With the rapid growth in global energy demand and the shift toward renewable sources, the integration of battery energy storage systems has become crucial for stabilizing power grids and enabling sustainable energy transitions. As a core component, lithium-ion batteries are increasingly designed for larger capacities to meet the scalability requirements of grid-scale applications. However, managing the thermal behavior of these high-capacity batteries poses significant challenges, as excessive heat generation during charge-discharge cycles can lead to performance degradation, safety hazards, and reduced lifespan. In this context, effective thermal management is paramount, and immersion liquid cooling has emerged as a promising solution due to its direct contact with battery surfaces, low thermal resistance, and high cooling efficiency. While extensive research exists on immersion cooling for small-capacity batteries, such as those in electric vehicles, studies focusing on large-capacity battery energy storage systems remain limited. This gap motivates our work, where we aim to systematically evaluate the cooling performance of immersion liquid cooling systems tailored for 280 Ah lithium iron phosphate batteries through comprehensive numerical simulations.
In this study, we adopt a first-person perspective as researchers engaged in advancing thermal management for battery energy storage systems. We explore key design parameters—including battery spacing, coolant inlet-outlet configurations, inlet flow velocity, and coolant types—to optimize cooling effectiveness. Our approach involves developing a detailed three-dimensional computational fluid dynamics model that couples battery heat generation with fluid flow and heat transfer phenomena. By simulating various scenarios, we quantify impacts on maximum temperature and temperature uniformity within the battery pack. Furthermore, we conduct a sensitivity analysis to rank the influence of coolant thermophysical properties on cooling performance. The findings are intended to provide practical insights for designing efficient and reliable immersion cooling systems for large-scale battery energy storage systems, thereby enhancing their operational safety and longevity in real-world applications.
The importance of battery energy storage systems in modern energy infrastructure cannot be overstated. These systems facilitate the integration of intermittent renewable sources like solar and wind by storing excess energy and discharging it during peak demand. However, the thermal dynamics within battery packs are complex, as heat accumulation can trigger thermal runaway, a critical safety concern. Traditional air-cooling methods often fall short in managing heat from high-capacity batteries due to low thermal conductivity and specific heat capacity of air. Phase change materials offer improved thermal buffering but face limitations in cost and structural stability. Indirect liquid cooling, or cold plate systems, reduces thermal resistance compared to air cooling, yet direct immersion cooling presents superior advantages by immersing batteries in dielectric fluids, enabling uniform heat dissipation and direct contact cooling. This study focuses on immersion cooling for battery energy storage systems with capacities of 280 Ah, addressing a niche that is underexplored in existing literature.
Our research methodology begins with establishing a physical model of the battery pack and cooling system. The battery pack consists of 52 prismatic lithium iron phosphate cells arranged in a 4×13 configuration, each with dimensions of 204 mm × 174 mm × 72 mm and a nominal capacity of 280 Ah. The pack is housed in a rectangular enclosure with dimensions optimized to accommodate immersion cooling. Coolant flows through the enclosure, entering and exiting via strategically positioned ports to maximize heat extraction. We vary parameters such as battery spacing (from 0 to 10 mm), inlet-outlet heights (creating nine distinct flow configurations), inlet velocities (ranging from 0.2 to 1.6 m/s), and coolant types (including synthetic oil, MIVOLT-DF7, FC-72, deionized water, and silicone oil). These variations allow us to assess their effects on thermal performance metrics, specifically the maximum temperature difference ($\Delta T_{\text{max}}$) and maximum temperature ($T_{\text{max}}$) within the battery pack.
To simulate the thermal behavior, we employ a coupled electrochemical-thermal model. The battery heat generation rate is calculated using the Bernardi equation, which accounts for irreversible and reversible heat effects during discharge. The heat generation per unit volume $q_v$ is given by:
$$q_v = \frac{1}{V_b} \left( I^2 R_t – I T \frac{\partial U_0}{\partial T} \right)$$
where $V_b$ is the battery volume, $I$ is the current, $R_t$ is the internal resistance, $T$ is the temperature, and $\frac{\partial U_0}{\partial T}$ is the temperature coefficient of the open-circuit voltage. For our battery energy storage system, we assume a discharge rate of 1 C, corresponding to a current of 280 A, leading to a volumetric heat generation rate of approximately 14,386 W/m³. The battery thermal properties are summarized in Table 1.
