Battery pack dynamic energy distribution method for hybrid unmanned aerial vehicle

Abstract

The invention discloses a battery pack dynamic energy distribution method for a hybrid unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicle battery pack energy distribution, and the method comprises the steps: S1, a system collects flight state parameters and battery pack state parameters in real time; and S2, in combination with a preset flight stage, the power of the battery pack and the power of the engine are dynamically adjusted, so that the hybrid power device has the minimum fuel consumption, and meanwhile, the SOC balance of the battery is maintained. According to the dynamic energy distribution method for the battery pack, the hybrid power device has the good fuel economic performance, accurate dynamic distribution of the energy of the battery pack is achieved, and the hybrid power device can meet the required power requirement under the more complex working condition.

Description

Technical Field

[0001] The present invention relates to the technical field of energy distribution of battery packs for unmanned aerial vehicles (UAVs), and in particular to a dynamic energy distribution method for battery packs of hybrid UAVs. Background Art

[0002] With the widespread application of drones in agriculture, logistics, surveying and mapping, and other fields, the demand for long flight time and high reliability is becoming increasingly urgent. However, traditional battery packs have problems with extensive energy distribution, such as insufficient power supply during high-power demand phases (such as takeoff), energy waste during low-load phases (such as cruising), and battery life degradation caused by overcharging and over-discharging. In addition, the contradiction between the increase in battery pack weight and the need for heat dissipation (such as liquid cooling increasing the burden on the fuselage) also limits the optimization space for energy distribution. Currently, drone battery pack energy distribution solutions mainly include the following categories:

[0003] 1. Dynamic power allocation control scheme

[0004] Chinese patent publication number CN119905985A discloses an energy management method for a hybrid power system for unmanned aerial vehicles (UAVs) based on fuzzy dual-closed-loop control. This solution, targeting a hybrid power system consisting of a hydrogen fuel cell and a lithium battery, achieves efficient energy transmission and distribution through a fuzzy power control outer loop (dynamically allocating power based on load demand power, power error, and lithium battery state of charge) and a fuzzy PI current control inner loop (generating precise drive signals), effectively improving endurance and energy utilization efficiency.

[0005] 2. Dual battery unit switching weight reduction solution

[0006] Chinese patent publication number CN119796559A discloses a battery pack device for drones. This solution utilizes a dual-battery unit design (a first battery unit and a second battery unit). When the first battery unit is depleted, power is automatically switched to the second battery unit, and the first battery unit is discarded to reduce weight, significantly improving the drone's ultimate endurance. By combining physical weight reduction with power switching, this solution addresses the pain point of traditional battery packs, where increased weight offsets the improved endurance.

[0007] 3. Multi-power supply collaborative power management solution

[0008] Chinese patent publication number CN119865063A discloses a drone power management system that integrates an onboard 44V power supply with an independent external 28V power supply interface. It dynamically manages the power distribution of each module through optocoupler-controlled switching tubes: the external power supply prioritizes powering the integrated control module, and automatically switches to the onboard power supply when it is removed. At the same time, diode isolation is used to prevent current backflow, ensuring power supply safety at all stages.

[0009] 4. Backup link redundancy allocation scheme

[0010] The technical solution of the "UAV power supply system" disclosed by Jigao.com in 2025 monitors the speed of the propeller motor. When the main power supply link fails (the speed is lower than the set value), the backup link (directly connected to the flight control battery) is closed, and the inherent flight control battery is used as a backup power source, avoiding the additional weight of the battery and improving the power supply reliability.

[0011] The shortcomings of existing solutions are essentially the contradiction between "technical design goals and actual application constraints": fuzzy control pursues dynamics but is limited by computing power and scenario coverage, dual-battery weight reduction relies on mechanical structure but introduces safety risks, multi-power coordination focuses on compatibility but sacrifices environmental reliability, and backup link redundancy emphasizes emergency response but ignores power matching. Summary of the Invention

[0012] The purpose of the present invention is to provide a dynamic energy distribution method for a battery pack of a hybrid UAV, so as to solve the problem of extensive energy distribution in the battery pack of a traditional hybrid UAV.

[0013] The technical solution of the present invention to solve the above technical problems is as follows:

[0014] A dynamic energy distribution method for a battery pack of a hybrid UAV, comprising:

[0015] S1: The system collects flight status parameters and battery pack status parameters in real time;

[0016] S2: Dynamically adjusts the power of the battery pack and the engine based on the preset flight phase, minimizing fuel consumption of the hybrid power unit while maintaining battery SOC balance.

[0017] Furthermore, in the above step S1, the flight status parameters include altitude, speed and acceleration; and the battery pack status parameters include SOC, temperature and internal resistance.

[0018] Furthermore, in the above step S2, the flight phase includes take-off, cruising and landing.

[0019] Furthermore, the above step S2 includes the following sub-steps:

[0020] S21: Construct the objective function of minimizing fuel consumption and the battery SOC boundary constraint;

[0021] S22: Based on the objective function of minimizing fuel consumption and the battery SOC boundary constraint, a Lagrangian function is constructed using weight parameters;

[0022] S23: Adjust the power of the engine and the battery according to the optimal conditions for solving the Lagrangian function.

[0023] Furthermore, in the above step S21, the objective function for minimizing fuel consumption is expressed as:

[0024] ;

[0025] Where, Indicates engine power, Indicates the battery charging and discharging power, Indicates the total duration, represents the objective function.

