In recent years, global environmental pollution, energy crises, and climate warming have intensified, prompting nations to pay increasing attention to novel energy technologies. In September 2020, China proposed the dual-carbon goals of “carbon peak” and “carbon neutrality,” adopting a series of policies and measures including adjusting the energy structure and promoting energy technology innovation to reduce the use of traditional fossil fuels and accelerate the replacement process with new renewable energy sources. Among these developments, energy storage technology has become one of the key technologies in the current new energy development strategy. Lithium-ion batteries, as one of the current new energy sources, offer advantages in power, capacity, and durability, and are thus widely used in commercial and household energy storage systems. However, lithium-ion battery cells generate a large amount of heat during charging and discharging. The heat sources include reaction heat, side reaction heat, Joule heat, and polarization reaction heat. The accumulation of heat affects the service life of battery cells, especially in large-capacity container-type energy storage cabinets, where each battery box contains multiple cells connected in series and parallel. If the heat cannot be dissipated in time, it not only reduces energy storage efficiency but can also lead to combustion or explosion in severe cases. Therefore, designing a thermal management system for the energy storage battery box to improve the heat dissipation effect of the lithium-ion battery pack is crucial to avoid life and property hazards caused by heat accumulation.
Currently, the cooling methods for energy storage battery boxes mainly include air cooling, liquid cooling, and phase change material cooling. Liquid cooling uses a liquid coolant as the heat exchange medium. The coolant is continuously delivered into the flow channel structure of the cold plate, and convective heat transfer occurs during the flow process, thereby removing the heat generated during battery operation. Liquid cooling is widely used in energy storage technology due to its high heat dissipation efficiency and low implementation cost. Numerous scholars have conducted research on how to improve the performance of liquid cooling. For example, some researchers proposed a bionic liquid cooling channel structure based on the heating characteristics of prismatic lithium-ion battery packs, which takes insect wing vein contours as a reference. Others put forward a bidirectional parallel liquid cooling channel structure and optimized the length of internal ribs to adjust the uniformity of flow distribution. Using the DOE optimal Latin hypercube design method combined with CFD simulation, some studies optimized the width of two inlet branches to improve flow consistency. Another study optimized the original parallel channel arrangement to a series-parallel combination, resulting in more uniform flow distribution and significantly improved heat dissipation. These studies indicate that for energy storage battery boxes, the most effective method to enhance liquid cooling heat dissipation is to optimize the liquid cooling channel structure, with the optimization directions being to improve flow distribution uniformity and reduce flow resistance. For large-capacity container-type energy storage cabinets, multiple battery packs are arranged to form large battery clusters, and the maximum temperature difference of the battery cluster can be reduced by optimizing the overall outlet position and the arrangement of each cold plate, thereby improving the overall heat dissipation performance of the energy storage cabinet.
In this study, I propose a novel liquid cooling channel structure processed by stamping for an energy storage battery box. Based on CFD numerical simulation, I establish a coupled thermal simulation model for the battery pack under this channel layout to identify the key factors affecting heat dissipation performance and flow resistance. To reduce the maximum temperature difference on the top faces of the battery pack and improve temperature uniformity, I perform targeted optimization of the channel structure, thereby achieving the goal of improving the heat dissipation performance of the energy storage battery box.
1. Numerical Model Establishment
1.1 Physical Model
The energy storage battery box studied in this work consists of a liquid cooling plate assembly and a battery pack. The liquid cooling plate assembly comprises a base plate, a channel plate, and inlet/outlet nozzles. The battery pack is composed of 52 cell monomers. The specific structural dimensions are listed in Table 1. The battery pack is placed on top of the base plate, and a thermally conductive structural adhesive with a thermal conductivity of 0.8 W/(m·K) is applied between the battery pack and the base plate to reduce thermal contact resistance. Foam cotton with a thermal conductivity of 0.022 W/(m·K) is placed between adjacent cell monomers to reduce heat transfer between them and prevent heat accumulation. The channel plate is processed by stamping to form groove-shaped channels. The base plate and the channel plate are joined by brazing to form the main body of the cold plate.
| Component | Dimension (mm) |
|---|---|
| Base plate (length × width × thickness) | 1145 × 800 × 1.2 |
| Channel plate (length × width × thickness) | 1145 × 800 × 1.0 |
| Cell monomer width | 173.8 ± 0.8 |
| Cell monomer thickness | 71.7 ± 0.8 |
| Cell monomer height | 207.2 ± 0.8 |
| Nozzle inner diameter | 10 |
The channel structure is 4.5 mm deep. The inlet adopts a one-to-three split pattern, meaning the inlet has three parallel branches, two of which further divide into sub-branches. The outlet adopts a two-to-one convergence pattern: all branches merge into two main channels that finally converge at the outlet. The paths of all branches are serpentine.
