In the context of global energy systems, the significance of electrochemical energy storage has grown substantially, with operational safety emerging as a critical concern. Lithium-ion batteries, particularly LiFePO4 types, serve as fundamental energy storage cells in large-scale applications due to their high energy density and longevity. However, the high packing density and substantial charge-discharge rates of these energy storage cells lead to significant heat accumulation. When temperatures exceed optimal operating ranges, the risk of thermal runaway increases, posing potential safety hazards. Consequently, advanced thermal management systems, especially liquid cooling with efficient heat dissipation capabilities, have become a focal point in both academic and industrial research. This study focuses on optimizing the liquid cooling performance for large-capacity energy storage modules, proposing a novel parallel serpentine flow channel design that balances cooling efficiency and energy consumption. Through computational simulations and experimental validation, we demonstrate the effectiveness of staggered cooling plate arrangements and differentiated flow velocity strategies in enhancing thermal homogeneity and reducing peak temperatures.
The thermal behavior of energy storage cells during operation is governed by multiple heat generation mechanisms, including electrochemical reaction heat, Joule heating from internal resistance, polarization heat, and side reaction heat. Heat transfer occurs via conduction, convection, and radiation. To model this, we consider a module comprising 30 LiFePO4 cells, each with a capacity of 208 Ah and dimensions of 170 mm × 200 mm × 50 mm, arranged in three columns with ten cells each and 40 mm spacing. The overall module size is 540 mm × 200 mm × 860 mm. For simulation purposes, we simplify the geometry by neglecting terminals and interconnects, as these have minimal impact on thermal distribution. The thermal properties of the energy storage cells are summarized in Table 1, and the cooling liquid (50% ethylene glycol solution) parameters are listed in Table 2.
| Parameter | Value |
|---|---|
| Density (kg/m³) | 2405 |
| Specific Heat Capacity (J/kg·K) | 1329 |
| Thermal Conductivity (W/m·K) | 3.72 (X), 28 (Z), 26 (Y) |
| Heat Generation Rate at 1C (W/m³) | 6362 |
| Parameter | Value |
|---|---|
| Density (kg/m³) | 1070 |
| Specific Heat Capacity (J/kg·K) | 3396 |
| Thermal Conductivity (W/m·K) | 0.399 |
| Dynamic Viscosity (kg/m·s) | 0.00339 |
The heat generation in energy storage cells can be modeled using the energy balance equation. For a single cell, the transient heat equation is given by:
$$ \rho c_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + \dot{q}_{gen} $$
where \( \rho \) is density, \( c_p \) is specific heat capacity, \( k \) is thermal conductivity, \( T \) is temperature, \( t \) is time, and \( \dot{q}_{gen} \) is the volumetric heat generation rate. For LiFePO4 energy storage cells under 1C charging, \( \dot{q}_{gen} \) is approximated as 6362 W/m³ based on experimental data. The cooling performance of the liquid cold plate is evaluated using the Navier-Stokes equations for fluid flow and energy equation for heat transfer:
$$ \rho \left( \frac{\partial \mathbf{v}}{\partial t} + \mathbf{v} \cdot \nabla \mathbf{v} \right) = -\nabla p + \mu \nabla^2 \mathbf{v} + \mathbf{f} $$
$$ \rho c_p \left( \frac{\partial T}{\partial t} + \mathbf{v} \cdot \nabla T \right) = k \nabla^2 T + \dot{q}_{fluid} $$
where \( \mathbf{v} \) is velocity, \( p \) is pressure, \( \mu \) is dynamic viscosity, and \( \dot{q}_{fluid} \) represents heat sources in the fluid. We designed three flow channel configurations: traditional serpentine, traditional parallel, and a novel parallel serpentine channel that combines parallel and serpentine features to optimize flow distribution and heat transfer. The parallel serpentine channel aims to reduce pressure drop while maintaining effective cooling, addressing the high energy consumption associated with traditional serpentine designs.
