Energy storage lithium battery systems have become integral to modern renewable energy applications due to their high energy density, long cycle life, and low self-discharge rates. However, the performance and longevity of these systems are heavily influenced by operating temperature and thermal uniformity. In containerized energy storage setups, limited space and密闭 conditions often lead to inadequate heat dissipation, resulting in temperature disparities and accelerated aging among cells. If temperatures exceed safe thresholds, risks such as thermal runaway or combustion escalate. Thus, enhancing the cooling efficiency of battery thermal management systems (BTMS) is critical. This study focuses on optimizing the air-cooled散热 performance of energy storage lithium battery packs through computational fluid dynamics (CFD) simulations, examining single-factor sensitivities and multi-factor interactions to achieve superior thermal control.
The thermal behavior of energy storage lithium battery cells during charge and discharge cycles involves complex heat generation and transfer mechanisms. The primary heat sources include the electrodes and the core, where electrochemical reactions occur. For the electrodes, heat generation arises from internal resistance, converting electrical energy into thermal energy. The heat generation rate for the poles can be modeled as:
$$ q_{\text{pol}} = \frac{I^2 R}{V_{\text{pol}}} $$
where \( R = \rho_{\text{pol}} \frac{l}{S} \). Here, \( I \) represents the current, \( R \) the resistance, \( \rho_{\text{pol}} \) the resistivity of the pole material, \( l \) the length, \( S \) the cross-sectional area, and \( V_{\text{pol}} \) the volume of the pole. For the core, the Bernardi model simplifies the energy storage lithium battery as a homogeneous entity with anisotropic thermal conductivity, accounting for reversible and irreversible heat. The total heat generation power is:
$$ \dot{Q} = \dot{Q}_p + \dot{Q}_r = I(E_{\text{ocv}} – U) + IT \frac{dE_{\text{ocv}}}{dT} $$
and the volumetric heat generation rate becomes:
$$ q_{\text{cor}} = \frac{I}{V_{\text{cor}}} \left[ (E_{\text{ocv}} – U) + T \frac{dE_{\text{ocv}}}{dT} \right] $$
where \( \dot{Q}_r \) is the reversible heat power, \( T \) the average temperature, \( \frac{dE_{\text{ocv}}}{dT} \) the entropy coefficient, \( \dot{Q}_p \) the irreversible heat power, \( V_{\text{cor}} \) the core volume, \( E_{\text{ocv}} \) the open-circuit voltage, and \( U \) the operating voltage. Heat transfer occurs via conduction, convection, and radiation, with conduction described by Fourier’s law:
$$ \rho C_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + q_{\text{pol}} + q_{\text{cor}} $$
and convection by Newton’s law of cooling:
$$ q = h_f (T_w – T_f) $$
Here, \( \rho \) is density, \( C_p \) specific heat capacity, \( k \) thermal conductivity, \( h_f \) the convective heat transfer coefficient, \( T_w \) the wall temperature, and \( T_f \) the fluid temperature. Radiation is neglected due to its minimal contribution.
To validate the thermal model, a single lithium iron phosphate (LiFePO4) energy storage lithium battery with a capacity of 32 A·h was simulated under 1C charge and discharge rates. The thermophysical parameters, calculated based on material compositions, are summarized in Table 1.
| Parameter | Value |
|---|---|
| Density (kg/m³) | Calculated from component masses |
| Specific Heat Capacity, \( C_p \) (J/(kg·K)) | 1917.46 |
| Longitudinal Thermal Conductivity, \( k_{\text{in}} \) (W/(m·K)) | 14.77 |
| Transverse Thermal Conductivity, \( k_{\text{thr}} \) (W/(m·K)) | 1.05 |
| Heat Generation Rate (Charge, 1C) (W/m³) | 4871.92 |
| Heat Generation Rate (Discharge, 1C) (W/m³) | 3953.60 |
The specific heat capacity and thermal conductivities were derived using mixture rules:
$$ C_p = \frac{\sum (c_i m_i)}{\sum m_i}, \quad k_{\text{in}} = \frac{\sum L_i}{\sum (L_i / \lambda_i)}, \quad k_{\text{thr}} = \frac{\sum (\lambda_i L_i)}{\sum L_i} $$
where \( c_i \), \( m_i \), \( L_i \), and \( \lambda_i \) are the specific heat, mass, thickness, and thermal conductivity of each layer, respectively. CFD simulations in Ansys Fluent showed temperature distributions with higher values during charging, and internal hotspots near the core. Experimental validation in a 25°C environment confirmed model accuracy, with maximum errors of 0.67% for charging and 0.46% for discharging, ensuring reliability for pack-level analysis.