1.2 Grid Generation
The 3D model is discretized using unstructured polyhedral meshes. To save computational cost, local mesh refinement is applied. Since the flow of the coolant inside the channel belongs to near-wall flow, boundary layer meshes are generated on the channel walls to accurately capture the flow and heat transfer conditions at the wall. The thickness of the first boundary layer is set to 0.2 mm to ensure a y+ value less than 5. The number of boundary layers is 3, with a growth rate of 1.1.
1.3 Governing Equations
The CFD numerical simulation of the energy storage battery box is performed using the Fluent solver based on the RANS (Reynolds-averaged Navier-Stokes) equations. The conservation equations for mass, momentum, and energy are given below:
$$
\frac{\partial \bar{u}_i}{\partial x_i} = 0 \quad \text{(1)}
$$
$$
\frac{\partial (\rho \bar{u}_i)}{\partial t} + \frac{\partial (\rho \bar{u}_i \bar{u}_j)}{\partial x_j} = -\frac{\partial \bar{p}}{\partial x_i} + \frac{\partial}{\partial x_j} \left[ \mu \left( \frac{\partial \bar{u}_i}{\partial x_j} + \frac{\partial \bar{u}_j}{\partial x_i} \right) – \rho \overline{u_i’ u_j’} \right] \quad \text{(2)}
$$
$$
\frac{\partial (\rho e)}{\partial t} + \frac{\partial (\rho e \bar{u}_i)}{\partial x_i} = \frac{\partial}{\partial x_i} \left( k_{\text{eff}} \frac{\partial T}{\partial x_i} \right) – \frac{\partial (\bar{u}_i \bar{p})}{\partial x_i} + \frac{\partial}{\partial x_i} \left( \tau_{ij} \bar{u}_i \right) \quad \text{(3)}
$$
In these equations, ρ, p, and μ represent the density, pressure, and dynamic viscosity of the fluid; \(\bar{u}_i\) is the mean velocity component; \(-\rho \overline{u_i’ u_j’}\) is the Reynolds stress tensor used to model turbulent fluctuations; e is the total energy per unit mass (e = h + u²/2, where h is enthalpy); and \(k_{\text{eff}}\) is the effective thermal conductivity of the fluid. Additionally, a turbulence model must be introduced to close the RANS equations. In this study, the two-equation SST k-ω model is adopted. Its governing equations are:
$$
\frac{\partial (\rho k)}{\partial t} + \frac{\partial (\rho k u_i)}{\partial x_i} = \frac{\partial}{\partial x_j} \left( \Gamma_k \frac{\partial k}{\partial x_j} \right) + G_k – Y_k + S_k \quad \text{(4)}
$$
$$
\frac{\partial (\rho \omega)}{\partial t} + \frac{\partial (\rho \omega u_i)}{\partial x_i} = \frac{\partial}{\partial x_j} \left( \Gamma_\omega \frac{\partial \omega}{\partial x_j} \right) + G_\omega – Y_\omega + D_\omega + S_\omega \quad \text{(5)}
$$
where k is the turbulent kinetic energy, ω is the specific dissipation rate, Gk represents the generation of turbulence kinetic energy due to mean velocity gradients, Gω is the generation of ω, Γk and Γω are the effective diffusivities of k and ω, Yk and Yω represent the dissipation of k and ω due to turbulence, Dω is the cross-diffusion term, and Sk and Sω are user-defined source terms.
1.4 Boundary Conditions
The computation is performed under steady-state, pressure-based conditions. The coolant is set as an incompressible fluid. Both the base plate and the channel plate are made of aluminum. The cell monomers are 314 Ah lithium iron phosphate (LFP) prismatic cells widely used in the current energy storage market. The coolant is a 50% by volume ethylene glycol solution. The specific material properties are listed in Table 2.
| Property | Coolant (50% EG) | Aluminum (Al) | LFP Cell |
|---|---|---|---|
| Density (kg/m³) | 1074.194 | 2719 | 2284 |
| Viscosity (kg/(m·s)) | 4.216e-3 | — | — |
| Specific heat (J/(kg·K)) | 3273 | 871 | 1060 |
| Thermal conductivity (W/(m·K)) | 0.382 | 202.4 | k_x=9.04, k_y=11, k_z=3.56 |
The heat generation power of each cell monomer under a 0.5 C discharge rate is approximately 14 W, and the initial temperature is set to 35 °C. The inlet boundary condition is a mass flow inlet with a flow rate of 5 L/min, and the initial coolant temperature is 18 °C. The Reynolds number Re is calculated as 2703.4, which falls in the range of 2300–4000, indicating a mixed laminar-turbulent flow regime. The outlet boundary condition is a pressure outlet with zero gauge pressure. All external surfaces of the cell monomers and the cold plate assembly are assumed adiabatic; natural convection with the ambient environment and thermal radiation within the system are temporarily neglected. The pressure-velocity coupling is handled by the SIMPLEC algorithm. Pressure discretization uses a second-order scheme, while momentum and energy discretization use second-order upwind schemes.
