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

Abstract

The invention 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: acquiring moving energy consumption of the energy storage robot to a plurality of candidate areas; obtaining a photovoltaic target area, wherein the photovoltaic target area is the area with the highest photovoltaic power generation net value in the plurality of candidate areas within the preset stay duration; a wind power target area is obtained, wherein the wind power target area is the area with the highest wind power generation net value in the multiple candidate areas within the preset staying duration; and under the condition that the area of the overlapped area of the photovoltaic target area and the wind power target area is smaller than or equal to a preset area threshold value, a power generation efficient area is obtained based on the photovoltaic target area and the wind power target area. Wherein in the preset staying duration, the total power generation amount of the power generation efficient area is larger 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 obtained by subtracting the moving energy consumption of the energy storage robot moving to the power generation efficient area from the sum of the photovoltaic power generation net value and the wind power generation net value in the preset staying duration.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy storage, in particular to an energy storage robot, an energy storage system and a control method of the energy storage robot. BACKGROUND

[0002] In the case that a user is in an outdoor scene and needs to use electricity, the user usually chooses an outdoor power supply. However, the weight of the outdoor power supply increases with the increase of the storage capacity, and it is difficult to balance the convenience and large capacity demand of outdoor power use. Therefore, the energy storage robot with autonomous mobile function can solve the above problems to a certain extent. The energy storage robot simultaneously carries two power generation components, photovoltaic components and wind power components, which can improve the power generation efficiency of the energy storage robot.

[0003] However, the power generation strategies of the wind power components and the photovoltaic components will conflict, making it difficult for the energy storage robot to balance the two to improve the power generation efficiency. SUMMARY

[0004] The present application provides an energy storage robot, an energy storage system and a control method of the energy storage robot.

[0005] The application provides a control method of an energy storage robot. The energy storage robot comprises a body, a photovoltaic assembly arranged on the body, a wind power assembly arranged on the body, a battery module arranged on the body, and a moving assembly arranged on the body. The photovoltaic assembly is configured to generate electric energy through photoelectric conversion and output the electric energy to the battery module. The wind power assembly is configured to generate electric energy through wind power and output the electric energy to the battery module. The battery module is configured to be capable of charging or discharging. The moving assembly is configured to drive the body to move. The control method comprises the following steps: obtaining a moving energy consumption Q_move of the energy storage robot when moving to a plurality of candidate regions P; obtaining a photovoltaic target region A, which is a region in the plurality of candidate regions P with the highest photovoltaic power generation net value Q_light of the photovoltaic assembly within a preset stay duration; obtaining a wind power target region B, which is a region in the plurality of candidate regions P with the highest wind power generation net value Q_wind of the wind power assembly within the preset stay duration; when an area of an overlapping region of the photovoltaic target region A and the wind power target region B is less than or equal to a preset area threshold, obtaining a power generation efficient region C based on the photovoltaic target region A and the wind power target region B, wherein a total power generation amount Q_final of the energy storage robot in the power generation efficient region C is greater than a larger one of a total power generation amount Q_final in the photovoltaic target region A and a total power generation amount Q_final in the wind power target region B within the preset stay duration, and the total power generation amount Q_final of the energy storage robot in the power generation efficient region C is a sum of the photovoltaic power generation net value Q_light and the wind power generation net value Q_wind within the preset stay duration minus the moving energy consumption Q_move of the energy storage robot when moving to the power generation efficient region C; and controlling the moving assembly to drive the body to move, so that the energy storage robot moves to the power generation efficient region C.

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

[0007] In some embodiments, the step of obtaining the moving energy consumption Q_move of the energy storage robot when moving to a plurality of candidate regions P in an environment comprises the following steps: dividing a geographical region to which the energy storage robot is going to move into a plurality of candidate regions P; obtaining a current position N of the energy storage robot; obtaining a travel path S from the current position N to each of the candidate regions P and a road condition on the travel path S; and obtaining the moving energy consumption Q_move of traveling to each of the candidate regions P based on a preset travel speed V and the road condition.

[0008] In some embodiments, the method further comprises: dividing the geographic area into a plurality of initial regions; obtaining a slope and an obstacle coverage rate of each of the initial regions based on contour information and ground information; and obtaining the candidate region P as the initial region that satisfies a preset slope and a preset coverage rate.

[0009] In some embodiments, the method further comprises: obtaining a parking photovoltaic power generation amount Q_L1 of the energy storage robot in each of the candidate regions P within the preset stay duration; obtaining a moving photovoltaic power generation amount Q_L2 of the energy storage robot in moving to the candidate region P based on the travel path S and the travel speed; obtaining a photovoltaic power generation net value Q_light of the energy storage robot corresponding to each of the candidate regions P based on the moving energy consumption Q_move, the moving photovoltaic power generation amount Q_L2, and the parking photovoltaic power generation amount Q_L1; and determining the photovoltaic target area A based on a plurality of the photovoltaic power generation net values Q_light.

[0010] In some embodiments, the method further comprises: obtaining a parking wind power generation amount Q_W1 of the energy storage robot in each of the candidate regions P within the preset stay duration; obtaining a moving wind power generation amount Q_W2 of the energy storage robot in moving to the candidate region P based on the travel path S and the travel speed; obtaining a wind power generation net value Q_wind of the energy storage robot corresponding to each of the candidate regions P based on the moving energy consumption Q_move, the moving wind power generation amount Q_W2, and the parking wind power generation amount Q_W1; and determining the wind target area B based on a plurality of the wind power generation net values Q_wind.

[0011] In some embodiments, the method further comprises: obtaining a light-wind potential ratio of the energy storage robot in each of the candidate regions P, the light-wind potential ratio being a ratio of the photovoltaic power generation amount to the wind power generation amount within the preset stay duration; screening a first region S1 from the candidate regions P, the light-wind potential ratio of the first region S1 being within a preset ratio range; respectively inflating the photovoltaic target area A, the wind target area B, and a connected path L between the photovoltaic target area A and the wind target area B to obtain an inflated region S2; obtaining a second region S3 as an intersection of the first region S1 and the inflated region S2; and obtaining the second region S3 with a maximum total power generation Q_final as the power generation efficient area C.

[0012] In some embodiments, the expansion of 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 expansion area S2 respectively comprises: expanding outward in a first condition with the photovoltaic target area A as a reference to obtain a first expansion area S21; expanding outward in a second condition with the wind power target area B as a reference to obtain a second expansion area S22; obtaining a connected path L between the photovoltaic target area A and the wind power target area B; expanding outward in a third condition with the connected path L as a reference to obtain a third expansion area S23; and taking the union of the first expansion area S21, the second expansion area S22, and the third expansion area S23 as the expansion area S2.

[0013] The application also provides a storage energy robot. The storage energy robot comprises a body, a photovoltaic assembly arranged on the body, a wind power assembly arranged on the body, a battery module arranged on the body, a moving assembly arranged on the body, and a controller. The photovoltaic assembly is configured to generate electric energy through photoelectric conversion and output to the battery module. The wind power assembly is configured to generate electric energy through wind power and output to the battery module. The battery module is configured to be able to charge or discharge. The moving assembly is configured to drive the body to move, and the controller is configured to execute the control method described in any of the embodiments.

