In the field of energy storage, the thermal management of energy storage cells is a critical aspect that ensures safe and efficient operation. As global energy demands continue to rise and renewable energy sources like hydropower, wind, and solar power become more prevalent, the importance of advanced energy storage systems has grown significantly. Energy storage cells, particularly lithium iron phosphate batteries, are widely used due to their high cycle life, energy density, and overall performance. However, these energy storage cells generate heat during charging and discharging cycles, which can lead to temperature non-uniformities and potential thermal runaway if not properly managed. Effective cooling is essential to maintain the temperature of energy storage cells within a safe range, typically between -20°C and 55°C, to prevent degradation and hazards. This study focuses on the cooling performance of modular immersed energy storage cell units, utilizing computational fluid dynamics (CFD) simulations and experimental validation to analyze temperature distribution and optimize design parameters.
The modular immersed energy storage system consists of energy storage cell units submerged in a cooling fluid, which directly contacts the cells to dissipate heat. This system includes components such as circulation pumps, plate heat exchangers, and external cooling units. The energy storage cells are arranged in modules, and the cooling fluid flows through channels to remove heat, ensuring that the temperature remains uniform and within acceptable limits. The design aims to achieve a maximum temperature below 35°C and a temperature difference of less than 3°C among the energy storage cells. This approach leverages the high efficiency and uniform cooling provided by liquid immersion, which reduces thermal resistance compared to indirect cooling methods like cold plates. The following sections detail the numerical simulation methodology, including governing equations, model setup, and boundary conditions, followed by an analysis of simulation results under various flow rates and experimental validation.
In numerical simulations, the cooling performance of energy storage cell modules is evaluated using CFD software. The fluid flow and heat transfer within the immersed system are governed by the fundamental laws of fluid dynamics and thermodynamics. The key equations include the mass conservation equation, momentum conservation equation, and energy conservation equation. For a three-dimensional, viscous, and unsteady flow, these equations are expressed as follows:
Mass conservation equation:
$$\frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) = 0$$
where $\rho$ is the fluid density, $t$ is time, and $\mathbf{v}$ is the velocity vector.
Momentum conservation equation:
$$\frac{\partial \mathbf{v}}{\partial t} + (\mathbf{v} \cdot \nabla) \mathbf{v} = -\frac{1}{\rho} \nabla p + \mathbf{f} + \nu \nabla^2 \mathbf{v}$$
where $p$ is the pressure, $\mathbf{f}$ represents body forces, and $\nu$ is the kinematic viscosity.
Energy conservation equation:
$$\frac{\partial (\rho T)}{\partial t} + \nabla \cdot (\rho \mathbf{u} T) = \nabla \cdot \left( \frac{k}{c_p} \nabla T \right) + S_h + \Phi$$
where $T$ is the temperature, $\mathbf{u}$ is the fluid velocity, $k$ is the thermal conductivity, $c_p$ is the specific heat capacity, $S_h$ is the heat source term, and $\Phi$ represents the viscous dissipation function.
These equations are solved iteratively with appropriate boundary conditions to predict the temperature distribution in the energy storage cell modules. The simulation model simplifies the energy storage cells as uniform heat generation bodies to reduce computational complexity while maintaining accuracy. The mesh for the model consists of approximately 2.14 million elements to capture detailed flow and thermal characteristics. Boundary conditions include an inlet coolant temperature of 20°C and flow rates based on the thermal load, with calculations derived from the energy balance equation:
$$P = \rho c_p \dot{V} \Delta T$$
where $P$ is the heat generation power, $\dot{V}$ is the volumetric flow rate, and $\Delta T$ is the temperature rise. For a single module with a heat generation of 16.5 W per energy storage cell, the flow rate is optimized to ensure a temperature rise of less than 5°C, leading to flow rates of 2.5 L/min and 3 L/min for analysis.
The simulation results for steady-state conditions at a flow rate of 2.5 L/min indicate that the maximum surface temperature of the energy storage cells reaches approximately 36.9°C, with a temperature difference of about 3.4°C. This exceeds the design requirements, highlighting the need for higher flow rates. At 3 L/min, the maximum temperature decreases to 35.7°C, and the temperature difference reduces to 3.2°C, which is closer to the targets but still not fully compliant. Therefore, transient simulations are conducted to reflect real-world operating conditions, including charging for 2 hours, resting for 0.5 hours, and discharging for 2 hours at a 0.5C rate. The transient analysis shows that at the end of charging, the maximum temperature is 31.8°C with a difference of 2.1°C; after resting, it drops to 28.6°C with a difference of 1.8°C; and at the end of discharging, it peaks at 33.9°C with a difference of 2.9°C. This confirms that a flow rate of 3 L/min per module (equivalent to 6 L/min for a single pack) meets the design criteria for energy storage cells.
