A fast charging control method and system for an unmanned aerial vehicle intelligent hangar

Abstract

The invention discloses a fast charging control method and system for an intelligent hangar of an unmanned aerial vehicle. The method comprises: performing a charging and discharging experiment according to a preset charging condition through a charging equivalent circuit model to obtain charging and discharging experimental data; constructing a hangar fast charging model based on the charging time, charging loss and battery life of the unmanned aerial vehicle; inputting the charging and discharging experimental data into the hangar fast charging model, optimizing and solving the hangar fast charging model by using an NSGA‑II algorithm, and determining an optimal hangar fast charging model; collecting the charging information of the unmanned aerial vehicle by a hangar charging system, inputting the charging information of the unmanned aerial vehicle into a preset hangar fast charging model to obtain a charging control instruction, and executing the charging control instruction by the hangar charging system; so as to improve the charging efficiency, shorten the charging time, prolong the battery life, and ensure the safety of the charging process.

Description

Technical Field

[0001] The present invention belongs to the field of unmanned aerial vehicle (UAV) intelligent hangar charging, and in particular relates to a fast charging system and a control method for the unmanned aerial vehicle (UAV) intelligent hangar. Background Art

[0002] Drone hangars, also known as drone airports or drone "nests", provide a place to protect drones from bad weather, theft or damage from wild animals. In addition, hangars can also charge drones and provide data transmission.

[0003] The endurance of drones is limited by the energy density and charge-discharge efficiency of batteries. Lithium-ion batteries or solid-state batteries are widely used due to their high energy density and relatively long cycle life; however, in the existing technology, drone hangars only have an automatic charging function, without considering the power and service life of the drone, resulting in low charging efficiency of drone hangars and shortened service life of drone batteries. Summary of the invention

[0004] The present invention provides a fast charging control method and system for an unmanned aerial vehicle intelligent hangar, so as to improve charging efficiency, shorten charging time, prolong battery life, and ensure the safety of the charging process.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is:

[0006] The first aspect of the present invention provides a fast charging control method for a UAV intelligent hangar, comprising:

[0007] The hangar charging system collects the UAV charging information, inputs the UAV charging information into the preset hangar fast charging model to obtain the charging control instruction, and executes the charging control instruction through the hangar charging system;

[0008] The hangar fast charging model construction process includes:

[0009] Construct a charging equivalent circuit model based on the drone battery; perform charging and discharging experiments through the charging equivalent circuit model according to preset charging conditions to obtain charging and discharging experimental data;

[0010] A hangar fast charging model is constructed based on the charging time, charging loss and battery life of the UAV. The charging and discharging experimental data are input into the hangar fast charging model, and the hangar fast charging model is optimized and solved using the NSGA-II algorithm. The optimal hangar fast charging model is determined through the Pareto optimal solution.

[0011] Furthermore, the process of constructing a charging equivalent circuit model based on the drone battery includes:

[0012] The charging equivalent circuit model includes a voltage source E, a first parallel RC unit and a second parallel RC unit; an ohmic internal resistance R0, a capacitor Cb, a voltage source E, a first parallel RC unit and a second parallel RC unit are connected in series in sequence;

[0013] The first parallel RC unit and the second parallel RC unit include a polarization internal resistance and a polarization capacitance arranged in parallel;

[0014] According to the UAV intelligent hangar, the charging equivalent circuit model is constructed and expressed as follows:

[0015] ;

[0016] In the formula, It is expressed as the terminal voltage of the charging equivalent circuit model; It is represented by the voltage of the voltage source E and the capacitor Cb; The current expressed as a charging equivalent circuit model; Expressed as the ohmic internal resistance of the battery; It is represented as the voltage of the first parallel RC unit; It is represented as the voltage of the second parallel RC unit;

[0017] The polarization resistance and polarization capacitance values ​​are obtained by fitting the charging and discharging parameters of the UAV battery.

[0018] Furthermore, a charge-discharge experiment is performed according to a preset charging condition through a charging equivalent circuit model to obtain charge-discharge experimental data, including:

[0019] Set the battery experimental temperature; perform constant current charging at 0.2C under constant temperature conditions. When the battery terminal voltage rises to 3.6V, perform constant voltage charging. The charging current drops until the current drops to 0.05C, which is considered the drone battery is fully charged.

[0020] Discharge at a current of 0.2C under constant temperature conditions until the battery terminal voltage drops to 2.0V; perform cyclic charge and discharge on the drone battery, and collect the charge and discharge experimental data of the drone battery.