[0014] The application also provides a storage energy system. The storage energy system comprises the storage energy robot described in any of the embodiments and a charging device configured to provide electric energy to the storage energy robot.

[0015] In the storage energy robot, the storage energy system, and the control method of the storage energy robot of the application, the controller can obtain the photovoltaic target area A and the wind power target area B, i.e. the optimal positions in two independent power generation modes, and in the case that the area of the overlapping region of the photovoltaic target area A and the wind power target area B is less than or equal to a preset area threshold, obtain the power generation efficient area C based on the photovoltaic target area A and the wind power target area B, and control the moving assembly to drive the body to move, so that the storage energy robot goes to the power generation efficient area C. Since the power generation efficient area C can take into account the power generation efficiency of the wind power assembly and the power generation efficiency of the photovoltaic assembly, the wind power assembly and the photovoltaic assembly can be made to generate power cooperatively after the storage energy robot reaches the power generation efficient area C, and the power generation efficiency of the storage energy robot can be improved. Further, the power generation efficient area C also takes into account the moving energy consumption Q_move of the storage energy robot, and the accuracy of the control method can be improved.

[0016] Additional aspects and advantages of the application will be in part apparent and in part pointed out hereinafter. BRIEF DESCRIPTION OF DRAWINGS

[0017] The above and / or additional aspects and advantages of the present application will become apparent and more readily appreciated from the following description, taken in conjunction with the following drawings in which:

[0018] Figure 1 is a perspective structural schematic diagram of a storage energy robot in a folded state according to some embodiments of the present application;

[0019] Figure 2 is a perspective structural schematic diagram of a storage energy robot in an unfolded state according to some embodiments of the present application;

[0020] Figure 3 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0021] Figure 4 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0022] Figure 5 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0023] Figure 6 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0024] Figure 7 is a scenario schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0025] Figure 8 is a scenario schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0026] Figure 9 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0027] Figure 10 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0028] Figure 11 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0029] Figure 12 is a flow schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0030] Figure 13 is a scenario schematic diagram of a control method of a storage energy robot according to some embodiments of the present application;

[0031] Figure 14is a scene schematic diagram of a control method of an energy storage robot of some embodiments of the present application;

[0032] Figure 15 is a structural schematic diagram of an energy storage system of some embodiments of the present application.

[0033] The reference signs in the detailed description of the embodiments are as follows:

[0034] Energy storage system 1000; charging device 300; energy storage robot 100; body 10; battery module 30; moving assembly 50; controller 70; wind assembly 80; blade assembly 81; first telescopic structure 83; photovoltaic assembly 90; photovoltaic piece 91; second telescopic structure 93. Detailed description of the embodiments

[0035] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below in combination with the drawings. In the following description, a large number of specific details are set forth in order to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without departing from the connotation of the present application, therefore the present application is not limited by the specific embodiments disclosed below.

[0036] In the description of the present application, it should be understood that the terms "center", "length", "upper", "lower", "front", "rear", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship shown in the drawings, and are only for the purpose of facilitating the description of the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, therefore it cannot be understood as a limitation on the present application.

[0037] In addition, the terms "first", "second" are only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is at least two, for example, two, three, etc., unless otherwise specifically limited.

[0038] In the present application, unless specifically defined otherwise, the terms "mounting", "connected", "connection" and the like should be construed broadly and can be, for example, fixedly connected, removably connected, or integrated into one body; can be mechanically connected, or electrically connected; can be directly connected, or indirectly connected via an intermediate medium; can be a communication relationship inside two elements, or an interaction relationship between two elements, unless specifically defined otherwise. The specific meanings of the above terms in the present application can be understood by those of ordinary skill in the art according to the specific circumstances.

[0039] In the present application, unless specifically defined otherwise, a first feature is "on" or "under" a second feature can be that the first and second features are in direct contact, or the first and second features are in indirect contact via an intermediate medium. Moreover, the first feature "above", "over" and "on" the second feature can be that the first feature is directly above or obliquely above the second feature, or only indicates that the horizontal height of the first feature is higher than that of the second feature. The first feature "below", "under" and "under" the second feature can be that the first feature is directly below or obliquely below the second feature, or only indicates that the horizontal height of the first feature is less than that of the second feature.

[0040] In the case that the user is in an outdoor scene and needs to use electricity, the user will usually choose an outdoor power supply. However, the weight of the outdoor power supply increases with the increase of the storage capacity, and it is difficult to balance the convenience and large capacity requirements of outdoor power use. Therefore, the energy storage robot with autonomous moving function can solve the above problems to a certain extent. The energy storage robot simultaneously carries two kinds of power generation components, photovoltaic components and wind power components, which can improve the power generation efficiency of the energy storage robot. However, the power generation strategies of the wind power components and the photovoltaic components will conflict, making it difficult for the energy storage robot to balance the two to improve the power generation efficiency. In order to solve this problem, the present application provides an energy storage robot 100 Figure 1 and Figure 2 as shown), an energy storage system 1000 Figure 15 as shown) and a control method of the energy storage robot Figures 3 to 6 , and Figures 9 to 12 as shown).

[0041] Please refer to Figure 1 and Figure 2The energy storage robot 100 of the embodiment of the present application includes a fuselage 10, a battery module 30 provided on the fuselage 10, a moving component 50 provided on the fuselage 10, a controller 70 provided on the fuselage 10, a wind power component 80 provided on the fuselage 10, and a photovoltaic component 90 provided on the fuselage. The battery module 30 is configured to be capable of charging or discharging. The moving component 50 is configured to drive the fuselage 10 to move. The wind power component 80 is configured to generate electrical energy through wind power and output it to the battery module 30. The photovoltaic component 90 is configured to generate electrical energy through photoelectric conversion and output it to the battery module 30.

[0042] The energy storage robot 100 is a power distribution device that integrates energy storage function, autonomous movement function and intelligent control function. The energy storage robot 100 can move autonomously to the target location according to the user's demand for electricity use and provide regular or temporary power supply. The energy storage robot 100 can be used for, but not limited to, outdoor camping, dynamic energy management, emergency rescue, microgrid support and other scenarios to meet the electricity needs of areas without power grids or unstable power. The power of the energy storage robot 100 can be provided by a rechargeable battery module 30, a non-rechargeable battery module 30 or a charging structure (such as a wind power component 80, a photovoltaic component 90) provided in the energy storage robot 100, so that the energy storage robot 100 has sufficient reserve power.

