Mobile energy storage charging robot

Abstract

The utility model relates to the technical field of new energy automobile charging, and discloses a mobile energy storage charging robot, which comprises a robot body, a charging pile and a platform dispatching system, and is characterized in that the robot body is provided with a high-capacity energy storage battery pack, a motor driving unit and an electric drive chassis; a charger, a charging and discharging switching controller and an anti-backflow terminal are arranged in the charging pile, the robot body is used for charging a new energy automobile, the charging pile is used for automatically switching to charge the robot body and receiving discharging of the robot body, and the platform dispatching system is used for managing resource distribution of the robot body and the charging pile of each city; according to the invention, the charging convenience and user experience are greatly improved, the problem of insufficient charging facilities is effectively relieved, the utilization rate of electric power resources is improved, deep fusion of new energy vehicles and smart grids is promoted, and an innovative solution is provided for promoting green travel and constructing a smart energy ecological system.

Description

Technical Field

[0001] The utility model relates to the technical field of new energy vehicle charging, and in particular to a mobile energy storage charging robot. Background Art

[0002] With the transformation of the global energy structure and the enhancement of environmental awareness, new energy vehicles are rapidly becoming popular due to their clean and low-carbon characteristics, and the demand for vehicle charging is also increasing. However, the rapid development of the new energy vehicle industry also faces many challenges, especially insufficient charging infrastructure, low charging efficiency and unbalanced grid load. Especially in the central area of ​​the city, land resources are tight, and it is difficult and costly to build fixed charging piles. It is also difficult to flexibly respond to fluctuations in charging demand at different times, resulting in "difficult charging" becoming a major bottleneck restricting the promotion of new energy vehicles; in addition, the peak and valley load differences of the power system are becoming increasingly significant, with a surge in electricity demand during peak hours and an excess of electricity resources during valley hours. This imbalance leads to a waste of electricity resources and increased pressure on grid operation. Utility Model Content

[0003] The purpose of the utility model is to solve the above problems and designs a mobile energy storage and charging robot.

[0004] The utility model provides a mobile energy storage charging robot, which includes a robot body, a charging pile and a platform scheduling system. The robot body is provided with a large-capacity energy storage battery pack, a motor drive unit, and an electric drive chassis. The charging pile is provided with a charger, a charge and discharge switching controller, and an anti-backflow terminal.

[0005] The robot body is used to provide charging services for new energy vehicles;

[0006] The charging pile is used to automatically switch to charge the robot body and receive the discharge of the robot body, wherein the charging pile is set in a parking lot or a public area;

[0007] The platform scheduling system is used to manage the resource allocation of the robot bodies and the charging piles in each city.

[0008] Optionally, in the first implementation of the utility model, the large-capacity energy storage battery pack is used to store the electric energy obtained from the power grid during off-peak hours and provide charging services for the target new energy vehicles during peak hours.

[0009] Optionally, in a second implementation of the utility model, the robot body is further provided with a visual positioning module, a charging docking module, a battery status monitoring module, an environmental adaptability component and a communication module, and the battery status monitoring module is connected to the large-capacity energy storage battery pack, wherein,

[0010] A visual positioning module is used to locate, avoid obstacles and navigate the robot, so that the robot can move accurately to the target new energy vehicle position, wherein the visual positioning module includes at least a laser radar, a camera, an ultrasonic sensor and an inertial measurement unit;

[0011] The charging docking module is equipped with a variety of charging interfaces, compatible with charging ports of new energy vehicles of different standards, and automatically identifies the vehicle type and selects the charging interface;

[0012] The battery status monitoring module is used to monitor the battery health status during the charging process and take safety measures immediately when abnormal conditions are detected;

[0013] Environmental adaptability components, including dustproof and waterproof housing, temperature adjustment components, over-temperature protection components, short-circuit protection components, and over-charge and over-discharge protection components;

[0014] The communication module is used to exchange data with the cloud service platform and transmit the robot's real-time location information and battery status information.

