Ecms energy management method for unmanned aerial vehicle oil-electric hybrid range extending system

Abstract

The application discloses an ECMS energy management method of an unmanned aerial vehicle oil-electric hybrid power range extending system, and comprises the following steps: S1, collecting data sets and fusing to obtain an altitude; S2, calculating total load demand power based on the altitude; S3, calculating an equivalent factor according to the altitude; S4, determining a cost function according to the equivalent factor; and S5, iterating according to the total load demand power and the cost function to output optimal engine power. The application adopts a more reasonable power distribution method, can effectively improve fuel efficiency, enhance the endurance of the unmanned aerial vehicle, and enable the unmanned aerial vehicle to fly for a longer time and in a more stable state in a plateau environment.

Description

Technical Field

[0001] This invention belongs to the field of energy management technology, specifically relating to an ECMS energy management method for a hybrid electric range extender system for unmanned aerial vehicles (UAVs). Background Technology

[0002] In practice, high-altitude areas (above 3000 meters) pose significant challenges for drones. The thin air, low air pressure, and low temperatures in these regions cause a sharp decline in the range of traditional pure electric drones. The combustion efficiency of hybrid range extenders also decreases significantly, and energy management strategies are completely unsuitable for these conditions. Existing energy management strategies share a common problem: they completely fail to consider the low-oxygen environment of high altitudes. Changes in engine combustion efficiency and the accuracy of battery SOC estimation are affected, leading to persistently high fuel consumption of the range extender, over-discharging or overcharging of the battery, and a substantial reduction in flight range.

[0003] The core idea of ​​existing hybrid-powered drone energy management strategies is to introduce an equivalent factor at each sampling time, use the equivalent factor to convert the electrical power consumed by the battery into virtual fuel consumption, then establish a unified Hamiltonian cost function through data, and achieve optimal power allocation by instantaneously minimizing the equivalent total fuel consumption.

[0004] Current ECMS strategies demonstrate some effectiveness in low-to-medium altitude plains environments; however, direct application to high-altitude UAV range extenders presents several challenges. First, the impact of low air pressure on engine combustion efficiency at high altitudes is overlooked. For every 1000 meters increase in altitude, atmospheric pressure decreases by approximately 12%, and air density decreases by about 10%. At an altitude of 4000 meters, naturally aspirated engines can experience a power loss of 30%-40%. Existing ECMS strategies still use BSFC (Breakpoint-Based Functional Fuel) models based on sea-level calibration data without altitude compensation corrections. This results in a significant deviation between the calculated optimal power point and actual conditions, reducing fuel economy by 10%-20%. Second, the power demand compensation mechanism for high-altitude environments is inadequate. Decreased air density reduces propeller efficiency; to maintain the same flight conditions, both motor speed and power output must be increased. Current strategies directly use the power demand of plains environments as input without compensating for power increments at high altitudes, leading to insufficient engine power reserves, frequent deep battery discharges, and accelerated engine aging. Third, the equivalent factor adaptive law is inadequate, lacking an adaptive term for high-altitude environments. Traditional A-ECMS's equivalent factor is driven solely by SOC deviation, failing to consider the coupled effects of altitude on battery charge / discharge characteristics and engine fuel economy. In high-altitude, low-temperature environments, lithium battery internal resistance increases and discharge efficiency decreases. Fixed equivalent factor adjustment parameters cannot adapt to this nonlinear change, leading to SOC trajectory drift and reduced control robustness. Fourth, multi-source sensor fusion lacks the ability to perceive high-altitude environments. Existing strategies rely solely on a single barometer or GPS to obtain altitude information, without temperature compensation or Kalman filtering fusion. Temperature differences in high-altitude areas exhibit certain characteristics; barometer temperature drift errors can reach ±50 meters, significantly impacting the accuracy of air density estimation and the actual effectiveness of power compensation. Summary of the Invention

[0005] To address the above problems, this invention proposes an ECMS energy management method for a hybrid electric range extender system for unmanned aerial vehicles (UAVs).

