Vehicle-mounted dual-mode unmanned aerial vehicle power supply system parameter design method

Abstract

The invention provides a parameter design method for a vehicle-mounted dual-mode unmanned aerial vehicle power supply system. The parameter design method comprises the following steps: step 1, establishing electrical connection between the dual-mode unmanned aerial vehicle power supply system and a pure electric vehicle platform; step 2, performing modeling on each performance parameter under the dual-mode unmanned aerial vehicle system; and step 3, designing a solution algorithm to solve the nonlinear optimization model, and realizing accurate optimization of the vehicle-mounted power supply power. According to the method, the energy optimization management model considering the power supply characteristics of the vehicle-mounted power battery is formed, the model is solved by adopting the particle swarm optimization algorithm, and compared with a traditional scheme, the power consumption of the mooring mode can be optimized, the mooring flight duration is prolonged, and the remaining endurance energy efficiency of the vehicle is considered.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicles, and particularly relates to a parameter design method for a vehicle-mounted dual-mode unmanned aerial vehicle power supply system. BACKGROUND

[0002] At present, most of the traditional vehicle-mounted dual-mode unmanned aerial vehicles are integrated on a fuel vehicle platform in an independent mounting manner. The dual-mode unmanned aerial vehicle power supply system is generally driven by an external power supply, a fuel generator or a vehicle-mounted force taking manner. The dual-mode unmanned aerial vehicle power supply system mainly includes an unmanned aerial vehicle body (including a power system), a flying battery, an on-board power supply, a tether cable and a line winding and unwinding device, and also needs to be equipped with an independent fuel generator and an AC / DC vehicle-mounted power supply and other supporting equipment. The traditional vehicle-mounted dual-mode unmanned aerial vehicle power supply system needs to be additionally installed with a generator and an AC / DC vehicle-mounted power supply, which increases the system weight and is generally only suitable for large load type or independent towing type vehicles.

[0003] With the vigorous development of pure electric vehicle platforms, some researches have explored the feasibility of the unmanned aerial vehicle power supply system taking power from the vehicle-mounted high-voltage power battery. Chinese patent application No. 202311357306.8, patent name: an unmanned aerial vehicle mobile nest and a control method and device thereof and an electric vehicle, discloses a vehicle-mounted unmanned aerial vehicle nest, which realizes electrical connection between the vehicle-mounted power battery of an electric vehicle and the nest to charge the flying unmanned aerial vehicle. This patent does not involve research in the field of tethered unmanned aerial vehicle power supply. At present, there is no research exploring the feasibility of the pure electric vehicle providing power for the tethered mode. Due to the limited vehicle-mounted energy reserve of the pure electric vehicle and the strict load limit, the traditional independent mounting manner has a low degree of electrical coupling with the pure electric vehicle platform, which may cause the weight of the unmanned aerial vehicle power supply system to exceed the load limit, resulting in a serious reduction in the endurance of the pure electric vehicle platform and even endangering the driving safety.

[0004] At present, the vehicle-mounted dual-mode unmanned aerial vehicle power supply system based on the pure electric vehicle platform also has the following defects in parameter design: 1) There is a lack of a general mathematical model for typical performance indicators such as system total weight, whole machine power consumption and flying endurance; 2) There is no general design method for the power supply system under multiple constraint requirements, which cannot complete the overall parameter design of the power supply system with a specified optimization target; 3) There is a lack of a solving method. The above defects make it difficult to realize vehicle-aircraft collaborative optimization, taking into account the unmanned aerial vehicle performance indicators and the residual endurance efficiency of the vehicle. SUMMARY

[0005] Purpose of the invention: This invention aims to solve the problems of weak electrical coupling between existing dual-mode UAV power supply systems and pure electric vehicle platforms, as well as the lack of systematic modeling and optimization objectives for performance indicators. It proposes a parameter design method for vehicle-mounted dual-mode UAV power supply systems based on pure electric vehicle platforms and an energy optimization management model considering the characteristics of vehicle-mounted power supply. The model is solved using the particle swarm optimization algorithm.

[0006] This invention specifically provides a method for designing parameters of a vehicle-mounted dual-mode unmanned aerial vehicle (UAV) power supply system, comprising the following steps:

[0007] Step 1: Establish the electrical connection between the dual-mode drone power supply system and the pure electric vehicle platform;

[0008] Step 2: Model the performance parameters of the dual-mode UAV system;

[0009] Step 3: Design a solution algorithm to solve the nonlinear optimization model and achieve accurate optimization of the vehicle power supply.

[0010] Step 1 includes: forming an energy coupling between the dual-mode drone power supply system and the pure electric vehicle platform power supply system, forming an energy transmission link between the vehicle power battery, high-voltage box and the drone's onboard power supply and backup battery in tethered mode;

[0011] The dual-mode UAV power supply system includes the UAV, launch battery, airborne power supply, backup battery, tether cable, and cable reel-in / reel-out device.

[0012] The pure electric vehicle platform includes a power battery, a high-voltage box, and an on-board charger.