For the energy storage lithium battery pack, a 1P8S configuration (eight cells in series) was modeled with equal spacing. A grid independence test established 250,000 cells as optimal, balancing computational efficiency and precision. Single-factor sensitivity analysis examined five parameters: cell spacing, inlet vent length, inlet air velocity, flow channel shape, and duct inclination angle. Table 2 outlines the experimental design for single-factor tests.
| Factor | Levels |
|---|---|
| Cell Spacing (mm) | 3, 5, 7, 9 |
| Inlet Vent Length (mm) | 55, 65, 75, 85 |
| Inlet Air Velocity (m/s) | 0.3, 0.5, 0.7, 0.9 |
| Flow Channel Shape | U-type, Z-type, T-type, I-type |
| Duct Inclination Angle (°) | 1.0, 1.5, 2.0, 2.5 |
Results indicated that cell spacing, inlet air velocity, and flow channel shape significantly impacted cooling performance, while vent length and duct angle had negligible effects. For instance, increasing cell spacing initially raised maximum cell temperatures due to reduced airflow efficiency, but at 9 mm, backflow decreased, improving散热. Higher inlet velocities above 0.5 m/s reduced temperatures but increased turbulence and backflow. Among channel shapes, Z-type offered the best balance, whereas T-type and I-type caused uneven cooling. These findings guided the multi-objective optimization.
Orthogonal experimental design L16(3^4) was employed, considering three key factors: cell spacing, inlet air velocity, and flow channel shape, each at four levels. The objective was to minimize the maximum cell temperature (\( T_{\text{max}} \)), maximum temperature difference within a cell (\( \Delta t \)), and average temperature difference across the pack (\( \Delta T \)). Table 3 presents the orthogonal array and results for \( T_{\text{max}} \).
| Experiment | Spacing (mm) | Velocity (m/s) | Channel Shape | \( T_{\text{max}} \) (°C) |
|---|---|---|---|---|
| S1 | 3 | 0.3 | U-type | 29.45 |
| S2 | 3 | 0.5 | T-type | 29.01 |
| S3 | 3 | 0.7 | I-type | 29.34 |
| S4 | 3 | 0.9 | Z-type | 29.41 |
| S5 | 5 | 0.3 | T-type | 29.56 |
| S6 | 5 | 0.5 | U-type | 29.31 |
| S7 | 5 | 0.7 | Z-type | 29.13 |
| S8 | 5 | 0.9 | I-type | 28.65 |
| S9 | 7 | 0.3 | I-type | 29.87 |
| S10 | 7 | 0.5 | Z-type | 29.71 |
| S11 | 7 | 0.7 | U-type | 29.46 |
| S12 | 7 | 0.9 | T-type | 29.21 |
| S13 | 9 | 0.3 | Z-type | 29.83 |
| S14 | 9 | 0.5 | I-type | 28.80 |
| S15 | 9 | 0.7 | T-type | 29.37 |
| S16 | 9 | 0.9 | U-type | 29.41 |
Range analysis revealed that for \( T_{\text{max}} \) and \( \Delta T \), the factor influence order was flow channel shape > inlet air velocity > cell spacing, with ranges of 1.57°C, 0.91°C, and 0.44°C, respectively. For \( \Delta t \), the order was flow channel shape > cell spacing > inlet air velocity. Optimal combinations—S3, S4, S8, and S14—were identified, and further evaluation based on temperature metrics and pressure drop (\( \Delta p \)) highlighted S14 (9 mm spacing, 0.5 m/s velocity, I-type channel) as the best, reducing \( T_{\text{max}} \) by 1.1°C and \( \Delta T \) by 23.98% compared to the baseline. The pressure drop equation:
$$ \Delta p = f \frac{L}{D} \frac{\rho v^2}{2} $$
where \( f \) is the friction factor, \( L \) the duct length, \( D \) the hydraulic diameter, \( \rho \) air density, and \( v \) velocity, confirmed lower energy consumption for S14. This optimization enhances the thermal uniformity and safety of energy storage lithium battery packs, crucial for sustainable energy systems.
In conclusion, this study demonstrates the effectiveness of CFD-based multi-objective optimization in improving the air-cooled散热 of energy storage lithium battery packs. Key factors like flow channel shape, inlet velocity, and cell spacing must be tailored to minimize temperatures and disparities. The optimized setup ensures reliable operation, prolonging the lifespan of energy storage lithium battery systems in renewable applications. Future work could integrate real-time control strategies for dynamic thermal management.