[0015] Optionally, in a third implementation of the present utility model, the battery status monitoring module includes a data acquisition submodule, a data denoising submodule, an initialization submodule, a construction submodule, an output result submodule, an update position submodule, a repeated iteration submodule and a status monitoring submodule, wherein:

[0016] A data acquisition submodule, used to acquire multi-dimensional data of the robot body, wherein the multi-dimensional data at least includes voltage, current, temperature, state of charge, and battery internal resistance;

[0017] A data denoising submodule, for performing data denoising on the multi-dimensional data by using wavelet packet transform, and performing standardization processing on the denoised multi-dimensional data to obtain standardized multi-dimensional data;

[0018] The initialization submodule is used to initialize the number of sparrow populations of the sparrow search algorithm, the number of iterations of the sparrow algorithm, and the algorithm optimization range;

[0019] Build a submodule to initialize the position of the sparrow population, build an LSTM neural network based on the initialized sparrow population parameters, input the standardized multi-dimensional data into the LSTM neural network, and obtain the model output results under different parameters;

[0020] The output result submodule is used to calculate the fitness of each sparrow individual according to the model output result by taking the root mean square error as the fitness function;

[0021] The position update submodule is used to select the individual with the smallest fitness value as the optimal position sparrow individual, and update the position of the sparrow population according to the optimal sparrow individual;

[0022] A repeated iteration submodule, used for repeated iteration, and after reaching a maximum number of iterations, outputting an optimization result of the sparrow search algorithm, wherein the optimization result at least includes a batch size, a learning rate, a layer number, and a neuron number;

[0023] The state monitoring submodule is used to build an LSTM neural network using the optimization results of the sparrow search algorithm, obtain a battery state prediction model, and monitor the battery health state through the battery state prediction model.

[0024] Optionally, in a fourth implementation of the present invention, the battery status monitoring module also includes a graded response module connected to the output end of the status monitoring submodule, and the graded response module is used to determine a corresponding warning level and safety measures according to the battery health status output by the status monitoring submodule.

[0025] Optionally, in a fifth implementation of the present utility model, the charging pile is further provided with a bidirectional charging module, an intelligent scheduling module and a user interaction interface, wherein:

[0026] A bidirectional charging module, using a bidirectional DC-DC converter, is used to charge the robot body and reversely transmit power according to the needs of the power grid;

[0027] An intelligent scheduling module, used to communicate with the platform scheduling system to perform charging or discharging operations of the charging pile;

[0028] The user interaction interface supports multiple language switching and is equipped with a touch screen for users to operate by themselves.

[0029] Optionally, in a sixth implementation of the present utility model, the platform scheduling system is provided with a prediction charging module, a cloud service platform and an energy efficiency optimization module, wherein:

[0030] Predictive charging module, which is used to analyze historical charging demand and grid load data to predict future charging demand;

[0031] Cloud service platform, used to centrally collect robot data and remotely monitor the status of all robots and charging piles;

[0032] The energy efficiency optimization module is used to dynamically adjust the charging and discharging strategies based on the peak and valley electricity prices in each city, the distribution of new energy vehicles, and the load of charging piles.

[0033] Optionally, in a seventh implementation of the present utility model, the predicted charging module includes an FCM clustering submodule, a data partitioning submodule and a model building submodule, wherein:

[0034] The FCM clustering submodule is used to form a data sample set according to historical charging demand and grid load data, perform FCM clustering on the data sample set, traverse the number of clustering targets, and select the best clustering target value according to the SC index and DBI index;

[0035] A data partitioning submodule, used to divide the data in the data sample set into different clusters, and obtain the cluster center line of each category, wherein each cluster represents samples with similar charging load characteristics;

[0036] The model building submodule is used to build a corresponding BiLSTM model for each cluster. At each moment, the forget gate receives the current load state and the hidden layer state of the previous moment, which are output by the activation function Sigmoid. The input of the input gate is transformed by a nonlinear function and superimposed with the output of the forget gate to obtain the updated memory unit state. The output gate dynamically controls the memory unit state to obtain the output hidden state of the LSTM. The BiLSTM model is used to learn and capture the time correlation and periodicity characteristics of the charging load to predict future charging demand.