[0021] Furthermore, a charge-discharge experiment is performed according to a preset charging condition through a charging equivalent circuit model to obtain charge-discharge experimental data, including:

[0022] The drone battery is discharged with a small current of 0.02C until the battery terminal voltage drops to 2.0V, and then charged with a small current of 0.02C until the battery terminal voltage rises to 3.6V. The drone battery is charged and discharged in a cycle, and the charge and discharge experimental data of the drone battery are collected.

[0023] Furthermore, the charging and discharging parameters of the UAV battery are fitted by curve fitting calculation to obtain polarization resistance and polarization capacitance, including:

[0024] When the battery terminal voltage of the drone battery slowly rises after the battery discharge is completed, the expression formula of the zero input response of the first parallel RC unit and the second parallel RC unit is:

[0025] ;

[0026] ; ;

[0027] When the voltage at the battery end of the drone battery slowly decreases during discharge, the zero input response of the first parallel RC unit and the second parallel RC unit is expressed as:

[0028] ;

[0029] ;

[0030] In the formula, Expressed as the initial voltage of the charging equivalent circuit model; are the initial polarization voltages of the first parallel RC unit, respectively; are the initial polarization voltage of the second parallel RC unit, respectively; It is represented as the time constant of the first parallel RC unit; It represents the time constant of the second parallel RC unit; t represents the charge and discharge time; It is represented as the voltage of the first parallel RC unit; It is represented as the voltage of the first parallel RC unit; It is represented as the total voltage of the first parallel RC unit and the second parallel RC unit; is the polarization internal resistance of the first parallel RC unit; is the polarization internal resistance of the second parallel RC unit; is the polarized capacitance of the first parallel RC unit; is the polarized capacitance of the second parallel RC unit; The discharge capacity expressed as a charging equivalent circuit model; Expressed as a natural exponential function.

[0031] Furthermore, the state of charge of the drone battery at time t is calculated using the following formula:

[0032] ;

[0033] In the formula, It is represented as the state of charge of the drone battery at time t; It is represented as the voltage of the first parallel RC unit at time t; It is represented as the voltage of the second parallel RC unit; Represented as the current of the drone battery at time t.

[0034] Furthermore, a hangar fast charging model is constructed based on the charging time, charging loss and battery life of the drone, and the expression formula is:

[0035] ;

[0036] st: ; ;

[0037] In the formula, is the objective function for charging the battery of the i-th UAV; F(t) represents the hangar fast charging model; m represents the number of UAVs; lb and ub are the lower and upper bound constraints of the variable x to be optimized, respectively; x represents the variable to be optimized; is the linear inequality constraint of the variable x to be optimized;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] In the formula, It is expressed as the charging loss function of the drone; It is expressed as the charging time function of the drone; It is expressed as the battery life evaluation function of the drone; , and It is expressed as setting weight; The actual amount of energy stored in the drone battery; is the energy input to the battery; It is represented as the state of charge of the drone battery at time t; Indicates the target state of charge for charging the drone battery; The charging rate of the drone battery; Indicates the actual capacity of the drone battery; is the terminal voltage of the charging equivalent circuit model at time t; It is expressed as the initial capacity of the drone battery; Objective function for charging the battery of the i-th drone.

[0044] Furthermore, the charging and discharging experimental data are input into the hangar fast charging model, and the hangar fast charging model is optimized and solved using the NSGA-II algorithm, and the optimal hangar fast charging model is determined through the Pareto optimal solution; including:

[0045] Set the population size M and the number of iterations N of the NSGA-II algorithm; randomly generate the initial population, where each individual represents the solution of the hangar fast charging model;

[0046] Input the charging and discharging experimental data into the hangar fast charging model to calculate the output value of the objective function corresponding to each individual;

[0047] The individuals in the population are stratified according to the non-dominated relationship; the individuals in each layer are sorted according to their output values ​​in the objective function; the crowding distance of each individual is calculated;

[0048] Individuals are selected according to non-dominated sorting and crowding distance, and the population is subjected to selection, crossover, and mutation genetic operations to generate the next generation of population; the iteration is repeated N times to output the optimal hangar fast charging model.

