Correspondingly, controller 70 is used to execute the methods in 031, 033, 035, and 037. More specifically, controller 70 is configured to: acquire the parking photovoltaic power generation Q_L1 of the energy storage robot 100 when it stays in each candidate area P within a preset dwell time; acquire the moving photovoltaic power generation Q_L2 of the energy storage robot 100 during its movement to the candidate area P based on the travel path S and travel speed; acquire the net photovoltaic power generation Q_light of the energy storage robot 100 for each candidate area P based on the moving energy consumption Q_move, the moving photovoltaic power generation Q_L2, and the parking photovoltaic power generation Q_L1; and determine the photovoltaic target area A based on multiple net photovoltaic power generation Q_light.

[0094] In the method of 031, the parking photovoltaic power generation Q_L1 refers to the electrical energy that the photovoltaic module 90 can generate within a preset dwell time when the energy storage robot 100 is located in the candidate area P and is stationary (i.e., in the "parking" state).

[0095] In the method of 033, the mobile photovoltaic power generation Q_L2 refers to the electrical energy generated by the photovoltaic module 90 when the energy storage robot 100 moves from its current position N to a candidate area P along its travel path S.

[0096] In method 035, for a candidate region P, the net photovoltaic power generation value Q_light of candidate region P is: the sum of mobile photovoltaic power generation Q_L2 and parking photovoltaic power generation Q_L1, minus the mobile energy consumption Q_move consumed by the energy storage robot 100 to move to the candidate region P. That is, Q_light = Q_L1 + Q_L2 - Q_move.

[0097] In method 03, after the controller 70 obtains the net photovoltaic power generation value Q_light corresponding to each candidate region P, it compares the net photovoltaic power generation value Q_light corresponding to all candidate regions P. The photovoltaic target region A is the candidate region P with the highest net photovoltaic power generation value Q_light among the candidate regions P.

[0098] Based on mobile energy consumption Q_move, mobile photovoltaic power generation Q_L2, and parked photovoltaic power generation Q_L1, the net photovoltaic power generation value Q_light for each candidate region P corresponding to the energy storage robot 100 is obtained. The net photovoltaic power generation value Q_light for each candidate region P takes into account the mobile photovoltaic power generation Q_L2, which can more comprehensively evaluate the electrical energy generated by the photovoltaic module 90. The net photovoltaic power generation value Q_light for each candidate region P also takes into account the mobile energy consumption Q_move, which can further improve the accuracy of the net photovoltaic power generation value Q_light.

[0099] Please see Figure 2 , Figure 10 and Figure 13 In some implementations, 05: Obtaining the wind target area B includes:

[0100] 051: Obtain the parking wind power generation Q_W1 of the energy storage robot 100 when it stays in each candidate area P within the preset dwell time;

[0101] 053: Based on the travel path S and travel speed, obtain the mobile wind power generation Q_W2 of the energy storage robot 100 during its movement to the candidate area P;

[0102] 055: Based on mobile energy consumption Q_move, mobile wind power generation Q_W2, and parked wind power generation Q_W1, obtain the net wind power generation value Q_wind for each candidate region P corresponding to the energy storage robot 100; and

[0103] 057: Based on multiple net wind power generation values ​​Q_wind, determine the wind power target area B.

[0104] Correspondingly, controller 70 is used to execute the methods in 051, 053, 055, and 057. More specifically, controller 70 is configured to: obtain the parking wind power generation Q_W1 of the energy storage robot 100 when it stays in each candidate area P within a preset dwell time; obtain the moving wind power generation Q_W2 of the energy storage robot 100 during its movement to the candidate area P based on the travel path S and travel speed; obtain the net wind power generation Q_wind of the energy storage robot 100 for each candidate area P based on the moving energy consumption Q_move, the moving wind power generation Q_W2, and the parking wind power generation Q_W1; and determine the wind power target area B based on multiple net wind power generation Q_wind values.