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

[0044] The battery module 30 is a module for storing and releasing electric energy in the energy storage robot 100, and is a core module in the energy storage robot 100. Based on different application scenarios of the energy storage robot 100, the energy storage robot 100 has different capacities, that is, the battery module 30 has different capacities. For example, in a small household or commercial energy storage robot 100, the capacity of the battery module 30 is usually several kilowatt-hours to several tens of kilowatt-hours. In an industrial energy storage robot 100, the capacity of the battery module 30 is usually several tens of kilowatt-hours to several hundred kilowatt-hours. The battery module 30 is accommodated in the body 10 and can be electrically connected with other functional elements. The battery module 30 can be a rechargeable battery module or a non-rechargeable battery module. In the case of a rechargeable battery module 30, the energy storage robot 100 can be charged by the charging device 300 (such as a charging pile) to supplement the electric energy of the energy storage robot 100. In the case of a non-rechargeable battery module 30, the energy storage robot 100 can replace the battery module 30 by the charging device 300 to supplement the electric energy of the energy storage robot 100. The charging device 300 is a device for providing electric energy to a device with energy storage function. For example, the charging device 300 can provide electric energy to a new energy vehicle, an energy storage robot 100 or other energy storage device. The present application takes the charging device 300 as a charging pile as an example for description. The charging device 300 provides electric energy to the energy storage robot 100 in the form of charging the battery module 30 in the energy storage robot 100, or in the form of replacing (battery replacement) the battery module 30 in the energy storage robot 100.

[0045] The moving assembly 50 is a component for moving the body 10 in the energy storage robot 100. The moving assembly 50 is arranged on the body 10, usually at the bottom of the body 10. The moving assembly 50 can include a driving element (not shown in the figure) and an executing element. The driving element is an element for providing power, such as a driving motor, an internal combustion engine and a pneumatic motor, etc. The executing element is an element for direct movement, such as a track and a wheel, etc. The driving element is directly connected with the executing element and directly transmits power to the executing element to drive the executing element to move, thereby realizing the movement of the moving assembly 50 driving the body 10. The movement of the moving assembly 50 driving the body 10 can be, but is not limited to, translation, rotation, and a combination of translation and rotation, etc. Further, the moving assembly 50 can also include a transmission element, which connects the driving element and the executing element, that is, the driving element is indirectly connected with the executing element through the transmission element. The driving element directly transmits power to the transmission element, and then transmits power to the executing element through the transmission element, so as to drive the executing element to move, thereby realizing the movement of the moving assembly 50 driving the body 10.

[0046] The controller 70 is a device for receiving signals, processing signals and issuing control instructions in the energy storage robot 100. The controller 70 includes a circuit board and a control chip disposed on the circuit board. The controller 70 is electrically connected with the battery module 30, the moving assembly 50 and the photovoltaic assembly 90 and the like to realize the control of the energy storage robot 100 (including the start and stop of the moving assembly 50, the acquisition of the state of the energy storage robot 100 and the switching control of the working mode of the energy storage robot 100 and the like). In some embodiments, the controller 70 is connected with the battery module 30, the moving assembly 50 and the photovoltaic assembly 90 and the like through a wired connection, and the wired connection has high reliability of electrical connection between the elements, and the control of the controller 70 on the battery module 30, the moving assembly 50 and the photovoltaic assembly 90 and the like is stable and rapid. In other ways, the controller 70 is connected with the battery module 30, the positioning module 30 and the moving assembly 50 and the like through a wireless connection, and compared with the wired connection, the wireless connection saves the electrical connectors for connection and does not occupy space.

[0047] The wind power assembly 80 is an assembly for generating electric energy by wind power and outputting to the battery module 30 in the energy storage robot 100. The wind power assembly 80 includes a blade assembly 81 and a first telescopic structure 83, the blade assembly 81 includes a blade and a generator, the blade and the generator are connected, the blade can be rotated by wind power to drive the generator to generate electric energy. The first telescopic structure 83 is connected with the body 10, the first telescopic structure 83 can receive the control instruction of the controller 70, the first telescopic structure 83 responds to the control instruction, and drives the blade assembly 81 to move relative to the body 10 to make the wind power assembly 80 in a retracted state or an unfolded state. In the retracted state, as shown in Figure 1 , the blade assembly 81 is attached to the body 10 or accommodated in the body 10, and the space occupied by the energy storage robot 100 is reduced. In the unfolded state, as shown in Figure 2 , the first telescopic structure 83 drives the blade assembly 81 to protrude relative to the body 10 and extend outward. The electric energy generated by the wind power assembly 80 can be transmitted to the battery module 30 for storage to provide supplemental power for the energy storage robot 100.

[0048] The photovoltaic assembly 90 includes a photovoltaic component 91 and a second telescopic structure 93, and the photovoltaic component 91 is disposed on the second telescopic structure 93. The photovoltaic component 91 is a solar conversion device for generating electric energy by photoelectric conversion. The photovoltaic component 91 can be a single crystal silicon, a polycrystalline silicon or a thin-film solar cell and the like different types of solar conversion devices. Among them, the user can select photovoltaic components 91 of different efficiencies and sizes according to the use requirements. The second telescopic structure 93 is connected with the body 10, the second telescopic structure 93 can receive the control instruction of the controller 70, the second telescopic structure 93 responds to the control instruction, and drives the photovoltaic component 91 to move relative to the body 10 to change the light receiving area of the photovoltaic component 91, so that the photovoltaic assembly 90 is in a retracted state or an unfolded state. In the retracted state, as shown inFigure 1 As shown, the second telescopic structure 93 drives the photovoltaic component 91 to be arranged on or in the body 10 in response to the control instruction, so as to reduce the space occupied by the energy storage robot 100. In the unfolded state, as shown, Figure 2 As shown, the second telescopic structure 93 drives the photovoltaic component 91 to protrude relative to the body 10 and extend outward, and the light-receiving area of the photovoltaic component 91 in the folded state is smaller than the light-receiving area of the photovoltaic component 91 in the unfolded state.

[0049] Further, in the unfolded state, the second telescopic structure 93 can also change the area of the photovoltaic component 91 protruding relative to the body 10. That is, in the unfolded state, the photovoltaic component 91 can be partially accommodated in the body 10 and partially extend outward from the body 10, and the proportion of the two can be controlled by the second telescopic structure 93. It can be understood that the part of the photovoltaic component 91 protruding relative to the body 10 can perform photoelectric conversion under illumination, and therefore the area of the part extending outward from the body 10 is also the light-receiving area of the photovoltaic component 91, which can represent the photoelectric conversion efficiency of the energy storage robot 100. It should be noted that the photovoltaic component 91 arranged on or in the body 10 can also perform photoelectric conversion in some scenarios. For example, the photovoltaic component 91 arranged on the outer surface of the body 10 can receive light and perform photoelectric conversion; for another example, the photovoltaic component 91 accommodated in the body 10 can receive light penetrating the body 10 and perform photoelectric conversion. That is, regardless of the state of the photovoltaic component 91, the present application does not limit the photoelectric conversion process of the photovoltaic component 91.

[0050] For example, when the energy storage robot 100 moves to an area with good illumination, an open space and a small wind speed, the energy storage robot 100 can be fixed at a certain position, and the photovoltaic component 91 can be fully extended from the body 20 to maximize photoelectric conversion. For example, when the energy storage robot 100 moves in a small area, the photovoltaic component 91 can be fully accommodated in the body 20 to prevent the photovoltaic component 91 from being scratched by branches and the like during movement. For example, when the energy storage robot 100 moves following a user, the photovoltaic component 91 can be partially accommodated in the body 20 and partially extend outward from the body 20, so as to avoid being scratched by branches and the like during movement and to perform photoelectric conversion during movement.