[0049] The second aspect of the present invention provides a fast charging control system for a UAV intelligent hangar, characterized in that it includes:

[0050] A charging control module is used to collect UAV charging information from a hangar charging system, input the UAV charging information into a preset hangar fast charging model to obtain a charging control instruction, and execute the charging control instruction through the hangar charging system;

[0051] An acquisition module is used to construct a charging equivalent circuit model according to the UAV intelligent hangar; and to perform a charging and discharging experiment through the charging equivalent circuit model according to preset charging conditions to obtain charging and discharging experimental data;

[0052] The optimization module is used to build a hangar fast charging model based on the charging time, charging loss and battery life of the drone; the charging and discharging experimental data are input into the hangar fast charging model, the hangar fast charging model is optimized and solved using the NSGA-II algorithm, and the optimal charging plan is determined through the Pareto optimal solution.

[0053] Furthermore, the charging equivalent circuit model includes a voltage source E, a first parallel RC unit and a second parallel RC unit; an ohmic internal resistance R0, a capacitor Cb, a voltage source E, a first parallel RC unit and a second parallel RC unit are connected in series in sequence;

[0054] The first parallel RC unit and the second parallel RC unit include polarization internal resistance and polarization capacitance arranged in parallel; wherein the polarization resistance value and the polarization capacitance value are obtained by fitting curve calculation based on the charging and discharging parameters of the drone battery;

[0055] According to the UAV intelligent hangar, the charging equivalent circuit model is constructed and expressed as follows:

[0056] ;

[0057] In the formula, It is expressed as the terminal voltage of the charging equivalent circuit model; It is expressed as the total voltage of the voltage source E and the capacitor Cb; The current expressed as a charging equivalent circuit model; Expressed as the ohmic internal resistance of the battery; It is represented as the voltage of the first parallel RC unit; is represented as the voltage of the second parallel RC unit.

[0058] Furthermore, the hangar charging system includes a charging controller and a PWM control switch; the DC power supply is electrically connected to the drone battery through the PWM control switch; the charging controller collects the battery terminal voltage status and sends it to the host computer; after receiving the control instruction sent by the host computer, the charging controller adjusts the charging current through the PWM control switch.

[0059] Furthermore, the charging controller includes a PWM controller, an error amplifier EA and a current amplifier CA; a DC power supply, a resistor The drone battery pack is connected in series and then grounded; the drone battery pack includes a number of drone batteries; resistors are connected in parallel at both ends of the drone battery pack and resistor ;

[0060] The PWM controller is electrically connected to the control port of the error amplifier EA; the REF port of the error amplifier EA is connected to a stable voltage; the VFB port of the error amplifier EA is electrically connected to a resistor and resistor The circuit between the error amplifier EA's PROG port and the resistor and resistor The PWM control switch and the resistor are connected in series and then grounded; In parallel; the resistor and resistor The capacitor C is connected in parallel at both ends;

[0061] The PROG port of the error amplifier EA is electrically connected to the input end of the current amplifier CA; the CSP port of the current amplifier CA is connected to the resistor One end, the CSN port of the current amplifier CA is connected to the resistor The other end: the CSP port and the resistor , CSN port and the resistor There is a resistor between .

[0062] Compared with the prior art, the present invention has the following beneficial effects:

[0063] The present invention constructs a charging equivalent circuit model according to a UAV battery; performs a charging and discharging experiment through the charging equivalent circuit model according to preset charging conditions to obtain charging and discharging experimental data; constructs a hangar fast charging model based on the charging time, charging loss and battery life of the UAV; inputs the charging and discharging experimental data into the hangar fast charging model, optimizes and solves the hangar fast charging model using the NSGA-II algorithm, and determines the optimal hangar fast charging model through the Pareto optimal solution; the optimized charging scheme and the NSGA-II algorithm are helpful to improve charging efficiency, reduce energy loss, and improve energy utilization efficiency.

[0064] The present invention constructs a charging equivalent circuit model based on the UAV battery; performs charging and discharging experiments through the charging equivalent circuit model according to preset charging conditions to obtain charging and discharging experimental data; and reduces the heat loss and polarization of the battery during the charging process by accurately controlling the charging process, which helps to reduce the battery aging speed and extend its service life. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flowchart of a method for fast charging of a drone intelligent hangar provided in Example 1 of the present invention;

[0066] Figure 2 A structural diagram of a charging equivalent circuit model provided in Embodiments 1 and 2 of the present invention;

[0067] Figure 3 This is an experimental comparison diagram of the charging equivalent circuit models provided in Examples 1 and 2 of the present invention;

[0068] Figure 4 This is a structural diagram of the hangar charging system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0069] The present invention will be further described below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the protection scope of the present invention.