[0013] The electrical connection between the dual-mode drone power supply system and the pure electric vehicle platform includes DC400V from the high-voltage box and DC400V from the on-board charger, specifically:

[0014] The output voltage of the power battery is DC384V~DC456V;

[0015] The on-board charger has an output voltage of DC400V. When connected to an external AC220 / AC380, the DC400V can be input to the high-voltage box to charge the power battery, and can also drive the power supply system of the drone in tethered mode.

[0016] In step 1, the launch mode uses a DC48V launch battery, and the energy transfer link is as follows:

[0017] Launch battery DC48V, drone bus DC 48V;

[0018] In tethered mode, a topology combining high-voltage power supply from the vehicle's power battery and step-down power supply from the airborne power source is adopted. The energy transmission link is as follows:

[0019] Power battery DC384V~456V, high voltage box DC400V, tether cable DC400V, airborne power supply or backup battery DC48V, UAV busbar DC48V.

[0020] Step 2 includes:

[0021] Step 2-1: Establish the system's total weight model;

[0022] The total weight of the dual-mode drone power supply system is M. total It is expressed as follows:

[0023]

[0024] in, Let N be the weight of the i-th drone, determined by the drone's design specifications, and there are a total of N. UAV Optional mobile suit; M PL M represents the load weight. BF The weight of the battery to be launched is expressed as Where V BF Q is the nominal voltage of the battery. BF To maximize battery capacity, D BF For the energy density of the flying battery; M AP The weight of the onboard power supply is expressed in M. AP =k AP P AP +b AP , where k AP P is the airborne power weight factor. AP b is the rated power of the airborne power supply. AP The base weight includes the weight of fixed components such as the power supply casing and heatsink; M BB The weight of the backup battery is expressed as... Q BB For backup battery capacity, V BB D is the nominal voltage of the battery pack. BB The energy density of the backup battery; The weight of the tethered cable in clause j is expressed as... L cable For cable length, The weight per unit length of the j-th type of tethered cable is N. cable Optional tether cable; M SU The weight of the monitoring unit; M GRD This refers to the weight of the wire take-up and untake-down device.

[0025] Step 2-2: Establish the total power consumption model of the dual-mode UAV;

[0026] The total power consumption of the drone includes the power system power consumption P. d Steady-state power consumption P of avionics system a and load steady-state power consumption P l ;

[0027] Calculate the total power consumption P of the drone in flight mode. total-F Total power consumption P of tethered mode drone total-T :

[0028] P total-F =P d-F +P a +P l (2),

[0029] P total-T =P d-T +P a +P l (3),

[0030] Among them, P d-F The power consumption of the propulsion system in flight mode is expressed as... Takeoff weight M in launch mode total-F Represented as: M total-F =M UAV +M BF +M PL n is the number of propellers, C L R is the propeller thrust coefficient, C is the propeller diameter, and R is the propeller diameter. M ρ is the propeller torque coefficient, ρ is the air density, and g is the gravitational acceleration.

[0031] Among them, P d-T The power consumption of the power system in tethered mode is expressed as... Takeoff weight M in tethered mode total-T Represented as: M total-T =M UAV +M AP +M BB +M cable +M PL .

[0032] Steps 2-3: Establish a coupled model of flight time, launch radius, and cruising speed in flight mode;

[0033] In flight mode, the flight time T flight This refers to the maximum flight time that the drone can sustain. The flight radius R... flight This refers to the maximum distance a drone can travel between its starting point and target point within the maximum time it can fly. The specific modeling is as follows:

[0034]

[0035] in, To improve the discharge efficiency of the battery; V flight S is the cruising speed; S is the wing area; C is the cruising speed. D0 is the zero-lift drag coefficient; K is the lift-induced drag factor.

[0036] Steps 2-4: Establish an optimization model for onboard power supply capacity under tethered mode;

[0037] The optimization objective is to minimize the tethered mode power consumption, within the constraints, even if the onboard output power P out Minimize, the objective function is:

[0038]

[0039] Among them, P out For vehicle-mounted output power; η DCDC The conversion efficiency of the airborne power supply; η rect For the conversion efficiency of the ground high-voltage box; V IN This is the input voltage for the high-voltage box; The resistivity of the conductor of the j-th type of tethered cable; Let J be the cross-sectional area of ​​the cable used for mooring cable in clause j.

[0040] The typical constraints include maximum system weight limit, minimum launch radius limit, maximum endurance limit, minimum payload capacity limit, and tethered altitude limit. The constraints are as follows:

[0041] M total ≤M total-req (8),

[0042] R flight ≥R flight-req (9),

[0043] T flight ≥T flight-req (10),

[0044] M PL ≥M PL-req (11),

[0045] L cable ≥L cable-req (12),

[0046] Where M total-req M is the maximum permissible total weight of the system. PL-req Minimum permissible load weight; L cable-req R is the minimum allowable length for tethered cables. flight-req For the minimum launch radius, T flight-req This is the minimum flight endurance.

[0047] The safety constraints include the rated power P of the airborne power supply. AP Emergency landing time of backup battery The two safety constraints are as follows:

[0048] P total-T ≤σP AP (13),

[0049]

[0050] Where σ is the airborne power supply safety factor; DOD is the depth of discharge. θ represents the discharge efficiency of the backup battery; θ is the safety factor for forced-land power consumption; T T-req This is the minimum time required for a forced landing.