[0051] Referring to Figure 2 , Figure 3 and Figure 13 , the present embodiment provides a control method of an energy storage robot. The control method comprises:

[0052] 01: acquiring the movement energy consumption Q_move of the energy storage robot 100 to the plurality of candidate areas P;

[0053] 03: Obtaining a photovoltaic target area A, where the photovoltaic target area A is the area with the highest photovoltaic power generation net value Q_light of the photovoltaic module 90 within a preset stay time among multiple candidate areas P;

[0054] 05: Obtaining a wind target area B, where the wind target area B is the area with the highest net wind power generation value Q_wind of the wind power assembly 80 within a preset stay time among the multiple candidate areas P;

[0055] 071: When the area of ​​the overlapping region between the photovoltaic target area A and the wind target area B is less than or equal to a preset area threshold, a high-efficiency power generation area C is obtained based on the photovoltaic target area A and the wind target area B, wherein, during the preset residence time, the total power generation Q_final of the energy storage robot 100 in the high-efficiency power generation area 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 target area B, and the total power generation Q_final of the energy storage robot 100 in the high-efficiency power generation area C is the sum of the net photovoltaic power generation value Q_light and the net wind power generation value Q_wind during the preset residence time, minus the movement energy consumption Q_move of the energy storage robot 100 to the high-efficiency power generation area C; and

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

[0057] Correspondingly, the controller 70 is used to execute the methods in 01, 03, 05 and 071. More specifically, the controller 70 is configured to: obtain the moving energy consumption Q_move of the energy storage robot 100 to multiple candidate areas P; obtain the photovoltaic target area A, which is the area in which the photovoltaic components 90 in the multiple candidate areas P have the highest net photovoltaic power generation value Q_light within the preset stay time; obtain the wind target area B, which is the area in which the wind components 80 in the multiple candidate areas P have the highest net wind power generation value Q_wind within the preset stay time; when the area of ​​the overlapping area of ​​the photovoltaic target area A and the wind target area B is less than or equal to the preset area threshold, obtain the power generation high efficiency area C based on the photovoltaic target area A and the wind target area B, which In the embodiment, within the preset stay time, the total power generation Q_final of the energy storage robot 100 in the high-efficiency power generation area 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 target area B. The total power generation Q_final of the energy storage robot 100 in the high-efficiency power generation area C is the sum of the net value of photovoltaic power generation Q_light and the net value of wind power generation Q_wind within the preset stay time minus the movement energy consumption Q_move of the energy storage robot 100 moving to the high-efficiency power generation area C, and the control of the mobile component 50 to drive the fuselage 10 to move, so that the energy storage robot 100 moves to the high-efficiency power generation area C.

[0058] Specifically, in the method of 01, the candidate region P refers to a position where the energy storage robot 100 can move to. There are multiple candidate regions P, and the controller 70 selects one candidate region P as the target position from the multiple candidate regions P. The target position refers to the position where the controller 70 selects the energy storage robot 100 to go from the current position N. The target position in this application is one of the overlapping region of the photovoltaic target area A and the wind target area B and the power generation efficient area C.

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

[0060] In the method of 03, the preset stay duration refers to the length of time that the energy storage robot 100 is expected to stay in the candidate region P. The photovoltaic target area A is the candidate region P with the highest net power generation value of the photovoltaic component 90 within the preset stay duration, without considering the power generation of the wind component 80. The photovoltaic target area A is the region with the highest photovoltaic power generation efficiency of the energy storage robot 100.

[0061] In the method of 05, the wind target area B is the candidate region P with the highest net power generation value of the wind component 80 within the preset stay duration, without considering the power generation of the photovoltaic component 90. The wind target area B is the region with the highest wind power generation efficiency of the energy storage robot 100.

[0062] In the method of 071, the preset area threshold is a threshold preset by the controller 70, and the preset area threshold is used to determine the degree of overlap between the photovoltaic target area A and the wind target area B. For example, the preset area threshold of the present application is 0. When the area of the overlapping region between the photovoltaic target area A and the wind target area B is less than or equal to 0, it means that the photovoltaic target area A and the wind target area B do not overlap. That is, there is no candidate region P that meets the conditions of the highest photovoltaic power generation efficiency and the highest wind power generation efficiency at the same time in the candidate region P. The controller 70 needs to obtain a new candidate region P, i.e., the high-efficiency power generation area C, based on the photovoltaic target area A and the wind target area B. The high-efficiency power generation area C is an area where the total power generation Q_final of the energy storage robot 100 is greater than the larger one of the total power generation Q_final when the energy storage robot 100 is only in the photovoltaic target area A and the total power generation Q_final when the energy storage robot 100 is only in the wind target area B within the preset stay duration. The total power generation Q_final is obtained by: the sum of the photovoltaic power generation net value Q_light and the wind power generation net value Q_wind in the area within the preset stay duration minus the moving energy consumption Q_move of the energy storage robot 100 moving to the high-efficiency power generation area C, i.e., Q_final = Q_light + Q_wind - Q_move. In this way, the total power generation Q_final not only considers the power generation efficiency of the wind component 80 and the power generation efficiency of the photovoltaic component 90, but also removes the energy consumption generated by moving to the high-efficiency power generation area C, thereby obtaining a more accurate total power generation Q_final, which helps to improve the accuracy of the control method. In the method of 09, the controller 70 controls the movement component 50 to move the body 10 so that the energy storage robot 100 goes to the high-efficiency power generation area C.

[0063] In the control method of the present application, the controller 70 can obtain the photovoltaic target area A and the wind target area B, i.e., the optimal positions in two independent power generation modes, and in the case that the area of the overlapping region between the photovoltaic target area A and the wind target area B is less than or equal to the preset area threshold, the controller 70 obtains the high-efficiency power generation area C based on the photovoltaic target area A and the wind target area B, and controls the movement component 50 to move the body 10, so that the energy storage robot 100 goes to the high-efficiency power generation area C. Since the high-efficiency power generation area C can take into account the power generation efficiency of the wind component 80 and the power generation efficiency of the photovoltaic component 90, the wind component 80 and the photovoltaic component 90 can be made to generate power cooperatively after the energy storage robot 100 reaches the high-efficiency power generation area C, which can improve the power generation efficiency of the energy storage robot 100. Further, the high-efficiency power generation area C also takes into account the moving energy consumption Q_move of the energy storage robot 100, which can improve the accuracy of the control method.

[0064] Please refer to Figure 2 , Figure 4 and Figure 13 In some embodiments, the control method further comprises:

[0065] 073: In the case that 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 mobile assembly 50 drives the energy storage robot 100 to the overlapping region.