[0070] Example 1

[0071] like Figure 1 As shown, this embodiment provides a method for fast charging of a drone smart hangar, including:

[0072] Construct a charging equivalent circuit model based on the drone battery, such as Figure 2As shown, the charging equivalent circuit model includes a voltage source E, a first parallel RC unit and a second parallel RC unit; the ohmic internal resistance R0, the capacitor Cb, the voltage source E, the first parallel RC unit and the second parallel RC unit are connected in series in sequence; the first parallel RC unit and the second parallel RC unit include a polarized internal resistance and a polarized capacitor arranged in parallel;

[0073] According to the UAV intelligent hangar, the charging equivalent circuit model is constructed and expressed as follows:

[0074] ;

[0075] In the formula, It is expressed as the terminal voltage of the charging equivalent circuit model; It is represented by the voltage of the voltage source E and the capacitor Cb; The current expressed as a charging equivalent circuit model; Expressed as the ohmic internal resistance of the battery; It is represented as the voltage of the first parallel RC unit; It is represented as the voltage of the second parallel RC unit;

[0076] The polarization resistance and capacitance values ​​are obtained by fitting curve calculation of the UAV battery charging and discharging parameters. The charging equivalent circuit model (improved PNGV model) and parameter identification method enable the charging system to adapt to batteries of different types and states, enhancing the versatility and adaptability of the system.

[0077] There are corresponding test experimental standards for obtaining the charging equivalent circuit model parameters. The model parameters can be divided into OCV curve and component parameters. The OCV curve is obtained directly through standard experiments, and the component parameters are obtained by identifying and calculating the current pulse experimental data. The OCV curve and component parameters are both in the form of SOC curve functions.

[0078] According to the preset charging conditions, the charging and discharging experiment is performed through the charging equivalent circuit model to obtain the charging and discharging experimental data, including:

[0079] (1) Standard charge and discharge experiment: First, set the battery test temperature; perform constant current charging at 0.2C under constant temperature conditions. When the battery terminal voltage rises to 3.6V, perform constant voltage charging. When the charging current drops to 0.05C, the drone battery is fully charged.

[0080] Discharge at a current of 0.2C under constant temperature conditions until the battery terminal voltage drops to 2.0V; perform cyclic charge and discharge on the drone battery, and collect the charge and discharge experimental data of the drone battery.

[0081] (2) Small current charge and discharge experiment: The drone battery is discharged with a small current of 0.02C until the battery terminal voltage drops to 2.0V, and then charged with a small current of 0.02C until the battery terminal voltage rises to 3.6V. The drone battery is charged and discharged cyclically to collect the charge and discharge experimental data of the drone battery.

[0082] During the low current charge and discharge process, the ohmic voltage and polarization effect of the battery are very small, so the charge and discharge curves are very close, and the ohmic voltage and polarization effect are just opposite during the charge and discharge process. When the charge and discharge data are summed, the positive and negative ohmic voltage and polarization voltage cancel each other out, and then the summed data is divided by 2 to obtain the battery open circuit voltage data, that is, the OCV curve.

[0083] The polarization resistance and polarization capacitance are obtained by fitting curves of the UAV battery charge and discharge parameters, including:

[0084] When the battery terminal voltage of the drone battery slowly rises after the battery discharge is completed, the expression formula of the zero input response of the first parallel RC unit and the second parallel RC unit is:

[0085] ;

[0086] ; ;

[0087] When the voltage at the battery end of the drone battery slowly decreases during discharge, the zero input response of the first parallel RC unit and the second parallel RC unit is expressed as:

[0088] ;

[0089] ;

[0090] In the formula, Expressed as the initial voltage of the charging equivalent circuit model; are the initial polarization voltages of the first parallel RC unit, respectively; are the initial polarization voltage of the second parallel RC unit, respectively; It is represented as the time constant of the first parallel RC unit; It represents the time constant of the second parallel RC unit; t represents the charge and discharge time; It is represented as the voltage of the first parallel RC unit; It is represented as the voltage of the first parallel RC unit; It is represented as the total voltage of the first parallel RC unit and the second parallel RC unit; is the polarization internal resistance of the first parallel RC unit; is the polarization internal resistance of the second parallel RC unit; is the polarized capacitance of the first parallel RC unit; is the polarized capacitance of the second parallel RC unit; The discharge capacity expressed as a charging equivalent circuit model; Expressed as a natural exponential function.

[0091] The curve fitting tool curve fitting in Matlab was used to obtain the polarization resistance parameters, and the polarization capacitance parameters were obtained from the time constant calculation formula.

[0092] Calculate the state of charge of the drone battery at time t, expressed as:

[0093] ;

[0094] In the formula, It is represented as the state of charge of the drone battery at time t; It is represented as the voltage of the first parallel RC unit at time t; It is represented as the voltage of the second parallel RC unit; Represented as the current of the drone battery at time t.

