An intelligent control method and system for the whole process of a charger nest

Abstract

The present application relates to power distribution control related technical field, specifically including a kind of for the intelligent control method and system of charger nest whole process, including: according to the side of unmanned aerial vehicle, determine the energy gap lower limit under the deadline limit and first safety margin;According to the side of charger nest, determine the power carrying upper limit under the time slice limit and second safety margin, dynamic matching is carried out, executes single stake power adjustment and multi-stake collaborative control.Solved only rely on fixed experience value setting charging parameter, energy demand and power carrying imbalance, the technical problem that the precision of charger nest whole process control is insufficient, realizes the dynamic matching of unmanned aerial vehicle side energy demand parameter and nest side power carrying parameter, executes single stake power adjustment and multi-stake collaborative control, improves charging efficiency and resource utilization, through safety margin quantization and dynamic scheduling optimization, reduce the task interruption of unmanned aerial vehicle due to energy shortage, improve the technical effect of the precision of charger nest whole process control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power distribution control, and particularly relates to an intelligent control method and system for a full process of a charging nest. BACKGROUND

[0002] As the core of energy supply for the continuous operation of a UAV, the operation efficiency and reliability of the charging nest directly determine the continuity of the UAV task execution. The energy scheduling of the charging nest needs to match the dynamic energy demand of the UAV, the power carrying capacity of the charging unit and the load fluctuation of the power grid, so as to avoid task interruption due to energy gap or equipment failure due to power overload.

[0003] The current charging nest control cannot adapt to the needs of complex operation scenarios, the energy demand and power carrying capacity are mismatched, the charging parameters are mainly set based on fixed experience values, the dynamic deduction is not combined with the task time window and the remaining battery capacity of the UAV, the charging efficiency is affected by insufficient power during the peak period of the power grid, and the power cannot be efficiently utilized for energy storage during the trough period, which cannot meet the fine management needs of the full process of the charging nest.

[0004] In summary, the prior art has the technical problems of setting charging parameters only relying on fixed experience values, energy demand and power carrying capacity mismatch, and insufficient precision of full process control of the charging nest. SUMMARY

[0005] The present application provides an intelligent control method and system for a full process of a charging nest, which aims to solve the technical problems of setting charging parameters only relying on fixed experience values, energy demand and power carrying capacity mismatch, and insufficient precision of full process control of the charging nest in the prior art.

[0006] In view of the above problems, the technical scheme of the present application is as follows:

[0007] In a first aspect, the present application provides an intelligent control method for a full process of a charging nest, wherein the method comprises: determining the lower limit of the energy gap and the first safety margin under the deadline according to the start time, the operation area and the flight duration of the flight task on the UAV side, and the state of the remaining battery capacity based on forward deduction and backward tracing analysis of the time window; determining the upper limit of the power carrying capacity and the second safety margin under the time slice according to the idle state of the charging position, the working state of the charging unit, the communication connection and the rated power of the charging unit based on forward deduction and backward tracing analysis of the time window; dynamically matching the lower limit of the energy gap and the first safety margin under the deadline, and the upper limit of the power carrying capacity and the second safety margin under the time slice, and performing single-pile power adjustment and multi-pile collaborative control.

[0008] Preferably, when the grid load is in the low period and the SOC deviation of the plurality of energy storage units in the energy storage system exceeds the dynamic balancing threshold, the energy storage charging mode is started through the bidirectional converter, and the group string level active balancing strategy is triggered to assist the plurality of energy storage units in the energy storage system to supplement power.

[0009] Preferably, when the grid load is in the peak period and the average SOC of the plurality of energy storage units in the energy storage system is higher than the dynamic power feeding threshold and the SOC standard deviation is less than the balancing tolerance threshold, the energy storage discharging mode is switched through the bidirectional converter, and the hierarchical power feeding strategy based on the load distribution weight is triggered to assist the plurality of charging units in the charger nest to supply power.

[0010] Preferably, according to the battery temperature and internal resistance data uploaded by the BMS; based on the battery temperature and internal resistance data, the Thevenin equivalent circuit model is used to analyze the thermal runaway risk online.

[0011] Preferably, if it is determined that the temperature rise rate exceeds the critical slope and the internal resistance mutation value is greater than the preset jump threshold, the energy demand sequence associated with the first safety margin and the charging scheduling sequence associated with the second safety margin are rolled back in a stack state.

[0012] Preferably, the scheduling instructions in the risk charging state are revoked and an abnormal event is defined, and the abnormal event is stored in a distributed database as a negative sample; the distributed database also includes full-process operation data, and a control model based on a hierarchical architecture is constructed based on the distributed database.

[0013] Preferably, the control model includes a data acquisition layer, a control decision layer, and an execution control layer; wherein the data acquisition layer of the control model acquires the battery remaining capacity state of the unmanned aerial vehicle, the charging unit working state, and the grid operation parameters through the BMS interface and the smart meter.

[0014] Preferably, the control decision layer re-executes dynamic matching according to the updated energy demand sequence and charging scheduling sequence, generates an updated charging scheduling strategy, and performs target weight balancing optimization between task completion rate and device life through NSGA-II multi-objective optimization, determines single-pile power setting value and multi-pile collaborative control instructions, and simultaneously uses a fuzzy PID controller to dynamically adjust single-pile output power.

[0015] Preferably, the execution control layer receives scheduling instructions through the charging unit controller and the energy storage converter supporting the Modbus TCP / IP protocol, performs single-pile output power and multi-pile collaborative control operations, and feeds back the charging current and SOC real-time state to the control decision layer.