[0066] Correspondingly, the controller 70 is configured to perform the method in 073. More specifically, the controller 70 is configured to: in the case that 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 mobile assembly 50 drives the energy storage robot 100 to the overlapping region.

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

[0068] Please refer to Figure 2 , Figure 5 and Figure 13 In some embodiments, 01: obtaining the moving energy consumption Q move of the energy storage robot 100 to a plurality of candidate regions P in the environment in which the energy storage robot 100 is located, comprises:

[0069] 011: dividing the geographical region to which the energy storage robot 100 is to be moved into a plurality of candidate regions P;

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

[0071] 015: obtaining the travel path S from the current position N to each candidate region P and the road conditions on the travel path S; and

[0072] 017: obtaining the moving energy consumption Q move of the travel to each candidate region P based on the preset travel speed V and the road conditions.

[0073] Correspondingly, the controller 70 is configured to perform the methods in 011, 013, 015 and 017. More specifically, the controller 70 is configured to: divide the geographical region to which the energy storage robot 100 is to be moved into a plurality of candidate regions P; obtain the current position N of the energy storage robot 100; obtain the travel path S from the current position N to each candidate region P and the road conditions on the travel path S; and obtain the moving energy consumption Q move of the travel to each candidate region P based on the preset travel speed V and the road conditions.

[0074] In the method of 011, the geographical region refers to the region range that the controller 70 pre-sets and that the energy storage robot 100 can need to move, i.e. Figure 13The white box area where the energy storage robot 100 is located. The geographical area acquisition can be based on various ways, for example, the controller 70 expands a certain range outward with the current position N of the energy storage robot 100 as the center, or according to the user's demand to preset a range of an area. The geographical area is used to limit the potential working range of the energy storage robot 100 in a manageable area, thereby reducing the complexity of subsequent controller 70 calculation. The division refers to the division of the geographical area into a plurality of smaller, independent sub-areas, which are candidate areas P. The candidate area P discretizes the geographical area, so that the controller 70 can analyze each candidate area P.

[0075] In the method of 013, the current position N is as described above, and the acquisition of the current position N is the basis for the controller 70 to acquire the moving energy consumption Q_move.

[0076] In the method of 015, the travel path S refers to the route of the energy storage robot 100 moving from the current position N to a candidate area P, and the controller 70 can acquire the travel path S through a path planning algorithm. The path planning algorithm includes but is not limited to genetic algorithm, simulated annealing algorithm, ant colony algorithm, Dijkstra algorithm or A* algorithm, etc., which are not limited herein. The road conditions of the present application include uphill, downhill and flat road. In some embodiments, the road conditions can also include road roughness and other information. It can be understood that the moving energy consumption Q_move of the energy storage robot 100 is different under different road conditions. For example, uphill usually consumes more moving energy consumption Q_move than flat road or downhill. Differentiating different road conditions can improve the accuracy of the acquired moving energy consumption Q_move.

[0077] In the method of 017, the preset travel speed V represents the average moving speed assumed by the controller 70 when acquiring the moving energy consumption Q_move. The preset travel speed V can be a fixed value, or a range dynamically adjusted according to the road conditions, which is not limited in the present application. After the controller 70 acquires the travel path S, the controller 70 divides the distance of the travel path S 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 power consumption of the energy storage robot 100 on the travel path S.

[0078] Dt1 is the distance of downhill walking of the energy storage robot 100, at this time, the energy storage robot 100 does work against gravity, and the battery module 30 consumes more electric energy per unit time when the energy storage robot 100 moves at the same speed V. Dt2 is the distance of uphill walking of the energy storage robot 100, at this time, the energy storage robot 100 does work to overcome gravity, and the battery module 30 consumes more electric energy per unit time when the energy storage robot 100 moves at the same speed V. Dt3 is the distance of walking on a flat road of the energy storage robot 100, at this time, the gravity center of the energy storage robot 100 does not change, and the battery module 30 does not consume electric energy per unit time when the energy storage robot 100 moves at the same speed V. Then, the controller 70 obtains the unit distance energy consumption corresponding to the three road conditions. The unit distance energy consumption is obtained by pre-calibration or experiment, the unit distance energy consumption of downhill is q1, the unit distance energy consumption of uphill is q2, and the unit distance energy consumption of flat road is q3. For each road condition, the controller 70 obtains the energy consumption of different road conditions based on the preset walking speed V, the distance of the road condition (downhill distance Dt1, uphill distance Dt2 or flat road distance Dt3) and the unit distance energy consumption, and adds the energy consumption of the three road conditions, that is, the moving energy consumption Q_move of each candidate area P. That is, Q_move = Q{Dt1, q1, V} + Q{Dt2, q2, V} + Q{Dt3, q3, V}.

[0079] Please refer to Figure 2 , Figure 6 , Figure 7 and Figure 8 In some embodiments, 011: the energy storage robot 100 divides the geographical area to be visited into a plurality of candidate areas P, including:

[0080] 0111: dividing the geographical area into a plurality of initial areas;

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

[0082] 0115: obtaining the initial area meeting the conditions of the slope being less than the preset slope and the obstacle coverage rate being less than the preset coverage rate as the candidate area P.

[0083] Correspondingly, the controller 70 is configured to execute the methods in 0111, 0113 and 0115. More specifically, the controller 70 is configured to: divide the geographical area into a plurality of initial areas; obtain the slope and obstacle coverage rate of each initial area based on the contour line information and the ground surface information; and obtain the initial area meeting the conditions of the slope being less than the preset slope and the obstacle coverage rate being less than the preset coverage rate as the candidate area P.

[0084] In the method of 0111, the initial regions refer to smaller sub-regions segmented in the geographical region to which the energy storage robot 100 is going to travel, according to predetermined rules (e.g., grid division, segmentation based on geographical features, etc.), and adjacent to each other. The initial regions represent all potential locations where the energy storage robot 100 can move and stay, and serve as the basis for subsequent screening of the candidate regions P.

[0085] In the method of 0113, the contour information represents the ups and downs of the terrain in the geographical region, and can reflect the slope of the geographical region. The surface information represents various features of the ground cover in the geographical region, such as vegetation types, water distribution, building distribution, and road network, etc., which can be derived from satellite images, remote sensing data, or a geographic information system (GIS) database. The controller 70 can obtain the slope of each initial region based on the contour information. The slope represents the degree of ground inclination. The controller 70 can obtain the obstacle coverage of each initial region based on the surface information. The obstacle coverage represents the proportion of the area of the initial region covered by obstacles (such as dense vegetation, water, steep rocks, building ruins, etc.) that are not or are difficult to pass through.

[0086] In the method of 0115, the initial region with a slope greater than or equal to the preset slope can cause the energy storage robot 100 to have difficulty moving, increase the moving energy consumption Q_move, and even roll over. The region with an obstacle coverage greater than or equal to the preset coverage can make the robot unable to pass through. The initial region with a slope greater than or equal to the preset slope is the non-candidate region F. The controller 70 obtains the initial region that satisfies the conditions of a slope less than the preset slope and an obstacle coverage less than the preset coverage as the candidate region P, which can directly exclude the regions (i.e., the initial region with a slope greater than or equal to the preset slope and the region with an obstacle coverage greater than or equal to the preset coverage) that are not suitable for the energy storage robot 100 to move or stay, reduce the number of candidate regions P, and thus reduce the computational burden of the controller 70, improve the computational efficiency and real-time performance of the control method.