[0006] The technical solution of this invention is: an ECMS energy management method for a hybrid electric range extender system for unmanned aerial vehicles (UAVs) comprising the following steps: S1. Collect the dataset and fuse it to obtain the altitude; S2. Calculate the total load power requirement based on altitude; S3. Calculate the equivalence factor based on the altitude; S4. Determine the cost function based on the equivalence factor; S5. Based on the total load demand power and cost function, perform iteration to output the optimal engine power.

[0007] Furthermore, S2 includes the following sub-steps: S21. Determine the air pressure value based on the altitude and the measured ambient temperature; S22. Calculate the current air density based on the measured ambient temperature and air pressure values; S23. Calculate the air density ratio based on the current air density; S24. Determine the power compensation coefficient based on the air density ratio and the standard power requirement for plains areas; S25. Calculate the total load power requirement based on the power compensation coefficient.

[0008] Furthermore, in S22, the current air density The expression is: ; in, This represents the air pressure value after Kalman filtering. This represents the gas constant for air. Indicates the measured ambient temperature; In S23, the air density is higher than... The expression is: ; in, This represents the standard sea-level density.

[0009] Furthermore, in S24, the power compensation coefficient The expression is: ; in, Indicates the air density influence coefficient. This represents the power compensation index, which is dimensionless, with a typical value of 0.5 to 0.8, and is calibrated based on the propeller load characteristics. This indicates the standard power requirement for plains areas. Indicates rated power; In S25, the total load power requirement The expression is: .

[0010] Furthermore, in S3, the equivalent factor The expression is: ; in, As the benchmark equivalent factor, For proportional gain, For integral gain, This is the altitude compensation coefficient. Indicates the target SOC reference value. express Real-time SOC express Real-time SOC Indicates reference temperature. express The ambient temperature is measured at all times.

[0011] Furthermore, S5 includes the following sub-steps: S51. Determine the battery power based on the total load power demand; S52. Determine the cost function based on the battery power and the equivalent factor; S53. Divide the engine power range using the golden ratio to obtain the first inner point and the second inner point; S54. If the cost function of the first interior point is less than the cost function of the second interior point, then shrink the right boundary of the engine power interval; otherwise, shrink the left boundary of the engine power interval and return to S53 to re-perform the interior point iteration until the width of the engine power interval is less than the convergence accuracy, and output the optimal engine power.

[0012] Furthermore, in S51, the battery power... The expression is: ; in, Indicates the total load power requirement. This indicates engine power.

[0013] Furthermore, in S52, the cost function The expression is: ; in, Indicates the equivalent factor. Indicates instantaneous fuel consumption. This indicates the battery efficiency at the current SOC and temperature. This indicates the battery power.

[0014] Furthermore, in S54, the optimal engine power The expression is: ; in, This indicates the left endpoint of the engine power range. This indicates the right endpoint of the engine's power range.

[0015] The beneficial effects of this invention are: (1) This invention establishes a three-dimensional adaptive equivalent factor, and adds altitude compensation and temperature correction techniques on the basis of traditional SOC feedback, so that the equivalent factor is closer to the actual situation and can better reflect the energy conversion cost on the plateau.

[0016] (2) This invention improves the SOC balance. Although the altitude will vary to different degrees and the temperature will fluctuate, the SOC can still remain within the specified range without large fluctuations.

[0017] (3) The present invention adopts a more reasonable power distribution method, which can effectively improve fuel efficiency, enhance the endurance of the UAV, enable it to fly for a longer time in the plateau environment, and make the flight state more stable. Attached Figure Description

[0018] Figure 1 A flowchart of the ECMS energy management method for a hybrid electric range extender system for unmanned aerial vehicles (UAVs). Detailed Implementation

[0019] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0020] like Figure 1 As shown, this invention provides an ECMS energy management method for a hybrid electric range extender system for unmanned aerial vehicles (UAVs), comprising the following steps: S1. Collect the dataset and fuse it to obtain the altitude; S2. Calculate the total load power requirement based on altitude; S3. Calculate the equivalence factor based on the altitude; S4. Determine the cost function based on the equivalence factor; S5. Based on the total load demand power and cost function, perform iteration to output the optimal engine power.

[0021] It can be achieved by using barometers, temperature sensors, GPS, and even integrating IMU (Inertial Measurement Unit) to collect altitude, temperature, and air pressure data in real time.