[0016] In a second aspect, this application provides an intelligent control system for the entire process of a charging station, wherein the system includes: a first analysis module: determining the lower limit of the energy gap and a first safety margin under a deadline based on forward extrapolation and backward tracing analysis of the start time, operating area, and flight duration of the flight mission on the UAV side, and the remaining battery power status based on a time window; a second analysis module: determining the upper limit of the power carrying capacity and a second safety margin under a time slice based on forward extrapolation and backward tracing analysis of the idle status of the charging position, the working status of the charging unit, and the communication connection on the charging station side, and the rated power of the charging unit based on a time window; and a dynamic matching module: dynamically matching the lower limit of the energy gap and the first safety margin under the deadline, and the upper limit of the power carrying capacity and the second safety margin under the time slice, and performing single-pile power adjustment and multi-pile collaborative control.

[0017] In summary, one or more technical solutions provided in this application achieve the following technical effects: determining the lower limit of energy deficit and the first safety margin, the upper limit of power capacity and the second safety margin, dynamically matching the energy demand parameters of the UAV side with the power capacity parameters of the charging station side, performing single-pile power adjustment and multi-pile collaborative control, improving charging efficiency and resource utilization, reducing mission interruptions of UAVs due to insufficient energy through safety margin quantification and dynamic scheduling optimization, and improving the accuracy of the entire process control of the charging station and charging station. Attached Figure Description

[0018] Figure 1 This application provides a flowchart illustrating an intelligent control method for the entire process of a charger nest.

[0019] Figure 2 This application provides a structural schematic diagram of an intelligent control system for the entire process of a charger nest.

[0020] Explanation of reference numerals in the attached diagram: First analysis module M100, Second analysis module M200, Dynamic matching module M300. Detailed Implementation

[0021] Example 1: The present application will be described in detail below with reference to the accompanying drawings, as follows... Figure 1 As shown, this application provides an intelligent control method for the entire process of a charger nest, wherein the method includes:

[0022] S1: Based on the starting time of the flight task, the operation area, the flight duration, and the state of the remaining battery power, the energy gap lower limit and the first safety margin under the deadline are determined through forward deduction and backward tracing analysis based on the time window; S2: Based on the idle state of the charging position, the working state of the charging unit, and the communication connection, the power carrying upper limit and the second safety margin under the time slice are determined through forward deduction and backward tracing analysis based on the time window based on the rated power of the charging unit.

[0023] Specifically, the forward deduction and backward tracing analysis of the time window is a time series-based analysis method for predicting and evaluating the energy demand of the unmanned aerial vehicle and the power carrying capacity of the charging nest. Further, the forward deduction is to predict the future energy demand or power carrying capacity from the current time point; the backward tracing is to review the past data from the current time point to verify the accuracy of the prediction and adjust the model parameters; the energy gap refers to the difference between the energy required by the unmanned aerial vehicle to complete the task and the current battery remaining energy; the first safety margin is the additional energy reserve based on the energy gap lower limit to ensure that the unmanned aerial vehicle can safely complete the task in the face of unexpected situations; the power carrying upper limit refers to the maximum charging power that the charging nest can provide within a certain time; the second safety margin is the additional power capacity reserved based on the power carrying upper limit to prevent overload during charging.

[0024] Execution steps: Energy demand analysis on the unmanned aerial vehicle side, specifically, the starting time of the flight task, the operation area, the flight duration, etc. are obtained through the flight control system of the unmanned aerial vehicle, and the remaining battery power state is obtained through the battery management system, further, the starting time of the flight task, the operation area, the flight duration of the unmanned aerial vehicle side are used to predict the energy demand of the unmanned aerial vehicle at each time point during the task through forward deduction of the time window; at the same time, the starting time of the flight task, the operation area, the flight duration of the unmanned aerial vehicle side are used for backward tracing analysis to review the experience data, verify the accuracy of the forward deduction, and adjust the model parameters according to the historical data; through forward deduction and backward tracing analysis, the energy gap lower limit under the deadline is determined, and the first safety margin is increased based on this.

[0025] The power bearing analysis is performed on the charger nest side. Specifically, the charging position idle state, the charging unit working state, and the communication connection state of the charger nest are obtained through the sensor network, and the rated power of the charging unit is obtained through the charging management system. Further, the idle state of the charging position, the working state of the charging unit, and the communication connection of the charger nest side are used for forward deduction of the time window to predict the power bearing capacity of the charger nest in each time slice. At the same time, the idle state of the charging position, the working state of the charging unit, and the communication connection of the charger nest side are used for backward tracing analysis to review the experience data and verify the accuracy of the forward deduction, and the model parameters are adjusted according to the historical data. Through forward deduction and backward tracing analysis, the upper limit of the power bearing under the time slice is determined, and a second safety margin is added on this basis.

[0026] In the above steps, through accurate energy demand analysis and power bearing evaluation, it is ensured that the unmanned aerial vehicle always has sufficient energy support during the task process, so that the unmanned aerial vehicle can return at the end of the task, and at the same time, the required power for the next flight task is completed to avoid the failure of the charger nest due to overload, improve the charging efficiency and resource utilization.

[0027] S3: dynamically match the energy gap lower limit and the first safety margin under the cut-off time limit, and the power bearing upper limit and the second safety margin under the time slice limit, and perform single-pile power adjustment and multi-pile collaborative control.