[0087] If the controller 70 obtains and calculates the information in the form of coordinate points, the number of coordinate points to be processed in the geographical region is too large, and the controller 70 will bear a very high computational burden. The controller 70 divides the geographical region into a limited number of initial regions (P+F), and further screens the candidate regions P that meet the conditions, so that the controller 70 does not need to obtain the moving energy consumption Q_move or the total power generation Q_final of each coordinate point. The coordinate points in the candidate region P can be regarded as a whole. Exemplarily, the data of the central coordinate point of the candidate region P can represent the data of all coordinate points in the candidate region P. In this way, the computational burden of the controller 70 can be reduced, the computational complexity can be reduced, and the real-time performance of the control method can be improved.

[0088] Please refer toFigure 2 、 Figure 9 and Figure 13 In some embodiments, 03: obtaining the photovoltaic target area A comprises:

[0089] 031: obtaining a parked photovoltaic power generation amount Q_L1 of the energy storage robot 100 staying in each candidate area P within a preset staying duration;

[0090] 033: obtaining a moving photovoltaic power generation amount Q_L2 of the energy storage robot 100 in moving to the candidate area P based on the travel path S and the travel speed;

[0091] 035: obtaining a photovoltaic power generation net value Q_light of the energy storage robot 100 corresponding to each candidate area P based on the moving energy consumption Q_move, the moving photovoltaic power generation amount Q_L2 and the parked photovoltaic power generation amount Q_L1; and

[0092] 037: determining the photovoltaic target area A based on the plurality of photovoltaic power generation net values Q_light.

[0093] Correspondingly, the controller 70 is configured to perform the methods in 031, 033, 035 and 037. More specifically, the controller 70 is configured to: obtain a parked photovoltaic power generation amount Q_L1 of the energy storage robot 100 staying in each candidate area P within a preset staying duration; obtain a moving photovoltaic power generation amount Q_L2 of the energy storage robot 100 in moving to the candidate area P based on the travel path S and the travel speed; obtain a photovoltaic power generation net value Q_light of the energy storage robot 100 corresponding to each candidate area P based on the moving energy consumption Q_move, the moving photovoltaic power generation amount Q_L2 and the parked photovoltaic power generation amount Q_L1; and determine the photovoltaic target area A based on the plurality of photovoltaic power generation net values Q_light.

[0094] In the method of 031, the parked photovoltaic power generation amount Q_L1 refers to the electric energy that the photovoltaic assembly 90 can generate within the preset staying duration if the energy storage robot 100 is located in the candidate area P and is stationary (i.e., in a "parked" state).

[0095] In the method of 033, the moving photovoltaic power generation amount Q_L2 refers to the electric energy that the photovoltaic assembly 90 generates in the travel path S of the energy storage robot 100 moving from the current position N to a certain candidate area P.

[0096] In the method of 035, for a candidate area P, the photovoltaic power generation net value Q_light of the candidate area P is: the sum of the moving photovoltaic power generation amount Q_L2 and the parked photovoltaic power generation amount Q_L1, minus the moving energy consumption Q_move of the energy storage robot 100 moving to the candidate area P. That is, Q_light = Q_L1 + Q_L2 - Q_move.

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

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

[0099] Please refer to Figure 2 , Figure 10 and Figure 13 In some embodiments, 05: obtaining the wind target area B includes:

[0100] 051: obtaining the parking wind power generation amount Q_W1 of the energy storage robot 100 staying in each candidate region P within a preset staying duration;

[0101] 053: obtaining the moving wind power generation amount Q_W2 of the energy storage robot 100 in the process of moving to the candidate region P based on the travel path S and the travel speed;

[0102] 055: obtaining the wind power generation net value Q_wind of the energy storage robot 100 corresponding to each candidate region P based on the moving energy consumption Q_move, the moving wind power generation amount Q_W2, and the parking wind power generation amount Q_W1; and

[0103] 057: determining the wind target area B based on the plurality of wind power generation net values Q_wind.

[0104] Correspondingly, the controller 70 is configured to execute the methods in 051, 053, 055, and 057. More specifically, the 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 stay time; obtain the moving wind power generation Q_W2 of the energy storage robot 100 when it moves 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 corresponding to 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 target area B based on the multiple net wind power generation values ​​Q_wind.

[0105] In the method of 051, the parking wind power generation Q_W1 refers to the electric energy that can be generated by the wind power component 80 within the preset stay 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 electric energy generated by the wind power component 80 when the energy storage robot 100 moves from the current position N to a candidate area P in the travel path S.

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

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

[0109] Based on the moving energy consumption Q_move, the moving wind power generation Q_W2, and the parked wind power generation Q_W1, the energy storage robot 100 obtains a net wind power generation value Q_wind for each candidate region P. This net wind power generation value Q_wind for each candidate region P takes into account the moving wind power generation Q_W2, enabling a more comprehensive assessment of the electrical energy generated by the wind power assembly 80. This net wind power generation value Q_wind for each candidate region P also considers the moving energy consumption Q_move, further improving its accuracy.

[0110] See also Figure 2 、 Figure 11 、 Figure 13 andFigure 14 In some embodiments, the power generation efficient area C is obtained based on the photovoltaic target area A and the wind target area B, including:

[0111] 0711: Obtain the light-wind potential ratio of the energy storage robot 100 in each candidate area P, the light-wind potential ratio being the ratio of the photovoltaic power generation amount to the wind power generation amount within a preset stay duration;

[0112] 0713: Screen out a first area S1 with the light-wind potential ratio within a preset ratio range from the candidate area P;

[0113] 0714: Dilate the photovoltaic target area A, the wind target area B, and the connecting path L between the photovoltaic target area A and the wind target area B to obtain a dilated area S2;

[0114] 0715: Obtain the intersection of the first area S1 and the dilated area S2 as a second area S3; and

[0115] 0717: Take the second area S3 with the maximum total power generation Q_final as the power generation efficient area C.

[0116] Correspondingly, the controller 70 is configured to perform the methods in 0711, 0713, 0714, 0715, and 0717. More specifically, the controller 70 is configured to: obtain the light-wind potential ratio of the energy storage robot 100 in each candidate area P, the light-wind potential ratio being the ratio of the photovoltaic power generation amount to the wind power generation amount within a preset stay duration; screen out a first area S1 with the light-wind potential ratio less than or equal to a preset ratio from the candidate area P; dilate the photovoltaic target area A, the wind target area B, and the path between the photovoltaic target area A and the wind target area B to obtain a dilated area S2; obtain the intersection of the first area S1 and the dilated area S2 as a second area S3; and take the second area S3 with the maximum total power generation Q_final as the power generation efficient area C.

