Abstract
As electric vehicles (EVs) gain widespread adoption, the thermal management of power batteries in low-temperature environments has become critical for ensuring safety and performance. This study presents our design of a flat ultra-thin micro heat pipe (FUMHP) applied to a 3.2V 50Ah lithium-ion battery pack, coupled with a segmented thermal resistance model to analyze heat dissipation and heating temperature fields. Results show that the segmented model accurately predicts dynamic temperature changes, while the FUMHP demonstrates ideal heating and cooling effects under various low-temperature and convection conditions, significantly reducing heat accumulation and preventing thermal runaway in EVs.
1. Introduction
In the context of global warming and environmental pollution, electric vehicles have emerged as a key solution for sustainable transportation. The performance of power batteries, however, is highly sensitive to temperature, particularly in low-temperature environments where issues like increased internal resistance, reduced capacity, and uneven temperature distribution degrade battery life and safety .
Our research focuses on addressing these challenges by leveraging heat pipe technology. Unlike traditional air-cooling or liquid-cooling systems, heat pipes offer bidirectional heat conduction, high thermal conductivity, and compact design, making them suitable for EV battery thermal management . The specific objectives are to:
- Design a FUMHP for low-temperature applications.
- Develop a segmented thermal resistance model to improve prediction accuracy.
- Evaluate the FUMHP’s heating and cooling performance through simulations and experiments.
2. Design of Flat Ultra-Thin Micro Heat Pipe
2.1 Working Principle and Structure
The FUMHP operates on the principle of phase change heat transfer, consisting of a shell, wick structure, and working fluid. The wick, made of sintered copper powder, facilitates liquid 回流 (reflux) from the condensation section to the evaporation section, while the flattened shape (thickness < 2 mm) enhances heat distribution across the battery surface .
The working fluid selection follows criteria such as boiling point, thermal stability, and non-toxicity. We chose water as the working fluid due to its suitable temperature range (-200°C to 2000°C) and high latent heat of vaporization, as shown in Table 1 .
| Working Fluid | Temperature Range (°C) | Boiling Point (°C) | Freezing Point (°C) |
|---|---|---|---|
| Ammonia | -60 to 1100 | -33 | -78 |
| Methanol | 10 to 130 | 64 | -98 |
| Water | 30 to 250 | 100 | 0 |
| Mercury | 250 to 650 | 361 | -39 |
| Sodium | 600 to 1200 | 892 | 98 |
2.2 Key Design Parameters
- Maximum heat transfer capacity: 8.0 W
- Filling ratio: 25% of the evaporation section volume
- Wick thickness: 0.12 mm
- Shell thickness: 0.16 mm
- Optimal effective length: 168.3 mm
The FUMHP’s operational condition is governed by the capillary pressure balance:\(\Delta P_c \geq \Delta P_p + \Delta P_i + \Delta P_g\) where \(\Delta P_c\) is the capillary pressure, \(\Delta P_p\) is the vapor flow resistance, \(\Delta P_i\) is the liquid flow resistance, and \(\Delta P_g\) is the gravitational pressure .
3. Segmented Thermal Resistance Model and Coupling Analysis
3.1 Model Development
Traditional integrated thermal resistance models overlook the dynamic heat transfer differences between evaporation and condensation sections, leading to calculation errors. We proposed a segmented model that divides the heat pipe into evaporation and condensation sections, each with distinct thermal resistance parameters .
The thermal resistance of the evaporation section (\(R^e\)) is:\(\begin{cases} R^e = R_{wall}^e + R_{wick}^e + R_{eva} \\ K^e = \frac{L^e}{R^e A^e} \end{cases}\) where \(R_{wall}^e\), \(R_{wick}^e\), and \(R_{eva}\) are the wall, wick, and evaporation thermal resistances, respectively; \(K^e\), \(L^e\), and \(A^e\) are the heat transfer coefficient, axial length, and cross-sectional area .
For the condensation section, the model accounts for both plain and finned tube segments:\(\begin{cases} R_1^c = R_{con1} + R_{wick1}^c + R_{wall}^c \\ K_1^c = \frac{L_1^c}{R_1^c A_1^c} \\ R_2^c = R_{con2} + R_{wick2}^c + R_{fin} \\ K_2^c = \frac{L_2^c}{R_2^c A_2^c} \end{cases}\) where \(R_{con1}\) and \(R_{con2}\) are thermal resistances of plain and finned tubes, and \(R_{fin}\) is the fin thermal resistance .