[0051] Step 3 includes:

[0052] Step 3-1, Mathematical Modeling: The objective function is mathematically modeled, and the formulas (1) to (14) in Step 2 are rearranged to obtain the on-board output power P in tethered mode. out The objective function and constraints are minimized, and the mathematical expression is as follows:

[0053]

[0054] Where st indicates that it is restricted;

[0055] Step 3-2: Improve the algorithm selection and constraint handling. The particle swarm optimization algorithm is selected to solve equation (15). The following improvements are made:

[0056] Nearest neighbor mapping for discrete variables: for N cable The cross-sectional area of ​​the tethered cable and N UAV For each type of drone, after the particle position is updated, the continuous values ​​of the tethered cable cross-sectional area and the drone configuration are mapped to the closest engineering specifications.

[0057] Normalized Dynamic Penalty Function: Hard constraints are handled using the normalized dynamic penalty function defined in equations (16-1) to (16-3). This penalty function... Unify the dimensions, and through w k (t) Dynamically adjust the intensity of the penalty.

[0058] Initial weight engineering calibration: initial weights w for each constraint k INIT Determined by sensitivity analysis.

[0059] New fitness function F adapt (x,t) is shown in the following equation:

[0060] Fadapt (x,t)=P out (x)+Φ(x,t) (16),

[0061] Among them, F adapt (x,t) is the newly constructed fitness function, P out (x) represents the original onboard power supply, Φ(x,t) represents the penalty term of the constraint condition, t represents the t-th iteration, and x represents the set of variables.

[0062]

[0063] Where k = 1, 2, ..., 7, To normalize the reference power, set in The highest hover power consumption among all candidate models; w k (t) is the piecewise assignment function for the k-th constraint; φ k (x) is the relative constraint penalty function for the kth term;

[0064]

[0065] φ k (x)=max(0,g k (x))k=1,2,…,7(16-3),

[0066] α is the initial weight; α is the decay coefficient; T MAX The maximum number of iterations is given; exp is the natural exponential function.

[0067] The seven inequality constraints in formulas (8) to (14) are transformed into a unified violation function g. k (x), defined as follows:

[0068]

[0069] Step 3-3: Algorithm parameter setting and particle initialization;

[0070] Steps 3-4 involve iterative solution and optimal solution update. Through the speed and position update iterative mechanism of the particle swarm optimization algorithm, the optimal solution that minimizes the on-board output power consumption and satisfies all constraints is gradually approached, while the iterative process is monitored in real time.

[0071] Step 3-3 includes:

[0072] Step 3-3-1: Set key parameters for the particle swarm optimization algorithm. Based on the variable dimensionality of this problem and engineering experience, combined with the accuracy requirements of the design task, set the population size N and the maximum number of iterations T. MAXInertia weight ω, individual learning factor c1, group learning factor c2, velocity limit v max Parameters, upper limit of position x max parameter.

[0073] Step 3-3-2: Perform particle initialization.

[0074] Initialization of continuous variables: Randomly generate initial values ​​for continuous variables within the feasible region.

[0075] Discrete variable initialization: randomly assigned from the optional specifications;

[0076] Post-initialization checks and adjustments: For each initial particle, calculate whether the seven constraints are met. If the constraints are not met, adjust the continuous variables until they are met, ultimately ensuring that the proportion of feasible solutions in the initial population is ≥60%.

[0077] Steps 3-4 include:

[0078] Step 3-4-1, each iteration executes the following process:

[0079] Fitness calculation: For each particle in the population, the fitness function F is modified according to equation (16). adapt (x,t) calculates the fitness value, where the penalty term Φ(x,t) for infeasible solutions is 0;

[0080] Discrete variable mapping and verification: Perform nearest neighbor mapping on discrete variables, then recalculate the fitness function and its continuous variables. If the value of the continuous variable exceeds the feasible region, mark the particle as infeasible and skip the current iteration.

[0081] Update individual optimal values ​​and global optimal values:

[0082] In particle velocity and position updates, the velocity update formula is as follows:

[0083] v m (t+1)=ω·v m (t)+c1·r1·(p best,m -x m (t))+c2·r2·(g best -x m (t)) (17),

[0084] Where r1 and r2 are random numbers between 0 and 1, increasing the randomness of the search and avoiding local optima; v m (t) represents the velocity of particle m in the t-th round; x m (t) represents the position of particle m in round t; ω is the inertial weight, indicating the extent to which it represents the original weight; p best,mLet p be the optimal individual extreme value of the m-th particle. If the current particle's fitness is better than its historical best fitness, then update the particle's p. best,m g best To find the global optimum, iterate through the p values ​​of all particles. best,m If a solution with better fitness exists, then update the population's g. best ;

[0085] The position update formula is:

[0086] x m (t+1)=x m (t)+v m (t+1) (18),

[0087] After the update, perform the nearest neighbor mapping in step 3-4-1 on the discrete variables, and check the feasible region for the continuous variables;

[0088] Convergence criterion: If the global optimal solution g is found in X1 consecutive iterations... best If the change in fitness is less than the threshold, or the maximum number of iterations has been reached, the iteration is terminated.