[0028] Specifically, dynamic matching refers to dynamically comparing and adjusting the energy demand parameters obtained from the unmanned aerial vehicle side, including the energy gap lower limit and the first safety margin, and the power bearing parameters obtained from the charger nest side, including the power bearing upper limit and the second safety margin, to ensure the balance and adaptation between the two; single-pile power adjustment refers to real-time adjusting the output power of a single charging pile according to the results of dynamic matching to meet the energy demand of the unmanned aerial vehicle, while not exceeding the power bearing upper limit of the charger nest; multi-pile collaborative control refers to collaborative control of multiple charging piles in the case of multiple charging piles, reasonable allocation of charging tasks, optimization of the use of charging resources, improvement of charging efficiency, and avoidance of resource waste or supply-demand imbalance.

[0029] The execution step: through the energy gap lower limit of the UAV side and the first safety margin, and the power bearing upper limit of the charger nest side and the second safety margin, the matching degree between the energy demand of the UAV and the power bearing capacity of the charger nest is determined, and further, at the current time point, whether the maximum charging power that the charger nest can provide can meet the energy demand of the UAV is analyzed; according to the matching degree, a single pile power adjustment strategy and a multi-pile cooperative control strategy are generated, and if the power bearing capacity of the charger nest can meet the energy demand of the UAV, the corresponding charging instruction is generated; if it cannot be met, the charging strategy is adjusted, for example, the charging power is reduced or is distributed to other charging piles.

[0030] The single pile power adjustment and the multi-pile cooperative control further adjust the output power of the single charging pile in real time according to the dynamic matching result, so as to ensure the safety of the charging process; in the case of multiple charging piles, the multiple charging piles are cooperatively controlled, and the charging task is reasonably distributed according to the power bearing capacity of each charging pile and the energy demand of the UAV. In the above steps, through dynamic matching and single pile power adjustment and multi pile cooperative control, the precise adaptation between the energy demand of the UAV and the power bearing capacity of the charger nest is ensured, the charging efficiency is improved, and resource waste or supply and demand imbalance is avoided; through single pile power adjustment and multi pile cooperative control, the UAV can safely return and complete charging at the end of the task, while avoiding faults of the charger nest due to overload.

[0031] Further, the single pile power adjustment and the multi pile cooperative control are executed, and the method of the application further comprises:

[0032] When the grid load is in a low valley period and the SOC deviation of the multiple energy storage units in the energy storage system exceeds the dynamic balancing threshold, the energy storage charging mode is started through the bidirectional converter, and the group string level active balancing strategy is triggered to assist the power compensation for the multiple energy storage units in the energy storage system.

[0033] Specifically, the grid load low valley period refers to a period of low electricity demand in the power system, usually at night or in specific off-peak periods. In these periods, the power supply capacity of the grid is relatively abundant, and the electricity price is relatively low, which is suitable for charging operation of the energy storage system; the energy storage system refers to a device that can store and release electric energy, which is usually used to balance the grid load and improve energy utilization efficiency. The energy storage system is composed of multiple energy storage units, each of which has its own state of charge; the SOC (State of Charge, state of charge) deviation refers to the difference between the states of charge of the energy storage units in the energy storage system. When the SOC deviation exceeds a certain threshold, it indicates that the distribution of electric quantity among the energy storage units is uneven, and balancing operation is needed.

[0034] The dynamic balancing threshold is used to determine whether the SOC deviation of the energy storage units in the energy storage system needs to be actively balanced, and when the SOC deviation exceeds the dynamic balancing threshold, the group string level active balancing strategy is started; the bidirectional converter is a device for bidirectional conversion of electric energy, including a charging mode and a discharging mode, and the bidirectional converter is used to control the charging process of the energy storage system; the group string level active balancing strategy is a balancing strategy for multiple energy storage units in the energy storage system, and the SOC of each unit tends to be consistent by transferring electric energy between the energy storage units, and the group string level active balancing strategy can be performed simultaneously during the charging process to optimize the performance of the energy storage system.

[0035] The execution steps are: monitoring and judging the load of the power grid, further, monitoring the load of the power grid in real time, obtaining the real-time load data of the power grid through a smart meter or a power grid management system, when it is detected that the load of the power grid is in a low valley period, entering the preparation stage of the energy storage charging mode; monitoring the SOC of each energy storage unit in the energy storage system in real time through the BMS, when it is detected that the SOC deviation of multiple energy storage units exceeds the preset dynamic balancing threshold, triggering the group string level active balancing strategy to reduce the single unit difference of each energy storage unit in the energy storage system; further, starting the energy storage charging mode, converting the alternating current of the power grid into direct current through the bidirectional converter to charge the energy storage system, and simultaneously performing the group string level active balancing strategy during the charging process to make the SOC of each energy storage unit consistent.

[0036] Preferably, during the charging process, auxiliary power is supplied to the energy storage units with lower SOC according to the actual SOC of each energy storage unit, to ensure uniform distribution of the SOC of all energy storage units and improve the charging efficiency. In the above steps, the energy storage is charged during the low load valley period of the power grid, the bidirectional converter starts the energy storage charging mode, and the group string level active balancing strategy is performed during the charging process to control the SOC deviation of each unit in the energy storage system within a certain range, to ensure uniform distribution of the electric quantity of each unit of the energy storage system and improve the overall performance and service life of the energy storage system.

[0037] Further, single-pile power adjustment and multi-pile cooperative control are performed, and the method of the application further comprises:

[0038] When the load of the power grid is in a peak period and the average SOC corresponding to the multiple energy storage units in the energy storage system is higher than the dynamic power feeding threshold and the SOC standard deviation is less than the balancing tolerance threshold, the bidirectional converter is switched to the energy storage discharging mode, and the hierarchical power feeding strategy based on the load distribution weight is triggered to assist the power supply for the multiple charging units in the charger nest.