[0022] By fusing these sensor data using Kalman filtering, a more accurate altitude can be obtained.

[0023] In this embodiment of the invention, S2 includes the following sub-steps: S21. Determine the air pressure value based on the altitude and the measured ambient temperature; S22. Calculate the current air density based on the measured ambient temperature and air pressure values; S23. Calculate the air density ratio based on the current air density; S24. Determine the power compensation coefficient based on the air density ratio and the standard power requirement for plains areas; S25. Calculate the total load power requirement based on the power compensation coefficient.

[0024] In this embodiment of the invention, in S22, the current air density The expression is: ; in, This represents the air pressure value after Kalman filtering. The gas constant for air is taken as 287.05 J / (kg·K) (or 287 J / (kg·K)). Indicates the measured ambient temperature; In S23, the air density is higher than... The expression is: ; in, This represents the standard sea-level density.

[0025] In this embodiment of the invention, in S24, the power compensation coefficient The expression is: ; in, Indicates the air density influence coefficient. This represents the power compensation index, which is dimensionless, with a typical value of 0.5 to 0.8, and is calibrated based on the propeller load characteristics. This indicates the standard power requirement for plains areas. Indicates rated power; In S25, the total load power requirement The expression is: .

[0026] In this embodiment of the invention, in S3, the equivalent factor The expression is: ; in, As the benchmark equivalent factor, For proportional gain, For integral gain, This is the altitude compensation coefficient. Indicates the target SOC reference value. express Real-time SOC express Real-time SOC Indicates reference temperature. express The ambient temperature is measured at all times.

[0027] The target SOC reference value is typically taken as 0.50 to 0.60. The reference temperature is generally taken as 25℃.

[0028] The reference equivalence factor is determined based on engine-generator efficiency and fuel calorific value. The gain is adjusted proportionally to the SOC, with typical values ​​between 2.0 and 5.0. It is the SOC integral-adjustable gain, with a typical value of 0.005 to 0.02. It is the altitude compensation coefficient, with a typical value of 0.0002 to 0.0005 per meter. This is the target reference value for SOC, which is typically between 0.50 and 0.60. This is a reference temperature, typically 25℃. The altitude compensation factor is used because engine power generation efficiency decreases in high-altitude areas, altering the "electrical energy-fuel" equivalence. The temperature compensation factor reflects the decrease in battery efficiency at low temperatures.

[0029] The equivalent factor is essentially the conversion ratio between electrical energy and fuel energy. In high-altitude environments, engine efficiency decreases, requiring more fuel to generate the same amount of electricity; therefore, the equivalent factor should be increased. Battery efficiency also decreases, and charging / discharging losses increase, further necessitating a larger equivalent factor. Furthermore, the higher the altitude and the lower the temperature, the greater the need for an increasingly larger equivalent factor.

[0030] The three-dimensional adaptive law of this invention uses proportional-integral (PI) control to control SOC deviation, while simultaneously superimposing altitude and temperature feedforward compensation to achieve robust adaptive adjustment of the equivalent factor. When the SOC is lower than the target value, the equivalent factor increases, thereby driving the engine to charge; when the SOC is higher than the target value, the equivalent factor decreases, thereby driving the battery to discharge. The altitude compensation term ensures that the equivalent factor automatically increases in high-altitude environments, reflecting the true energy conversion cost.

[0031] In this embodiment of the invention, S5 includes the following sub-steps: S51. Determine the battery power based on the total load power demand; S52. Determine the cost function based on the battery power and the equivalent factor; S53. Divide the engine power range using the golden ratio to obtain the first inner point and the second inner point; S54. If the cost function of the first interior point is less than the cost function of the second interior point, then shrink the right boundary of the engine power interval; otherwise, shrink the left boundary of the engine power interval and return to S53 to re-perform the interior point iteration until the width of the engine power interval is less than the convergence accuracy, and output the optimal engine power.

[0032] Substitute the equivalent factor into the cost function. Combining the engine BSFC diagram with completed plateau correction and the battery charge / discharge efficiency model, the system employs the golden section search algorithm within each control cycle (100ms) to search the engine power range. Fast search for optimal power allocation: Initialize interval endpoints a and b, and take the golden ratio. Calculate interior points .