2. Results and Discussion
2.1 Model Validation
To validate the numerical model, I first simulate the thermal behavior of a single cell monomer under 1 C, 2 C, and 3 C discharge rates using the same solver and boundary conditions as reported in a previous experimental study. The comparison between the simulated and experimental surface temperature evolution over time shows excellent agreement with negligible errors, confirming that the Fluent solver can accurately reproduce the heat transfer characteristics of the cell monomer.
2.2 Grid Independence Study
A grid independence study is essential to balance accuracy and computational cost. Three different mesh sizes are generated, as listed in Table 3. For each mesh, the total outlet pressure and the average temperature on the top faces of the battery pack are monitored. The results are presented in Table 4.
| Mesh ID | Max element size (mm) | Min element size (mm) | Number of elements |
|---|---|---|---|
| #1 | 17 | 1.5 | 2,817,518 |
| #2 | 16 | 1.2 | 4,055,112 |
| #3 | 15 | 1 | 5,547,631 |
| Mesh ID | Outlet total pressure (Pa) | Deviation (%) | Avg. top temperature (°C) | Deviation (%) |
|---|---|---|---|---|
| #1 | 705.07 | — | 34.17 | — |
| #2 | 701.46 | 0.51 | 34.51 | 0.99 |
| #3 | 700.81 | 0.09 | 34.77 | 0.75 |
The deviations among the three meshes are all below 1%, indicating grid independence. To save computational time, Mesh #2 is used for subsequent analyses.
2.3 Flow Field and Temperature Field Analysis
A CFD simulation is performed for the energy storage battery box under a 0.5 C discharge rate. The internal flow field of the channel and the overall temperature field of the battery pack are analyzed. The total pressure distribution inside the channel shows that the maximum total pressure occurs at the inlet, and the pressure gradually decreases as the coolant flows through the channel. The pressure drop between the inlet and outlet is 9.828 kPa. The streamline and velocity distributions indicate relatively uniform flow distribution, stable overall velocity, and no severe recirculation or large vortices at the corners. The effective utilization of the channel is high.
The coolant temperature distribution shows that the lowest temperature occurs at the inlet, while the outlet temperature reaches 20.49 °C, yielding an inlet-outlet temperature difference of 2.49 °C. As the coolant flows through the channel, it continuously exchanges heat with the base plate through convection, so the temperature of the coolant gradually rises along each branch. The four branches corresponding to the outlet have the highest coolant temperature and the most unfavorable heat dissipation conditions.
The top face temperature distribution of the battery pack is evaluated by measuring the positive and negative terminal temperatures of each cell monomer and calculating the maximum and minimum values to obtain the maximum temperature difference. This is a key indicator for assessing temperature uniformity. The minimum temperature is 33.77 °C, appearing in the first row of cells, while the maximum temperature is 35.17 °C, appearing in the fourth row. The maximum temperature difference is 1.40 °C, indicating relatively uniform temperature distribution. The average temperature of the first and third rows is significantly lower than that of the second and fourth rows. This is because the first and third rows are located directly above the three inlet branches, where the coolant temperature is lower, leading to a larger temperature difference between the coolant and the base plate under the battery pack, thus more convective heat transfer. Conversely, the fourth row is located directly above the four outlet branches where the coolant temperature is highest, resulting in the least convective heat transfer and the highest temperature.
2.4 Effect of Inlet Flow Rate on Heat Dissipation
The inlet flow rate is a key factor affecting both the heat dissipation performance of the battery pack and the flow resistance of the channel. To quantify its influence, five different inlet flow rates (5 L/min, 8 L/min, 10 L/min, 12 L/min, and 15 L/min) are simulated. The top face temperature distributions show that as the flow rate increases, the overall temperature of the battery pack decreases, indicating improved cooling. When the flow rate is 5 L/min, the highest and lowest temperatures occur in the fourth and first rows, respectively. However, as the flow rate increases, the highest and lowest temperature points shift to the second and third rows, suggesting that the increased velocity changes the convective heat transfer coefficients and alters the thermal conditions of the cells.