[0039] Specifically, the power grid load peak period refers to a period of high demand for electricity in the power system, usually during the day or at specific peak times; the average SOC refers to the average value of the state of charge of multiple energy storage units in the energy storage system, which is used to evaluate the overall power level of the energy storage system; the dynamic feeding threshold is used to determine whether the energy storage system has sufficient power to discharge, and when the average SOC is higher than the dynamic feeding threshold, the energy storage system is considered to have sufficient power to discharge; the SOC standard deviation is a statistical quantity that measures the uniformity of the SOC distribution of each energy storage unit in the energy storage system, and the smaller the SOC standard deviation, the more uniform the SOC distribution.

[0040] The equalization tolerance threshold is used to determine whether the SOC distribution is uniform enough, and when the SOC standard deviation is less than the equalization tolerance threshold, it means that the power distribution in the energy storage system is relatively uniform; the bidirectional converter switches to the energy storage discharge mode refers to the bidirectional converter switching from the charging mode to the discharging mode, converting the direct current in the energy storage system into alternating current to power the charging units in the charging nest; the hierarchical feeding strategy based on load distribution weight is a strategy that allocates the power released by the energy storage system according to the load of each charging unit in the charging nest according to a certain weight, which can optimize power distribution and improve charging efficiency.

[0041] Execution steps: Real-time monitoring of the load of the power grid, obtaining real-time load data of the power grid through intelligent meters or power grid management systems, when detecting that the load of the power grid is in the peak period, entering the preparation stage of the energy storage discharge mode; through the BMS, real-time monitoring of the SOC of each energy storage unit in the energy storage system, when detecting that the average SOC of multiple energy storage units in the energy storage system is higher than the preset dynamic feeding threshold, and the SOC standard deviation is less than the preset equalization tolerance threshold, triggering the hierarchical feeding strategy based on load distribution weight, specifically, determining the priority of each level of feeding according to the peak shaving and load reduction target, and preferentially feeding the key load distribution feeding power marked by the power grid management system, including the power supply line of the transportation hub; according to the real-time voltage drop data of the line, dynamically adjusting the remaining discharge power of the energy storage system, offsetting the voltage loss caused by line impedance through output compensation current, maintaining stable power supply voltage, at the same time, feeding the charging nest power supply bus according to the preset proportion, directly bearing part of the electricity demand of the charging pile through energy storage discharge, sharing the load pressure of the distribution network.

[0042] The energy storage discharging mode is started, specifically, the direct current in the energy storage system is converted into alternating current by the bidirectional converter to power the charging units in the charging nest. During the discharging process, according to the load conditions of each charging unit in the charging nest, the load distribution weight is preset for hierarchical power supply, and more power is provided for the charging unit with higher load to ensure efficient charging process and optimize the performance of the energy storage system. According to the actual load conditions of each charging unit, more power is provided for the charging unit with higher load to ensure efficient charging process. Preferably, the energy storage discharging is performed during the peak load period of the power grid to reduce the dependence on the power grid, and the hierarchical power supply is performed according to the load distribution weight to reasonably distribute the power of the energy storage system to each charging unit, so that the charging demand of the charging nest during the peak period is met, and the power utilization efficiency of the energy storage system is optimized.

[0043] Further, the method of the application further comprises:

[0044] According to the battery temperature and internal resistance data uploaded by the BMS; based on the battery temperature and internal resistance data, the Thevenin equivalent circuit model is used to analyze the thermal runaway risk online.

[0045] Specifically, the BMS (Battery Management System, battery management system) is used to monitor and manage the state of the battery, including the voltage, current, temperature, internal resistance and other parameters of the battery. Specifically, during the operation of the battery, the battery temperature reflects the thermal state of the battery, and the internal resistance data is closely related to the health status and charge-discharge performance of the battery. The Thevenin equivalent circuit model is a model that describes the electrical behavior of the battery. The Thevenin equivalent circuit model is used to simulate the voltage, current and internal resistance characteristics of the battery to analyze the health status and thermal runaway risk of the battery online. Further, by using the real-time collected battery data and the Thevenin equivalent circuit model analysis, it is evaluated whether the battery has a risk of thermal runaway. Thermal runaway refers to the uncontrolled rise in internal temperature of the battery, which may lead to damage of the battery and even a dangerous state of fire and explosion.

[0046] The execution step: the temperature and internal resistance data of the battery are collected in real time by the BMS, uploaded to the control center through the sensor network, and used for subsequent analysis and processing; the collected battery temperature and internal resistance data are analyzed by using the Thevenin equivalent circuit model, the Thevenin model describes the behavior of the battery through equivalent circuit parameters including open circuit voltage, polarization resistance and capacitance, and can reflect the voltage and current changes of the battery under different working conditions; according to the calculation results of the Thevenin model, it is evaluated whether the battery has the risk of thermal runaway, specifically, the change rate of the battery temperature and the mutation of the internal resistance are monitored, if the temperature rising rate exceeds the critical value, or the internal resistance suddenly increases, it indicates that abnormal reaction occurs in the battery, and there is a risk of thermal runaway.

[0047] In actual application process, if the risk of thermal runaway is detected, a warning signal is immediately sent out, and corresponding measures are taken, such as reducing the charging power, stopping the charging operation or starting the cooling system, to avoid the damage of the battery due to overheating, so as to prevent the occurrence of thermal runaway event, preferably, by monitoring the temperature and internal resistance of the battery in real time, the risk of thermal runaway is warned in advance, and the Thevenin equivalent circuit model is used for online analysis, so that the abnormal state of the battery can be found and handled in time, to prevent the occurrence of thermal runaway event, ensure the smooth progress of the charging process, ensure the safety of the battery and improve the reliability of the charging.