[0089] If the penalty term Φ(x,t) for satisfying the constraints in X² consecutive iterations of the global optimal solution is less than a threshold, then the iteration is accelerated. After terminating the iteration, the output g is determined. best The optimal parameter combination is selected. Otherwise, the iteration continues, starting with fitness calculation.

[0090] Step 3-4-2: Monitor and correct the iterative process.

[0091] Step 3-4-2 includes: outputting key information every X3 iterations, including: the current globally optimal tethered power consumption P. out Total system weight M total Launch radius R flight Battery life T flight Variables such as the total weight M of the system. total Launch radius R flight Battery life T flight If a sustained overrun occurs due to constraints, the penalty function weight w is adjusted. j (t) or expand the feasible region of the variable, such as the upper limit of velocity v. max Position limit x max Re-iterate;

[0092] If a constraint is not met, analyze the reason and perform the corresponding correction:

[0093] If the feasible region of the dependent variable is too narrow: adjust the variable parameters and repeat steps 3-3 to 3-4.

[0094] If the algorithm gets stuck in a local optimum: increase the population size or adjust the inertia weights, and iterate again;

[0095] If there is a conflict in the constraints: feedback should be sent to the design task, the constraints should be adjusted through negotiation, and then the solution should be obtained again.

[0096] The present invention also provides an electronic device, including a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method.

[0097] The present invention also provides a storage medium storing a computer program or instructions that, when the computer program or instructions are run on a computer, execute the steps of the method described.

[0098] The parameter design method for a vehicle-mounted dual-mode UAV power supply system described in this invention, compared with existing technologies, has the following advantages:

[0099] 1. Establish the electrical coupling relationship between the power supply system of the pure electric vehicle platform and the power supply system of the dual-mode UAV. Compared with the traditional fuel vehicle platform, it reduces the number of components such as fuel generator and AC / DC vehicle power supply, thus reducing the total weight of the UAV power supply system.

[0100] 2. Establish a refined parameter model for the dual-mode UAV power supply system, and construct correlation models such as "system weight-energy efficiency" coordination and "endurance-flight radius" coordination to achieve multi-parameter collaborative optimization and improve the overall system performance;

[0101] 3. An energy optimization management model considering the power supply characteristics of the vehicle's power battery is formed, and the particle swarm optimization algorithm is used to solve the model. Compared with the traditional solution, it can optimize the power consumption in tethered mode, increase the tethered flight time, and take into account the remaining range energy efficiency of the vehicle. Attached Figure Description

[0102] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, and the advantages of the present invention in the above and / or other aspects will become clearer.

[0103] Figure 1 This is a schematic diagram illustrating the design steps of the present invention.

[0104] Figure 2 This is a schematic diagram of the electrical coupling relationship between the unmanned aerial vehicle power supply system and the pure electric vehicle platform of the present invention.

[0105] Figure 3 Flowchart for solving the optimization model of on-board power supply capacity in tethered mode. Detailed Implementation

[0106] This invention provides a method for designing parameters of a vehicle-mounted dual-mode unmanned aerial vehicle (UAV) power supply system. Many methods and approaches exist for implementing this technical solution; the following description is merely a preferred embodiment. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of this invention, and these improvements and modifications should also be considered within the scope of protection of this invention. All components not explicitly stated in this embodiment can be implemented using existing technologies.

[0107] The steps of the present invention will be described in detail below with reference to the accompanying drawings:

[0108] A flowchart of the parameter design method for a vehicle-mounted dual-mode UAV power supply system is shown below. Figure 1 As shown, the process includes the following steps:

[0109] Step 1: Establish the electrical connection between the dual-mode drone power supply system and the pure electric vehicle platform.

[0110] The electrical coupling diagram between the UAV power supply system and the pure electric vehicle platform provided in this application embodiment is shown below. Figure 2 As shown.

[0111] The dual-mode drone power supply system mainly includes the drone, launch battery, onboard power supply, backup battery, tether cable, and cable retraction / release device. The pure electric vehicle platform mainly includes a power battery, high-voltage box, and onboard charger.

[0112] The electrical connection between the dual-mode drone system and the vehicle system mainly consists of DC 400V from the high-voltage box and DC 400V from the onboard charger. Specifically:

[0113] The output voltage of the power battery is DC384V~DC456V, and the output voltage of the on-board charger is DC400V. When an external AC220 / AC380 is connected, this voltage can be input into the high-voltage box to charge the power battery, or it can be used as the main power source in tethered mode.

[0114] In step 1, the present invention uses a launch battery (DC48V) in the launch mode, and the energy transmission link is as follows:

[0115] Launch battery DC48V, drone bus DC 48V.

[0116] In tethered mode, a topology of high-voltage power supply from the vehicle's power battery + step-down power supply from the airborne power supply is adopted, and the energy transmission link is as follows:

[0117] Power battery DC 384V~456V, high voltage box DC400V, tether cable DC400V, airborne power supply / backup battery DC48V, UAV bus DC 48V.