[0105] In the method of 051, the wind power generation Q_W1 refers to the electrical energy that the wind turbine component 80 can generate within a preset dwell time when the energy storage robot 100 is located in the candidate area P and is stationary (i.e., in the "parking" state).

[0106] In the method of 053, the mobile wind power generation Q_W2 refers to the electrical energy generated by the wind turbine component 80 when the energy storage robot 100 moves from its current position N to a candidate area P along its travel path S.

[0107] In the 055 method, for a candidate region P, the net wind power generation value Q_wind of the candidate region P is: the sum of mobile wind power generation Q_W2 and parked wind power generation Q_W1, minus the mobile energy consumption Q_move consumed by the energy storage robot 100 when moving to the candidate region P, that is, Q_wind = Q_W1 + Q_W2 - Q_move.

[0108] In method 057, after the controller 70 obtains the net wind power generation value Q_wind corresponding to each candidate region P, it compares the net wind power generation values ​​Q_wind corresponding to all candidate regions P. The wind power target area B is the candidate region P with the highest net wind power generation value Q_wind among the candidate regions P.

[0109] Based on mobile energy consumption Q_move, mobile wind power generation Q_W2, and parked wind power generation Q_W1, the net wind power generation value Q_wind for each candidate region P corresponding to the energy storage robot 100 is obtained. The net wind power generation value Q_wind for each candidate region P takes into account the mobile wind power generation Q_W2, which can more comprehensively evaluate the electrical energy generated by the wind turbine component 80. The net wind power generation value Q_wind for each candidate region P also takes into account the mobile energy consumption Q_move, which can further improve the accuracy of the net wind power generation value Q_wind.

[0110] Please see Figure 2 , Figure 11 , Figure 13 and Figure 14 In some implementations, obtaining a high-efficiency power generation zone C based on photovoltaic target zone A and wind power target zone B includes:

[0111] 0711: Obtain the solar-wind potential ratio of the energy storage robot 100 in each candidate region P. The solar-wind potential ratio is the ratio of photovoltaic power generation to wind power generation within a preset residence time.

[0112] 0713: Select the first region S1 from candidate region P whose solar-wind potential ratio is within the preset range;

[0113] 0714: Expand the photovoltaic target area A, the wind power target area B, and the connecting path L between the photovoltaic target area A and the wind power target area B to obtain the expanded region S2;

[0114] 0715: Obtain the intersection of the first region S1 and the expanded region S2 to form the second region S3; and

[0115] 0717: The second region S3 with the largest total power generation Q_final is designated as the high-efficiency power generation zone C.

[0116] Correspondingly, controller 70 is used to execute the methods in 0711, 0713, 0714, 0715, and 0717. More specifically, controller 70 is configured to: obtain the solar-wind potential ratio of the energy storage robot 100 in each candidate region P, where the solar-wind potential ratio is the ratio of photovoltaic power generation to wind power generation within a preset dwell time; select a first region S1 in candidate region P whose solar-wind potential ratio is less than or equal to a preset ratio; expand the photovoltaic target region A, the wind target region B, and the path between the photovoltaic target region A and the wind target region B to obtain expanded regions S2; obtain the intersection of the first region S1 and the expanded region S2 as the second region S3; and select the second region S3 with the largest total power generation Q_final as the high-efficiency power generation region C.

[0117] In the 0711 method, the solar-wind potential ratio represents the difference between the net photovoltaic power generation value Q_light and the net wind power generation value Q_wind within a candidate region P. If the solar-wind potential ratio is greater than a preset range, it indicates that the power generation capacity of photovoltaic modules 90 within candidate region P is strong, while the power generation capacity of wind turbine modules 80 is weak, and the power generation of wind turbine modules 80 cannot be fully utilized. If the solar-wind potential ratio is less than a preset range, it indicates that the power generation capacity of photovoltaic modules 90 within candidate region P is weak, while the power generation capacity of wind turbine modules 80 is strong, and the power generation of photovoltaic modules 90 cannot be fully utilized.