[0048] Further, the online analysis of the risk of thermal runaway, the method of the present application comprises:

[0049] If it is determined that the temperature rising rate exceeds the critical slope and the internal resistance mutation is greater than the preset jump threshold, the energy demand sequence associated with the first safety margin, the charging scheduling sequence associated with the second safety margin are rolled back in stack state.

[0050] Specifically, the temperature rise rate refers to the rising speed of the battery temperature per unit time, usually expressed in °C / min or °C / h, and is an important indicator for evaluating the thermal state of the battery. An excessively high temperature rise rate may indicate abnormal reactions inside the battery. The critical slope is used to determine whether the temperature rise rate is within a safe range. If the temperature rise rate exceeds the critical slope, it indicates that the battery temperature is rising too fast, and there is a risk of thermal runaway. The internal resistance mutation refers to a significant change in the internal resistance of the battery within a short period of time, usually expressed in Ω. The preset jump threshold is used to determine whether the internal resistance change is abnormal. If the internal resistance mutation exceeds the preset jump threshold, it indicates that the internal resistance of the battery has changed significantly, and there is a risk of thermal runaway. Stack state rollback is a data structure operation similar to the last-in-first-out feature of a stack. Specifically, the stack state rollback refers to the system undoing the latest charging scheduling instruction and returning to the previous safe state according to the preset rules to avoid potential thermal runaway risks. The energy demand sequence refers to the sequence of the energy demand of the unmanned aerial vehicle related to the first safety margin, including the predicted value of the energy demand and the safety margin. The charging scheduling sequence refers to the sequence of the charging machine nest charging scheduling related to the second safety margin, including charging power, charging time, and other parameters.

[0051] The execution steps are as follows: monitor the temperature rise rate and internal resistance change of the battery. If the temperature rise rate exceeds the preset critical slope and the internal resistance mutation is greater than the preset jump threshold, it is determined that the battery has a risk of thermal runaway. Once it is determined that there is a risk of thermal runaway, the stack state rollback mechanism is triggered immediately to undo the latest charging scheduling instruction and return to the previous safe state. The energy demand sequence is rolled back. Specifically, for the energy demand sequence associated with the first safety margin, the latest energy demand prediction and safety margin adjustment are undone, and the previous energy demand prediction value is returned to the stable state. The charging scheduling sequence is rolled back. Specifically, for the charging scheduling sequence associated with the second safety margin, the latest charging power and charging time adjustment are undone, and the previous charging power and time settings are returned to the stable state. In the above steps, through the stack state rollback mechanism, measures can be taken quickly when potential risks are detected, and unsafe charging scheduling instructions can be undone to return to a safe state. By undoing unsafe charging scheduling instructions in a timely manner, the safety risk of the battery is reduced, and the safety of the charging process is ensured.

[0052] Further, the stack state rollback is performed, and the method of the present application further comprises:

[0053] The scheduling instruction in the risk charging state is undone, and an abnormal event is defined. The abnormal event is stored in a distributed database as a negative sample. The distributed database further includes full-process operation data, and a control model based on a hierarchical architecture is constructed based on the distributed database.

[0054] Specifically, the revocation of the scheduling instruction in the risk charging state refers to canceling the current charging scheduling instruction being performed to prevent further charging operation from causing battery damage or safety problems when the battery is detected to have a risk of thermal runaway; defining an abnormal event refers to marking the detected thermal runaway risk event as an abnormal event; a negative sample refers to a data sample that does not meet normal conditions or expected results, and the abnormal event is stored in the database as a negative sample for training and optimizing the control model.

[0055] The distributed database refers to a database system that stores data in multiple nodes, has high availability, high scalability and high fault tolerance, and is used to store full-process running data and abnormal event data; the full-process running data refers to all data generated during the operation of the charger nest, including the flight task data of the unmanned aerial vehicle, the battery state data, and the charging scheduling data; the control model based on the hierarchical architecture refers to a model architecture that divides the control logic into multiple levels, including the data acquisition layer, the control decision layer and the execution control layer, preferably, the modularity and scalability of the system are improved to facilitate management and optimization.

[0056] The execution step is: when the battery is detected to have a risk of thermal runaway, the current charging scheduling instruction is immediately revoked, and the charging of the battery in the risk state is stopped; the thermal runaway risk event is defined as an abnormal event, and relevant data are recorded, including the temperature rise rate of the battery, the internal resistance mutation, the charging power, and the charging time; the abnormal event is stored in the distributed database as a negative sample, and the full-process running data, including the flight task data of the unmanned aerial vehicle, the battery state data, and the charging scheduling data, are also stored in the distributed database; based on the data in the distributed database, a control model based on the hierarchical architecture is constructed, which can continuously learn and optimize to improve the identification and response capabilities for the risk of thermal runaway. In the above steps, by storing the abnormal event as a negative sample in the distributed database, specifically, by analyzing the negative sample data, the thresholds of the temperature rise rate and the internal resistance mutation are adjusted to improve the accuracy of risk assessment; based on the full-process running data and the abnormal event data in the distributed database, the control model is constructed, which can continuously optimize the control strategy to improve the reliability and stability of the system.