[0095] A pulse experiment was conducted at the SOC point of 0.5, and the current and voltage data were recorded as the input of the simulation to obtain the simulation waveform of the battery terminal voltage of the charging equivalent circuit model. Figure 3 As shown, the two curves are the simulation curve and the measured value respectively. It can be seen that the charging equivalent circuit model (improved PNGV model) has a smaller error and can more accurately describe the polarization phenomenon of lithium iron phosphate batteries, thereby more realistically reflecting the behavior of the battery during the charging and discharging process.

[0096] Based on the charging time, charging loss and battery life of the drone, a hangar fast charging model is constructed, and the expression formula is:

[0097] ;

[0098] st: ; ;

[0099] In the formula, The objective function for charging the battery of the i-th UAV; m represents the number of UAVs; lb and ub are the lower and upper bound constraints of the variable x to be optimized, respectively; x represents the variable to be optimized; is the linear inequality constraint of the variable x to be optimized;

[0100] ;

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] In the formula, It is expressed as the charging loss function of the drone; It is expressed as the charging time function of the drone; It is expressed as the battery life evaluation function of the drone; , and It is expressed as setting weight; The actual amount of energy stored in the drone battery; is the energy input to the battery; It is represented as the state of charge of the drone battery at time t; Indicates the target state of charge for charging the drone battery; The charging rate of the drone battery; Indicates the actual capacity of the drone battery; is the terminal voltage of the charging equivalent circuit model at time t; It is expressed as the initial capacity of the drone battery; Objective function for charging the battery of the i-th drone.

[0106] The charging and discharging experimental data are input into the hangar fast charging model, and the hangar fast charging model is optimized and solved using the NSGA-II algorithm. The optimal hangar fast charging model is determined through the Pareto optimal solution; including:

[0107] Set the population size M and the number of iterations N of the NSGA-II algorithm; randomly generate the initial population, where each individual represents the solution of the hangar fast charging model;

[0108] Input the charging and discharging experimental data into the hangar fast charging model to calculate the output value of the objective function corresponding to each individual;

[0109] The individuals in the population are stratified according to the non-dominated relationship; the individuals in each layer are sorted according to their output values ​​in the objective function; the crowding distance of each individual is calculated;

[0110] Individuals are selected according to non-dominated sorting and crowding distance, and the next generation of population is generated by selection, crossover and mutation genetic operations. The optimal hangar fast charging model is outputted after N repeated iterations. By optimizing the charging strategy and improving the charging efficiency, energy consumption and operating costs are reduced, which has good economy.

[0111] The hangar charging system collects the UAV charging information, inputs the UAV charging information into the pre-set hangar fast charging model to obtain the charging control instructions, and executes the charging control instructions through the hangar charging system; by precisely controlling the charging process, the heat loss and polarization of the battery during the charging process are reduced, which helps to reduce the aging rate of the battery and extend its service life.

[0112] Example 2

[0113] like Figures 2 to 3 As shown, this embodiment provides a fast charging control system for a UAV intelligent hangar, and the fast charging control system can execute the fast charging control method described in Example 1. The fast charging control system includes:

[0114] A charging control module is used to collect UAV charging information from a hangar charging system, input the UAV charging information into a preset hangar fast charging model to obtain a charging control instruction, and execute the charging control instruction through the hangar charging system;

[0115] An acquisition module is used to construct a charging equivalent circuit model according to the UAV intelligent hangar; and to perform a charging and discharging experiment through the charging equivalent circuit model according to preset charging conditions to obtain charging and discharging experimental data;

[0116] The optimization module is used to build a hangar fast charging model based on the charging time, charging loss and battery life of the drone; the charging and discharging experimental data are input into the hangar fast charging model, the hangar fast charging model is optimized and solved using the NSGA-II algorithm, and the optimal charging plan is determined through the Pareto optimal solution.

[0117] like Figure 3 As shown, the charging equivalent circuit model includes a voltage source E, a first parallel RC unit and a second parallel RC unit; an ohmic internal resistance R0, a capacitor Cb, a voltage source E, a first parallel RC unit and a second parallel RC unit are connected in series in sequence;

[0118] The first parallel RC unit and the second parallel RC unit include polarization internal resistance and polarization capacitance arranged in parallel; wherein the polarization resistance value and the polarization capacitance value are obtained by fitting curve calculation based on the charging and discharging parameters of the drone battery;

[0119] According to the UAV intelligent hangar, the charging equivalent circuit model is constructed and expressed as follows:

[0120] ;

[0121] In the formula, It is expressed as the terminal voltage of the charging equivalent circuit model; It is expressed as the total voltage of the voltage source E and the capacitor Cb; The current expressed as a charging equivalent circuit model; Expressed as the ohmic internal resistance of the battery; It is represented as the voltage of the first parallel RC unit; is represented as the voltage of the second parallel RC unit.