[0037] In the technical solution provided by the utility model, the mobile energy storage charging robot includes a robot body, a charging pile and a platform scheduling system. The robot body is used to provide charging services for new energy vehicles, the charging pile is used to automatically switch to charge the robot body and receive the discharge of the robot body, and the platform scheduling system is used to manage the resource allocation of the robot body and the charging piles in each city; the application greatly improves the convenience of charging and user experience, especially in urban centers with tight parking resources, can effectively alleviate the problem of insufficient charging facilities, improve the utilization rate of power resources, and promote the deep integration of new energy vehicles and smart grids, providing innovative solutions for promoting green travel and building a smart energy ecosystem. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] By reading the detailed description of the preferred embodiment below, various other advantages and benefits will become clear to those skilled in the art. The accompanying drawings are only for the purpose of illustrating the preferred embodiment and are not to be considered as limiting the present invention.

[0039] Figure 1 A schematic diagram of the structure of a mobile energy storage and charging robot provided in an embodiment of the utility model;

[0040] Figure 2 A schematic diagram of the structure of the robot body provided by an embodiment of the utility model;

[0041] Figure 3 A schematic diagram of the structure of a charging pile provided in an embodiment of the utility model;

[0042] Figure 4A schematic diagram of the structure of a platform scheduling system provided in an embodiment of the utility model. DETAILED DESCRIPTION

[0043] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the drawings in the present application only serve the purpose of explanation and description and are not used to limit the scope of protection of the present application. In addition, it should be understood that the schematic drawings are not drawn in real proportion. The flowchart used in this application shows the operations implemented according to some embodiments of the present application. It should be understood that the operations of the flowchart can be implemented out of sequence, and the steps without logical context can be reversed in order or implemented simultaneously. In addition, those skilled in the art can add one or more other operations to the flowchart under the guidance of the content of the present application, or remove one or more operations from the flowchart.

[0044] In addition, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0045] It should be noted that the term "comprising" will be used in the embodiments of the present application to indicate the existence of the features declared thereafter, but does not exclude the addition of other features.

[0046] For ease of understanding, the specific process of the embodiment of the utility model is described below. Figure 1-4 The mobile energy storage charging robot includes a robot body 101, a charging pile 102 and a platform scheduling system 103. The robot body is provided with a large-capacity energy storage battery pack 1011, a motor drive unit 1012, and an electric drive chassis 1013. The charging pile is provided with a charger 1021, a charge and discharge switching controller 1022, and an anti-backflow terminal 1023.

[0047] The robot body is used to provide charging services for new energy vehicles;

[0048] A charging pile, used to automatically switch to charge the robot body and receive the discharge of the robot body, wherein the charging pile is set in a parking lot or a public area;

[0049] The platform scheduling system is used to manage the resource allocation of robot bodies and charging piles in each city.

[0050] In this embodiment, a large-capacity energy storage battery pack is used to store the electric energy obtained from the power grid during off-peak hours and provide charging services for target new energy vehicles during peak hours.

