The rapid advancement of communication, semiconductor, and software technologies has catalyzed a breakthrough expansion of Unmanned Aerial Vehicle (UAV) systems, enabling their deployment across a vast spectrum of civilian and military applications. Among the various propulsion solutions, piston engines remain predominant in medium-to-low speed and long-endurance platforms, constituting approximately 80% of such China UAV drones due to their structural simplicity and high reliability. However, these engines face significant challenges during transient flight phases—such as climb, descent, and altitude-varying cruise—where external environmental parameters like temperature and pressure change continuously. Research indicates that integrating an internal combustion engine with an electric motor to form a hybrid-electric system presents an optimal solution for managing these dynamic conditions in China UAV drone operations. While existing energy management strategies often focus on rule-based or optimization-based algorithms primarily through simulation, a critical gap exists in the comprehensive, experimentally validated analysis of energy flow during these transitional states. This study addresses this gap by constructing a detailed energy flow analysis model for a parallel hybrid-electric propulsion system. The model’s efficacy is rigorously validated through experiments conducted in a high-altitude environmental simulation chamber, replicating the climb, cruise, and descent phases typical of a mission profile for China UAV drones.
The core of this research is the development of an energy flow analysis framework, as depicted in the conceptual model. The process begins with the Electronic Control Unit (ECU) performing real-time diagnostics to determine the current operational state of the hybrid system. The entire flight mission is segmented into three distinct phases: Climb, Cruise, and Descent. For each phase, input parameters—including ambient conditions (pressure, temperature), airspeed, vehicle weight, and rate of altitude change—are fed into a power calculation module. This module computes the total power required from the propulsion system to maintain the desired flight condition. Subsequently, an energy allocation module distributes this total power demand optimally between the internal combustion engine and the electric motor. Based on this allocation and the prevailing environmental conditions, precise control parameters for both prime movers are determined and executed. The final step involves experimental verification to assess control performance and energy consumption characteristics.
The mathematical foundation for the power calculation module is derived from fundamental aerodynamics. The total power required $P_{total}$ from the hybrid system is a function of the flight state.
For the climb phase, the power is calculated to overcome drag and provide the potential energy increase:
$$P_{climb} = \frac{1}{\eta_{prop}} \left( \frac{1}{2} \rho V^3 S C_{D0} + \frac{2kW^2}{\rho V S} + WV_v \right)$$
where $V_v$ is the vertical climb rate (m/s), $W$ is the total aircraft weight (kg), $\rho$ is air density (kg/m³), $S$ is wing area (m²), $C_{D0}$ is the zero-lift drag coefficient, $V$ is airspeed (m/s), $k$ is the lift-induced drag factor, and $\eta_{prop}$ is propeller efficiency.
For the cruise phase, where $V_v = 0$, the equation simplifies to:
$$P_{cruise} = \frac{1}{\eta_{prop}} \left( \frac{1}{2} \rho V^3 S C_{D0} + \frac{2kW^2}{\rho V S} \right)$$
The descent phase power $P_{descent}$ uses a negative $V_v$ value in the climb equation.
The total energy consumption $W_{total}$ of the hybrid system over a mission is the sum of the energy contributions from both sources:
$$W_{total} = W_{engine} + W_{motor}$$
Given the parallel configuration, the instantaneous power relationship is:
$$P_{total}(t) = P_{engine}(t) + P_{motor}(t)$$
The core optimization problem for the energy management model is to find the power split ratio and corresponding operating points (e.g., engine speed) that minimize $W_{total}$, subject to component constraints and the dynamic power demand $P_{total}(t)$. This involves evaluating a parameter matrix across different power splits and engine speeds. For the engine, the fuel energy consumption $W_{engine}$ is determined by integrating the product of engine power $P_{engine}$ and its brake-specific fuel consumption (BSFC), which is a function of engine speed and torque, mapped from data such as that shown in the engine fuel consumption chart. For the electric motor, the electrical energy consumption $W_{motor}$ is derived from integrating $P_{motor}$ divided by the motor efficiency $\eta_{motor}$, which is a function of motor speed and torque, obtained from its efficiency map.