Using computational fluid dynamics (CFD) simulations in Fluent, we analyzed the thermal and hydraulic performance of these channels. The mesh size was set to 2 mm, resulting in approximately 16.48 million elements. Simulations were conducted under an ambient temperature of 27°C, with a 1C charging rate for 1 hour. The cooling liquid inlet temperature was 25°C, and inlet velocity was varied to assess performance. The results, summarized in Table 3, show that the traditional serpentine channel achieved the lowest maximum temperature and temperature difference but had the highest pressure drop (1252.5 Pa), leading to significant pumping energy. The traditional parallel channel had the lowest pressure drop (101.175 Pa) but poorer cooling, with a maximum temperature of 32.881°C and temperature difference of 7.476°C. The parallel serpentine channel offered a compromise, with a maximum temperature of 32.610°C, temperature difference of 7.077°C, and pressure drop of 748.641 Pa, representing a 59.77% reduction in pumping energy compared to the serpentine design.
| Flow Channel Type | Max Temperature (°C) | Temperature Difference (°C) | Pressure Drop (Pa) |
|---|---|---|---|
| Traditional Serpentine | 32.481 | 6.892 | 1252.5 |
| Traditional Parallel | 32.881 | 7.476 | 101.175 |
| Parallel Serpentine | 32.610 | 7.077 | 748.641 |
To validate the simulations, we fabricated the parallel serpentine cold plates using aluminum alloy via friction stir welding and integrated them into an experimental setup. The energy storage module was housed in a stainless steel enclosure measuring 1244 mm × 794 mm × 260 mm, with cooling liquid circulated by a variable-speed pump and maintained at 25°C by a chiller. Temperature sensors were placed at the center of each cell in the outer and middle columns to monitor thermal distribution. Experiments included natural convection tests and liquid cooling tests under different configurations: symmetric versus staggered cold plate arrangements, and uniform versus differentiated flow velocities. For natural convection, the module reached a maximum temperature of 40.3°C after 1C charging, with a temperature rise of 13.2°C. With liquid cooling, the parallel serpentine system reduced the maximum temperature to 34.3°C under symmetric arrangement and 0.1 m/s inlet velocity, demonstrating a significant improvement.
Further optimization involved staggered cold plate installation, where inlets alternated between sides of the module. This approach reduced the maximum temperature by 0.3°C and decreased the temperature difference in the outer column by 25%, from 1.2°C to 0.9°C, as shown in Table 4. The staggered arrangement enhances thermal homogeneity by balancing coolant pathways, reducing hotspots in the energy storage cells.
| Cell ID | Symmetric Installation Max Temp (°C) | Staggered Installation Max Temp (°C) |
|---|---|---|
| 1-1 | 32.6 | 32.7 |
| 1-2 | 33.2 | 33.1 |
| 1-3 | 33.5 | 33.0 |
| 1-4 | 33.7 | 33.6 |
| 1-5 | 33.4 | 33.3 |
| 1-6 | 33.7 | 33.5 |
| 1-7 | 33.2 | 33.6 |
| 1-8 | 33.6 | 33.3 |
| 1-9 | 33.8 | 33.2 |
| 1-10 | 33.8 | 32.9 |
| Row-wise ΔT | 1.2 | 0.9 |
Additionally, we investigated flow velocity differentiation, where outer cold plates operated at 0.1 m/s and inner plates at 0.3 m/s, maintaining the same total flow rate as a uniform 0.2 m/s case. This strategy further lowered the maximum temperature to 32.7°C, compared to 32.9°C under uniform flow, as detailed in Table 5. The differentiated flow addresses the higher heat accumulation in central energy storage cells by allocating more coolant to critical areas, optimizing both cooling and energy efficiency.
| Flow Setting | Max Module Temperature (°C) |
|---|---|
| Natural Convection (0 m/s) | 40.3 |
| Uniform Flow (0.1 m/s) | 34.3 |
| Uniform Flow (0.2 m/s) | 32.9 |
| Differentiated Flow (Outer 0.1 m/s, Inner 0.3 m/s) | 32.7 |
Error analysis between experimental and simulation data revealed maximum absolute errors of 1.94°C (5.6% error rate) for symmetric installation and 1.42°C (4.3% error rate) for uniform flow experiments. These discrepancies arise from simplifications in the thermal model, such as assuming constant internal resistance and material properties, as well as practical factors like manufacturing tolerances, environmental conditions, and measurement inaccuracies. Despite these, the models consistently predicted trends, validating the parallel serpentine design’s efficacy.
The optimization of thermal management for energy storage cells is crucial for ensuring safety and longevity in high-density applications. Our parallel serpentine flow channel reduces pressure drop by over 40% compared to traditional serpentine designs while maintaining effective cooling. The staggered arrangement and flow velocity differentiation further enhance performance, demonstrating that tailored coolant distribution can mitigate thermal gradients without increasing energy consumption. Future work will explore dynamic control strategies and integration with phase change materials to address transient heat loads in energy storage cells. In conclusion, this study provides a scalable solution for thermal management in large-capacity energy storage systems, contributing to safer and more efficient battery technologies.