[0118] In the method of 0713, if the solar-wind potential ratio is within a preset range, it indicates that the power generation efficiency of the photovoltaic module 90 and the wind power module 80 in candidate region P is relatively balanced, and the total power generation of the energy storage robot 100 will not decrease significantly due to drastic fluctuations in a single energy source (solar or wind power). The first region S1 refers to the set of candidate regions P whose solar-wind potential ratio is within a preset threshold range, that is, the first region S1 includes one or more candidate regions P. Figure 14 The boxes marked with a blank asterisk represent candidate regions in the first region S1, i.e. Figure 14 The first region S1 includes 9 candidate regions P, and the solar-wind potential ratio of these 9 candidate regions P is within a preset threshold range.

[0119] In the method of 0714, the connecting path L between photovoltaic target area A and wind power target area B is the travel route between them. The energy storage robot 100 can move along the connecting path L between photovoltaic target area A and wind power target area B. The controller 70 can obtain the connecting path L through a path planning algorithm. The path planning algorithm includes, but is not limited to, genetic algorithm, simulated annealing algorithm, ant colony algorithm, Dijkstra's algorithm, or A* algorithm, etc., and is not limited here. It is understood that photovoltaic target area A and wind power target area B are themselves areas with high power generation efficiency, and high power generation area C is usually likely to appear around photovoltaic target area A and wind power target area B, as well as near the connecting path L. "Expansion" refers to expanding photovoltaic target area A, wind power target area B, and connecting path L outward by a certain range. Taking photovoltaic target area A as an example, expansion can be a circular range with a certain radius expanded outward from photovoltaic target area A as the center, or expansion can be based on candidate areas, screening areas adjacent to the boundary of photovoltaic target area A, and is not limited here. Obtaining the expansion region S2 avoids the controller 70 from calculating all candidate regions, narrowing the range of the potential high-efficiency power generation region C, thereby reducing the computational complexity of the controller 70 and improving the efficiency and real-time performance of the controller 70.

[0120] In the method of 0715, the second region S3 represents the intersection of areas that have a good solar-wind potential ratio (belonging to the first region S1) and are located near the photovoltaic target area A, the wind power target area B, and the connecting path L (belonging to the expansion region S2). The second region S3 further narrows the range of the potential high-efficiency power generation area C, further reduces the computational complexity of the controller 70, and improves the efficiency and real-time performance of the controller 70.

[0121] In the method of 0717, the second region S3 with the largest total power generation Q_final is designated as the high-efficiency power generation zone C, ensuring that the high-efficiency power generation zone C selected by the energy storage robot 100 maximizes the power acquisition capability of the energy storage robot 100.

[0122] Please see Figure 2 , Figure 12 and Figure 14 In some embodiments, the photovoltaic target area A, the wind power target area B, and the path between the photovoltaic target area A and the wind power target area B are expanded to obtain the expanded region S2, including:

[0123] 07141: Taking the photovoltaic target area A as a reference, expand outwards according to the first condition to obtain the first expansion area S21;

[0124] 07143: Based on the wind target area B, expand outwards according to the second condition to obtain the second expansion area S22;

[0125] 07145: Obtain the connectivity path L between photovoltaic target area A and wind power target area B;

[0126] 07147: Based on the connected path L, expand outwards according to the third condition to obtain the third expansion region S23; and

[0127] 07149: The union of the first expansion region S21, the second expansion region S22, and the third expansion region S23 is taken as the expansion region S2.

[0128] Correspondingly, controller 70 is used to execute the methods in 07141, 07143, 07145, 07147, and 07149. More specifically, controller 70 is configured to: expand outwards from photovoltaic target area A based on a first condition to obtain a first expansion area S21; expand outwards from wind target area B based on a second condition to obtain a second expansion area S22; obtain a connection path L between photovoltaic target area A and wind target area B; expand outwards from the connection path L based on a third condition to obtain a third expansion area S23; and take the union of the first expansion area S21, the second expansion area S22, and the third expansion area S23 as the expansion region S2.