[0118] Step 2 involves modeling the performance parameters of the dual-mode UAV system, including the following steps:

[0119] Step 2-1: Establish the total system weight model

[0120] Total weight of the unmanned aerial vehicle system (M) total It is expressed as follows:

[0121]

[0122] Step 2-2: Establish the total power consumption model of the dual-mode UAV power supply system;

[0123] In this invention, the total power consumption of the UAV includes the power system power consumption P. d Steady-state power consumption P of avionics system a and load steady-state power consumption P l Establish the total power consumption of the launch mode. Total power consumption in tethered mode As shown in equations (2) and (3) respectively:

[0124] P total-F =P d-F +P a +P l (2),

[0125] P total-T =P d-T +P a +P l (3),

[0126] Steps 2-3: Establish coupled modeling of "endurance time - launch radius - cruising speed" in launch mode;

[0127] In flight mode, the flight time T flight This refers to the maximum flight time that the drone can sustain. The flight radius R... flight This refers to the maximum distance a drone can travel between its starting point and target point within the maximum time it can fly. The specific modeling is as follows:

[0128]

[0129] Steps 2-4: Establish an optimization model for onboard power supply capacity under tethered mode;

[0130] The optimization objective is to minimize the tethered mode power consumption, while keeping the constraints within acceptable limits, even if the onboard output power P... out Minimize, objective function:

[0131]

[0132] The typical constraints include maximum system weight limit, minimum launch radius limit, maximum endurance limit, minimum payload capacity limit, and tethered altitude limit. The constraints are as follows:

[0133] M total ≤M total-req (8),

[0134] R flight ≥R flight-req (9),

[0135] T flight ≥T flight-req (10),

[0136] M PL ≥M PL -req (11),

[0137] L cable ≥L cable -req (12),

[0138] The safety constraints include the rated power P of the airborne power supply. AP Backup battery forced landing time T flight The two safety constraints are as follows:

[0139] P total-T ≤σP AP (13),

[0140]

[0141] Step 3: Design a solution algorithm to solve the nonlinear optimization model and achieve precise optimization of the vehicle power supply capability.

[0142] like Figure 3 As shown below, the application process and optimization effect of the parameter design method described in this invention in actual engineering are further explained in conjunction with specific design tasks and parameter data.

[0143] 1. Design Task Definition

[0144] Taking an aerial reconnaissance operation scenario as an example, a dual-mode UAV power supply system needs to be integrated into a pure electric vehicle platform. The design specifications are as follows:

[0145] a) Minimum load weight M PL-min =3kg;

[0146] b) Minimum tethered flight altitude L min-cable =200m;

[0147] c) Minimum launch operation radius R req =10km;

[0148] d) Minimum flight endurance T F-req =50 min;

[0149] e) Minimum total system weight M total-req =50kg;

[0150] f) Backup battery forced landing time T T-req =3min;

[0151] Optional Specifications Library:

[0152] a) Unmanned aerial vehicle (UAV) models: 3 multi-rotor platforms, parameters are shown in Table 1;

[0153] b) Mooring cable: 3 specifications, parameters are shown in Table 2;

[0154] c) Launch battery: energy density 250Wh / kg, nominal voltage 48V;

[0155] d) Backup battery: Energy density 260Wh / kg, nominal voltage 48V, depth of discharge (DOD) 0.8;

[0156] e) The safety factor σ of the airborne power supply is 0.9, and the safety factor θ of the forced landing power consumption is 1.3;

[0157] Table 1 Parameters of Candidate UAV Models

[0158]

[0159] Table 2 Candidate Tether Cable Specifications

[0160]

[0161] 2. Parameter Modeling and Initialization

[0162] Step 2-1: System Total Weight Model

[0163]

[0164] Among them, monitoring unit M SU Weight 1.8kg, cable take-up and unwinding device M GRD Weight 10kg.

[0165] Step 2-2: Power Consumption Model

[0166] Steady-state power consumption P of avionics system a =80W, steady-state power consumption under load P l =120W. Air density ρ is taken as 1.225 kg / m³. 3 The acceleration due to gravity is g = 9.81 m / s². 2 .

[0167] Step 3: Algorithm Parameter Settings

[0168] Population size N=50

[0169] Maximum number of iterations t max =500

[0170] The inertia weight ω decreases linearly from 0.8 to 0.35.

[0171] Learning factors c1 = 2.0, c2 = 2.0

[0172] Speed ​​limit v max =0.2·x max

[0173] Initial penalty function weights: Total system weight constraint radius constraint Battery life constraints Load weight constraint mooring height restraint Power constraints Forced landing time constraints

[0174] Discrete variables: Model i∈{1,2,3}, Cable j∈{1,2,3}

[0175] Continuous variable: Capacity of the battery in flight Q BAT ∈[0,+∞]Ah, backup battery capacity Q BB ∈[0,+∞]Ah, cable length L cable ∈[L cable,min ,+∞]m.

