Abstract
This study investigates the cooling performance of modular immersion energy storage battery units using computational fluid dynamics (CFD) simulations and experimental validation. The research focuses on optimizing the thermal management system to ensure battery temperatures remain below 35°C with a temperature difference under 3°C. Simulations were conducted at flow rates of 2.5 L·min⁻¹ and 3.0 L·min⁻¹, with results showing that a flow rate of 3.0 L·min⁻¹ meets design requirements. Experimental validation confirmed the accuracy of the CFD model, with a maximum temperature of 33°C (vs. 33.9°C in simulation) and a temperature difference of 2.7°C (vs. 2.9°C in simulation). These findings highlight the critical role of fluid dynamics in enhancing the thermal efficiency and safety of energy storage battery systems.
Introduction
The rapid growth of renewable energy sources, such as solar and wind power, has intensified the demand for efficient energy storage solutions. Lithium iron phosphate (LiFePO₄) batteries, widely used in energy storage systems due to their high energy density and cycle stability, require robust thermal management to prevent overheating and thermal runaway. Effective cooling ensures uniform temperature distribution, prolongs battery life, and mitigates safety risks.
Current cooling technologies for energy storage batteries include air cooling and liquid cooling. While air cooling is cost-effective, its poor thermal uniformity limits its application in high-power scenarios. Liquid cooling, particularly immersion cooling, offers superior heat dissipation and temperature homogeneity by directly submerging batteries in dielectric fluid. This study explores the cooling performance of modular immersion energy storage battery units using CFD simulations and experimental validation, aiming to optimize thermal management for large-scale applications.
Methodology
1. Modular Immersion Energy Storage System Design
The modular system comprises:
- Immersion battery unit modules: Housing LiFePO₄ batteries submerged in dielectric coolant.
- Coolant circulation system: Includes pumps, plate heat exchangers, and external cooling units.
- Monitoring and control systems: Track temperature, flow rate, and pressure.
The system operates by circulating coolant through battery modules, transferring heat to external cooling units, and maintaining temperatures within a safe range (-20°C to 55°C).
2. Numerical Simulation
Governing Equations
The CFD model solves the Navier-Stokes equations for fluid flow and energy transfer:
- Mass Conservation:
∂ρ∂t+∇⋅(ρv)=0∂t∂ρ+∇⋅(ρv)=0
- Momentum Conservation:
∂v∂t+(v⋅∇)v=1ρ∇⋅σ+f∂t∂v+(v⋅∇)v=ρ1∇⋅σ+f
- Energy Conservation:
∂(ρT)∂t+∇⋅(ρUT)=∇⋅(hcp∇T)+Sh+Φ∂t∂(ρT)+∇⋅(ρUT)=∇⋅(cph∇T)+Sh+Φ
Model Parameters
- Battery specifications: 280 Ah LiFePO₄ cells (164 mm × 72 mm × 194 mm).
- Thermal conductivity: 14 W/(m·K) (X/Z-axis), 2.5 W/(m·K) (Y-axis).
- Heat generation: 16.5 W per cell during 0.5P charge/discharge.
- Mesh: ~2.14 million cells for a single IP26S module.
Boundary Conditions
- Inlet coolant temperature: 20°C.
- Flow rates: 2.5 L·min⁻¹ and 3.0 L·min⁻¹.
- Maximum allowable temperature rise: 5°C.
The relationship between flow rate (vv) and temperature rise (ΔTΔT) is derived as:P=CρvΔTP=CρvΔT
where CC is specific heat capacity, and ρρ is coolant density.
3. Experimental Setup
A prototype immersion battery module was tested under 0.5P charge/discharge cycles. Key components included:
- Immersion battery unit module.
- Chiller unit for coolant temperature control.
- Data acquisition system for real-time temperature monitoring.
Results and Discussion
1. Simulation Results
Flow Rate: 2.5 L·min⁻¹
- Maximum cell temperature: 36.9°C.
- Temperature difference: 3.4°C.
Flow Rate: 3.0 L·min⁻¹
- Maximum cell temperature: 33.9°C.
- Temperature difference: 2.9°C.
The higher flow rate reduced both maximum temperature and temperature gradient, meeting design criteria.
2. Experimental Validation
| Parameter | Simulation | Experiment |
|---|---|---|
| Maximum Temperature (°C) | 33.9 | 33.0 |
| Temperature Difference (°C) | 2.9 | 2.7 |
Experimental data closely matched simulation results, validating the CFD model. Temperature peaks occurred at the end of charge/discharge cycles, consistent with transient simulations.
3. Thermal Performance Analysis
The table below summarizes key metrics for energy storage battery cooling:
| Flow Rate (L·min⁻¹) | Max Temp (°C) | Temp Difference (°C) |
|---|---|---|
| 2.5 | 36.9 | 3.4 |
| 3.0 | 33.9 | 2.9 |
Increasing coolant flow rate enhances heat dissipation, ensuring safer operation of energy storage battery systems.
Conclusion
This study demonstrates that a flow rate of 3.0 L·min⁻¹ effectively maintains energy storage battery temperatures below 35°C with a temperature difference under 3°C. The strong correlation between CFD simulations and experimental results validates the reliability of numerical models in optimizing thermal management systems. These insights are critical for advancing immersion cooling technologies in large-scale energy storage applications, ensuring both efficiency and safety.
References
- Hales, A., et al. (2019). Journal of the Electrochemical Society, 166(12), 2383–2395.
- Liu, D., et al. (2015). Chinese Journal of Scientific Instrument, 36(1), 1–16.
- Wang, H. T., et al. (2020). Applied Thermal Engineering, 178, 115591.
- Wang, F. J. (2004). Computational Fluid Dynamics Analysis: CFD Software Principles and Applications. Tsinghua University Press.