With the rapid growth of renewable energy integration, the demand for efficient thermal management in energy storage batteries has become critical. This study focuses on optimizing liquid cooling structures for lithium iron phosphate (LiFePO₄) energy storage batteries to enhance operational performance and lifespan. Two serpentine channel configurations—transverse and longitudinal layouts—are analyzed using ANSYS Fluent, with emphasis on pressure drop, temperature uniformity, and flow rate effects.
1. Battery Thermal Model and Governing Equations
The heat generation rate in lithium-ion energy storage batteries is modeled using the Bernardi equation:
$$
q = \frac{1}{V_b} \left[ (E_0 – U) – T \frac{dE_0}{dT} \right] = \frac{I}{V_b} \left[ I^2 R – IT \frac{dE_0}{dT} \right]
$$
where \( V_b \) is the battery volume, \( E_0 \) the open-circuit voltage, \( U \) the terminal voltage, and \( R \) the internal resistance. The cooling plate’s fluid dynamics are governed by:
Mass Conservation
$$
\frac{\partial \rho_w}{\partial t} + \nabla \cdot (\rho_w \mathbf{v}) = 0
$$
Momentum Conservation
$$
\frac{\partial \mathbf{v}}{\partial t} + (\mathbf{v} \cdot \nabla) \mathbf{v} = -\frac{\nabla p}{\rho_w} + \frac{\mu}{\rho_w} \nabla^2 \mathbf{v} + \mathbf{g}
$$
Energy Conservation
$$
\frac{\partial}{\partial t} (\rho_w c_{pw} T_w) + \nabla \cdot (-k_w \nabla T_w + \rho_w c_{pw} T_w \mathbf{v}) = 0
$$
2. Serpentine Channel Configurations
Two cooling plate designs are evaluated:
| Configuration | Channel Length (mm) | Cross-Section (mm²) |
|---|---|---|
| Transverse Layout | 1,776 | 4×17 |
| Longitudinal Layout | 1,898 | 4×17 |
3. Simulation Results
3.1 Pressure Distribution
Pressure drop (\( \Delta P \)) comparisons at 0.1 g/s flow rate:
| Configuration | Inlet Pressure (Pa) | Outlet Pressure (Pa) | \( \Delta P \) (Pa) |
|---|---|---|---|
| Transverse | 31,240 | 0 | 31.24 |
| Longitudinal | 31,923 | 0 | 31.92 |
3.2 Temperature Uniformity
Maximum temperature (\( T_{\text{max}} \)) and gradient (\( \Delta T \)):
| Configuration | \( T_{\text{max}} \) (°C) | \( \Delta T \) (°C) |
|---|---|---|
| Transverse | 30.59 | 0.59 |
| Longitudinal | 30.50 | 0.50 |
3.3 Flow Rate Impact
Increasing coolant flow rate improves cooling but raises energy loss:
| Flow Rate (g/s) | \( T_{\text{max}} \) (°C) | \( \Delta P \) (Pa) |
|---|---|---|
| 0.1 | 30.50 | 31.92 |
| 0.3 | 29.87 | 112.45 |
| 0.5 | 29.21 | 198.73 |
4. Multi-Objective Optimization
A Pareto front analysis balances \( T_{\text{max}} \) and \( \Delta P \):
$$
\text{Minimize } \left[ T_{\text{max}}, \Delta P \right] \\
\text{Subject to: } 0.1 \leq \dot{m} \leq 0.5 \, \text{g/s}, \quad \text{Re} \leq 2,300
$$
5. Conclusion
Longitudinal serpentine channels achieve superior cooling uniformity (ΔT = 0.50°C) for energy storage batteries but incur higher pressure drops. Flow rates above 0.3 g/s reduce \( T_{\text{max}} \) by 1.29°C at the cost of 6.3× higher \( \Delta P \). Future work will explore hybrid cooling strategies to optimize energy efficiency while maintaining thermal stability in large-scale energy storage battery systems.