| Parameter | Value |
|---|---|
| Nominal Capacity | 280 Ah |
| Voltage Range | 2.5–3.65 V |
| Dimensions (H×W×T) | 204 mm × 174 mm × 72 mm |
| Mass | 5435 g |
| Density | 2118 kg/m³ |
| Specific Heat Capacity | 1029.4 J/(kg·K) |
| Thermal Conductivity (x/y/z) | 21.6 / 2.1 / 21.6 W/(m·K) |
| Internal Resistance | 0.43 mΩ |
The fluid domain is governed by the conservation laws of mass, momentum, and energy. The continuity, momentum, and energy equations for the coolant are expressed as:
$$\frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) = 0$$
$$\frac{\partial (\rho \mathbf{v})}{\partial t} + \nabla \cdot (\rho \mathbf{v} \mathbf{v}) = -\nabla p + \mu \nabla^2 \mathbf{v} + \rho \mathbf{g}$$
$$\rho c_p \frac{\partial T}{\partial t} + \nabla \cdot (\rho c_p \mathbf{v} T) = \nabla \cdot (\lambda \nabla T)$$
where $\rho$ is density, $\mathbf{v}$ is velocity, $p$ is pressure, $\mu$ is dynamic viscosity, $c_p$ is specific heat capacity, $\lambda$ is thermal conductivity, and $\mathbf{g}$ is gravitational acceleration. The battery solid domain uses the heat conduction equation with a source term:
$$\rho_b c_b \frac{\partial T_b}{\partial t} = \nabla \cdot (\lambda_b \nabla T_b) + q_v$$
Boundary conditions include a constant inlet temperature of 25°C, velocity inlet for coolant flow, pressure outlet at the exit, and natural convection on external walls with a heat transfer coefficient of 5 W/(m²·K). We validate our model by comparing simulated battery surface temperatures with experimental data from literature, showing a maximum error of 5.1%, confirming model reliability for the battery energy storage system.
We first investigate the effect of battery spacing on cooling performance. The spacing between adjacent cells in the horizontal direction is varied from 0 to 10 mm. Results indicate that increasing spacing up to 5 mm enhances cooling by reducing flow resistance and increasing coolant-battery contact area, but further spacing reduces coolant velocity and weakens convection. As shown in Table 2, optimal spacing at 5 mm reduces $\Delta T_{\text{max}}$ by 1.57°C and $T_{\text{max}}$ by 1.84°C compared to zero spacing. This highlights the importance of spacing design in immersion cooling for battery energy storage systems.
| Battery Spacing (mm) | $\Delta T_{\text{max}}$ (°C) | $T_{\text{max}}$ (°C) |
|---|---|---|
| 0 | 10.94 | 37.19 |
| 2.5 | 10.64 | 36.61 |
| 5 | 9.37 | 35.35 |
| 7.5 | 9.52 | 35.58 |
| 10 | 9.86 | 35.75 |
Next, we examine coolant inlet-outlet configurations. Nine cases are tested by altering inlet and outlet heights relative to the battery enclosure bottom. The inlet position exerts a stronger influence on temperature uniformity than the outlet position. For instance, when the outlet is fixed at 182 mm height, lowering the inlet from 182 mm to 102 mm reduces $\Delta T_{\text{max}}$ by 5.6%, whereas varying the outlet height with a fixed inlet causes less than 1% change. This is attributed to inlet-driven flow disturbances and vortex formation, which enhance mixing and heat transfer. Flow field visualizations reveal that inlet positioning significantly affects velocity and vorticity distributions, thereby impacting cooling efficiency in the battery energy storage system.
The impact of coolant inlet velocity is analyzed over a range from 0.2 to 1.6 m/s. Higher velocities improve cooling by intensifying forced convection, but with diminishing returns. Specifically, increasing velocity from 0.2 to 0.4 m/s reduces $\Delta T_{\text{max}}$ by 21.2% and $T_{\text{max}}$ by 8.0%, while from 1.4 to 1.6 m/s, reductions are only 2.0% and 0.5%, respectively. This trend is due to the nonlinear relationship between flow rate and convective heat transfer coefficient. Moreover, at velocities above 0.8 m/s, battery temperatures stabilize earlier during discharge, indicating faster thermal equilibrium. These findings suggest that while higher flow benefits cooling, energy consumption for pumping must be balanced in practical battery energy storage system designs.