[0129] In the method of 07141, in one example, the first condition refers to expanding a circle of a predetermined radius outwards from the photovoltaic target area A, thereby forming an area larger than the photovoltaic target area A. In another example (also... Figure 14 (In the selected implementation method), the first condition refers to obtaining candidate regions adjacent to the boundary based on the boundary of the photovoltaic target area A. Figure 14 The first expansion region S21 consists of 12 candidate regions.

[0130] In the method of 07143, the second condition refers to obtaining candidate regions adjacent to the boundary based on the boundary of the wind target area B. Figure 14 The second expansion region S22 consists of 12 candidate regions.

[0131] In the method of 07145, the connecting path L between the photovoltaic target area A and the wind power target area B is obtained. The connecting path L refers to the route connecting the photovoltaic target area A and the wind power target area B. The connecting path L can be the most direct straight line between two points (in the ideal case where there are no obstacles or terrain restrictions), or it can be a route that the energy storage robot 100 can travel through, obtained by the controller 70 through a path planning algorithm.

[0132] In the method of 07147, the third condition is to obtain the candidate region P on both sides of the connected path L, where the distance between the center point of the candidate region and the connected path L is less than the width of the candidate region. The width of the candidate region is the maximum distance between any two points on the boundary of the candidate region. Figure 14 The third expansion region S23 consists of 17 candidate regions.

[0133] In the method of 07149, the union of the first expansion region S21, the second expansion region S22, and the third expansion region S23 is taken as the expansion region S2. Figure 14 The expansion region S2 consists of 38 candidate regions.

[0134] The expanded region S2 can reduce the computational range of the controller 70, avoiding a blind and time-consuming global search of the entire geographic area. This improves the efficiency of the algorithm and the accuracy of the search.

[0135] Please see Figure 1 , Figure 2 and Figure 15 This application also provides an energy storage system 1000. The energy storage system 1000 includes an energy storage robot 100 according to any of the above embodiments and a charging device 300, wherein the charging device 300 is configured to provide electrical energy to the energy storage robot 100.

[0136] Specifically, in the above embodiments, the energy storage system 1000 is a system for storing, scheduling, and utilizing energy. The energy storage system 1000 includes an energy storage device (energy storage robot 100) that provides energy for scheduling or utilizing energy, and an energy supply device that supplies power to the energy storage robot 100. The energy storage system 1000 can be any system possessing the above functions, for example: the energy storage system 1000 is a cleaning system, the energy storage robot 100 is a cleaning robot, the energy supply device is a power supply base station, and the power supply base station supplies power to the cleaning robot so that the cleaning robot can use electrical energy to move; the energy storage system 1000 is a logistics system, the energy storage robot 100 is a logistics robot, the energy supply device is a charging pile, and the charging pile supplies power to the logistics robot so that the logistics robot can use electrical energy to move; the energy storage system 1000 is a new energy vehicle system, the energy storage robot 100 is a new energy vehicle, the energy supply device is a charging pile, and the charging pile supplies power to the new energy vehicle so that the new energy vehicle can use electrical energy to move. Please further combine... Figure 15This application takes the energy storage system 1000 as an example of a power dispatching system. In this case, the energy storage robot 100 is a mobile energy storage power source, and the power supply device is the charging pile 300. The charging pile 300 supplies power to the energy storage robot 100 so that the energy storage robot 100 can move using electrical energy and perform power dispatching and utilization.

[0137] It should be noted that the specific structure and properties of the charging device 300 in this embodiment are exactly the same as those of the charging device 300 in the above embodiment, and the specific structure and properties of the energy storage robot 100 in this embodiment are exactly the same as those of the energy storage robot 100 in the above embodiment, and will not be explained again here.

