Energy storage lithium battery systems have become integral to modern renewable energy applications due to their high energy density, long cycle life, and reliability. However, the performance and longevity of these systems are highly dependent on thermal management, as excessive temperatures or uneven thermal distribution can lead to reduced efficiency, accelerated aging, or even catastrophic failures like thermal runaway. In this study, we focus on optimizing the air-cooled heat dissipation for energy storage lithium battery packs, leveraging computational fluid dynamics (CFD) simulations to analyze key factors and achieve multi-objective improvements. The energy storage lithium battery pack, typically arranged in confined spaces like containerized systems, requires efficient cooling to maintain operational temperatures within the optimal range of 15–25°C. We begin by establishing a thermal model based on the heat generation and transfer mechanisms of lithium iron phosphate batteries, validate it through experimental comparisons, and then explore single-factor and multi-factor interactions to enhance cooling performance. Our goal is to reduce the maximum temperature and improve temperature uniformity in the energy storage lithium battery pack, ensuring safer and more efficient operation.
The thermal behavior of energy storage lithium battery systems is governed by complex electrochemical and heat transfer processes. For a typical 1P8S battery pack configuration, consisting of eight series-connected cells, we developed a physical model that ignores minor components like terminals and welding seats to simplify simulations. The battery cells are spaced equally, and the cooling airflow is managed through various channel designs. The energy storage lithium battery pack’s thermal performance is critical, as uneven cooling can exacerbate aging differences and increase the risk of thermal events. To address this, we employ a comprehensive mathematical framework that accounts for heat generation rates and convective heat transfer, enabling accurate predictions of temperature distributions.
The heat generation in an energy storage lithium battery arises from both reversible and irreversible processes during charging and discharging. Based on the Bernardi model, the heat generation rate can be expressed as a function of current, voltage, and temperature. The irreversible heat, resulting from internal resistance, is given by:
$$ \dot{Q}_p = I (E_{ocv} – U) $$
where \( I \) is the current, \( E_{ocv} \) is the open-circuit voltage, and \( U \) is the working voltage. The reversible heat, associated with entropy changes, is:
$$ \dot{Q}_r = I T \frac{dE_{ocv}}{dT} $$
Thus, the total heat generation power becomes:
$$ \dot{Q} = \dot{Q}_p + \dot{Q}_r = I (E_{ocv} – U) + I T \frac{dE_{ocv}}{dT} $$
For the battery core, the volumetric heat generation rate is derived as:
$$ q_{cor} = \frac{I}{V_{cor}} \left[ (E_{ocv} – U) + T \frac{dE_{ocv}}{dT} \right] $$
where \( V_{cor} \) is the volume of the battery core. Additionally, the heat generated at the poles due to resistance is calculated using:
$$ q_{pol} = \frac{I^2 R}{V_{pol}} $$
with \( R = \rho_{pol} \frac{l}{S} \), where \( \rho_{pol} \) is the resistivity, \( l \) is the length, and \( S \) is the cross-sectional area. These equations form the basis for simulating the thermal dynamics of the energy storage lithium battery pack.
Heat transfer within the energy storage lithium battery pack involves conduction, convection, and radiation, though radiation is negligible compared to the other modes. The conduction process is described by Fourier’s law:
$$ \rho C_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + q_{pol} + q_{cor} $$
where \( \rho \) is the density, \( C_p \) is the specific heat capacity, \( k \) is the thermal conductivity, and \( T \) is temperature. For convective cooling, Newton’s law of cooling applies:
$$ q = h_f (T_w – T_f) $$
with \( h_f \) as the convective heat transfer coefficient, \( T_w \) as the wall temperature, and \( T_f \) as the fluid temperature. In our simulations, we used ANSYS Fluent to solve these equations under steady-state conditions, employing the Realizable k-ε turbulence model and coupled algorithm for accuracy. The boundary conditions included velocity inlets and pressure outlets, with ambient air at 25°C and atmospheric pressure. The energy storage lithium battery pack’s external surfaces were set to natural convection, while internal surfaces used coupled heat transfer for forced convection.
To ensure model reliability, we first validated the single cell model against experimental data. The thermal properties of the energy storage lithium battery were calculated based on material parameters, as summarized in the following table:
| Component | Material | Density (kg/m³) | Specific Heat (J/(kg·K)) | Thermal Conductivity (W/(m·K)) |
|---|---|---|---|---|
| Positive Pole | Aluminum | 2719 | 871 | 202.40 |
| Negative Pole | Copper | 8978 | 381 | 387.60 |
| Positive Electrode | LiFePO4 | 2910 | 1339 | 0.45 |
| Negative Electrode | Graphite | 1550 | 1320 | 129.00 |
| Separator | PP/PE/PP | 492 | 1978 | 0.33 |
| Electrolyte | C3H4O3 | 1321 | 1930 | 0.45 |
| Case | Stainless Steel | 7750 | 502.4 | 15.10 |
The overall specific heat capacity of the energy storage lithium battery was computed using a weighted average:
$$ C_p = \frac{\sum_{i=1}^{n} (c_i m_i)}{\sum_{i=1}^{n} m_i} $$
and the thermal conductivities in the longitudinal and transverse directions were determined via series and parallel resistance methods:
$$ k_{in} = \frac{\sum_{i=1}^{n} L_i}{\sum_{i=1}^{n} (L_i / \lambda_i)} $$
$$ k_{thr} = \frac{\sum_{i=1}^{n} (\lambda_i L_i)}{\sum_{i=1}^{n} L_i} $$
For a 32 Ah battery at 1C charge and discharge rates, the heat generation rates were calculated as 4871.92 W/m³ and 3953.60 W/m³, respectively. The simulated temperature distributions showed higher temperatures during charging, with hotspots in the core region, aligning with experimental measurements from a controlled test platform. The maximum errors between simulation and experiment were 0.67% for charging and 0.46% for discharging, confirming the model’s accuracy for the energy storage lithium battery system.
