In the development of electric vehicles (EVs), the thermal management of lithium-ion battery packs is a critical aspect that directly impacts performance, safety, and longevity. As the primary power source for EVs, the EV battery pack must operate within an optimal temperature range to ensure efficient discharge cycles and prevent degradation. High temperatures or uneven thermal distribution can lead to accelerated aging, reduced capacity, and even safety hazards such as thermal runaway. Therefore, designing an effective cooling system for the EV battery pack is essential for enhancing the overall efficiency and reliability of electric vehicles. Among various cooling methods, air-cooled systems are favored for their simplicity, low cost, and high energy density, as they do not require complex internal flow channels or additional components. However, traditional air-cooled EV battery pack designs often suffer from inadequate cooling performance, particularly in terms of temperature uniformity. In this study, I explore the optimization of an air-cooled EV battery pack thermal management system by incorporating turbulence structures, such as staggered arrangements and baffles, to improve heat dissipation and temperature homogeneity. Through finite element simulations, I analyze the effects of different configurations and operational parameters on the thermal behavior of the EV battery pack, aiming to provide insights for practical applications in the automotive industry.
The core of this research focuses on a forced air-cooled EV battery pack composed of 18650 lithium-ion cells, which are commonly used in EV applications due to their high energy density and reliability. The EV battery pack model consists of 24 cells arranged in a modular design, with external dimensions of 130 mm in length, 90 mm in width, and 70 mm in height. Each cell is spaced 2 mm apart to allow for airflow, and the pack features multiple inlet and outlet configurations to facilitate cooling. The primary goal is to enhance the cooling efficiency by introducing turbulence structures that disrupt laminar airflow and promote better heat exchange between the air and the battery surfaces. This approach is particularly relevant for EV battery packs, where compact packaging and weight constraints necessitate innovative thermal solutions. To evaluate the performance, I employ computational fluid dynamics (CFD) simulations using a finite element method, which allows for detailed analysis of temperature distributions, airflow patterns, and heat transfer rates within the EV battery pack.
The thermal characteristics of lithium-ion batteries are governed by internal heat generation during charge and discharge cycles. The total heat generation rate \( Q_z \) in an EV battery pack can be expressed as the sum of reaction heat \( Q_r \), Joule heating \( Q_j \), and polarization heat \( Q_p \), as shown in the following equations:
$$ Q_z = Q_r + Q_j + Q_p $$
$$ Q_r = \frac{n m Q I}{M F} $$
$$ Q_j = I^2 R_j $$
$$ Q_p = I_p^2 R_p $$
where \( n \) is the number of batteries in the EV battery pack, \( m \) is the electrode mass, \( Q \) is the internal chemical reaction heat, \( I \) is the current, \( M \) is the molar mass, \( F \) is Faraday’s constant, \( R_j \) is the internal resistance, \( R_p \) is the polarization resistance, and \( I_p \) is the current due to polarization effects. For simulation purposes, the batteries are assumed to have uniform material properties and heat generation, with radiation effects neglected due to their minimal impact compared to conduction and convection. The thermophysical properties of the EV battery pack components are aggregated using weighted averages based on volume fractions, as detailed in Table 1. This simplification allows for efficient modeling while maintaining accuracy in predicting thermal behavior.
| Component | Density (kg/m³) | Specific Heat Capacity (J/(kg·K)) | Thermal Conductivity (W/(m·K)) |
|---|---|---|---|
| Positive Electrode | 2,291.62 | 1,172.8 | 1.85 |
| Negative Electrode | 5,031.67 | 700.0 | 5.00 |
| Aluminum Foil | 2,700.00 | 870.0 | 200.00 |
| Separator | 1,200.00 | 700.0 | 1.00 |
| Copper Foil | 9,000.00 | 381.0 | 380.00 |
| Casing | 7,800.00 | 478.0 | 16.80 |
| Aggregated EV Battery Pack | 3,450.25 | 850.5 | 45.75 |
The heat generation rate for individual cells in the EV battery pack is calculated using the Bernardi model, which accounts for electrochemical reactions and entropy changes:
$$ Q = \frac{1}{V} \left[ I (E_o – E) – T \frac{\partial E_o}{\partial T} \right] $$
where \( V \) is the battery volume, \( E_o \) is the open-circuit voltage, \( E \) is the working voltage, \( T \) is the temperature, and \( \frac{\partial E_o}{\partial T} \) is the entropy coefficient. This model is validated against experimental data for 18650 cells at discharge rates of 1C to 4C, ensuring reliability for the EV battery pack simulations. The airflow within the EV battery pack is modeled as a turbulent flow using the RNG k-ε turbulence model, which is suitable for high-velocity flows and accounts for heat transfer variations. The governing equations for mass, momentum, and energy conservation are:
