With the rapid advancement of drone technology, Unmanned Aerial Vehicles (UAVs) have become integral across various sectors, including surveillance, delivery, and environmental monitoring. As the demand for extended operational capabilities grows, the need for efficient charging solutions becomes paramount. Charging compartments are essential for supporting continuous UAV missions, but the lithium-ion batteries commonly used in these systems are prone to overheating during charging, which can lead to prolonged charging times and safety hazards. The optimal charging temperature range for lithium batteries is between 20°C and 45°C; exceeding this range accelerates battery degradation and increases risks. This study explores a thermal management system leveraging a Flat Plate Micro Heat Pipe Array (MHPA) integrated with direct refrigerant cooling to address these challenges in UAV applications. By focusing on the unique requirements of drone technology, we aim to enhance battery performance and longevity through innovative heat dissipation methods.
The integration of MHPA into UAV battery compartments offers a lightweight and efficient solution for thermal regulation. Traditional cooling methods, such as air cooling, often fall short due to low heat transfer efficiency, while liquid cooling and phase-change materials add complexity and weight, making them less suitable for compact Unmanned Aerial Vehicles. In contrast, MHPA provides superior thermal conductivity, uniformity, and rapid response, making it ideal for the dynamic operational environments of drone technology. Our research involves developing a comprehensive model of the thermal system, validating it through experimental data, and analyzing key factors like heat pipe condensation temperature, environmental conditions, and charging rates. The findings demonstrate that MHPA-based systems can effectively maintain battery temperatures within safe limits, even under high charging rates, thereby supporting the reliability and efficiency of UAV operations.
In this paper, we first detail the physical and mathematical models used in our simulations, including assumptions and governing equations for heat transfer and fluid dynamics. We then present experimental validation to ensure model accuracy, followed by an in-depth analysis of how various parameters influence battery thermal behavior. Tables and equations are employed to summarize results and relationships, providing a clear understanding of the system’s performance. The implications of this research extend to improving the sustainability and safety of drone technology, contributing to the broader adoption of Unmanned Aerial Vehicles in critical applications.
The physical model of the thermal management system comprises the battery, air domain, heat pipe, and cooling components, as illustrated in the following representation. The MHPA is integrated into the battery compartment, with its evaporator section in direct contact with the battery and the condenser section exposed to the external environment for heat dissipation. This setup minimizes thermal resistance and enhances heat transfer efficiency, which is crucial for the compact design of UAV systems.
Key specifications of the model include a battery dimensions of 127 mm × 48 mm × 8 mm, with a rated capacity of 5.2 Ah and a maximum charging rate of 3C. The thermal properties are defined by a specific heat capacity of 1100 J/(kg·K), thermal conductivities of 35 W/(m·K) in the xy-direction and 0.48 W/(m·K) in the z-direction, and a density of 2630 kg/m³. The MHPA has a thickness of 3 mm, uses acetone as the working fluid with a fill ratio of 50%, and exhibits a thermal conductivity of 14,000 W/(m·K), a specific heat capacity of 880 J/(kg·K), and a density of 2700 kg/m³. The air domain is characterized by a specific heat capacity of 1006.43 J/(kg·K), thermal conductivity of 0.0242 W/(m·K), and density of 1.225 kg/m³. To simplify the analysis, we assume the battery acts as a uniform heat source, with constant material properties and negligible contact resistances, radiation, and convective effects within the compartment. These assumptions allow us to focus on the core heat transfer mechanisms relevant to drone technology.
The mathematical framework governing the thermal management system is based on fundamental conservation laws. The mass conservation equation ensures continuity in the fluid domain:
$$ \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{u}) = 0 $$
where \( \rho \) is the fluid density, \( t \) is time, and \( \mathbf{u} \) is the velocity vector. The momentum equation accounts for fluid motion and pressure effects:
$$ \frac{\partial (\rho \mathbf{u})}{\partial t} + \nabla \cdot (\rho \mathbf{u} \mathbf{u}) = -\nabla P + \nabla \cdot (\mu \nabla \mathbf{u}) $$
Here, \( \mu \) represents the dynamic viscosity, and \( P \) is the pressure. The energy equation models heat transfer within the system:
$$ \frac{\partial (\rho c_p T)}{\partial t} + \nabla \cdot (\rho c_p \mathbf{u} T) = \nabla \cdot (\lambda \nabla T) + S_h $$
In this equation, \( c_p \) is the specific heat capacity, \( T \) is the temperature, \( \lambda \) is the thermal conductivity, and \( S_h \) denotes internal heat sources. For natural convection in the air domain, the Rayleigh number \( Ra \) determines flow characteristics:
$$ Ra = \frac{g \beta \Delta T L^3 \rho}{\mu \alpha} $$