[0176] 3. Iterative solution process

[0177] Iterations 1-50: After population initialization, feasible solutions accounted for 62%. The globally optimal solution appeared in the combination of aircraft type 2 and cable 2, with a tethering power consumption of approximately 3550W, but the launch radius was only 4.2km, failing to meet the constraint. The penalty function was dynamically adjusted, and the radius constraint weight was increased from the initial 1.2 to 1.8.

[0178] Iterations from rounds 51 to 150: Through discrete variable mapping, the combination of aircraft type 1 and cable type 1 gradually becomes superior due to its light weight and low power consumption. A feasible solution is obtained in round 148: aircraft type 1 + cable type 1 + 50Ah launch battery + 4.5Ah backup battery, tethered power consumption 2845W, launch radius 10km, flight time 55min, total system weight 38kg.

[0179] Iterations 201-400: The algorithm enters the fine-grained search phase. The backup battery capacity is reduced to 4Ah, and the cable length is optimized to 100m. The global optimum is found in iteration 398: tethered power consumption is reduced to 2128W.

[0180] Iterations 401-500: Convergence speed slows down, fitness change is less than 0.3%. A better solution is found in iteration 442: a combination of model 1 and cable 1, with a cable cross-sectional area of ​​0.5mm². 2 Although line loss increases, the reduced weight leads to better overall power consumption, with tethered power consumption maintained at 2128W. Starting from round 442, the fitness change is less than 0.05% for 30 consecutive iterations, satisfying the convergence condition, and the algorithm terminates.

[0181] 4. Optimize the output results. The final optimal parameter combination is as follows:

[0182] 1) Model selection: Model 1, weight 8.5kg

[0183] 2) Load capacity: 3.5kg

[0184] 3) Mooring cable: Specification 1, length 200m, weight 2.8kg

[0185] 4) Launch battery: 50Ah capacity, 10kg weight

[0186] 5) Backup battery: 4Ah capacity, 1.4kg weight

[0187] 6) Onboard power supply: Rated power 3kW, weight 4kg

[0188] 7) Total system weight: 32kg

[0189] 8) Flight mode performance: Flight time 52 minutes, flight radius 10km

[0190] 9) Tethered mode power consumption: 2128W on-board output power

[0191] 10) Backup battery forced landing time: 4.2 min

[0192] 5. Comparative Analysis

[0193] Compared with the traditional independent mounting solution for gasoline vehicles:

[0194] Weight reduction: The total weight of this system is 32kg, which is 29.8% less than the traditional solution (including a 5kW generator + AC / DC power supply, with a total weight of about 45kg), effectively alleviating the load pressure on the pure electric platform.

[0195] Improved energy efficiency: The tethered power consumption is 2128W, which is 29% lower than the traditional solution (about 3000W). Based on the calculation that the power battery reserves 10kWh capacity for tethered flight, the tethered operation time can be extended by about 1.37 hours.

[0196] Collaborative optimization: Through coupled modeling of "endurance-radius-speed", the flight mode has an endurance of 52 minutes, exceeding the design target by 4%, thus improving the performance margin.

[0197] Algorithm efficiency: The improved iteration termination condition of the particle swarm optimization algorithm can achieve convergence within 450 rounds, which is about 10% faster than the standard particle swarm optimization algorithm.

[0198] The optimization results show that the parameter design method described in this invention can accurately predict system performance, and the optimization results achieve efficient electrical coupling and energy collaborative management between the pure electric vehicle platform and the dual-mode UAV power supply system.

Claims

1. A method for designing parameters of a vehicle-mounted dual-mode unmanned aerial vehicle (UAV) power supply system, characterized in that, Includes the following steps: Step 1: Establish the electrical connection between the dual-mode drone power supply system and the pure electric vehicle platform; Step 2: Model the performance parameters of the dual-mode UAV system; Step 3: Design a solution algorithm to solve the nonlinear optimization model and achieve accurate optimization of the vehicle power supply.

2. The method according to claim 1, characterized in that, Step 1 includes: forming an energy coupling between the dual-mode drone power supply system and the pure electric vehicle platform power supply system, forming an energy transmission link between the vehicle power battery, high-voltage box and the drone's onboard power supply and backup battery in tethered mode; The dual-mode UAV power supply system includes the UAV, launch battery, airborne power supply, backup battery, tether cable, and cable reel-in / reel-out device. The pure electric vehicle platform includes a power battery, a high-voltage box, and an on-board charger. The electrical connection between the dual-mode drone power supply system and the pure electric vehicle platform includes DC400V from the high-voltage box and DC400V from the on-board charger, specifically: The output voltage of the power battery is DC384V~DC456V; The on-board charger has an output voltage of DC400V. When connected to an external AC220 / AC380, the DC400V can be input to the high-voltage box to charge the power battery, and can also drive the power supply system of the drone in tethered mode.

3. The method according to claim 2, characterized in that, In step 1, the launch mode uses a DC48V launch battery, and the energy transfer link is as follows: Launch battery DC48V, drone bus DC 48V; In tethered mode, a topology combining high-voltage power supply from the vehicle's power battery and step-down power supply from the airborne power source is adopted. The energy transmission link is as follows: Power battery DC384V~456V, high voltage box DC400V, tether cable DC400V, airborne power supply or backup battery DC48V, UAV busbar DC48V.