[0033] Plateau correction The chart shows the instantaneous fuel consumption. The battery power is then calculated based on the current load. The cost function is then calculated by combining the battery efficiency at the current SOC and temperature with the equivalence factor. Comparison. and ,like Then the right boundary will shrink. Otherwise, shrink the left boundary. The process involves recalculating the interior point iterations until the interval width is less than the convergence accuracy (e.g., 50W), ultimately outputting the optimal engine power. This achieves the control of minimizing instantaneous equivalent fuel consumption in high-altitude environments, thereby minimizing the instantaneous equivalent total fuel consumption.

[0034] The optimized instructions are sent to the engine to control the controller and battery management system. The new battery status, engine status, and SOC are read, then the process returns to step one and repeats in a loop.

[0035] In this embodiment of the invention, in S51, the battery power... The expression is: ; in, Indicates the total load power requirement. This indicates engine power.

[0036] In this embodiment of the invention, in S52, the cost function The expression is: ; in, Indicates the equivalent factor. Indicates instantaneous fuel consumption. This indicates the battery efficiency at the current SOC and temperature. This indicates the battery power.

[0037] In this embodiment of the invention, in S54, the optimal engine power... The expression is: ; in, This indicates the left endpoint of the engine power range. This indicates the right endpoint of the engine's power range.

[0038] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. An ECMS energy management method for a hybrid electric range extender system for unmanned aerial vehicles (UAVs), characterized in that, Includes the following steps: S1. Collect the dataset and fuse it to obtain the altitude; S2. Calculate the total load power requirement based on altitude; S3. Calculate the equivalence factor based on the altitude; S4. Determine the cost function based on the equivalence factor; S5. Based on the total load demand power and cost function, perform iteration to output the optimal engine power.

2. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 1, characterized in that, S2 includes the following sub-steps: S21. Determine the air pressure value based on the altitude and the measured ambient temperature; S22. Calculate the current air density based on the measured ambient temperature and air pressure values; S23. Calculate the air density ratio based on the current air density; S24. Determine the power compensation coefficient based on the air density ratio and the standard power requirement for plains areas; S25. Calculate the total load power requirement based on the power compensation coefficient.

3. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 2, characterized in that, In S22, the current air density The expression is: ； in, This represents the air pressure value after Kalman filtering. This represents the gas constant for air. Indicates the measured ambient temperature; In S23, the air density ratio The expression is: ； in, This represents the standard sea-level density.

4. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 2, characterized in that, In S24, the power compensation coefficient The expression is: ； in, Indicates the air density influence coefficient. Indicates the power compensation index. This indicates the standard power requirement for plains areas. Indicates rated power; In S25, the total load power demand The expression is: 。 5. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 1, characterized in that, In S3, the equivalent factor The expression is: ； in, As the benchmark equivalent factor, For proportional gain, For integral gain, This is the altitude compensation coefficient. Indicates the target SOC reference value. express Real-time SOC express Real-time SOC Indicates reference temperature. express The ambient temperature is measured at all times.

6. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 1, characterized in that, S5 includes the following sub-steps: S51. Determine the battery power based on the total load power demand; S52. Determine the cost function based on the battery power and the equivalent factor; S53. Divide the engine power range using the golden ratio to obtain the first inner point and the second inner point; S54. If the cost function of the first interior point is less than the cost function of the second interior point, then shrink the right boundary of the engine power interval; otherwise, shrink the left boundary of the engine power interval and return to S53 to re-perform the interior point iteration until the width of the engine power interval is less than the convergence accuracy, and output the optimal engine power.

7. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 6, characterized in that, In S51, the battery power The expression is: ； in, Indicates the total load power requirement. This indicates engine power.

8. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 6, characterized in that, In S52, the cost function The expression is: ； in, Indicates the equivalent factor. Indicates instantaneous fuel consumption. This indicates the battery efficiency at the current SOC and temperature. This indicates the battery power.

9. The ECMS energy management method for the UAV hybrid electric range extender system according to claim 6, characterized in that, In S54, the optimal engine power The expression is: ； in, This indicates the left endpoint of the engine power range. This indicates the right endpoint of the engine's power range.

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