[0122] like Figure 4 As shown, the hangar charging system includes a charging controller and a PWM control switch; the DC power supply is electrically connected to the drone battery through the PWM control switch; the charging controller collects the battery terminal voltage status and sends it to the host computer; after receiving the control instruction sent by the host computer, the charging controller adjusts the charging current through the PWM control switch.

[0123] LTC4012 is used as a charging controller, which includes a PWM controller, an error amplifier EA and a current amplifier CA; a DC power supply, a resistor The drone battery pack is connected in series and then grounded; the drone battery pack includes a number of drone batteries; resistors are connected in parallel at both ends of the drone battery pack and resistor ;

[0124] The PWM controller is electrically connected to the control port of the error amplifier EA; the REF port of the error amplifier EA is connected to a stable voltage; the VFB port of the error amplifier EA is electrically connected to a resistor and resistor The circuit between the error amplifier EA's PROG port and the resistor and resistor The PWM control switch and the resistor are connected in series and then grounded; In parallel; the resistor and resistor The capacitor C is connected in parallel at both ends;

[0125] The PROG port of the error amplifier EA is electrically connected to the input end of the current amplifier CA; the CSP port of the current amplifier CA is connected to the resistor One end, the CSN port of the current amplifier CA is connected to the resistor The other end: the CSP port and the resistor , CSN port and the resistor There is a resistor between .

[0126] In this embodiment , , , ,resistance The current sampling resistor has a fixed bias of 11.67uA, and the output current of CA is Calculated as:

[0127] ;

[0128] The voltage at the PROG port of the error amplifier EA is:

[0129] ;

[0130] In the formula, D is the duty cycle of the PWM control switch; Represented as the input current of the drone battery pack.

[0131] The host computer uses Labwindows / CVI for control. The host computer acts as a communication host and sends instructions. The instructions are divided into: collecting voltage, setting current, and controlling switches. After receiving the voltage collection instruction, the charging controller returns the battery terminal voltage status. After receiving the current setting or switch control instruction, it controls the PWM control switch duty cycle and then returns the execution information.

[0132] Table 1, constant current pulse charging scheme verification

[0133] ;

[0134] The host computer samples the battery terminal voltage data regularly and performs fast charging control to determine the charging end point. The charging process is controlled according to the optimized fast charging scheme, and the charging time is recorded after the charging is completed. The charged battery is discharged in a standard manner on the battery test system, the discharge power is recorded, and the charging loss is calculated. As shown in Table 1, taking constant current pulse charging as an example, the experimental results obtained are compared with the results of the optimized simulation; there is an error between the charging experimental results and the simulation results, but the error is within the allowable range. It can be concluded that the scheme obtained by optimization is correct, and it is feasible to use the equivalent circuit model for the optimization simulation of the intelligent battery in the drone hangar.

[0135] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, etc.) containing computer-usable program code. M , optical storage, etc.).