4. The method according to claim 3, characterized in that, Step 2 includes: Step 2-1: Establish the system's total weight model; The total weight of the dual-mode drone power supply system is M. total It is expressed as follows: in, Let N be the weight of the i-th drone body, and there are a total of N. UAV Optional mobile suit; M PL M represents the load weight. BF The weight of the battery to be launched is expressed as Where V BF Q is the nominal voltage of the battery. BF To maximize battery capacity, D BF For the energy density of the flying battery; M AP The weight of the onboard power supply is expressed in M. AP =k AP P AP +b AP , where k AP P is the airborne power weight factor. AP b is the rated power of the airborne power supply. AP Based on weight; M BB The weight of the backup battery is expressed as... Q BB For backup battery capacity, V BB D is the nominal voltage of the battery pack. BB The energy density of the backup battery; The weight of the tethered cable in clause j is expressed as... L cable For cable length, The weight per unit length of the j-th type of tethered cable is N. cable Optional tether cable; M SU The weight of the monitoring unit; M GRD The weight of the wire take-up and unwinding device; Step 2-2: Establish the total power consumption model of the dual-mode UAV; The total power consumption of the drone includes the power system power consumption P. d Steady-state power consumption P of avionics system a and load steady-state power consumption P l ; Calculate the total power consumption P of the drone in flight mode. total-F Total power consumption P of tethered mode drone total-T : P total-F =P d-F +P a +P l (2), P total-T =P d-T +P a +P l (3), Among them, P d-F The power consumption of the propulsion system in flight mode is expressed as... Takeoff weight M in launch mode total-F Represented as: M total-F =M UAV +M BF +M PL n is the number of propellers, C L R is the propeller thrust coefficient, C is the propeller diameter, and R is the propeller diameter. M ρ is the propeller torque coefficient, ρ is the air density, and g is the acceleration due to gravity. Among them, P d-T The power consumption of the power system in tethered mode is expressed as... Takeoff weight M in tethered mode total-T Represented as: M total-T =M UAV +M AP +M BB +M cable +M PL ; Steps 2-3: Establish a coupled model of flight time, launch radius, and cruising speed in flight mode; In flight mode, the flight time T flight The maximum flight time of the drone; the flight radius R flight This refers to the maximum distance a drone can travel between its starting point and target point within the maximum time it can fly. The specific modeling is as follows: in, To improve the discharge efficiency of the battery; V flight S is the cruising speed; S is the wing area; C is the cruising speed. D0 K is the zero-lift drag coefficient; K is the lift-induced drag factor. Steps 2-4: Establish an optimization model for onboard power supply capacity under tethered mode; The optimization objective is to minimize the tethered mode power consumption, within the constraints, even if the onboard output power P out Minimize, the objective function is: Among them, P out For vehicle-mounted output power; η DCDC The conversion efficiency of the airborne power supply; η rect For the conversion efficiency of the ground high-voltage box; V IN The input voltage of the high-voltage box; ρ j cable S represents the resistivity of the conductor of the j-th type of tethered cable; j cable Let J be the cross-sectional area of ​​the cable used for mooring in clause j; The typical constraints include maximum system weight limit, minimum launch radius limit, maximum endurance limit, minimum payload capacity limit, and tethered altitude limit. The constraints are as follows: M total ≤M total-req (8), R flight ≥R flight-req (9), T flight ≥T flight-req (10), M PL ≥M PL -req (11), L cable ≥L cable-req (12), Where M total-req M is the maximum permissible total weight of the system. PL-req Minimum permissible load weight; L cable-req R is the minimum allowable length for tethered cables. flight-req For the minimum launch radius, T flight-req Minimum flight endurance; The safety constraints include the rated power P of the airborne power supply. AP Emergency landing time of backup battery The two safety constraints are as follows: P total-T ≤σP AP (13), Where σ is the airborne power supply safety factor; DOD is the depth of discharge. θ represents the discharge efficiency of the backup battery; θ is the safety factor for forced-land power consumption; T T-req This is the minimum time required for a forced landing.