3.2 Coupling with Battery Thermal Model
The segmented model was coupled with a battery thermal model using tetrahedral meshing, resulting in:
- Total mesh elements: 3,493,037
- Contact surfaces: 130
- Solid components: 74
- Nodes: 568,647
Thermal properties of the heat pipe and battery were set as:
- Density: \(5059 ~kg/m^3\)
- Specific heat: 103.6 J/(kg·K)
Boundary conditions considered two scenarios: one side of the battery cooled by the heat pipe and the other by air, or both sides cooled by heat pipes .
4. Experimental Validation and Results
4.1 Heat Dissipation Performance
We conducted heat dissipation simulations under a 3C discharge rate, comparing the segmented and integrated models. Key results are summarized in Table 2 and Figure 3.
| Cooling Method | Discharge Rate (C) | Max Battery Pack Temperature (°C) | Max Temperature Difference (°C) | Max Single-Cell Temperature Difference (°C) |
|---|---|---|---|---|
| Natural convection (no heat pipe) | 1 | 26.5 | 6.3 | 4.5 |
| 2 | 31.6 | 7.8 | 4.7 | |
| 3 | 39.6 | 8.6 | 5.9 | |
| Forced convection (no heat pipe) | 1 | 23.6 | 5.78 | 4.3 |
| 2 | 26.8 | 7.1 | 4.4 | |
| 3 | 33.6 | 8.2 | 5.6 | |
| Natural convection (FUMHP) | 1 | 21.9 | 5.6 | 4.2 |
| 2 | 24.9 | 6.7 | 4.4 | |
| 3 | 30.6 | 7.8 | 5.3 | |
| Forced convection (FUMHP) | 1 | 13.2 | 3.4 | 2.7 |
| 2 | 17.2 | 4.0 | 3.1 | |
| 3 | 21.3 | 4.3 | 3.5 |
The segmented model showed a maximum temperature of 31.2°C, significantly lower than the integrated model’s 35.7°C. The maximum temperature difference in the battery pack was 6.8°C (segmented) vs. 8.9°C (integrated), confirming the segmented model’s superior accuracy in predicting thermal distribution .
4.2 Heating Performance
Heating experiments were conducted at initial temperatures of 0°C, -10°C, and -20°C using 180W heat sources, comparing direct battery heating and FUMHP heating.
- Direct battery heating: Achieved 20°C in the evaporation section within 260s, with the condensation section at 9°C.
- FUMHP heating: Took 300s to reach 20°C, but showed more uniform temperature distribution across cells .
The FUMHP demonstrated an isothermal characteristic, with the minimum temperature difference between evaporation and condensation sections reducing to 2.0°C under prolonged discharge, verifying its efficiency in low-temperature environments .
5. Discussion
5.1 Thermal Management Efficiency
The FUMHP significantly outperformed traditional cooling methods. Under forced convection with the FUMHP, the battery pack’s maximum temperature remained below 21.3°C even at a 3C discharge rate, while natural convection without a heat pipe led to temperatures exceeding 39.6°C. This confirms the FUMHP’s ability to maintain optimal battery operating temperatures in low environments .
5.2 Model Accuracy
The segmented thermal resistance model reduced prediction errors by accounting for dynamic heat transfer in different sections. Compared to the integrated model, it more accurately reflected real-world temperature trends, especially as the battery state of charge (SOC) decreased below 0.65, where heat generation rates increase .
5.3 Practical Implications for EVs
Our findings indicate that the FUMHP:
- Prevents thermal runaway by reducing heat accumulation.
- Improves battery life and performance in cold climates.
- Is scalable for both small and large EV battery systems .
6. Conclusion
In this study, we designed a flat ultra-thin micro heat pipe and developed a segmented thermal resistance model for EV power batteries in low-temperature environments. Key conclusions include:
- The segmented model accurately predicts battery pack temperature fields, with maximum temperatures 4.5°C lower than the integrated model.
- The FUMHP achieves ideal heating and cooling performance, reducing maximum temperature differences to within 5.0°C under forced convection.
- These results provide critical technical support for designing efficient thermal management systems in electric vehicles, enhancing both safety and operational stability.
Future work will focus on optimizing the FUMHP for extreme low temperatures and integrating intelligent control systems to further improve thermal management efficiency.