[0138] When the energy storage robot 100 arrives at the charging device 300, the charging device 300 can charge or replace the battery module 30 of the energy storage robot 100. Taking charging as an example, the charging device 300 can charge the energy storage robot 100 via wired charging or wireless charging. When the charging device 300 charges the energy storage robot 100 via wired charging, the energy storage robot 100 connects to the physical plug (such as Type 1, Type 2, GB / T, or a customized interface) of the charging device 300 through a connection device (not shown) or a guide device (not shown). In this case, the charging process of the charging device 300 to charge the energy storage robot 100 is simple, reliable, and fast. When the charging device 300 charges the energy storage robot 100 via wireless charging, both the energy storage robot 100 and the charging device 300 are equipped with induction coils. The induction coil of the charging device 300 generates a magnetic field and transfers energy to the energy storage robot 100 through the induction coil of the energy storage robot 100 to charge the battery module 30 of the energy storage robot 100. At this time, the charging device 300 and the energy storage robot 100 do not need to contact each other, so the energy storage robot 100 will not experience wear and tear, resulting in a better appearance and a longer service life. When the charging device 300 is swapping the battery for the energy storage robot 100, it directly replaces the battery module 30 with a fully charged one. This results in faster energy replenishment and a longer working time for the energy storage robot 100.

[0139] Since the energy storage system 1000 in this application embodiment includes an energy storage robot 100, it is understood that the energy storage system 1000 includes at least the same beneficial effects as the energy storage robot 100. Therefore, the beneficial effects of the energy storage system 1000 are described above with reference to the beneficial effects of the energy storage robot 100, and will not be repeated here.

[0140] The technical features of the embodiments described above can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification. Furthermore, other implementation methods can be derived from the above embodiments, allowing for structural and logical substitutions and changes without departing from the scope of this disclosure.

Claims

1. A control method for an energy storage robot, the energy storage robot comprising a body, a photovoltaic module disposed on the body, a wind power module disposed on the body, a battery module disposed on the body, and a moving component disposed on the body, wherein the photovoltaic module is configured to generate electrical energy through photoelectric conversion and output it to the battery module, the wind power module is configured to generate electrical energy through wind power and output it to the battery module, the battery module is configured to be capable of charging or discharging, and the moving component is configured to drive the body to move, characterized in that... The control method includes: Obtain the energy consumption Q_move of the energy storage robot as it moves to multiple candidate regions P; Obtain photovoltaic target area A, which is the area with the highest net photovoltaic power generation value Q_light of the photovoltaic module within a preset dwell time among the multiple candidate areas P; Obtain wind target area B, where wind target area B is the area with the highest net wind power generation value Q_wind of the wind turbine component within the preset dwell time among the multiple candidate areas P; If the area of ​​the overlapping region between the photovoltaic target area A and the wind power target area B is less than or equal to a preset area threshold, a high-efficiency power generation zone C is obtained based on the photovoltaic target area A and the wind power target area B. Within the preset dwell time, the total power generation Q_final of the energy storage robot in the high-efficiency power generation zone C is greater than the larger of the total power generation Q_final in the photovoltaic target area A and the total power generation Q_final in the wind power target area B. The total power generation Q_final of the energy storage robot in the high-efficiency power generation zone C is the sum of the net photovoltaic power generation Q_light and the net wind power generation Q_wind within the preset dwell time, minus the energy consumption Q_move of the energy storage robot moving to the high-efficiency power generation zone C. The mobile component is controlled to move the body so that the energy storage robot can move to the high-efficiency power generation zone C.

2. The control method according to claim 1, characterized in that, The control method further includes: If the area of ​​the overlapping region between the photovoltaic target area A and the wind power target area B is greater than the area threshold, the mobile component will drive the energy storage robot to the overlapping region.

3. The control method according to claim 2, characterized in that, The step of obtaining the energy consumption Q_move of the energy storage robot moving to multiple candidate areas P in its environment includes: The geographical area that the energy storage robot will travel to is divided into multiple candidate areas P; Obtain the current position N of the energy storage robot; Obtain the travel path S from the current location N to each of the candidate regions P and the road conditions along the travel path S; and Based on the preset travel speed V and the road conditions, the travel energy consumption Q_move for traveling to each of the candidate areas P is obtained.

