In the rapidly evolving field of electric vehicles (EVs), the thermal management of power batteries is a critical factor influencing performance, safety, and longevity. As a researcher focused on advancing EV technology, I have conducted an in-depth study on the thermal balance of China EV battery systems, specifically utilizing co-simulation with ABAQUS and ANSYS. This approach allows for a comprehensive analysis of heat generation and dissipation in lithium iron phosphate (LiFePO4) batteries, which are commonly used in EVs due to their stability and efficiency. The importance of maintaining optimal operating temperatures for EV power battery units cannot be overstated, as deviations can lead to reduced efficiency, accelerated aging, or even safety hazards. In this article, I will detail the methodology, including 3D modeling with CATIA, mesh generation in ABAQUS, and thermal simulations in ANSYS Fluent, with a focus on forced air cooling strategies. By incorporating multiple formulas and tables, I aim to provide a thorough understanding of how to achieve thermal equilibrium in China EV battery packs under various environmental conditions. The findings emphasize the significance of tailored heating and cooling protocols to ensure the reliability of EV power battery systems in real-world applications.
The foundation of this study lies in the development of accurate simulation models for the China EV battery. We began by creating a 3D model of a single LiFePO4 battery and a battery pack using CATIA software. The single battery model included essential components such as the positive and negative tabs and the core, with dimensions of 155.0 mm in length, 102.0 mm in width, and 10.0 mm in thickness. The tabs measured 15.0 mm in length, 5.0 mm in width, and 0.5 mm in thickness. For the battery pack, we assembled five single batteries in a stacked arrangement, with a 5 mm gap between them filled with silicone gel to enhance thermal conductivity. This setup is representative of typical EV power battery configurations in China, where compact design and efficient heat management are paramount. The material properties used in the model are summarized in Table 1, which includes density, specific heat capacity, and thermal conductivity for each component. These parameters are crucial for simulating the thermal behavior of the China EV battery under different scenarios.
| Component | Material | Density (kg/m³) | Specific Heat Capacity (J/(kg·K)) | Thermal Conductivity (W/(m·K)) |
|---|---|---|---|---|
| Tab | Nickel | 8900.0 | 440 | 90.00 |
| Internal | Solvent, Salt | 1.3 | 700 | 1.58 |
| Positive Electrode | LiFePO4 | 1300.0 | 1269 | 1.58 |
| Negative Electrode | Graphite | 2100.0 | 710 | 129.00 |
| Silicone Gel | Silicon Compound | 1820.0 | 1280 | 2.00 |
Following the modeling phase, we employed ABAQUS for mesh generation due to its robustness in handling complex solid mechanics and nonlinear problems. The Mesh module was used to discretize the single battery and battery pack models into finite elements. For the single battery, we applied a 1.5 mm hexahedral swept mesh, resulting in 57,568 nodes and 49,088 elements. The battery pack mesh comprised 345,248 nodes and 329,488 elements, ensuring high resolution for accurate thermal analysis. This meticulous meshing is vital for capturing the intricate heat transfer phenomena in EV power battery systems, particularly in China, where environmental factors can vary widely. To simulate heat generation during operation, we utilized the classic Bernardi equation, which assumes uniform heat generation in lithium-ion batteries. The heat generation rate \( q \) is given by:
$$ q = \frac{1}{V_b} \left( I^2 R_0 + I T \frac{dE_0}{dT} \right) $$
where \( V_b \) is the volume of the single battery (0.0001581 m³), \( I \) is the discharge current (A), \( R_0 \) is the internal resistance (2 mΩ), and \( T \frac{dE_0}{dT} \) is a term related to electrochemical reactions, valued at 11.16 mV based on reference data. This formula was integral to setting up the simulation conditions, as it defines the heat source within the China EV battery during discharge. For instance, at a discharge current of 20 A, the heat generation rate calculates to approximately 8,708.4 W/m³, while at 30 A, it rises to about 20,652.8 W/m³. These values were applied as steady heat sources in our simulations to replicate real-world operating conditions for EV power battery units.
We then proceeded to heating simulation analysis using ABAQUS to evaluate the temperature distribution in the battery pack under different environmental conditions. The initial battery temperature was set to 20°C, and we applied varying heat fluxes to the surface to assess how the China EV battery responds to external heating. This is particularly relevant for maintaining optimal performance in cold climates, where the EV power battery must be heated to prevent capacity loss. For an environmental temperature of 0°C, we applied heat fluxes of 0.15 W/m², 0.20 W/m², and 0.25 W/m². The results, summarized in Table 2, show that a heat flux of 0.15 W/m² yielded the smallest temperature difference (28.1°C) and kept the maximum temperature within the optimal range of 0–40°C. Similarly, at environmental temperatures of 20°C and 40°C, applying a heat flux of 0.05 W/m² resulted in the most uniform temperature distribution, with differences of 9.2°C and 9.2°C, respectively. These findings highlight the need for adaptive heating strategies in China EV battery systems to ensure efficient operation across diverse climates.