[0051] In this embodiment, the robot body is also provided with a visual positioning module 1014, a charging docking module 1015, a battery status monitoring module 1016, an environmental adaptability component 1017 and a communication module 1018. The battery status monitoring module is connected to a large-capacity energy storage battery pack, wherein:

[0052] The visual positioning module is responsible for the positioning, obstacle avoidance and navigation of the robot, so that the robot can accurately move to the target new energy vehicle position. The visual positioning module includes at least a laser radar, a camera, an ultrasonic sensor, and an inertial measurement unit;

[0053] The charging docking module is equipped with a variety of charging interfaces, compatible with charging ports of new energy vehicles of different standards, and automatically identifies the vehicle type and selects the charging interface;

[0054] The battery status monitoring module is used to monitor the battery health status during the charging process and take safety measures immediately when abnormal conditions are detected;

[0055] Environmental adaptability components, including dustproof and waterproof housing, temperature adjustment components, over-temperature protection components, short-circuit protection components, and over-charge and over-discharge protection components;

[0056] The communication module is used to exchange data with the cloud service platform and transmit the robot's real-time location information and battery status information.

[0057] In this embodiment, the battery status monitoring module includes a data acquisition submodule, a data denoising submodule, an initialization submodule, a construction submodule, an output result submodule, an update position submodule, a repeated iteration submodule and a status monitoring submodule, wherein:

[0058] A data acquisition submodule is used to acquire multi-dimensional data of the robot body, wherein the multi-dimensional data at least includes voltage, current, temperature, state of charge, and battery internal resistance;

[0059] A data denoising submodule is used to perform data denoising on multi-dimensional data by using wavelet packet transform, and to perform standardization processing on the denoised multi-dimensional data to obtain standardized multi-dimensional data;

[0060] The initialization submodule is used to initialize the number of sparrow populations of the sparrow search algorithm, the number of iterations of the sparrow algorithm, and the algorithm optimization range;

[0061] Build a submodule to initialize the position of the sparrow population, build an LSTM neural network based on the initialized sparrow population parameters, input the standardized multi-dimensional data into the LSTM neural network, and obtain the model output results under different parameters;

[0062] The output result submodule is used to calculate the fitness of each sparrow individual according to the model output result by taking the root mean square error as the fitness function;

[0063] The position update submodule is used to select the individual with the smallest fitness value as the optimal position sparrow individual, and update the position of the sparrow population according to the optimal sparrow individual;

[0064] A repeated iteration submodule is used for repeated iteration, and after reaching the maximum number of iterations, the optimization result of the sparrow search algorithm is output, wherein the optimization result at least includes the batch number, the learning rate, the number of layers, and the number of neurons;

[0065] The status monitoring submodule is used to build an LSTM neural network using the optimization results of the sparrow search algorithm, obtain a battery status prediction model, and monitor the battery health status through the battery status prediction model.

[0066] In this embodiment, the battery status monitoring module also includes a hierarchical response module connected to the output end of the status monitoring submodule, and the hierarchical response module is used to determine the corresponding warning level and safety measures according to the battery health status output by the status monitoring submodule.

[0067] In this embodiment, the charging pile is further provided with a bidirectional charging module 1024, an intelligent scheduling module 1025 and a user interaction interface 1026, wherein:

[0068] The bidirectional charging module uses a bidirectional DC-DC converter to charge the robot body and send power in the reverse direction according to the grid demand;

[0069] Intelligent scheduling module, used to communicate with the platform scheduling system to perform charging or discharging operations of the charging pile;

[0070] The user interaction interface supports multiple language switching and is equipped with a touch screen for users to operate by themselves.

[0071] In this embodiment, the platform scheduling system is provided with a prediction charging module 1031, a cloud service platform 1032 and an energy efficiency optimization module 1033, wherein:

[0072] Predictive charging module, which is used to analyze historical charging demand and grid load data to predict future charging demand;

[0073] Cloud service platform, used to centrally collect robot data and remotely monitor the status of all robots and charging piles;

[0074] The energy efficiency optimization module is used to dynamically adjust the charging and discharging strategies based on the peak and valley electricity prices in each city, the distribution of new energy vehicles, and the load of charging piles.