[0117] In the method of 0711, the light-wind potential ratio represents the gap between the photovoltaic power generation net value Q_light and the wind power generation net value Q_wind in a candidate area P. If the light-wind potential ratio is greater than the preset ratio range, it means that the photovoltaic component 90 in the candidate area P has strong power generation capacity, while the wind component 80 has weak power generation capacity, which cannot fully utilize the power generation of the wind component 80. If the light-wind potential ratio is less than the preset ratio range, it means that the photovoltaic component 90 in the candidate area P has weak power generation capacity, while the wind component 80 has strong power generation capacity, which cannot fully utilize the power generation of the photovoltaic component 90.

[0118] In the method of 0713, if the light-wind potential ratio is within the preset ratio range, it indicates that the power generation efficiency of the photovoltaic component 90 and the wind power component 80 of the candidate region P is relatively balanced, and the total power generation of the energy storage robot 100 will not be greatly reduced due to the drastic fluctuation of a single energy (light energy or wind energy). The first region S1 refers to a set of candidate regions P whose light-wind potential ratio is within the preset threshold range, that is, the first region S1 includes one or more candidate regions P. Figure 14 The hollow white star box is a candidate region in the first region S1, that is Figure 14 The first region S1 in 0713 includes 9 candidate regions P, and the light-wind potential ratio of the 9 candidate regions P is within the preset threshold range.

[0119] In the method of 0714, the communication path L between the photovoltaic target area A and the wind power target area B is the travel route between the photovoltaic target area A and the wind power target area B, and the energy storage robot 100 can walk along the communication path L between the photovoltaic target area A and the wind power target area B. The controller 70 can obtain the communication 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 algorithm or A* algorithm, etc., which is not limited here. It can be understood that the photovoltaic target area A and the wind power target area B themselves are regions with high power generation efficiency, and the regions around the photovoltaic target area A and the wind power target area B, as well as the nearby of the communication path L, are also easy to appear the high power generation efficiency region C. "Inflation" refers to the expansion of the photovoltaic target area A, the wind power target area B and the communication path L to a certain range. Taking the photovoltaic target area A as an example, the inflation can be a circular range expanded outward with the photovoltaic target area A as the center, and the inflation can also be based on the candidate region, and the region adjacent to the boundary of the photovoltaic target area A is screened, which is not limited here. Obtaining the inflation region S2 avoids the calculation of all candidate regions by the controller 70, reduces the range of the potential high power generation efficiency region C, and thus reduces the operation complexity of the controller 70 and improves the efficiency and real-time performance of the controller 70.

[0120] In the method of 0715, the second region S3 represents the intersection of the regions that have a good light-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 communication path L (belonging to the inflation region S2). The second region S3 further reduces the range of the potential high power generation efficiency region C, further reduces the operation 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 maximum total power generation Q_final is taken as the high power generation efficiency region C, which ensures that the high power generation efficiency region C selected by the energy storage robot 100 maximizes the power acquisition capability of the energy storage robot 100.

[0122] Please refer toFigure 2 、 Figure 12 and Figure 14 In some embodiments, the photovoltaic target area A, the wind target area B, and a path between the photovoltaic target area A and the wind target area B are respectively inflated to obtain an inflated area S2, including:

[0123] 07141: expanding outward from the photovoltaic target area A to obtain a first expanded area S21 according to a first condition;

[0124] 07143: expanding outward from the wind target area B to obtain a second expanded area S22 according to a second condition;

[0125] 07145: obtaining a connected path L between the photovoltaic target area A and the wind target area B;

[0126] 07147: expanding outward from the connected path L to obtain a third expanded area S23 according to a third condition; and

[0127] 07149: taking a union of the first expanded area S21, the second expanded area S22, and the third expanded area S23 as the inflated area S2.

[0128] Correspondingly, the controller 70 is configured to perform the methods in 07141, 07143, 07145, 07147, and 07149. More specifically, the controller 70 is configured to: expand outward from the photovoltaic target area A to obtain a first expanded area S21 according to a first condition; expand outward from the wind target area B to obtain a second expanded area S22 according to a second condition; obtain a connected path L between the photovoltaic target area A and the wind target area B; expand outward from the connected path L to obtain a third expanded area S23 according to a third condition; and take a union of the first expanded area S21, the second expanded area S22, and the third expanded area S23 as the inflated area S2.

[0129] In the method of 07141, in one example, the first condition refers to expanding outward from the photovoltaic target area A to a circle with a preset radius, thereby forming a larger range than the photovoltaic target area A. Figure 14 In another example (also in the selected embodiment), the first condition refers to obtaining candidate areas adjacent to the boundary of the photovoltaic target area A according to the boundary. Figure 14 In this example, the first expanded area S21 includes 12 candidate areas.

[0130] In the method of 07143, correspondingly, the second condition refers to obtaining candidate areas adjacent to the boundary of the wind target area B according to the boundary. Figure 14 In this example, the second expanded area S22 includes 12 candidate areas.

[0131] In the method of 07145, a connected path L between the photovoltaic target area A and the wind target area B is obtained. The connected path L refers to a route connecting the photovoltaic target area A and the wind target area B. The connected path L can be the most direct straight line between the two points (in an ideal case without obstacles or terrain restrictions), or a route that the energy storage robot 100 can pass through obtained by the controller 70 through a path planning algorithm.

[0132] In the method of 07147, the third condition is that the candidate area P on both sides of the connected path L has a center point whose distance to the connected path L is less than the width of the candidate area. Figure 14 The third expansion area S23 is composed of 17 candidate areas.

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

[0134] The inflation area S2 can reduce the calculation range of the controller 70, avoiding blind and time-consuming global search on the entire geographic area. It can improve the efficiency of the algorithm and the accuracy of the search.

[0135] Please refer to Figure 1 , Figure 2 and Figure 15 , the application also provides an energy storage system 1000. The energy storage system 1000 includes the energy storage robot 100 of any of the above embodiments and the charging device 300, which 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) for scheduling or utilizing energy, and an energy supply device for supplying energy to the energy storage robot 100. The energy storage system 1000 can be any system with the above functions, for example: the energy storage system 1000 is a cleaning system, the energy storage robot 100 is a cleaning robot, and the energy supply device is a power supply base station that supplies power to the cleaning robot to enable the cleaning robot to move using electrical energy; the energy storage system 1000 is a logistics system, the energy storage robot 100 is a logistics robot, and the energy supply device is a charging pile that supplies power to the logistics robot to enable the logistics robot to move using electrical energy; the energy storage system 1000 is a new energy vehicle system, the energy storage robot 100 is a new energy vehicle, and the energy supply device is a charging pile that supplies power to the new energy vehicle to enable the new energy vehicle to move using electrical energy. Please further refer to Figure 15The energy storage system 1000 is taken as an example for the power scheduling system, and at this time, the energy storage robot 100 is a movable energy storage power supply, and the energy 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 by using the electric energy and schedule and use the electric energy.