[0057] Further, based on the distributed database, a control model based on the hierarchical architecture is constructed, and the method of the application comprises:

[0058] The control model comprises a data acquisition layer, a control decision layer, and an execution control layer; wherein the data acquisition layer of the control model acquires the remaining battery capacity state of the unmanned aerial vehicle, the working state of the charging unit, and the power grid operation parameters through the BMS interface and the intelligent electric meter.

[0059] Specifically, the control model is used to realize intelligent control of the whole process of the charger nest. Through a hierarchical architecture, the functions of data collection, decision making and execution control are realized. The data collection layer is used to collect various data related to the operation of the charger nest, including the battery state of the unmanned aerial vehicle, the charging unit state and the power grid operation parameters. The control decision layer refers to analyzing and processing the data provided by the data collection layer to generate control strategies and scheduling instructions. The execution control layer refers to executing specific control operations according to the instructions of the control decision layer, such as adjusting the charging power, controlling the charging process, etc. The BMS interface, i.e. the interface of the battery management system, is used to communicate with the BMS to obtain detailed state information of the battery, including the remaining power, voltage, current, temperature, internal resistance. The smart meter is used to monitor the operation parameters of the power grid in real time, including voltage, current, power, power quality.

[0060] Execution step: The battery state data refers to the real-time acquisition of the remaining power state of the unmanned aerial vehicle battery through the BMS interface, including the voltage, current, temperature and internal resistance of the battery, which is used to evaluate the health status and remaining power of the battery. The charging unit state data refers to the working state of the charging unit obtained through the sensors and communication interfaces of the charging unit, including the charging power, charging current and charging voltage, which is used to evaluate the running state and performance of the charging unit. The power grid operation parameters refer to the operation parameters of the power grid obtained through the smart meter, including the voltage, current, power and power quality of the power grid, which are used to evaluate the running state and load condition of the power grid. The collected data is transmitted to the data collection layer of the control model through the communication network. The data collection layer performs preliminary processing and formatting on the collected data to ensure the accuracy and reliability of the data. The data collection layer integrates data from different sources to form a complete data set, which is then transmitted to the control decision layer for decision analysis. The data collection, transmission and integration of the data collection layer deeply integrate the whole process of the charger nest, ensuring the efficiency and adaptability of the control model.

[0061] The control model includes a data collection layer, a control decision layer and an execution control layer. Further, the control model is used to monitor the power grid load change and the state of charge of the energy storage system in real time, and to adjust the power distribution weight of each hierarchical power supply in a cycle until the power grid load exits the peak period or the average SOC of multiple energy storage units in the energy storage system falls below the dynamic power supply threshold.

[0062] Further, the control model includes a data collection layer, and the method of the application includes:

[0063] The control decision layer re-executes dynamic matching according to the updated energy demand sequence and the charging scheduling sequence, generates an updated charging scheduling strategy, and balances the target weight between the task completion rate and the equipment life through NSGA-II multi-objective optimization to determine the single-pile power setting value and the multi-pile collaborative control instruction, and simultaneously uses a fuzzy PID controller to dynamically adjust the single-pile output power.

[0064] Specifically, the control decision layer refers to a level in the control model for analyzing and processing data from the data collection layer to generate control strategies and scheduling instructions; the energy demand sequence refers to the energy demand sequence of the unmanned aerial vehicle at different time points, including energy consumption prediction and safety margin during the task; the charging scheduling sequence refers to the charging scheduling sequence of the charging nest at different time points, including charging power, charging time, and other parameters; dynamic matching refers to real-time adjustment of the charging strategy according to the energy demand of the unmanned aerial vehicle and the power carrying capacity of the charging nest to ensure supply-demand balance; NSGA-II (Non-dominated Sorting Genetic Algorithm II) multi-objective optimization is a non-dominated sorting genetic algorithm used for target weight balancing optimization between task completion rate and equipment life.

[0065] The task completion rate refers to the probability of the unmanned aerial vehicle completing the task, which is closely related to energy demand and charging strategy; the equipment life refers to the service life of the charging nest and the battery of the unmanned aerial vehicle, which is closely related to the charging strategy and operating conditions; the single-pile power setting value refers to the output power setting value of a single charging pile; the multi-pile collaborative control instruction refers to the collaborative control instruction between multiple charging piles for optimizing charging resource allocation; the fuzzy PID controller refers to a controller combining fuzzy logic and PID control for dynamically adjusting single-pile output power to improve control accuracy and adaptability.

[0066] Execution steps: the control decision layer re-executes dynamic matching according to the updated energy demand sequence and the charging scheduling sequence, including re-computing the energy demand of the unmanned aerial vehicle and the power carrying capacity of the charging nest to ensure balance between the two; the control decision layer balances the target weight between the task completion rate and the equipment life through the NSGA-II multi-objective optimization algorithm, specifically, the task completion rate is used to ensure that the unmanned aerial vehicle has enough energy to complete the task and improve the task completion rate; the equipment life is used to reasonably control the charging process and reduce the wear and tear on the equipment; the control decision layer generates an updated charging scheduling strategy according to the optimization results, including the single-pile power setting value and the multi-pile collaborative control instruction, and further, in the case of grid voltage fluctuations, the control decision layer uses a fuzzy PID controller to dynamically adjust the single-pile output power to ensure the stability and adaptability of the charging process.

[0067] Further, the control model includes a control decision layer, and the method includes:

[0068] The execution control layer receives scheduling instructions from the control decision layer through the charging unit controller supporting the Modbus TCP / IP protocol and the energy storage converter, performs single-pile output power and multi-pile collaborative control operations, and feeds back real-time charging current and SOC state to the control decision layer.