Figure 11 (not shown) illustrates the relationship between the average top temperature and the flow rate. The average temperature decreases with increasing flow rate, but the rate of decrease diminishes. Specifically, as the flow rate increases from 5 L/min to 8 L/min, 10 L/min, 12 L/min, and 15 L/min, the corresponding decreases in average temperature are 0.30 °C/(L/min), 0.18 °C/(L/min), 0.12 °C/(L/min), and 0.08 °C/(L/min), respectively. This indicates that the improvement in cooling gradually levels off. Regarding the maximum temperature difference (MTD), when the flow rate increases from 5 L/min to 8 L/min, the MTD drops from 1.40 °C to 1.22 °C. However, as the flow rate further increases to 15 L/min, the MTD gradually rises back to 1.39 °C. This suggests that for this channel structure, the best temperature uniformity is achieved at a flow rate of around 8 L/min.
The flow resistance and system thermal resistance as functions of flow rate are shown in Figure 12 (not shown). As the flow rate increases from 5 L/min to 15 L/min, the system thermal resistance decreases slightly from 23.58 °C/kW to 21.79 °C/kW, while the flow resistance increases significantly from 9.828 kPa to 59.441 kPa. This indicates that increasing the flow rate yields limited enhancement of heat dissipation but causes a substantial increase in flow resistance, thereby raising the pump power consumption and impairing the energy efficiency of the energy storage battery box. Therefore, it is necessary to reasonably control the inlet flow rate to maintain a balance between heat dissipation performance and flow resistance.
3. Channel Structure Optimization
To reduce the maximum temperature difference on the top faces of the battery pack and improve temperature uniformity, I perform targeted optimization of the channel structure shown in Figure 3. Under the inlet flow rate of 5 L/min, the lowest temperature appears in the first row and the highest in the fourth row. Therefore, reducing the temperature of the fourth row by adjusting the outlet branch structure can lower the maximum temperature difference. Based on boundary layer theory and field synergy principle, enhancing the disturbance of the coolant flow helps break the thermal boundary layer and strengthen convective heat transfer. Thus, adding turbulence-inducing structures (convex hulls) inside the outlet branches can improve the heat dissipation of the fourth row. The specific optimization replaces the four original outlet branches with two branches, each equipped with an equal number of equally spaced convex hulls.
To evaluate the flow disturbance, the swirling strength method is used. The velocity gradient tensor is decomposed as:
$$
\nabla u = \mathbf{S} + \mathbf{\Omega} \quad \text{(6)}
$$
where S is the strain rate tensor and Ω is the vorticity tensor. The swirling strength is defined as the imaginary part of the complex eigenvalue of ∇u.
The coupled thermal simulations are performed for the original channel and for optimized channels with different numbers of convex hull pairs (3, 4, and 10 pairs). The vortex structures inside the flow field and the top face temperature distributions are compared. The results show that the addition of convex hulls generates noticeable vortex structures downstream of each hull, indicating enhanced flow disturbance. As the number of convex hulls increases, the top face temperature of the fourth row decreases, confirming that the convective heat transfer in the outlet branches is improved.
Further analysis of the average swirling strength in the outlet branches and the maximum temperature difference versus the number of convex hull pairs is presented in Figure 16 (not shown). The average swirling strength increases with the number of convex hulls, but the rate of increase gradually slows. Simultaneously, the MTD decreases with increasing convex hulls, and the reduction also tends to plateau. This indicates that while convex hulls are beneficial for enhancing flow disturbance and improving temperature uniformity, adding too many hulls yields diminishing returns. Therefore, the number of convex hulls should be optimized considering both thermal performance and manufacturing feasibility.
4. Conclusions
In this work, I propose a novel liquid cooling channel structure for an energy storage battery box processed by stamping. A coupled thermal CFD simulation is conducted under this channel layout. The results show that at an inlet flow rate of 5 L/min, the flow resistance is 9.828 kPa, and the maximum temperature difference on the top faces of the battery pack is 1.40 °C, indicating good temperature uniformity. As the inlet flow rate increases, the flow resistance rises sharply while the improvement in heat dissipation performance gradually diminishes, demonstrating a fatiguing effect. Hence, it is crucial to control the inlet flow rate reasonably to maintain a balance between flow resistance and heat dissipation.
Targeted optimization is performed by adding convex hull structures in the outlet branches to enhance flow disturbance and strengthen convective heat transfer. The influence of the number of convex hulls on the maximum temperature difference of the battery pack is analyzed. The results indicate that increasing the number of convex hulls improves temperature uniformity, but the enhancement tends to saturate. Therefore, the selection of the optimal number of convex hulls should also account for manufacturing process constraints.
This study provides a reference for the thermal management design of lithium-ion battery packs in energy storage systems, highlighting the importance of optimizing flow channel geometry and controlling operating parameters to achieve efficient and uniform cooling of energy storage batteries.