CN-122009572-A - Fuel cell unmanned aerial vehicle power scheduling method and system based on flight working conditions

CN122009572ACN 122009572 ACN122009572 ACN 122009572ACN-122009572-A

Abstract

The embodiment of the invention provides a fuel cell unmanned aerial vehicle power scheduling method and system based on a flight working condition, and belongs to the technical field of unmanned aerial vehicle energy management. The scheduling method comprises the steps of obtaining flight sensing data of an unmanned aerial vehicle and historical load records of an auxiliary battery, determining a dynamic load safety threshold value at the current moment according to the flight sensing data and the historical load records, reading the dynamic load safety threshold value to calculate total power requirements, generating a primary power distribution scheme according to the total power requirements to carry out primary distribution on output power of the fuel battery and the auxiliary battery, detecting actual output of the auxiliary battery, and generating a correction power distribution scheme according to the actual output of the auxiliary battery to carry out secondary distribution on the output power of the fuel battery and the auxiliary battery. The dynamic load safety threshold is adjusted based on the real-time flight working condition and the auxiliary battery historical load, so that decision misjudgment caused by abrupt change of the working condition due to the fixed threshold is effectively avoided, and the accuracy and the adaptability of scheduling are improved.

Inventors

ZHANG TAILEI
CHEN ZHIHANG
LEI JIAJIE
LIU WENZHANG
HAN LU
Si Guangxuan
YU ZHIWEI
LI DA
LIU JUN
Ruan Xianxiang

Assignees

安徽送变电工程有限公司

Dates

Publication Date: 20260512
Application Date: 20260206

Claims (10)