| Flow Rate (L/min) | Simulation Type | Maximum Temperature (°C) | Temperature Difference (°C) | Compliance with Design |
|---|---|---|---|---|
| 2.5 | Steady-State | 36.9 | 3.4 | No |
| 3.0 | Steady-State | 35.7 | 3.2 | No |
| 3.0 | Transient (Charging End) | 31.8 | 2.1 | Yes |
| 3.0 | Transient (Resting End) | 28.6 | 1.8 | Yes |
| 3.0 | Transient (Discharging End) | 33.9 | 2.9 | Yes |
To validate the simulation findings, an experimental setup is designed based on the optimized flow rate of 3 L/min per module. The test rig includes a single pack of immersed energy storage cell units, a chiller for coolant supply, a charge-discharge integrated system, and high-voltage cabinets. Temperature sensors are attached to the terminals of the energy storage cells to monitor variations during operation. The experiment involves multiple charge-discharge cycles at a 0.5C rate, with each cycle consisting of 2 hours charging, 0.5 hours resting, and 2 hours discharging. The data collected over four complete cycles show that the maximum temperature during charging is 33°C, and during discharging, it is 32.5°C, with an overall peak of 33°C. The maximum temperature difference between energy storage cells is approximately 2.7°C. These results align closely with the transient simulation values, confirming the accuracy of the CFD model. The consistency between simulation and experiment demonstrates that CFD can be a reliable tool for designing and optimizing cooling systems for energy storage cells.
The experimental data further reveal that the highest temperatures and largest differences occur at the end of charging and discharging phases, which is consistent with the simulation trends. This emphasizes the importance of transient analysis in capturing real-world dynamics. The close agreement between experimental and simulated values for energy storage cell temperatures and differences validates the use of this methodology in future designs. For instance, the simulation accurately predicted the temperature distribution, with hotspots located away from the coolant outlet due to flow path design and radiative effects. This insight can guide improvements in module layout and fluid dynamics to enhance cooling performance for energy storage cells.
In addition to the primary analysis, several factors influence the cooling performance of energy storage cells. The thermal conductivity of the cooling fluid, the arrangement of cells within the module, and the flow distribution all play crucial roles. The cooling fluid used in this study has high insulation properties to ensure safety while providing efficient heat transfer. The module design incorporates gaps and pads between energy storage cells to prevent short circuits and manage heat flow. The thermal resistance model can be described using Fourier’s law of heat conduction:
$$q = -k \nabla T$$
where $q$ is the heat flux, and $k$ is the thermal conductivity. For the energy storage cells, the anisotropic thermal conductivity is considered, with values of 14 W/(m·K) in the X and Z directions and 2.5 W/(m·K) in the Y direction. The interstitial pads have a conductivity of 0.05 W/(m·K), which affects the overall thermal management.
To further optimize the system, parametric studies can be conducted using the following relation for heat transfer efficiency:
$$\eta = \frac{\dot{Q}_{\text{actual}}}{\dot{Q}_{\text{max}}}$$
where $\dot{Q}_{\text{actual}}$ is the actual heat transfer rate, and $\dot{Q}_{\text{max}}$ is the maximum possible heat transfer rate. This can be tied to the Nusselt number (Nu) for convective heat transfer:
$$\text{Nu} = \frac{h L}{k_f}$$
where $h$ is the convective heat transfer coefficient, $L$ is the characteristic length, and $k_f$ is the fluid thermal conductivity. For the immersed system, the Reynolds number (Re) and Prandtl number (Pr) are also critical:
$$\text{Re} = \frac{\rho v D}{\mu}, \quad \text{Pr} = \frac{\mu c_p}{k_f}$$
where $D$ is the hydraulic diameter, and $\mu$ is the dynamic viscosity. These dimensionless numbers help in scaling and comparing different designs for energy storage cell cooling.
| Parameter | Experimental Value | Simulated Value | Deviation |
|---|---|---|---|
| Maximum Temperature (°C) | 33.0 | 33.9 | 0.9 |
| Temperature Difference (°C) | 2.7 | 2.9 | 0.2 |
| Charging End Temperature (°C) | 33.0 | 31.8 | 1.2 |
| Discharging End Temperature (°C) | 32.5 | 33.9 | 1.4 |
The implications of this research extend to larger-scale energy storage systems, where multiple modules are integrated. The modular approach allows for scalability and ease of maintenance, while the immersion cooling technique offers superior thermal performance compared to traditional air or indirect liquid cooling. Future work could explore multi-phase cooling or advanced materials to further enhance the efficiency of energy storage cells. Additionally, the CFD model can be refined to include more detailed physics, such as turbulent flow models or coupled electro-thermal simulations, to better predict the behavior of energy storage cells under varying operational conditions.
In conclusion, this study demonstrates that a flow rate of 3 L/min per module effectively maintains the temperature of energy storage cells below 35°C with a difference under 3°C, meeting design requirements. The combination of CFD simulations and experimental validation provides a robust framework for analyzing and optimizing cooling performance. The results show strong agreement between simulated and experimental values, confirming the reliability of the numerical approach. This methodology can be applied to the development of advanced immersed energy storage systems, contributing to safer and more efficient energy storage solutions. The insights gained here underscore the importance of thermal management in prolonging the lifespan and ensuring the safety of energy storage cells, which are pivotal in the transition to renewable energy sources.