In the pursuit of advancing thermal management for energy storage systems, I focus on a liquid-cooled battery module comprising 52 individual energy storage cells. This study aims to enhance散热 performance and temperature uniformity through computational fluid dynamics (CFD) simulations. Energy storage cells are critical components in modern applications, and their efficiency is highly dependent on thermal conditions. By developing and validating a thermal model, I analyze the impact of various design parameters, such as inlet-outlet diameters and cooling structures, on the thermal behavior of energy storage cells. The optimization of these factors ensures safer and more reliable operation, which is essential for prolonging the lifespan of energy storage cells in high-demand scenarios.
The physical model consists of a battery pack with dimensions 1160 mm (L) × 810 mm (W) × 245 mm (H), housing 52 series-connected energy storage cells, each with a capacity of 280 Ah. These energy storage cells utilize lithium iron phosphate (LiFePO4) as the cathode material and carbon (C) as the anode. To simplify the simulation, I homogenize the battery materials, treating each energy storage cell as a uniform heat source and neglecting minor components like caps and vents. Thermal insulation foam is placed between energy storage cells to minimize radiative heat transfer. I investigate three liquid cooling configurations: a baseplate cooling structure (referred to as “baseplate cooling”), a side plate cooling structure (“side plate cooling”), and a combined baseplate and side plate cooling structure (“base-side plate cooling”). The cooling plates feature symmetric flow channels, with the baseplate having支流 channels of 33 mm width and 3.5 mm height, and the side plate having channels of 4.5 mm width and 7 mm height. Key material properties for the energy storage cells, electrodes, busbars, cooling plates, and coolant are summarized in Table 1.
| Component | Density (kg/m³) | Specific Heat Capacity (J/(kg·°C)) | Thermal Conductivity (W/(m·K)) | Viscosity (mPa·s) |
|---|---|---|---|---|
| Energy Storage Cells | 2024 | 964 | 3.56 (x), 9.04 (y), 11.0 (z) | – |
| Electrodes | 2700 | 900 | 234 | – |
| Busbars | 2719 | 871 | 234 | – |
| Cooling Plates | 2719 | 871 | 234 | – |
| Coolant | 1073.35 | 3281 | 0.38 | 3.94 |
For the mathematical modeling, I assume constant thermophysical properties and incompressible coolant flow with uniform velocity. The energy storage cells are treated as homogeneous heat sources. The governing equations for the coolant in the cooling plates include mass, momentum, and energy conservation. The mass conservation equation is given by:
$$\frac{\partial \rho_{\text{liq}}}{\partial t} + \nabla \cdot (\rho_{\text{liq}} \vec{v}) = 0$$
where $\rho_{\text{liq}}$ is the coolant density, and $\vec{v}$ is the velocity vector. The momentum conservation equation is expressed as:
$$\frac{\partial (\rho_{\text{liq}} \vec{v})}{\partial t} + \nabla \cdot (\rho_{\text{liq}} \vec{v} \vec{v}) = -\nabla P$$
with $P$ representing pressure. The energy conservation equation is:
$$\rho_{\text{liq}} c_{\text{liq}} \frac{\partial T_{\text{liq}}}{\partial t} + \nabla \cdot (\rho_{\text{liq}} c_{\text{liq}} \vec{v} T_{\text{liq}}) = \nabla \cdot (\lambda_{\text{liq}} \nabla T_{\text{liq}})$$
where $c_{\text{liq}}$ is the specific heat capacity, $T_{\text{liq}}$ is the temperature, and $\lambda_{\text{liq}}$ is the thermal conductivity of the coolant. To assess temperature uniformity across the energy storage cells, I calculate the temperature standard deviation $T_{\delta}$ using:
$$T_{\text{avg}} = \frac{\int_{A_{\text{wall}}} T \, dA}{\int_{A_{\text{wall}}} dA}$$
$$T_{\delta} = \sqrt{\frac{\int_{A_{\text{wall}}} (T – T_{\text{avg}})^2 \, dA}{\int_{A_{\text{wall}}} dA}}$$
where $T_{\text{avg}}$ is the average surface temperature of the energy storage cells. For system energy consumption, I consider pump power $P_w$ and chiller power $P_c$, calculated as:
$$P_w = V \Delta p = V (p_{\text{in}} – p_{\text{out}})$$
$$P_c = \frac{\rho V c_p (T_{\text{amb}} – T_{\text{in}})}{\eta_{\text{COP}}}$$
Here, $V$ is the volumetric flow rate, $\Delta p$ is the pressure difference, $c_p$ is the coolant specific heat capacity, $T_{\text{amb}}$ is the ambient temperature, $T_{\text{in}}$ is the inlet temperature, and $\eta_{\text{COP}}$ is the chiller coefficient of performance, assumed to be 5.