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

[0138] When the energy storage robot 100 reaches 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 in a wired charging mode or a wireless charging mode. In the case where the charging device 300 charges the energy storage robot 100 in a wired charging mode, the energy storage robot 100 is connected to the physical plug (such as Type 1, Type 2, GB / T or customized interface) of the charging device 300 through a connecting device (not shown in the figure) or a guiding device (not shown in the figure). At this time, the process of charging the energy storage robot 100 by the charging device 300 is simple and reliable, and the charging speed is relatively fast. In the case where the charging device 300 charges the energy storage robot 100 in a wireless charging mode, the energy storage robot 100 and the charging device 300 are respectively provided with an induction coil. The induction coil of the charging device 300 generates a magnetic field and transmits 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 be in contact, the energy storage robot 100 will not be worn, the appearance of the energy storage robot 100 is better and the service life is longer. In the case where the charging device 300 replaces the battery module 30 of the energy storage robot 100, the charging device 300 directly replaces the battery module 30 of the energy storage robot 100. At this time, the energy storage robot 100 can be quickly replenished with energy, and the energy storage robot 100 can work for a longer time.

[0139] Since the energy storage system 1000 in the present embodiment includes the energy storage robot 100, it can be understood that the energy storage system 1000 at least includes the same beneficial effects as the energy storage robot 100. Therefore, the beneficial effects of the energy storage system 1000 are referred to the beneficial effects of the energy storage robot 100 introduced above, and will not be repeated here.

[0140] The technical features of the above-described embodiments can be combined in any manner. For the sake of brevity, not all possible combinations of the technical features described above are described, however, it is to be understood that the scope of protection is not limited to the features described above. Furthermore, other embodiments can be derived from the above-described embodiments by means of structural and logical equivalents, and changes in the scope of the disclosure are intended to be covered.

Claims

1. A control method for an energy storage robot, the energy storage robot comprising a body, a photovoltaic assembly disposed on the body, a wind power assembly disposed on the body, a battery module disposed on the body, and a mobile assembly disposed on the body, wherein the photovoltaic assembly is configured to generate electrical energy through photoelectric conversion and output it to the battery module, the wind power assembly 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 mobile assembly is configured to drive the body to move, characterized in that: The control method includes: Obtaining the movement energy consumption Q_move of the energy storage robot to multiple candidate areas P; Acquire a photovoltaic target area A, where the photovoltaic target area A is an area in the plurality of candidate areas P where the photovoltaic module has the highest net photovoltaic power generation value Q_light within a preset stay time; Acquire a wind target area B, where the wind target area B is an area in the plurality of candidate areas P where the wind power component has the highest net wind power generation value Q_wind within the preset stay time; When the area of ​​the overlapping region of the photovoltaic target area A and the wind target area B is less than or equal to a preset area threshold, a high-efficiency power generation area C is obtained based on the photovoltaic target area A and the wind target area B, wherein, within the preset stay time, the total power generation Q_final of the energy storage robot in the high-efficiency power generation area 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 target area B, and the total power generation Q_final of the energy storage robot in the high-efficiency power generation area C is the sum of the net photovoltaic power generation value Q_light and the net wind power generation value Q_wind within the preset stay time, minus the movement energy consumption Q_move of the energy storage robot moving to the high-efficiency power generation area C; and The moving assembly is controlled to drive the body to move, so that the energy storage robot moves to the high-efficiency power generation area C.

2. The control method according to claim 1, characterized in that: The control method further includes: When 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 moving component drives the energy storage robot to the overlapping region.

3. The control method according to claim 2, characterized in that: The obtaining of the movement energy consumption Q_move of the energy storage robot to the plurality of candidate areas P in the environment includes: Divide the geographical area to which the energy storage robot is to go into a plurality of candidate areas P; Obtaining the current position N of the energy storage robot; Obtaining a travel path S from the current position N to each candidate area P and a road condition on the travel path S; and The moving energy consumption Q_move for traveling to each candidate area P is obtained based on the preset traveling speed V and the road condition.

4. The control method according to claim 3, characterized in that: The step of dividing the geographical area to which the energy storage robot is to go into a plurality of candidate areas P includes: dividing the geographic area into a plurality of initial areas; Obtaining the slope and obstacle coverage of each of the initial areas based on contour line information and surface information; and The initial area satisfying the conditions that the slope is less than a preset slope and the obstacle coverage is less than a preset coverage is obtained as the candidate area P.

5. The control method according to claim 4, characterized in that: The obtaining of the photovoltaic target area A comprises: Obtaining the parking photovoltaic power generation Q_L1 of the energy storage robot when it stays in each candidate area P within the preset stay time; Acquire the mobile photovoltaic power generation Q_L2 of the energy storage robot in the process of moving to the candidate area P based on the travel path S and the travel speed; Based on the moving energy consumption Q_move, the moving photovoltaic power generation Q_L2 and the parking photovoltaic power generation Q_L1, obtaining the photovoltaic power generation net value Q_light of the energy storage robot corresponding to each candidate area P; and The photovoltaic target area A is determined based on a plurality of the photovoltaic power generation net values ​​Q_light.

6. The control method according to claim 4, characterized in that: The obtaining of the wind target area B comprises: Obtaining the parking wind power generation Q_W1 of the energy storage robot when it stays in each candidate area P within the preset stay time; Acquire the mobile wind power generation Q_W2 of the energy storage robot in the process of moving to the candidate area P based on the travel path S and the travel speed; Based on the moving energy consumption Q_move, the moving wind power generation Q_W2 and the parking wind power generation Q_W1, obtaining the net wind power generation value Q_wind of the energy storage robot corresponding to each candidate area P; and The wind target area B is determined based on a plurality of the wind power net values ​​Q_wind.

7. The control method according to claim 4, characterized in that: The obtaining of the high-efficiency power generation area C based on the photovoltaic target area A and the wind power target area B includes: Obtaining a solar-wind potential ratio of the energy storage robot in each candidate area P, where the solar-wind potential ratio is a ratio of photovoltaic power generation to wind power generation within the preset stay time; Screening out a first region S1 in the candidate region P, wherein the solar-to-wind potential ratio is within a preset ratio range; Expanding the photovoltaic target area A, the wind target area B, and the communication path L between the photovoltaic target area A and the wind target area B to obtain an expanded area S2; Obtaining the intersection of the first region S1 and the expanded region S2 as the second region S3; and The second region S3 where the total power generation Q_final is the largest is regarded as the high-efficiency power generation region C.

8. The control method according to claim 7, characterized in that: The expanding the photovoltaic target area A, the wind target area B, and the path between the photovoltaic target area A and the wind target area B to obtain the expanded area S2 includes: Taking the photovoltaic target area A as a reference, expanding it in all directions according to the first condition to obtain a first expansion area S21; Taking the wind target area B as a reference, expanding it in all directions according to the second condition to obtain a second expansion area S22; Acquire a communication path L between the photovoltaic target area A and the wind target area B; Taking the connecting path L as a reference, expand in all directions according to the third condition to obtain a third expansion area S23; and The union of the first expansion area S21 , the second expansion area S22 , and the third expansion area S23 is used as the expansion region S2 .

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

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

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