| Environmental Temperature (°C) | Heat Flux (W/m²) | Minimum Temperature (°C) | Maximum Temperature (°C) | Temperature Difference (°C) |
|---|---|---|---|---|
| 0 | 0.15 | 0.1 | 28.2 | 28.1 |
| 0.20 | 0.1 | 37.6 | 37.5 | |
| 0.25 | 0.1 | 46.9 | 46.8 | |
| 20 | 0.05 | 20.2 | 29.4 | 9.2 |
| 0.10 | 20.3 | 38.6 | 18.3 | |
| 0.15 | 20.3 | 47.8 | 27.5 | |
| 40 | 0.05 | 40.4 | 49.6 | 9.2 |
| 0.10 | 40.5 | 58.7 | 18.2 | |
| 0.15 | 40.6 | 67.7 | 27.1 |
The temperature distribution during heating simulations exhibited a U-shaped pattern, with higher temperatures in the central region between the tabs and lower temperatures at the tabs and bottom. This is attributed to the current concentration in the central area and the better heat dissipation at the tabs, which serve as current pathways. Such insights are crucial for designing effective thermal management systems for EV power battery packs in China, where minimizing temperature gradients can enhance battery life and safety. To further illustrate the model, consider the following visualization of the battery pack setup, which underscores the importance of precise engineering in China EV battery design.
In addition to heating analysis, we conducted forced air cooling simulations using ANSYS Fluent to evaluate散热效果 for the China EV battery. Forced air cooling is a common method in EV power battery thermal management due to its simplicity and cost-effectiveness. We compared two ventilation structures: parallel flow and U-shaped flow. In the parallel flow structure, air enters from one side and exits from the opposite side, promoting uniform cooling across the battery pack. In contrast, the U-shaped flow structure involves air entering from the top and exiting from the bottom, which may enhance heat removal but could lead to less uniform temperature distribution. The simulations were performed under different discharge currents and cooling conditions, with an environmental temperature of 30°C. For example, at a discharge current of 20 A, wind speed of 5 m/s, and convective heat transfer coefficient of 15 W/(m²·K), the parallel flow structure resulted in a temperature range of 30.6–31.1°C and a difference of 0.5°C, whereas the U-shaped flow led to a range of 30.9–31.9°C and a difference of 1.0°C. Similarly, at 30 A, wind speed of 8 m/s, and convective heat transfer coefficient of 20 W/(m²·K), the parallel flow maintained a smaller temperature difference (1.1°C) compared to the U-shaped flow (2.0°C). These results are consolidated in Table 3, demonstrating the superiority of parallel flow for achieving thermal balance in China EV battery systems.
| Cooling Type | Current (A) | Wind Speed (m/s) | Convective Heat Transfer Coefficient (W/(m²·K)) | Minimum Temperature (°C) | Maximum Temperature (°C) | Temperature Difference (°C) |
|---|---|---|---|---|---|---|
| Parallel Flow | 20 | 5 | 15 | 30.6 | 31.1 | 0.5 |
| 30 | 8 | 20 | 31.0 | 32.1 | 1.1 | |
| U-Shaped Flow | 20 | 5 | 15 | 30.9 | 31.9 | 1.0 |
| 30 | 8 | 20 | 31.4 | 33.4 | 2.0 |
The analysis reveals that parallel flow ventilation provides more uniform cooling for the EV power battery, which is essential for maintaining stability in China’s diverse operating environments. The lower temperature at the tabs in both structures indicates effective heat dissipation at these points, but the overall temperature homogeneity is better with parallel flow. This aligns with the goal of optimizing China EV battery performance, as reduced temperature gradients minimize thermal stress and prolong battery life. To quantify the heat dissipation efficiency, we can refer to the heat transfer equation for forced convection:
$$ q = h A (T_s – T_\infty) $$
where \( q \) is the heat transfer rate, \( h \) is the convective heat transfer coefficient, \( A \) is the surface area, \( T_s \) is the surface temperature, and \( T_\infty \) is the environmental temperature. In our simulations, adjusting the wind speed and \( h \) value allowed us to achieve desirable thermal balance. For instance, at wind speeds of 5 m/s and 8 m/s with corresponding \( h \) values of 15 W/(m²·K) and 20 W/(m²·K), the China EV battery reached a stable thermal state, underscoring the importance of these parameters in EV power battery design.
Based on the simulation results, we designed a comprehensive thermal balance system for the China EV battery. The heating conditions require applying a heat flux of 0.15 W/m² at an environmental temperature of 0°C and 0.05 W/m² at 20°C and 40°C, with an initial battery temperature of 20°C. For cooling, the parallel flow structure is recommended, with scenarios involving wind speeds of 5 m/s and convective heat transfer coefficients of 15 W/(m²·K) or wind speeds of 8 m/s and coefficients of 20 W/(m²·K). These settings ensure that the EV power battery maintains a relatively stable temperature, crucial for reliable vehicle operation in China. The integration of ABAQUS and ANSYS co-simulation has proven effective in optimizing these parameters, providing a robust framework for future developments in China EV battery technology.
In conclusion, this study underscores the critical role of thermal management in enhancing the performance and safety of China EV battery systems. Through detailed simulations, we have demonstrated that adaptive heating and forced air cooling with parallel flow ventilation can achieve optimal thermal balance for EV power battery packs. The use of advanced software tools like CATIA, ABAQUS, and ANSYS has enabled precise modeling and analysis, contributing valuable insights for the EV industry. As the demand for electric vehicles grows in China and globally, continued research into thermal management strategies will be essential for advancing EV power battery technology and ensuring sustainable transportation solutions.