4. The control method according to claim 3, characterized in that, The process of dividing the geographical area to which the energy storage robot will travel into multiple candidate areas P includes: The geographical region is divided into multiple initial regions; Based on contour line information and surface information, the slope and obstacle coverage of each initial region are obtained; and The initial region that satisfies the conditions of having a slope less than a preset slope and an obstacle coverage rate less than a preset coverage rate is selected as the candidate region P.

5. The control method according to claim 4, characterized in that, The acquisition of photovoltaic target area A includes: Get the parking photovoltaic power generation Q_L1 of the energy storage robot when it stays in each of the candidate areas P within the preset dwell time; Based on the travel path S and the travel speed, the mobile photovoltaic power generation Q_L2 of the energy storage robot during its movement to the candidate area P is obtained; Based on the mobile energy consumption Q_move, the mobile photovoltaic power generation Q_L2, and the parking photovoltaic power generation Q_L1, obtain the net photovoltaic power generation value Q_light for each candidate region P corresponding to the energy storage robot; and The photovoltaic target area A is determined based on multiple net photovoltaic power generation values ​​Q_light.

6. The control method according to claim 4, characterized in that, The acquisition of wind target area B includes: Get the parking wind power generation Q_W1 of the energy storage robot when it stays in each of the candidate areas P within the preset dwell time; Based on the travel path S and the travel speed, the mobile wind power generation Q_W2 of the energy storage robot during the process of moving to the candidate area P is obtained; Based on the mobile energy consumption Q_move, the mobile wind power generation Q_W2, and the parked wind power generation Q_W1, obtain the net wind power generation value Q_wind for each candidate region P corresponding to the energy storage robot; and Based on multiple net wind power generation values ​​Q_wind, the wind power target area B is determined.

7. The control method according to claim 4, characterized in that, The process of obtaining a high-efficiency power generation zone C based on the photovoltaic target zone A and the wind power target zone B includes: The solar-wind potential ratio of the energy storage robot in each candidate region P is obtained, wherein the solar-wind potential ratio is the ratio of photovoltaic power generation to wind power generation within the preset dwell time. In the candidate region P, a first region S1 is selected where the solar-wind potential ratio is within a preset range. The photovoltaic target area A, the wind power target area B, and the connecting path L between the photovoltaic target area A and the wind power target area B are expanded to obtain the expanded area S2; Obtain the intersection of the first region S1 and the expanded region S2 to form the second region S3; and The second region S3, which has the largest total power generation Q_final, is designated as the high-efficiency power generation zone C.

8. The control method according to claim 7, characterized in that, The step of expanding the photovoltaic target area A, the wind power target area B, and the path between the photovoltaic target area A and the wind power target area B to obtain the expanded region S2 includes: Based on the photovoltaic target area A, the area is expanded outwards under the first condition to obtain the first expansion area S21; Based on the wind target area B, the second expansion area S22 is obtained by expanding outwards according to the second condition. Obtain the connection path L between the photovoltaic target area A and the wind power target area B; Based on the connected path L, expand outwards according to the third condition to obtain the third expansion region S23; and The union of the first expansion region S21, the second expansion region S22, and the third expansion region S23 is taken as the expansion region S2.

9. An energy storage robot, characterized in that: The energy storage robot includes a body, a photovoltaic module mounted on the body, a wind turbine mounted on the body, a battery module mounted on the body, a moving component mounted on the body, and a controller. The photovoltaic module is configured to generate electrical energy through photoelectric conversion and output it to the battery module. The wind turbine is configured to generate electrical energy through wind power and output it to the battery module. The battery module is configured to be able to charge or discharge. The moving component is configured to drive the body to move. The controller is configured to execute the control method according to any one of claims 1-8.

10. An energy storage system, characterized in that, include: The energy storage robot as described in claim 9; and A charging device configured to provide electrical energy to the energy storage robot.

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