1. The fuel cell unmanned aerial vehicle power scheduling method based on the flight working condition is characterized by comprising the following steps of: Acquiring flight sensing data of the unmanned aerial vehicle and a historical load record of an auxiliary battery; determining a dynamic load safety threshold at the current time according to the flight sensing data and the historic symbol load record, wherein the dynamic load safety threshold comprises the following steps: A dynamic load safety threshold is calculated according to equation (1), ,(1) Wherein, the Is that A dynamic load safety threshold value for the moment of time, To assist in the nominal baseline threshold value of the battery, The amplitude coefficient is adjusted for the working condition, Is that The difference between the time airspeed rate of change and the preset cruise rate of change, The activation term in the form of a hyperbolic tangent function, As a factor of the sensitivity of the sensor, To accumulate penalty coefficients, integral terms To be in past time window Actual output power of internal auxiliary battery Exceeding rated power Square integral of (a); Writing the dynamic load safety threshold value into a shared state database; Reading the dynamic load safety threshold to calculate a total power demand; Generating a primary power distribution scheme according to the total power demand to primarily distribute the output power of the fuel cell and the auxiliary battery; Detecting an actual output of the auxiliary battery; and generating a correction power distribution scheme according to the actual output of the auxiliary battery so as to redistribute the output power of the fuel battery and the auxiliary battery.
2. The scheduling method of claim 1, wherein acquiring flight sensing data of the unmanned aerial vehicle and historical load records of the auxiliary battery comprises Acquiring flight sensing data of the unmanned aerial vehicle; Analyzing the current flight phase according to the flight sensing data; extracting working condition characteristic parameters in the current flight stage; A history load record of the auxiliary battery is obtained.
3. The scheduling method of claim 1, wherein writing the dynamic load security threshold to a shared state database comprises: establishing a shared database based on memory mapping, wherein the shared database comprises a state flag bit, a threshold storage area and a decision instruction area; writing the dynamic load security threshold into the threshold storage area; refreshing the data of the threshold storage area in a millisecond level period, and updating the working condition confidence level according to the current calculation in the state flag bit to evaluate the data freshness.
4. A scheduling method according to claim 3, wherein determining the total power demand from the dynamic load safety threshold comprises: The total power demand is calculated according to equation (2), ,(2) Wherein, the For the estimated total power demand to be the same, A static power value mapped directly for the current flight sensing data, For a preset gradient coefficient of total power with height, For the current vertical climb speed, For the preset gradient coefficient of the total power along with the change of airspeed, For the current horizontal acceleration rate, In order to calculate the communication processing delay time constant, For the quality of the unmanned aerial vehicle, Is a power conversion efficiency coefficient.
5. The scheduling method of claim 4, wherein generating a primary power allocation scheme to initially allocate output power of the fuel cell and auxiliary battery based on the total power demand comprises: Determining an optimum efficiency output point of the fuel cell; Determining an output power of the fuel cell based on the optimal efficiency output point; subtracting the output power of the fuel cell from the estimated total power demand to obtain a theoretical demand value of the auxiliary battery; judging whether the theoretical demand value is smaller than the dynamic load safety threshold value or not; Adjusting the output of the fuel cell and the auxiliary battery according to the output power of the fuel cell and the theoretical demand value of the auxiliary battery under the condition that the theoretical demand value is less than the dynamic load safety threshold value; and adjusting the output point of the fuel cell to share the load in the case that the theoretical demand value is judged not to be smaller than the dynamic load safety threshold value.
6. The scheduling method of claim 5, wherein generating a modified power distribution scheme to redistribute output power of the fuel cell and auxiliary cell based on an actual output of the fuel cell, comprises: Calculating the absolute value of the difference between the actual output of the auxiliary battery and the dynamic load safety threshold; judging whether the absolute value is smaller than a first preset threshold value or not; Sending out an early warning signal under the condition that the absolute value is smaller than the first preset threshold value; Writing the early warning signal into a status flag bit of the shared database; generating a negotiation request code according to the early warning signal; Writing the negotiation request code into a decision instruction area of the shared database; responding to the early warning signal and generating a correction scheme to redistribute the output power of the fuel cell and the auxiliary battery; triggering a cooperative control loop.
7. The scheduling method of claim 6, wherein responding to the pre-warning signal and generating a correction scheme to redistribute the output power of the fuel cell and auxiliary battery comprises: reading the negotiation request code and interrupting the primary power allocation scheme; acquiring optimal auxiliary battery power and optimal fuel battery power based on a rapid optimizing algorithm of a target cost function; generating a control instruction according to the optimal auxiliary battery power and the optimal fuel battery power; the control instruction is sent to a bottom-layer motor controller and a DC/DC converter; the bottom layer motor controller and the DC/DC converter receive the control instruction and redistribute the output power of the fuel cell and the auxiliary battery; Writing the optimal auxiliary battery power and the optimal fuel battery power into a decision instruction area of a shared database; the underlying motor controller and DC/DC converter are notified that the reassignment is in effect.
8. The scheduling method of claim 7, wherein triggering the cooperative control loop comprises: judging whether the actual output of the auxiliary battery is larger than the dynamic load safety threshold value or not; starting a countdown monitor under the condition that the actual output of the auxiliary battery is judged to be greater than the dynamic load safety; detecting a countdown of the countdown monitor; Judging whether the countdown of the countdown monitor is finished or not; judging whether the actual output of the auxiliary battery is greater than the dynamic load safety threshold value under the condition that the countdown of the countdown monitor is judged to be ended; Under the condition that the actual output of the auxiliary battery is judged to be greater than the dynamic load safety, executing hardware protection; and under the condition that the actual output of the auxiliary battery is not larger than the dynamic load safety threshold value, the early warning is released and the primary power distribution scheme is executed.
9. The scheduling method of claim 7, wherein the fast optimizing algorithm based on the objective cost function obtains the optimal auxiliary battery power and the optimal fuel cell power, comprising: the target cost function is determined according to equation (3), ,(3) Wherein, the In order to optimize the value of the objective function, For the hydrogen consumption rate of the fuel cell, As a fuel economy weighting factor, For the optimal auxiliary battery power to be solved, As a penalty term for the potential energy field, To prevent the denominator from being a very small positive number of zero, Weight coefficients are penalized for security.
10. A fuel cell unmanned power scheduling system based on flight conditions, wherein the scheduling system comprises a processor for performing the scheduling method of any one of claims 1 to 9.