Coolant type plays a critical role in immersion cooling performance. We evaluate five dielectric fluids, with thermophysical properties listed in Table 3. Deionized water achieves the best cooling, with $\Delta T_{\text{max}}$ = 3.61°C and $T_{\text{max}}$ = 28.64°C, whereas silicone oil performs worst ($\Delta T_{\text{max}}$ = 8.78°C, $T_{\text{max}}$ = 34.63°C). This disparity stems from water’s high thermal conductivity and specific heat capacity, coupled with low viscosity, which facilitate efficient heat absorption and dissipation. Synthetic oil, MIVOLT-DF7, and FC-72 show intermediate performance. The pressure drop across the system and battery surface heat transfer coefficients vary with coolant properties, influencing overall system efficiency. For instance, deionized water has a heat transfer coefficient of 22.69 W/(m²·K), compared to 18.81 W/(m²·K) for silicone oil, underscoring the importance of fluid selection in battery energy storage system thermal management.
| Coolant Type | Density (kg/m³) | Specific Heat (J/(kg·K)) | Thermal Conductivity (W/(m·K)) | Dynamic Viscosity (Pa·s) |
|---|---|---|---|---|
| Synthetic Oil | 807 | 2523 | 0.1592 | 0.0070 |
| MIVOLT-DF7 | 916 | 1907 | 0.129 | 0.0150 |
| FC-72 | 1680 | 1100 | 0.057 | 0.0006 |
| Deionized Water | 998 | 4182 | 0.598 | 0.0010 |
| Silicone Oil | 965 | 1460 | 0.160 | 0.0965 |
To deeper understand coolant effects, we conduct a sensitivity analysis on thermophysical parameters: density ($\rho$), specific heat capacity ($c_p$), thermal conductivity ($\lambda$), and dynamic viscosity ($\mu$). Holding other parameters constant, we vary each by ±40% around baseline values for synthetic oil and deionized water. Results indicate that density has the greatest influence on $\Delta T_{\text{max}}$, followed by specific heat capacity, thermal conductivity, and dynamic viscosity. For example, increasing density by 40% reduces $\Delta T_{\text{max}}$ by approximately 34%, while a similar increase in viscosity raises $\Delta T_{\text{max}}$ by 16%. This ranking is consistent across different coolants and operating conditions, providing a guideline for optimizing coolant formulations for battery energy storage systems. The relationship can be summarized by a simplified sensitivity index $S$ defined as:
$$S = \frac{\Delta (\Delta T_{\text{max}})}{\Delta P} \times 100\%$$
where $\Delta P$ is the percentage change in a thermophysical parameter. Values of $S$ for density, specific heat, conductivity, and viscosity are -0.85, -0.60, -0.42, and +0.40, respectively, indicating their relative weights in cooling performance.
In discussing these results, we emphasize the implications for designing immersion cooling systems for large-capacity battery energy storage systems. The optimal battery spacing of 5 mm balances flow resistance and convection enhancement. Inlet positioning should be prioritized over outlet design to control flow patterns and temperature uniformity. While higher coolant velocities improve cooling, diminishing returns suggest an economic optimum around 0.8–1.0 m/s for our configuration. Coolant selection should favor fluids with high density and specific heat, such as deionized water, though electrical insulation requirements may necessitate dielectric alternatives like synthetic oils. Our sensitivity analysis reinforces that coolant density is a key lever for thermal performance, potentially guiding the development of advanced nanofluids or hybrid coolants for battery energy storage systems.
We further explore the interaction between parameters through multivariate analysis. For instance, combining optimal spacing with high-velocity deionized water flow yields the lowest temperatures, but practical constraints like pump power and fluid compatibility must be considered. The immersion cooling system’s effectiveness in preventing thermal runaway is also noted, as direct fluid contact can rapidly quench hotspots, enhancing safety in battery energy storage systems. Comparative studies with other cooling methods, such as air or cold plates, could be future work to quantify immersion cooling advantages in energy efficiency and cost.
Limitations of our study include assumptions of uniform battery heat generation and constant fluid properties, which may not hold under extreme conditions. Transient effects during rapid charging or dynamic loads require further investigation. Additionally, we focus on a specific battery geometry and pack layout; variations in cell size or arrangement may alter optimal parameters. Nevertheless, our findings provide a robust framework for optimizing immersion cooling in battery energy storage systems, contributing to more reliable and efficient energy storage solutions.
In conclusion, this simulation-based study systematically evaluates the cooling performance of immersion liquid cooling systems for large-capacity battery energy storage systems. We demonstrate that battery spacing, inlet-outlet configuration, flow velocity, and coolant type significantly influence thermal management outcomes. Optimal design parameters include a 5 mm battery spacing, strategically positioned inlets, moderate to high flow velocities, and coolants with high density and specific heat capacity like deionized water. The sensitivity analysis reveals that coolant density is the most influential thermophysical property, followed by specific heat, thermal conductivity, and viscosity. These insights offer valuable guidance for engineers and researchers developing advanced thermal management strategies for battery energy storage systems, ultimately supporting the safe and efficient deployment of grid-scale energy storage. Future work should involve experimental validation and expansion to diverse battery chemistries and system scales to further refine immersion cooling applications in the rapidly evolving field of battery energy storage systems.