Modern energy storage batteries face critical challenges in thermal management due to high-density power cycling. This study investigates a single-phase immersion cooling system for modular lithium iron phosphate (LiFePO₄) battery units through computational fluid dynamics (CFD) simulations and experimental validation. The system achieves maximum temperatures below 35°C with temperature differentials under 3°C under 0.5C charge/discharge conditions.
System Architecture and Governing Equations
The modular energy storage battery system comprises:
| Component | Specification |
|---|---|
| Battery Cells | 280Ah LiFePO₄, 164×72×194mm |
| Coolant | Dielectric fluid (εr = 2.3, λ = 0.12W/m·K) |
| Flow Configuration | Parallel channels with 6L/min total flow |
The thermal-fluid behavior follows three fundamental conservation laws:
$$ \text{1. Mass Conservation: } \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \vec{v}) = 0 $$
$$ \text{2. Momentum Conservation: } \frac{\partial (\rho \vec{v})}{\partial t} + \nabla \cdot (\rho \vec{v} \vec{v}) = -\nabla p + \mu \nabla^2 \vec{v} $$
$$ \text{3. Energy Conservation: } \rho c_p \left( \frac{\partial T}{\partial t} + \vec{v} \cdot \nabla T \right) = \nabla \cdot (k \nabla T) + q_{\text{gen}} $$
Numerical Simulation and Experimental Validation
CFD analysis of the energy storage battery module employed a 2.14-million-element mesh with boundary conditions:
| Parameter | Value |
|---|---|
| Inlet Temperature | 20°C ± 0.5°C |
| Cell Heat Generation | 16.5W @ 0.5C rate |
| Anisotropic Conductivity | λx,z = 14W/m·K, λy = 2.5W/m·K |
Transient simulation results revealed critical thermal characteristics:
$$ \Delta T_{\text{max}} = T_{\text{surface}} – T_{\text{coolant}} = \frac{q”_{\text{gen}} L^2}{2k_{\text{eff}}} $$
| Operating Phase | Simulated Tmax (°C) | Experimental Tmax (°C) |
|---|---|---|
| Charge Completion | 31.8 | 33.0 |
| Discharge Completion | 33.9 | 32.5 |
Thermal Performance Optimization
The energy storage battery system demonstrates superior cooling efficiency through:
$$ \text{Nusselt Number: } Nu = \frac{hD_h}{k_f} = 0.023 \mathrm{Re}^{0.8} \mathrm{Pr}^{0.4} $$
| Flow Rate (L/min) | ΔTcell-cell (°C) | Cooling Efficiency (%) |
|---|---|---|
| 2.5 | 3.4 | 82.7 |
| 3.0 | 2.9 | 91.4 |
Conclusion
This immersion cooling strategy for energy storage battery systems achieves:
- Maximum temperature differential: 2.9°C (simulation) vs 2.7°C (experimental)
- Peak temperature reduction of 18.2% compared to conventional air cooling
- Flow rate optimization at 3L/min per module (6L/min per pack)
The numerical model shows excellent agreement with experimental data (R² = 0.96), confirming its validity for designing large-scale energy storage battery systems. Future work will focus on multi-phase cooling enhancement and cycle life prediction under variable thermal loads.