Building on the validated single-cell model, we developed a 1P8S energy storage lithium battery pack model and performed a grid independence test. Results indicated that beyond 250,000 cells, temperature and pressure differences stabilized, so this mesh size was adopted for balance between precision and computational cost. We then conducted single-factor sensitivity analysis to assess the impact of various parameters on cooling performance, as outlined below:
| Factor | Levels | Impact on Maximum Temperature | Impact on Temperature Uniformity |
|---|---|---|---|
| Battery Spacing | 3, 5, 7, 9 mm | Initial increase then decrease | Improved at larger spacings |
| Inlet Vent Length | 55, 65, 75, 85 mm | Negligible change | Minimal effect |
| Inlet Air Velocity | 0.3, 0.5, 0.7, 0.9 m/s | Significant reduction above 0.5 m/s | Increased温差 at higher velocities |
| Flow Channel Shape | U, Z, T, I-type | Varies widely; I-type lowers min temperature | Z-type offers best uniformity |
| Duct Angle | 1.0°, 1.5°, 2.0°, 2.5° | Minor influence | Limited improvement |
Our analysis revealed that battery spacing, inlet air velocity, and flow channel shape are the most influential factors for the energy storage lithium battery pack’s cooling performance. For instance, increasing battery spacing initially raised temperatures due to reduced airflow efficiency but improved at 9 mm with less reflux. Inlet velocities above 0.5 m/s lowered maximum temperatures but exacerbated temperature differences. Among channel shapes, Z-type provided the best balance, while I-type reduced minimum temperatures but increased maxima. Duct angle and vent length had minimal effects, making them less critical for optimization.
We proceeded with multi-objective optimization using an orthogonal experimental design, considering battery spacing, inlet velocity, and flow channel shape as factors with four levels each. The L16(3^4) orthogonal array was employed to minimize experimental runs while capturing interactions. The design and results are summarized in the following tables:
| Experiment | Battery Spacing (mm) | Inlet Velocity (m/s) | Flow Channel | Max Temp (°C) | Max Cell ΔT (°C) | Average ΔT (°C) |
|---|---|---|---|---|---|---|
| S1 | 3 | 0.3 | U | 29.45 | 0.52 | 3.58 |
| S2 | 3 | 0.5 | T | 29.01 | 0.66 | 3.71 |
| S3 | 3 | 0.7 | I | 29.34 | 0.84 | 4.04 |
| S4 | 3 | 0.9 | Z | 29.41 | 0.89 | 4.11 |
| S5 | 5 | 0.3 | T | 29.56 | 0.67 | 4.26 |
| S6 | 5 | 0.5 | U | 29.31 | 0.76 | 4.01 |
| S7 | 5 | 0.7 | Z | 29.13 | 0.72 | 3.59 |
| S8 | 5 | 0.9 | I | 28.65 | 0.75 | 3.59 |
| S9 | 7 | 0.3 | I | 29.13 | 0.87 | 3.83 |
| S10 | 7 | 0.5 | Z | 29.94 | 0.71 | 4.64 |
| S11 | 7 | 0.7 | U | 28.37 | 0.46 | 3.07 |
| S12 | 7 | 0.9 | T | 29.21 | 0.85 | 3.91 |
| S13 | 9 | 0.3 | Z | 29.13 | 0.44 | 3.83 |
| S14 | 9 | 0.5 | I | 28.80 | 0.55 | 3.50 |
| S15 | 9 | 0.7 | T | 29.21 | 1.01 | 3.91 |
| S16 | 9 | 0.9 | U | 29.21 | 1.33 | 3.91 |
Range analysis of the orthogonal experiments showed that for maximum temperature and average temperature difference, the factor importance order is flow channel shape > inlet velocity > battery spacing, with ranges of 1.57°C, 0.91°C, and 0.44°C, respectively. For maximum cell temperature difference, the order is flow channel shape > battery spacing > inlet velocity, with ranges of 0.41°C, 0.37°C, and 0.09°C. This underscores the dominance of flow channel design in optimizing the energy storage lithium battery pack’s thermal performance. The optimal combination, identified as S14 (9 mm spacing, 0.5 m/s velocity, I-type channel), reduced the maximum temperature by 1.10°C to 28.80°C and the average temperature difference by 1.10°C to 3.50°C, compared to the baseline. Although some configurations like S3 and S8 offered lower temperatures, they involved higher pressure drops and power consumption, making S14 the most efficient choice for practical applications.
In conclusion, our study demonstrates the effectiveness of CFD-based optimization for air-cooled heat dissipation in energy storage lithium battery packs. Through single-factor and multi-objective analyses, we identified key parameters and achieved significant improvements in temperature control and uniformity. The optimized setup with 9 mm spacing, 0.5 m/s inlet velocity, and I-type flow channel ensures reliable operation, enhancing the safety and longevity of energy storage lithium battery systems. Future work could explore hybrid cooling methods or dynamic operational conditions to further advance thermal management for energy storage lithium battery packs.