[0136] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0139] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A fast charging control method for a UAV intelligent hangar, characterized in that: include: The hangar charging system collects the UAV charging information, inputs the UAV charging information into the preset hangar fast charging model to obtain the charging control instruction, and executes the charging control instruction through the hangar charging system; The hangar fast charging model construction process includes: The process of building a charging equivalent circuit model based on the drone battery includes: The charging equivalent circuit model includes a voltage source E, a first parallel RC unit and a second parallel RC unit; an ohmic internal resistance R0, a capacitor Cb, a voltage source E, a first parallel RC unit and a second parallel RC unit are connected in series in sequence; The first parallel RC unit and the second parallel RC unit include a polarization internal resistance and a polarization capacitance arranged in parallel; According to the UAV intelligent hangar, the charging equivalent circuit model is constructed and expressed as follows: ； In the formula, It is expressed as the terminal voltage of the charging equivalent circuit model; It is represented by the voltage of the voltage source E and the capacitor Cb; The current expressed as a charging equivalent circuit model; Expressed as the ohmic internal resistance of the battery; It is represented as the voltage of the first parallel RC unit; It is represented as the voltage of the second parallel RC unit; The polarization resistance and polarization capacitance values ​​are obtained by fitting the charging and discharging parameters of the drone battery. Performing a charge and discharge experiment according to a preset charging condition through a charging equivalent circuit model to obtain charge and discharge experimental data; Based on the charging time, charging loss and battery life of the drone, a hangar fast charging model is constructed, and the expression formula is: ； st: ; ; In the formula, is the objective function for charging the battery of the i-th UAV; F(t) represents the hangar fast charging model; m represents the number of UAVs; lb and ub are the lower and upper bound constraints of the variable x to be optimized, respectively; x represents the variable to be optimized; is the linear inequality constraint of the variable x to be optimized; ； ； ； ； ； In the formula, Expressed as the charging loss function of the drone, It is expressed as the charging time function of the drone; It is expressed as the battery life evaluation function of the drone; , and It is expressed as setting weight; The actual amount of energy stored in the drone battery; is the energy input to the battery; It is represented as the state of charge of the drone battery at time t; Indicates the target state of charge for charging the drone battery; The charging rate of the drone battery; Indicates the actual capacity of the drone battery; is the terminal voltage of the charging equivalent circuit model at time t; It is expressed as the initial capacity of the drone battery; The charging and discharging experimental data are input into the hangar fast charging model, and the hangar fast charging model is optimized and solved using the NSGA-II algorithm to obtain the Pareto optimal solution. The optimal hangar fast charging model is determined through the Pareto optimal solution.

2. The fast charging control method according to claim 1, characterized in that: According to the preset charging conditions, the charging and discharging experiment is performed through the charging equivalent circuit model to obtain the charging and discharging experimental data, including: Set the battery experimental temperature; perform constant current charging at 0.2C under constant temperature conditions. When the battery terminal voltage rises to 3.6V, perform constant voltage charging. The charging current drops until the current drops to 0.05C, which is considered the drone battery is fully charged. Discharge at a current of 0.2C under constant temperature conditions until the battery terminal voltage drops to 2.0V; perform cyclic charge and discharge on the drone battery, and collect the charge and discharge experimental data of the drone battery.

3. The fast charging control method according to claim 1, characterized in that: According to the preset charging conditions, the charging and discharging experiment is performed through the charging equivalent circuit model to obtain the charging and discharging experimental data, including: The drone battery is discharged with a small current of 0.02C until the battery terminal voltage drops to 2.0V, and then charged with a small current of 0.02C until the battery terminal voltage rises to 3.6V. The drone battery is charged and discharged in a cycle, and the charge and discharge experimental data of the drone battery are collected.

4. The fast charging control method according to claim 1, characterized in that: The polarization resistance and polarization capacitance are obtained by fitting curves of the UAV battery charge and discharge parameters, including: When the battery terminal voltage of the drone battery slowly rises after the battery discharge is completed, the expression formula of the zero input response of the first parallel RC unit and the second parallel RC unit is: ； ； ； When the voltage at the battery end of the drone battery slowly decreases during discharge, the zero input response of the first parallel RC unit and the second parallel RC unit is expressed as: ； ； In the formula, Expressed as the initial voltage of the charging equivalent circuit model; are the initial polarization voltages of the first parallel RC unit, respectively; are the initial polarization voltage of the second parallel RC unit, respectively; It is represented as the time constant of the first parallel RC unit; It represents the time constant of the second parallel RC unit; t represents the charge and discharge time; It is represented as the total voltage of the first parallel RC unit and the second parallel RC unit; is the polarization internal resistance of the first parallel RC unit; is the polarization internal resistance of the second parallel RC unit; is the polarized capacitance of the first parallel RC unit; is the polarized capacitance of the second parallel RC unit; Expressed as the discharge capacity of the charging equivalent circuit model, Expressed as a natural exponential function.

5. The fast charging control method according to claim 4, characterized in that: Calculate the state of charge of the drone battery at time t, expressed as: ； In the formula, It is represented as the state of charge of the drone battery at time t; It is represented as the voltage of the first parallel RC unit at time t; It is represented as the voltage of the second parallel RC unit; Represented as the current of the drone battery at time t.