[0069] Specifically, the execution control layer refers to a level in the control model responsible for performing specific control operations according to the instructions of the control decision layer; the Modbus TCP / IP protocol refers to an industrial automation communication protocol based on TCP / IP network, used to realize data communication between devices; specifically, the charging unit controller and the energy storage converter receive and send data through this protocol; the charging unit controller is used to control the output power of a single charging pile and the equipment of the charging process; the energy storage converter is used to control the charging and discharging process of the energy storage system, which can convert DC power into AC power or vice versa; SOC refers to the state of charge of the battery, indicating the percentage of the remaining battery capacity; real-time state feedback refers to the execution control layer feeding back real-time data such as charging current and SOC during the charging process to the control decision layer for dynamic adjustment and optimization.

[0070] Execution step: the execution control layer receives scheduling instructions from the control decision layer through the charging unit controller supporting the Modbus TCP / IP protocol and the energy storage converter, including single-pile output power set value and multi-pile collaborative control instructions; according to the received scheduling instructions, the charging unit controller adjusts the output power of the single pile to ensure that the charging pile charges according to the set power; the energy storage converter performs charging and discharging operations of the energy storage system according to the instructions to optimize energy utilization efficiency; in the above steps, through the operation of the execution control layer, the charging strategy can be dynamically adjusted according to real-time data, the charging resource allocation can be optimized, the precise control and dynamic optimization of the charging process can be ensured, and the charging efficiency and safety can be improved.

[0071] In summary, the beneficial effects of the embodiments of the present application are:

[0072] The first safety margin and the power bearing upper limit are dynamically matched, and single-pile power adjustment and multi-pile cooperative control are performed. The application provides an intelligent control method and system for the whole process of the charger nest. The energy gap lower limit and the first safety margin, the power bearing upper limit and the second safety margin are determined, the energy demand parameters on the unmanned aerial vehicle side are dynamically matched with the power bearing parameters on the nest side, single-pile power adjustment and multi-pile cooperative control are performed, the charging efficiency and resource utilization are improved, the safety margin is quantified and dynamically scheduled and optimized, the task interruption of the unmanned aerial vehicle caused by insufficient energy is reduced, and the technical effect of improving the accuracy of the whole process control of the charger nest is achieved.

[0073] Embodiment two, based on the same inventive concept as the intelligent control method for the whole process of the charger nest in the foregoing embodiments, as shown in the application embodiment, the application embodiment provides an intelligent control system for the whole process of the charger nest, wherein the system comprises: Figure 2

[0074] The first analysis module M100: according to the starting time, operation area and flight time of the flight task on the unmanned aerial vehicle side, and the residual state of the battery based on the forward deduction and backward tracing analysis of the time window, the energy gap lower limit and the first safety margin under the deadline are determined.

[0075] The second analysis module M200: according to the idle state, working state and communication connection of the charging position on the charger nest side, and the rated power of the charging unit based on the forward deduction and backward tracing analysis of the time window, the power bearing upper limit and the second safety margin under the time slice are determined.

[0076] The dynamic matching module M300: the energy gap lower limit and the first safety margin under the deadline, and the power bearing upper limit and the second safety margin under the time slice are dynamically matched, and single-pile power adjustment and multi-pile cooperative control are performed.

[0077] Further, the dynamic matching module M300 is further used to perform the following method:

[0078] ​When the grid load is in the low valley period and the SOC deviation of the plurality of energy storage units in the energy storage system exceeds the dynamic balancing threshold, the energy storage charging mode is started through the bidirectional converter, and the group string level active balancing strategy is triggered to assist the plurality of energy storage units in the energy storage system to supplement power.

[0079] Further, the dynamic matching module M300 is also used to execute the following method:

[0080] When the grid load is in the peak period and the average SOC corresponding to the plurality of energy storage units in the energy storage system is higher than the dynamic power feeding threshold while the SOC standard deviation is less than the balancing tolerance threshold, the energy storage discharging mode is switched through the bidirectional converter, and the hierarchical power feeding strategy based on the load distribution weight is triggered to assist the plurality of charging units in the charger nest to supply power.

[0081] Further, the intelligent control system for the whole process of the charger nest is also used to execute the following method:

[0082] According to the battery temperature and internal resistance data uploaded by the BMS; based on the battery temperature and internal resistance data, the Thevenin equivalent circuit model is used to analyze the thermal runaway risk online.

[0083] Further, the intelligent control system for the whole process of the charger nest is also used to execute the following method:

[0084] If it is determined that the temperature rise rate exceeds the critical slope and the internal resistance mutation is greater than the preset jump threshold, the energy demand sequence associated with the first safety margin and the charging scheduling sequence associated with the second safety margin are rolled back in a stack state.

[0085] Further, the intelligent control system for the whole process of the charger nest is also used to execute the following method:

[0086] The scheduling instruction in the risk charging state is revoked and an abnormal event is defined, and the abnormal event is stored in a distributed database as a negative sample; the distributed database also includes whole process operation data, and a control model based on a hierarchical architecture is constructed based on the distributed database.

[0087] Further, the intelligent control system for the whole process of the charger nest is also used to execute the following method:

[0088] The control model includes a data acquisition layer, a control decision layer, and an execution control layer; wherein the data acquisition layer of the control model acquires the battery remaining capacity state of the unmanned aerial vehicle, the charging unit working state, and the grid operation parameter through the BMS interface and the smart meter.