5. The method according to claim 4, characterized in that, Step 3 includes: Step 3-1, Mathematical Modeling: The objective function is mathematically modeled, and the formulas (1) to (14) in Step 2 are rearranged to obtain the on-board output power P in tethered mode. out The objective function and constraints are minimized, and the mathematical expression is as follows: Where st indicates that it is restricted; Step 3-2, algorithm selection and constraint handling improvement; select particle swarm optimization algorithm to solve equation (15), and make the following improvements: Nearest neighbor mapping for discrete variables: for N cable The cross-sectional area of ​​the tethered cable and N UAV For this type of drone, after the particle position is updated, the continuous values ​​of the tether cable cross-sectional area and the drone model are mapped to the closest engineering specifications. Normalized dynamic penalty function: The normalized dynamic penalty function defined by equations (16-1) to (16-3) is used to handle hard constraints. The normalized dynamic penalty function is obtained through... Unify the dimensions, and through w k (t) Dynamically adjust the intensity of the penalty; Initial weight engineering calibration: initial weights for each constraint Determined by sensitivity analysis; New fitness function F adapt (x,t) is shown in the following equation: F adapt (x,t)=P out (x)+Φ(x,t) (16), Among them, F adapt (x,t) is the newly constructed fitness function, P out (x) represents the original onboard power supply, Φ(x,t) represents the penalty term of the constraint condition, t represents the t-th iteration, and x represents the set of variables; Where k = 1, 2, ..., 7, To normalize the reference power, set in The highest hover power consumption among all candidate models; w k (t) is the piecewise assignment function for the k-th constraint; φ k (x) is the relative constraint penalty function for the kth term; φ k (x)=max(0,g k (x))k=1,2,…,7(16-3), α is the initial weight; α is the decay coefficient; T MAX The maximum number of iterations is given; exp is the natural exponential function. The seven inequality constraints in formulas (8) to (14) are transformed into a unified violation function g. k (x), defined as follows: Step 3-3: Algorithm parameter setting and particle initialization; Steps 3-4 involve iterative solution and optimal solution update. Through the speed and position update iterative mechanism of the particle swarm optimization algorithm, the optimal solution that minimizes the on-board output power consumption and satisfies all constraints is gradually approached, while the iterative process is monitored in real time.

6. The method according to claim 5, characterized in that, Step 3-3 includes: Step 3-3-1: Set key parameters for the particle swarm optimization algorithm; based on the problem's variable dimensions and engineering experience, combined with the accuracy requirements of the design task, set the population size N and the maximum number of iterations T. MAX Inertia weight ω, individual learning factor c1, group learning factor c2, velocity limit v max Parameters, upper limit of position x max parameter; Step 3-3-2: Perform particle initialization; Initialization of continuous variables: Randomly generate initial values ​​for continuous variables within the feasible region; Discrete variable initialization: randomly assigned from the optional specifications; Initialization checks and adjustments: For each initial particle, calculate whether the constraints meet the 7 constraints. If the constraints are not met, adjust the continuous variables until the constraints are met, and finally ensure that the proportion of feasible solutions in the initial population is greater than or equal to the threshold.

7. The method according to claim 6, characterized in that, Steps 3-4 include: Step 3-4-1, each iteration executes the following process: Fitness calculation: For each particle in the population, the fitness function F is modified according to equation (16). adapt (x,t) calculates the fitness value, where the penalty term Φ(x,t) for infeasible solutions is 0; Discrete variable mapping and verification: Perform nearest neighbor mapping on discrete variables, then recalculate the fitness function and continuous variables. If the value of the continuous variable is outside the feasible region, mark the particle as infeasible and skip the current iteration. Update individual optimal values ​​and global optimal values: In particle velocity and position updates, the velocity update formula is as follows: v m (t+1)=ω·v m (t)+c1·r1·(p best,m -x m (t))+c2·r2·(g best -x m (t)) (17), Where r1 and r2 are random numbers between 0 and 1; v m (t) represents the velocity of particle m in the t-th round; x m (t) represents the position of particle m in round t; ω represents the inertial weight; p best,m g represents the optimal individual extreme value of the m-th particle. best To find the global optimum, iterate through the p values ​​of all particles. best,m If a solution with better fitness exists, then update the population's g. best ; The position update formula is: x m (t+1)=x m (t)+v m (t+1) (18), After the update, perform the nearest neighbor mapping in step 3-4-1 on the discrete variables, and check the feasible region for the continuous variables; Convergence criterion: If the global optimal solution g is found in X1 consecutive iterations... best If the change in fitness is less than the threshold, or the maximum number of iterations has been reached, the iteration is terminated. If the penalty term Φ(x,t) for satisfying the constraints in X2 consecutive iterations of the global optimal solution is less than the threshold, then the iteration is accelerated, and after terminating the iteration, g is output. best If the optimal parameter combination is selected, otherwise the fitness calculation is skipped and the iteration continues. Step 3-4-2: Monitor and correct the iterative process.

8. The method according to claim 7, characterized in that, Step 3-4-2 includes: outputting key information every X3 iterations, including: the current globally optimal tethered power consumption P. out Total system weight M total Launch radius R flight Battery life T flight If the total weight of the system is M total Launch radius R flight Battery life T flight If a sustained overspending occurs, the penalty function weight w is adjusted. j (t) or expand the feasible region of the variable, such as the upper limit of velocity v. max Position limit x max Re-iterate; If a constraint is not met, analyze the reason and perform the corresponding correction: If the feasible region of the dependent variable is too narrow: adjust the variable parameters and repeat steps 3-3 to 3-4. If the algorithm gets stuck in a local optimum: increase the population size or adjust the inertia weights, and iterate again; If there is a conflict in the constraints: feedback should be sent to the design task, the constraints should be adjusted through negotiation, and then the solution should be obtained again.

9. An electronic device, characterized in that, It includes a processor and a memory, the memory storing program code that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 8.

10. A storage medium, characterized in that, It stores a computer program or instructions that, when run on a computer, perform the steps of the method as described in any one of claims 1 to 8.

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