Description

Fuel cell unmanned aerial vehicle power scheduling method and system based on flight working conditions Technical Field The invention relates to the technical field of unmanned aerial vehicle energy management, in particular to a fuel cell unmanned aerial vehicle power scheduling method and system based on flight working conditions. Background With the wide application of fuel cell technology in the unmanned aerial vehicle field, how to realize efficient and reliable power scheduling becomes a key for improving the endurance capacity and the task reliability of the unmanned aerial vehicle. Particularly in complex flight tasks, the unmanned aerial vehicle needs to deal with frequent working condition changes, which puts higher demands on the real-time performance and adaptability of the energy management system. In the related art, a fuel cell unmanned aerial vehicle power scheduling system realizes power distribution through a fixed threshold protection mechanism. Such systems typically employ a preset power threshold to overload the auxiliary battery, and trigger a protection mechanism directly when an overrun in power is detected. However, this fixed threshold based protection approach has significant drawbacks. Because the flight working conditions are complex and changeable, the fixed threshold cannot adapt to the power demand characteristics of different flight stages, and the problem that decision misjudgment is easy to generate when the working conditions are suddenly changed, so that excessive response of a protection mechanism is caused to cause task interruption exists. The method is characterized in that in the climbing or accelerating stage of the unmanned aerial vehicle, the power demand naturally increases, but the fixed threshold value can trigger protection too early to forcefully reduce the power output, and in the cruising stage, the threshold value can not provide effective protection in time due to too high setting. Disclosure of Invention The embodiment of the invention aims to provide a fuel cell unmanned aerial vehicle power scheduling method and system based on flight conditions, which solve the problem that a fixed power threshold cannot adapt to power demand characteristics of unmanned aerial vehicles in different flight phases. In order to achieve the above object, an embodiment of the present invention provides a fuel cell unmanned aerial vehicle power scheduling method based on a flight condition, the scheduling method including: Acquiring flight sensing data of the unmanned aerial vehicle and a historical load record of an auxiliary battery; determining a dynamic load safety threshold at the current time according to the flight sensing data and the historic symbol load record, wherein the dynamic load safety threshold comprises the following steps: A dynamic load safety threshold is calculated according to equation (1), ,(1) Wherein, the Is thatA dynamic load safety threshold value for the moment of time,To assist in the nominal baseline threshold value of the battery,The amplitude coefficient is adjusted for the working condition,Is thatThe difference between the time airspeed rate of change and the preset cruise rate of change,The activation term in the form of a hyperbolic tangent function,As a factor of the sensitivity of the sensor,To accumulate penalty coefficients, integral termsTo be in past time windowActual output power of internal auxiliary batteryExceeding rated powerSquare integral of (a); Writing the dynamic load safety threshold value into a shared state database; Reading the dynamic load safety threshold to calculate a total power demand; Generating a primary power distribution scheme according to the total power demand to primarily distribute the output power of the fuel cell and the auxiliary battery; Detecting an actual output of the auxiliary battery; and generating a correction power distribution scheme according to the actual output of the auxiliary battery so as to redistribute the output power of the fuel battery and the auxiliary battery. Optionally, taking flight sensing data of the unmanned aerial vehicle and historical load records of the auxiliary battery, including Acquiring flight sensing data of the unmanned aerial vehicle; Analyzing the current flight phase according to the flight sensing data; extracting working condition characteristic parameters in the current flight stage; A history load record of the auxiliary battery is obtained. Optionally, writing the dynamic load security threshold to a shared state database includes: establishing a shared database based on memory mapping, wherein the shared database comprises a state flag bit, a threshold storage area and a decision instruction area; writing the dynamic load security threshold into the threshold storage area; refreshing the data of the threshold storage area in a millisecond level period, and updating the working condition confidence level according to the current calculation in the st