where \( g \) is gravitational acceleration, \( \beta \) is the volumetric expansion coefficient, \( \Delta T \) is the temperature difference, \( L \) is the characteristic length, and \( \alpha \) is the thermal diffusivity. Calculations yield \( Ra = 3.12 \times 10^5 \), indicating laminar flow, which simplifies the analysis for UAV applications. Condensation on surfaces is modeled using a phase change rate equation:
$$ \dot{m}_{\text{phase}} = \frac{\rho D}{D + C_{\text{phase}} \delta} (y_i – y_{\text{sat}}) $$
where \( D \) is the diffusion coefficient, \( \delta \) is the distance to the wall, \( C_{\text{phase}} \) is the phase change constant, \( y_i \) is the vapor mass fraction, and \( y_{\text{sat}} \) is the saturation mass fraction. The battery heat generation is derived from the Bernardi model:
$$ q = \frac{I}{V_b} \left( I R_b + T \frac{\partial U_0}{\partial T} \right) $$
Here, \( I \) is the current, \( V_b \) is the battery volume, \( R_b \) is the internal resistance, and \( U_0 \) is the open-circuit voltage. The internal resistance \( R_b \) varies with temperature \( T \) and state of charge (SOC), as summarized in the following table based on experimental data:
| Parameter | Value Range | Description |
|---|---|---|
| SOC | 0–1 | State of charge |
| Temperature | –20°C to 60°C | Operating range |
| Internal Resistance | Fitted equation | Depends on SOC and T |
The internal resistance is expressed as a function of SOC and temperature:
$$ R_b = 2.904 – 5.424 SOC – 1.006 SOC^2 + 34.446 SOC^3 – 75.76 SOC^4 – 39.81 SOC^5 + 3.478 T – 0.198 T^2 + 0.0054 T^3 – 6.978 \times 10^{-5} T^4 + 3.488 \times 10^{-7} T^5 $$
This equation highlights the complex interplay between battery operational states and thermal behavior, which is critical for optimizing drone technology. Experimental validation involved charging the battery at rates of 1C, 2C, and 3C under controlled conditions, with temperature measurements compared to simulation results. The average error was less than 1°C, confirming model reliability for Unmanned Aerial Vehicle systems.
Grid independence tests were conducted to ensure computational accuracy. A mesh size of 1 mm with five layers in the air domain was selected, resulting in 487,858 elements and a maximum liquid film thickness below 0.1 μm. Time steps of 1 second balanced precision and efficiency. The table below outlines the operational conditions analyzed in this study:
| Case | Condenser Temperature (°C) | Ambient Temperature (°C) | Ambient Humidity (%) | Air Domain Thickness (mm) |
|---|---|---|---|---|
| 1 | 6, 8, 10, 12, 14, 16 | 38 | 60 | 1 |
| 2 | 8 | 8, 16, 22, 30, 38, 46 | 60 | 1 |
| 3 | 8 | 38 | 20, 40, 60, 80, 100 | 1 |
| 4 | 8 | 38 | 60 | 0.2, 0.4, 0.6, 0.8, 1 |
The analysis focuses on two phases: cooling and charging. During cooling, the battery temperature decreases without internal heat generation, while charging involves heat production. Results indicate that lower condenser temperatures enhance cooling efficiency but may increase temperature non-uniformity. For instance, at a condenser temperature of 6°C, the battery temperature drops rapidly, reaching approximately 40°C within 600 seconds, whereas at 16°C, the temperature stabilizes around 25°C after 1800 seconds. Environmental temperature has minimal impact on battery cooling due to the dominant role of MHPA heat transfer, but humidity influences are negligible, as the primary heat dissipation mechanism is radiation-driven. The air domain thickness significantly affects cooling rates; a thickness of 0.2 mm reduces the temperature to 25°C in 391 seconds, compared to 1807 seconds for 1 mm, illustrating a 78.4% improvement in cooling speed. This is crucial for drone technology, where rapid thermal management can enhance mission readiness.
Charging phase evaluations at 1C, 2C, and 3C rates show that the MHPA system maintains temperatures within the optimal range of 20–45°C. At 3C charging, the peak temperature is 36.98°C, with a maximum temperature difference of 13.25°C across the battery surface. The following equation summarizes the heat dissipation effectiveness:
$$ \eta = \frac{T_{\text{initial}} – T_{\text{final}}}{T_{\text{initial}} – T_{\text{ambient}}} $$
where \( \eta \) represents the cooling efficiency, \( T_{\text{initial}} \) is the starting temperature, \( T_{\text{final}} \) is the stabilized temperature, and \( T_{\text{ambient}} \) is the ambient temperature. For UAV applications, this efficiency ensures battery longevity and safety. The table below compares temperature metrics under different charging rates:
| Charging Rate | Maximum Temperature (°C) | Temperature Difference (°C) | Time to Stabilize (s) |
|---|---|---|---|
| 1C | 17 | 4.58 | 3600 |
| 2C | 29 | 9.77 | 1800 |
| 3C | 36.98 | 13.25 | 1200 |
In conclusion, the integration of Flat Plate Micro Heat Pipe Array technology in Unmanned Aerial Vehicle charging compartments provides an effective thermal management solution. Key recommendations include minimizing the air domain thickness to 0.2 mm for enhanced heat transfer and maintaining condenser temperatures between 12°C and 16°C to balance cooling performance and temperature uniformity. These strategies support the advancement of drone technology by ensuring reliable battery operation under varying conditions. Future work could explore hybrid systems combining MHPA with other cooling methods to further optimize thermal regulation for evolving UAV demands.