In the numerical model, I set the initial temperature of the energy storage cells to 25°C and apply a constant convective heat transfer coefficient of 5.0 W/(m²·K) at the battery-air interface to simulate natural convection. For boundary conditions, I use a velocity inlet and pressure outlet, with coolant inlet velocity varying between 0.1 m/s and 0.9 m/s and inlet temperature ranging from 25°C to 31°C. The heat generation rates for the energy storage cells under different discharge rates (0.5C, 0.75C, 1C) are derived from efficiency losses, as shown in Table 2. The simulations employ the finite volume method with the SIMPLE algorithm for pressure-velocity coupling and the k-ε turbulence model with standard wall functions.
| Discharge Rate | Efficiency | Heat Generation Power (kW) | Volumetric Heat Source (W/m³) |
|---|---|---|---|
| 0.5C | 94% | 0.699 | 5363.7 |
| 0.75C | 93% | 1.223 | 6257.7 |
| 1C | 92% | 1.864 | 14303.2 |
Grid independence is verified to ensure simulation accuracy. For the baseplate cooling structure, mesh sizes of 639,899, 1,221,407, and 2,205,010 cells yield maximum temperatures of 34.14°C, 34.07°C, and 34.08°C, respectively, with relative deviations below 5%. Similarly, for the side plate cooling structure, meshes of 3,760,587, 4,404,253, and 5,117,049 cells result in maximum temperatures of 33.21°C, 33.18°C, and 33.17°C. For the base-side plate cooling structure, meshes of 5,141,271, 8,082,392, and 9,748,194 cells give maximum temperatures of 30.96°C, 30.93°C, and 30.97°C. Based on this, I select mesh counts of 1,221,407 for baseplate cooling, 4,404,253 for side plate cooling, and 5,141,271 for base-side plate cooling to balance computational efficiency and precision.
The impact of inlet-outlet diameter on the thermal performance of energy storage cells is analyzed under constant flow rate conditions, with an initial battery temperature of 25°C, coolant temperature of 25°C, and a discharge rate of 1C. As the diameter increases from 5 mm to 10 mm, the maximum temperature of the energy storage cells decreases from 44.3°C to 42.6°C, indicating improved heat dissipation due to more uniform coolant distribution. However, further increase to 15 mm raises the temperature to 44.2°C, likely due to turbulent effects causing localized回流. Thus, an optimal diameter of 10 mm is selected for subsequent analyses to maximize the cooling efficiency for energy storage cells.
I evaluate the thermal effects of different cooling structures on energy storage cells under varying discharge rates, with an initial temperature of 25°C, coolant temperature of 25°C, and velocity of 0.9 m/s. For the 1C discharge rate, the maximum temperatures are 42.5°C for baseplate cooling, 40.8°C for side plate cooling, and 36.5°C for base-side plate cooling. At 0.75C, the values are 36.9°C, 35.7°C, and 32.8°C, respectively, and at 0.5C, they are 34.1°C, 33.2°C, and 31.0°C. These results demonstrate that side plate cooling reduces the maximum temperature by over 1.7°C compared to baseplate cooling, while base-side plate cooling achieves a reduction of more than 6.0°C, significantly enhancing the safety and stability of energy storage cells. Temperature uniformity is also improved; for instance, at 1C, the temperature differences are 16.8°C for baseplate cooling, 15.3°C for side plate cooling, and 11.3°C for base-side plate cooling. The standard deviations of temperature are 4.65°C, 3.38°C, and 2.86°C, respectively, indicating that side plate and base-side plate structures promote more uniform temperature distribution among energy storage cells, which is crucial for longevity and performance.
Energy consumption analysis focuses on side plate and base-side plate cooling structures, with a temperature上限 of 45°C for safe operation of energy storage cells. By varying inlet velocity (0.1–0.9 m/s) and temperature (25–31°C), I derive the minimum velocity required to maintain temperatures below 45°C, as summarized in Table 3. For side plate cooling, the lowest energy consumption occurs at an inlet temperature of 25°C and velocity of 0.4 m/s, with a system power of 338.30 W. For base-side plate cooling, the optimal point is at 25°C and 0.2 m/s, consuming 135.01 W. This shows that base-side plate cooling reduces energy consumption by a factor of 2.51 compared to side plate cooling, highlighting its efficiency for thermal management of energy storage cells.
| Case | Cooling Structure | Inlet Temperature (°C) | Minimum Inlet Velocity (m/s) | System Energy Consumption (W) |
|---|---|---|---|---|
| Case 1 | Side Plate | 25 | 0.4 | 338.30 |
| Case 2 | Side Plate | 27 | 0.6 | 538.71 |
| Case 3 | Side Plate | 29 | 0.8 | 1228.42 |
| Case 4 | Base-Side Plate | 25 | 0.2 | 135.01 |
| Case 5 | Base-Side Plate | 27 | 0.3 | 204.09 |
| Case 6 | Base-Side Plate | 29 | 0.3 | 389.86 |
| Case 7 | Base-Side Plate | 31 | 0.4 | 191.87 |
In conclusion, this study underscores the importance of optimizing liquid cooling systems for energy storage cells to achieve enhanced thermal performance and energy efficiency. The inlet-outlet diameter significantly influences the maximum temperature of energy storage cells, with a 10 mm diameter providing the best balance. Among the cooling structures, side plate cooling improves temperature uniformity and reduces peak temperatures, while base-side plate cooling offers superior散热 and energy savings. These findings provide valuable insights for designing thermal management systems that ensure the reliable operation of energy storage cells in various applications, contributing to the advancement of sustainable energy solutions. Future work could explore dynamic operating conditions and material innovations to further optimize the performance of energy storage cells.