[0089] Further, the intelligent control system for the whole process of the charger nest is also used to execute the following method:

[0090] The control decision layer re-executes dynamic matching according to the updated energy demand sequence and the charging scheduling sequence, generates an updated charging scheduling strategy, and balances the target weight between the task completion rate and the device life through NSGA-II multi-objective optimization to determine the single-pile power setting value and the multi-pile collaborative control instruction, and simultaneously uses a fuzzy PID controller to dynamically adjust the single-pile output power.

[0091] Further, the intelligent control system for the whole process of the charger nest is also used to execute the following method:

[0092] The execution control layer receives scheduling instructions through the charging unit controller supporting the Modbus TCP / IP protocol and the energy storage converter, executes single-pile output power and multi-pile collaborative control operations, and feeds back the charging current and the real-time SOC state to the control decision layer.

[0093] As described above, any step can be stored as computer instructions or programs in an unrestricted computer memory and can be called and recognized by an unrestricted computer processor, and no redundant limitation is made here.

[0094] Further, the above technical solution only embodies the preferred technical solution of the technical solution of the embodiments of the present application, and some variations of certain parts made by the person skilled in the art of the present application also embody the principles of the novel embodiments of the present application. Obviously, the person skilled in the art can make various modifications and variations to the present application without departing from the scope of the present application.

Claims

1. A method for intelligent control of the whole process of a charger nest, characterized in that, The method comprises: According to the starting time of the flight task, the operation area, the flight duration, and the residual battery capacity state on the side of the unmanned aerial vehicle, the forward deduction and backward tracing analysis based on the time window are performed to determine the lower limit of the energy gap and the first safety margin under the deadline; According to the idle state of the charging position, the working state of the charging unit, and the communication connection on the side of the charging nest, the forward deduction and backward tracing analysis based on the time window are performed on the rated power of the charging unit to determine the upper limit of the power carrying capacity and the second safety margin under the time slice; The lower limit of the energy gap and the first safety margin under the deadline, and the upper limit of the power carrying capacity and the second safety margin under the time slice are dynamically matched to perform single-pile power adjustment and multi-pile collaborative control. In addition, the method comprises: According to the battery temperature and internal resistance data uploaded by the BMS; Based on the battery temperature and internal resistance data, the Thevenin equivalent circuit model is used for online analysis of thermal runaway risk; The online analysis of thermal runaway risk comprises: If it is determined that the temperature rise rate exceeds the critical slope and the internal resistance mutation value is greater than the preset jump threshold, the energy demand sequence associated with the first safety margin and the charging scheduling sequence associated with the second safety margin are rolled back in a stack state; The stack state rollback further comprises: The scheduling instructions in the risk charging state are revoked, an abnormal event is defined, and the abnormal event is stored in a distributed database as a negative sample; The distributed database further comprises full-process operation data, and a control model based on a hierarchical architecture is constructed based on the distributed database; The control model based on the hierarchical architecture is constructed based on the distributed database, which comprises: The control model comprises a data acquisition layer, a control decision layer, and an execution control layer; The data acquisition layer of the control model acquires the battery residual capacity state, the charging unit working state, and the power grid operation parameters through the BMS interface and the smart meter; The data acquisition layer of the control model comprises: The control decision layer re-executes dynamic matching based on the updated energy demand sequence and charging scheduling sequence, generates an updated charging scheduling strategy, and performs target weight balance optimization between task completion rate and device life through NSGA-II multi-objective optimization to determine single-pile power setting value and multi-pile collaborative control instructions, and dynamically adjusts single-pile output power using a fuzzy PID controller; The control decision layer of the control model comprises: The execution control layer receives scheduling instructions through the charging unit controller and energy storage converter supporting Modbus TCP / IP protocol, performs single-pile output power and multi-pile collaborative control operations, and feeds back the charging current and SOC real-time state to the control decision layer.

2. The intelligent control method for the whole process of the charger nest according to claim 1, characterized in that, The method for performing single-pile power adjustment and multi-pile collaborative control further comprises: When the power grid load is in a low valley period and the SOC deviation of multiple energy storage units in the energy storage system exceeds a dynamic balance threshold, the energy storage charging mode is started through the bidirectional converter, and a group string level active balancing strategy is triggered to assist the multiple energy storage units in the energy storage system to supplement power.

3. The intelligent control method for the whole process of the charger nest according to claim 2, characterized in that, The method of performing single-pile power adjustment and multi-pile collaborative control further comprises: When the grid load is in a peak period, the average SOC of the plurality of energy storage units in the energy storage system is higher than the dynamic power feeding threshold, and the SOC standard deviation is less than the balance tolerance threshold, the bidirectional converter is switched to the energy storage discharging mode, and a hierarchical power feeding strategy based on the load distribution weight is triggered to assist power supply for the plurality of charging units in the charger nest.

4. An intelligent control system for the whole process of a charger nest, characterized in that, The steps of the intelligent control method for the whole process of the charger nest according to any one of claims 1-3, the system comprises: A first analysis module: based on the start time, operation area and flight duration of the flight task on the unmanned aerial vehicle side, and the state of the remaining battery power, the energy gap lower limit and the first safety margin under the deadline are determined through forward deduction and backward tracing analysis based on the time window; A second analysis module: based on the idle state of the charging position, the working state of the charging unit and the communication connection on the charger nest side, and the rated power of the charging unit, the power carrying upper limit and the second safety margin under the time slice are determined through forward deduction and backward tracing analysis based on the time window; A dynamic matching module: dynamically matches the energy gap lower limit and the first safety margin under the deadline, and the power carrying upper limit and the second safety margin under the time slice, and performs single-pile power adjustment and multi-pile collaborative control.

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