[0075] In this embodiment, the prediction charging module includes an FCM clustering submodule, a data partitioning submodule and a model building submodule, wherein:

[0076] The FCM clustering submodule is used to form a data sample set based on historical charging demand and grid load data, perform FCM clustering on the data sample set, traverse the number of clustering targets, and select the best clustering target value based on the SC index and DBI index;

[0077] The data partitioning submodule is used to divide the data in the data sample set into different clusters and obtain the cluster center line of each category, where each cluster represents samples with similar charging load characteristics;

[0078] The model building submodule is used to build the corresponding BiLSTM model for each cluster. At each moment, the forget gate receives the current load state and the hidden layer state of the previous moment, which is output by the activation function Sigmoid. The input of the input gate is transformed by a nonlinear function and superimposed with the output of the forget gate to obtain the updated memory unit state. The output gate memory unit state is dynamically controlled to obtain the output hidden state of the LSTM. The BiLSTM model is used to learn and capture the time correlation and periodicity characteristics of the charging load to predict future charging demand.

[0079] Through the implementation of the above scheme, the mobile energy storage charging robot includes a robot body, a charging pile and a platform scheduling system. The robot body is used to provide charging services for new energy vehicles, the charging pile is used to automatically switch to charge the robot body and receive the discharge of the robot body, and the platform scheduling system is used to manage the resource allocation of the robot body and charging piles in various cities; this application greatly improves the convenience of charging and user experience, especially in urban centers with tight parking resources, can effectively alleviate the problem of insufficient charging facilities, improve the utilization rate of power resources, and promote the deep integration of new energy vehicles and smart grids, providing innovative solutions for promoting green travel and building a smart energy ecosystem.

[0080] The above shows and describes the basic principle, main features and advantages of the utility model. Those skilled in the art should understand that the utility model is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the utility model and are not used to limit the utility model. Without departing from the spirit and scope of the utility model, the utility model may have various changes and improvements, which fall within the scope of the utility model to be protected. The scope of protection of the utility model is defined by the attached claims and their equivalents.

Claims

1. A mobile energy storage and charging robot, characterized in that: The mobile energy storage charging robot includes a robot body, a charging pile and a platform scheduling system. The robot body is provided with a large-capacity energy storage battery pack, a motor drive unit, and an electric drive chassis. The charging pile is provided with a charger, a charge and discharge switching controller, and an anti-backflow terminal. The robot body is used to provide charging services for new energy vehicles; The charging pile is used to automatically switch to charge the robot body and receive the discharge of the robot body, wherein the charging pile is set in a parking lot or a public area; The platform scheduling system is used to manage the resource allocation of the robot bodies and the charging piles in each city.

2. A mobile energy storage charging robot as claimed in claim 1, characterized in that: The large-capacity energy storage battery pack is used to store the electric energy obtained from the power grid during off-peak hours and provide charging services for target new energy vehicles during peak hours.

3. A mobile energy storage charging robot as claimed in claim 1, characterized in that: The robot body is also provided with a visual positioning module, a charging docking module, a battery status monitoring module, an environmental adaptability component and a communication module. The battery status monitoring module is connected to the large-capacity energy storage battery pack, wherein: A visual positioning module is used to locate, avoid obstacles and navigate the robot, so that the robot can move accurately to the target new energy vehicle position, wherein the visual positioning module includes at least a laser radar, a camera, an ultrasonic sensor and an inertial measurement unit; The charging docking module is equipped with a variety of charging interfaces, compatible with charging ports of new energy vehicles of different standards, and automatically identifies the vehicle type and selects the charging interface; The battery status monitoring module is used to monitor the battery health status during the charging process and take safety measures immediately when abnormal conditions are detected; Environmental adaptability components, including dustproof and waterproof housing, temperature adjustment components, over-temperature protection components, short-circuit protection components, and over-charge and over-discharge protection components; The communication module is used to exchange data with the cloud service platform and transmit the robot's real-time location information and battery status information.