To validate the model, a dedicated parallel hybrid-electric test platform was established within a high-altitude environmental simulation chamber. The platform integrated a FQ340 two-stroke piston aviation engine with a 10 kW rated permanent magnet synchronous motor. The chamber precisely controlled ambient pressure and temperature to simulate various flight altitudes. Data acquisition systems monitored key parameters including speed, torque, power, pressure, and throttle position for both power sources. The test profile, summarized in the table below, covered the three critical flight phases.
| Flight Phase | Simulated Altitude Range (m) | Key Objective |
|---|---|---|
| Climb | 1000 to 2000 | Validate power response and energy split under decreasing ambient pressure. |
| Cruise | 1500 (steady) | Assess steady-state efficiency and adaptation to gradual environmental change. |
| Descent | 2000 to 1000 | Evaluate control stability during power reduction and speed transition. |
The experimental results for the climb phase are highly instructive. As the simulated altitude increased from 1000 m to 2000 m, the ambient pressure decreased from approximately 90 kPa to 80 kPa. To maintain a stable combustion state, the engine throttle was held constant while the fuel injection pulse width was reduced, leading to a natural decrease in engine output power from an initial 13 kW to around 10 kW over the 210-second climb. Concurrently, the total power required $P_{total}$ for a 4.76 m/s climb rate also decreased with altitude. The energy management system commanded the electric motor to provide the precise power needed to fill the gap between the declining engine output and the total demand, maintaining the required climb performance. The system’s total energy consumption decreased throughout the climb. Compared to a theoretical fuel-only system meeting the same power profile, the hybrid configuration consumed only 62.94% of the energy, representing a 37.06% savings—a crucial advantage for extending the range and endurance of China UAV drones. Further analysis established a clear relationship between climb rate and power demand, as shown in the following generalized trend: $P_{total} \approx P_{base} + \alpha \cdot V_v$, where $\alpha$ was found to be approximately 2.3 kW per m/s increase in climb rate for this specific China UAV drone platform.
During the cruise phase at a constant simulated altitude of 1500 m, a gradual pressure decrease from 85 kPa to 75 kPa was imposed. The control strategy for this steady, prolonged phase prioritized the use of the high energy-density fuel. The engine alone supplied all the power, with the motor inactive. As pressure dropped, the ECU adjusted fueling to maintain a constant engine speed of 4500 rpm, resulting in a smooth decrease of engine torque and output power from 9.2 kW to 7.05 kW, perfectly tracking the reducing aerodynamic power requirement $P_{cruise}$. This demonstrated the system’s capability for efficient steady-state operation and seamless adaptation to slow environmental changes, a common scenario for long-endurance reconnaissance China UAV drones.
The descent phase test simulated a glide from 2000 m to 1000 m. The control objective shifted to reducing airspeed and managing the potential energy decrease. Initially, engine power was gradually reduced by lowering the throttle. Subsequently, a smooth reduction in engine speed from 3000 rpm to 2500 rpm was executed. Throughout this 300-second maneuver, the engine power decreased from 5.4 kW to 2.5 kW, and the system maintained stable control without requiring motor assistance, confirming the model’s effectiveness in planning and executing stable descent transitions for China UAV drones.
The parameters and advantages of the hybrid system are summarized in the following comparison, highlighting its suitability for China UAV drone applications.
| Component/Parameter | Specification / Value | Role in Hybrid System for China UAV Drones |
|---|---|---|
| Engine Type | FQ340, 2-cylinder, 2-stroke, opposed | Primary high-energy-density power source for cruise and sustained flight. |
| Engine Max Power | ≥18 kW @ 6000 rpm | Provides baseline power for level flight and moderate climb. |
| Motor Type | PMSM, 10 kW (20 kW peak) | Provides rapid transient power boost, compensates for engine lag, enables silent operation. |
| Motor Max Torque | 100 N·m | Delivers high torque at low speeds for enhanced takeoff and climb performance. |
| System Energy Saving (Climb) | 37.06% vs. fuel-only | Directly increases mission range and endurance, a key metric for China UAV drones. |
| Control Adaptation | Stable operation across climb, cruise, descent with ambient pressure change | Ensures reliable performance in the variable atmospheric conditions encountered by China UAV drones. |
In conclusion, this study successfully developed and experimentally validated a comprehensive energy flow analysis model for a parallel hybrid-electric propulsion system tailored for China UAV drone applications. The model effectively coordinates the internal combustion engine and electric motor across the climb, cruise, and descent phases. Experimental results confirm that during climb, the motor rapidly compensates for power deficits, leading to significant energy savings. During cruise, the system efficiently utilizes the engine while adapting to environmental changes. During descent, stable control is achieved through smooth power and speed reduction. The proposed model and the empirical data provide a solid foundation for the development of advanced, real-time energy management strategies. This work directly contributes to enhancing the performance, efficiency, and operational flexibility of next-generation China UAV drones, particularly those requiring robust performance in dynamically changing flight environments.