6. The fast charging control method according to claim 1, characterized in that: The charging and discharging experimental data are input into the hangar fast charging model, and the hangar fast charging model is optimized and solved using the NSGA-II algorithm. The optimal hangar fast charging model is determined through the Pareto optimal solution; including: Set the population size M and the number of iterations N of the NSGA-II algorithm; randomly generate the initial population, where each individual represents the solution of the hangar fast charging model; Input the charging and discharging experimental data into the hangar fast charging model to calculate the output value of the objective function corresponding to each individual; The individuals in the population are stratified according to the non-dominated relationship; the individuals in each layer are sorted according to their output values ​​in the objective function; the crowding distance of each individual is calculated; Individuals are selected according to non-dominated sorting and crowding distance, and the population is subjected to selection, crossover, and mutation genetic operations to generate the next generation of population; the iteration is repeated N times to output the optimal hangar fast charging model.

7. A fast charging control system for a drone smart hangar, characterized in that: include: A charging control module is used to collect UAV charging information from a hangar charging system, input the UAV charging information into a preset hangar fast charging model to obtain a charging control instruction, and execute the charging control instruction through the hangar charging system; An acquisition module is used to construct a charging equivalent circuit model according to the UAV intelligent hangar; and to perform a charging and discharging experiment through the charging equivalent circuit model according to preset charging conditions to obtain charging and discharging experimental data; The optimization module is used to build a hangar fast charging model based on the charging time, charging loss and battery life of the UAV; the charging and discharging experimental data are input into the hangar fast charging model, the hangar fast charging model is optimized and solved using the NSGA-II algorithm, and the optimal charging solution is determined through the Pareto optimal solution; The charging equivalent circuit model includes a voltage source E, a first parallel RC unit and a second parallel RC unit; an ohmic internal resistance R0, a capacitor Cb, a voltage source E, a first parallel RC unit and a second parallel RC unit are connected in series in sequence; The first parallel RC unit and the second parallel RC unit include polarization internal resistance and polarization capacitance arranged in parallel; wherein the polarization resistance value and the polarization capacitance value are obtained by fitting curve calculation based on the charging and discharging parameters of the drone battery; The acquisition module constructs a charging equivalent circuit model according to the UAV intelligent hangar, and the expression formula is: ； In the formula, It is expressed as the terminal voltage of the charging equivalent circuit model; It is represented by the voltage of the voltage source E and the capacitor Cb; The current expressed as a charging equivalent circuit model; Expressed as the ohmic internal resistance of the battery; It is represented as the voltage of the first parallel RC unit; It is represented as the voltage of the second parallel RC unit; The optimization module constructs a hangar fast charging model based on the charging time, charging loss and battery life of the UAV, and the expression formula is: ； st: ; ; In the formula, is the objective function for charging the battery of the i-th UAV; F(t) represents the hangar fast charging model; m represents the number of UAVs; lb and ub are the lower and upper bound constraints of the variable x to be optimized, respectively; x represents the variable to be optimized; is the linear inequality constraint of the variable x to be optimized; ； ； ； ； ； In the formula, Expressed as the charging loss function of the drone, It is expressed as the charging time function of the drone; It is expressed as the battery life evaluation function of the drone; , and It is expressed as setting weight; The actual amount of energy stored in the drone battery; is the energy input to the battery; It is represented as the state of charge of the drone battery at time t; Indicates the target state of charge for charging the drone battery; The charging rate of the drone battery; Indicates the actual capacity of the drone battery; is the terminal voltage of the charging equivalent circuit model at time t; Represents the initial capacity of the drone battery.

8. The fast charging control system according to claim 7, characterized in that: The hangar charging system includes a charging controller and a PWM control switch; the DC power supply is electrically connected to the drone battery through the PWM control switch; the charging controller collects the battery terminal voltage status and sends it to the host computer; after receiving the control instruction sent by the host computer, the charging controller adjusts the charging current through the PWM control switch.

9. The fast charging control system according to claim 8, characterized in that: The charging controller includes a PWM controller, an error amplifier EA and a current amplifier CA; a DC power supply, a resistor The drone battery pack is connected in series and then grounded; the drone battery pack includes a number of drone batteries; resistors are connected in parallel at both ends of the drone battery pack and resistor ; The PWM controller is electrically connected to the control port of the error amplifier EA; the REF port of the error amplifier EA is connected to a stable voltage; the VFB port of the error amplifier EA is electrically connected to a resistor and resistor The circuit between the error amplifier EA's PROG port and the resistor and resistor The PWM control switch and the resistor are connected in series and then grounded; In parallel; the resistor and resistor The capacitor C is connected in parallel at both ends; The PROG port of the error amplifier EA is electrically connected to the input end of the current amplifier CA; the CSP port of the current amplifier CA is connected to the resistor One end, the CSN port of the current amplifier CA is connected to the resistor The other end: the CSP port and the resistor , CSN port and the resistor There is a resistor between .

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