4. A mobile energy storage charging robot as claimed in claim 3, characterized in that: The battery status monitoring module includes a data acquisition submodule, a data noise reduction submodule, an initialization submodule, a construction submodule, an output result submodule, an update position submodule, a repeated iteration submodule and a status monitoring submodule, wherein: A data acquisition submodule, used to acquire multi-dimensional data of the robot body, wherein the multi-dimensional data at least includes voltage, current, temperature, state of charge, and battery internal resistance; A data denoising submodule, for performing data denoising on the multi-dimensional data by using wavelet packet transform, and performing standardization processing on the denoised multi-dimensional data to obtain standardized multi-dimensional data; The initialization submodule is used to initialize the number of sparrow populations of the sparrow search algorithm, the number of iterations of the sparrow algorithm, and the algorithm optimization range; Build a submodule to initialize the position of the sparrow population, build an LSTM neural network based on the initialized sparrow population parameters, input the standardized multi-dimensional data into the LSTM neural network, and obtain the model output results under different parameters; The output result submodule is used to calculate the fitness of each sparrow individual according to the model output result by taking the root mean square error as the fitness function; The position update submodule is used to select the individual with the smallest fitness value as the optimal position sparrow individual, and update the position of the sparrow population according to the optimal sparrow individual; A repeated iteration submodule, used for repeated iteration, and after reaching a maximum number of iterations, outputting an optimization result of the sparrow search algorithm, wherein the optimization result at least includes a batch size, a learning rate, a layer number, and a neuron number; The state monitoring submodule is used to build an LSTM neural network using the optimization results of the sparrow search algorithm, obtain a battery state prediction model, and monitor the battery health state through the battery state prediction model.

5. A mobile energy storage charging robot as claimed in claim 4, characterized in that: The battery status monitoring module also includes a graded response module connected to the output end of the status monitoring submodule, and the graded response module is used to determine the corresponding warning level and safety measures according to the battery health status output by the status monitoring submodule.

6. A mobile energy storage and charging robot as claimed in claim 1, characterized in that: The charging pile is also provided with a bidirectional charging module, an intelligent scheduling module and a user interaction interface, wherein: A bidirectional charging module, using a bidirectional DC-DC converter, is used to charge the robot body and reversely transmit power according to the needs of the power grid; An intelligent scheduling module, used to communicate with the platform scheduling system to perform charging or discharging operations of the charging pile; The user interaction interface supports multiple language switching and is equipped with a touch screen for users to operate by themselves.

7. A mobile energy storage and charging robot as claimed in claim 1, characterized in that: The platform scheduling system is provided with a prediction charging module, a cloud service platform and an energy efficiency optimization module, wherein: Predictive charging module, which is used to analyze historical charging demand and grid load data to predict future charging demand; Cloud service platform, used to centrally collect robot data and remotely monitor the status of all robots and charging piles; The energy efficiency optimization module is used to dynamically adjust the charging and discharging strategies based on the peak and valley electricity prices in each city, the distribution of new energy vehicles, and the load of charging piles.

8. A mobile energy storage charging robot as claimed in claim 7, characterized in that: The predicted charging module includes an FCM clustering submodule, a data partitioning submodule and a model building submodule, wherein: The FCM clustering submodule is used to form a data sample set according to historical charging demand and grid load data, perform FCM clustering on the data sample set, traverse the number of clustering targets, and select the best clustering target value according to the SC index and DBI index; A data partitioning submodule, used to divide the data in the data sample set into different clusters, and obtain the cluster center line of each category, wherein each cluster represents samples with similar charging load characteristics; The model building submodule is used to build a corresponding BiLSTM model for each cluster. At each moment, the forget gate receives the current load state and the hidden layer state of the previous moment, which are output by the activation function Sigmoid. The input of the input gate is transformed by a nonlinear function and superimposed with the output of the forget gate to obtain the updated memory unit state. The output gate dynamically controls the memory unit state to obtain the output hidden state of the LSTM. The BiLSTM model is used to learn and capture the time correlation and periodicity characteristics of the charging load to predict future charging demand.

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