[0026] Furthermore, in the above step S21, the expression of the battery SOC boundary constraint is: SOC(t)≥SOCmin, SOC(t)≤SOCmax; where SOC(t) represents the SOC value at time t, SOCmin represents the minimum SOC value, and SOCmax represents the maximum SOC value.

[0027] Furthermore, in the above step S22, the Lagrangian function The expression is:

[0028] ;

[0029] Where, represents the weight parameter, represents the derivative of the weight parameter.

[0030] Furthermore, in the above step S23, the Lagrangian function is derived using the variational method. When SOC(t)≥SOCmin, the engine power generation is reduced and the battery discharge power is increased; when SOC(t)=SOCmin, the engine power generation is increased and the battery discharge power is reduced.

[0031] The present invention has the following beneficial effects:

[0032] The battery pack dynamic energy distribution method for a hybrid UAV of the present invention enables the hybrid power device to have better fuel economy performance and realizes precise dynamic distribution of battery pack energy, so that the hybrid power device can meet the required power requirements under more complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a flow chart of the battery pack dynamic energy distribution method for hybrid drones of the present invention. DETAILED DESCRIPTION

[0034] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0035] Please refer to Figure 1This embodiment provides a method for dynamic energy distribution of a battery pack for a hybrid UAV, which includes the following steps:

[0036] S1: The system collects flight status parameters in real time, including altitude, speed, and acceleration; it also collects battery pack status parameters, including SOC, temperature, and internal resistance;

[0037] S2: Combined with preset flight phases, including takeoff, cruise and landing, the power of the battery pack and the engine are dynamically adjusted to minimize the fuel consumption of the hybrid power unit while maintaining the battery SOC balance.

[0038] Wherein, step S2 includes the following sub-steps:

[0039] S21: Construct the objective function of minimizing fuel consumption and the battery SOC boundary constraint;

[0040] S22: Based on the objective function of minimizing fuel consumption and the battery SOC boundary constraint, a Lagrangian function is constructed using weight parameters;

[0041] S23: Adjust the power of the engine and the battery according to the optimal conditions for solving the Lagrangian function.

[0042] In step S21, the objective function for minimizing fuel consumption is expressed as:

[0043] ;

[0044] Where, Indicates engine power, Indicates the battery charging and discharging power, Indicates the total duration, represents the objective function.

[0045] The expression of the battery SOC boundary constraint is: SOC(t)≥SOCmin, SOC(t)≤SOCmax; where SOC(t) represents the SOC value at time t, SOCmin represents the minimum SOC value, and SOCmax represents the maximum SOC value.

[0046] In S22, the Lagrangian function The expression is:

[0047] ;

[0048] Where, represents the weight parameter, represents the derivative of the weight parameter.

[0049] In S23, the Lagrangian function is derived using the variational method. When SOC(t) ≥ SOCmin, the engine power generation is reduced and the battery discharge power is increased; when SOC(t) = SOCmin, the engine power generation is increased and the battery discharge power is reduced.

[0050] The battery pack dynamic energy distribution method for a hybrid UAV of this embodiment enables the hybrid power device to have better fuel economy performance and realizes precise dynamic distribution of battery pack energy, so that the hybrid power device can meet the required power requirements under more complex working conditions.

[0051] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A dynamic energy distribution method for a battery pack of a hybrid UAV, characterized in that: include: S1: The system collects flight status parameters and battery pack status parameters in real time; S2: Dynamically adjusts the power of the battery pack and the engine based on the preset flight phase, minimizing fuel consumption of the hybrid power unit while maintaining battery SOC balance.

2. The method for dynamic energy distribution of a battery pack for a hybrid UAV according to claim 1, characterized in that: In step S1 , the flight status parameters include altitude, speed, and acceleration; the battery pack status parameters include SOC, temperature, and internal resistance.

3. The method for dynamic energy distribution of a battery pack for a hybrid UAV according to claim 1, characterized in that: In step S2, the flight phase includes take-off, cruising and landing.

4. The method for dynamic energy distribution of a battery pack for a hybrid UAV according to claim 3, characterized in that: Step S2 includes the following sub-steps: S21: Construct the objective function of minimizing fuel consumption and the battery SOC boundary constraint; S22: Based on the objective function of minimizing fuel consumption and the battery SOC boundary constraint, a Lagrangian function is constructed using weight parameters; S23: Adjust the power of the engine and the battery according to the optimal conditions for solving the Lagrangian function.

5. The method for dynamic energy distribution of a battery pack for a hybrid UAV according to claim 4, characterized in that: In step S21, the objective function for minimizing fuel consumption is expressed as: ； Where, Indicates engine power, Indicates the battery charging and discharging power, Indicates the total duration, represents the objective function.

6. The method for dynamic energy distribution of a battery pack for a hybrid UAV according to claim 5, characterized in that: In step S21, the expression of the battery SOC boundary constraint is: SOC(t)≥SOCmin, SOC(t)≤SOCmax; where SOC(t) represents the SOC value at time t, SOCmin represents the minimum SOC value, and SOCmax represents the maximum SOC value.

7. The method for dynamic energy distribution of a battery pack for a hybrid UAV according to claim 6, characterized in that: In step S22, the Lagrangian function The expression is: ； Where, represents the weight parameter, represents the derivative of the weight parameter.

8. The method for dynamic energy distribution of a battery pack for a hybrid UAV according to claim 5, characterized in that: In step S23, the Lagrangian function is derived using the variational method. When SOC(t)≥SOCmin, the engine power generation is reduced and the battery discharge power is increased; when SOC(t)=SOCmin, the engine power generation is increased and the battery discharge power is reduced.

Images