$$ \frac{\partial (\rho u_i)}{\partial x_i} = 0 $$
$$ \frac{\partial (\rho u_i u_j)}{\partial x_j} = -\frac{\partial p}{\partial x_i} + \frac{\partial}{\partial x_j} \left[ \mu_e \left( \frac{\partial u_i}{\partial x_j} + \frac{\partial u_j}{\partial x_i} \right) \right] + \rho g_i $$
$$ \frac{\partial (u_i (\rho E + p))}{\partial x_i} = \frac{\partial}{\partial x_i} \left[ \left( \lambda + \frac{C_p \mu_T}{Pr_T} \right) \frac{\partial T}{\partial x_i} + u_i \tau_{ij} \right] $$
where \( \rho \) is air density, \( u \) is velocity, \( p \) is pressure, \( \mu_e \) is effective viscosity, \( C_p \) is specific heat capacity, \( \lambda \) is thermal conductivity, \( E \) is total energy, and \( \tau_{ij} \) is the stress tensor. The turbulence kinetic energy \( k \) and dissipation rate \( \varepsilon \) are given by:
$$ \frac{\partial}{\partial x_i} (\rho k u_i) = \frac{\partial}{\partial x_j} \left[ \left( \mu + \frac{\mu_T}{\sigma_k} \right) \frac{\partial k}{\partial x_j} \right] + \mu_T \Gamma – \rho \varepsilon $$
$$ \frac{\partial}{\partial x_i} (\rho \varepsilon u_i) = \frac{\partial}{\partial x_j} \left[ \left( \mu + \frac{\mu_T}{\sigma_\varepsilon} \right) \frac{\partial \varepsilon}{\partial x_j} \right] + C_{\varepsilon 1} \mu_T \Gamma \frac{\varepsilon}{k} – C_{\varepsilon 2} \rho \frac{\varepsilon^2}{k} $$
The heat conduction within the EV battery pack is described by:
$$ \rho_b C_{p,b} \frac{\partial T}{\partial t} = k_b \nabla^2 T + q $$
where \( \rho_b \), \( C_{p,b} \), and \( k_b \) are the density, specific heat capacity, and thermal conductivity of the battery, respectively, and \( q \) is the volumetric heat source. The convective heat transfer at the battery-air interface follows Newton’s law of cooling:
$$ -\lambda \left( \frac{\partial T}{\partial n} \right)_w = h (t_w – t_f) $$
with \( h \) as the convective heat transfer coefficient, \( t_w \) as the wall temperature, and \( t_f \) as the air temperature.
For the EV battery pack simulations, boundary conditions are set with an inlet air velocity ranging from 2 m/s to 8 m/s and a constant temperature of 25°C (300 K), while the outlet is at atmospheric pressure. The air properties are summarized in Table 2, which are used to define the fluid domain in the CFD model. Mesh independence is verified by testing different grid sizes, and a mesh with over 1.5 million elements is selected to ensure accuracy without excessive computational cost. The validation of the single battery model against experimental data confirms the reliability of the approach for the full EV battery pack analysis.
| Property | Value |
|---|---|
| Density (kg/m³) | 1.225 |
| Specific Heat Capacity (J/(kg·K)) | 1,006.43 |
| Thermal Conductivity (W/(m·K)) | 0.0242 |
| Dynamic Viscosity (kg/(m·s)) | 1.7894 × 10⁻⁵ |
The initial configuration of the EV battery pack involves a parallel arrangement of cells with inlets and outlets positioned at opposite ends, creating a longitudinal airflow path. At an inlet air velocity of 2 m/s, the temperature distribution shows a maximum temperature of 32.11°C and a minimum of 25.15°C, resulting in a temperature difference of 6.96°C. This indicates poor thermal uniformity, with hotter spots near the outlet due to preheated air. To improve cooling, I investigate a staggered arrangement, where cells are offset to disrupt airflow and enhance turbulence. This modification reduces the maximum temperature to 31.44°C and the temperature difference to 6.29°C, demonstrating a 0.67°C improvement in uniformity for the EV battery pack. The staggered layout increases flow resistance and promotes mixing, which is beneficial for heat dissipation in compact EV battery pack designs.
Further optimization involves adding aluminum baffles to the EV battery pack to create deliberate turbulence structures. These baffles are placed symmetrically within the pack, with dimensions of 140 mm × 40 mm × 2 mm, and they force airflow to deviate and interact more effectively with battery surfaces. With baffles installed and inlets repositioned to shorter pathways, the temperature distribution becomes more uniform. At 2 m/s inlet velocity, the maximum temperature drops to 30.84°C and the temperature difference to 5.69°C, which is 0.60°C lower than the staggered arrangement without baffles. This highlights the efficacy of turbulence structures in enhancing the thermal management of the EV battery pack. The impact of inlet air velocity on cooling performance is systematically evaluated, as shown in Table 3, which summarizes key thermal metrics for different velocities. The data reveals that higher velocities generally improve cooling, but with diminishing returns beyond 6 m/s due to increased flow resistance and potential over-cooling effects.
| Inlet Air Velocity (m/s) | Maximum Temperature (°C) | Minimum Temperature (°C) | Temperature Difference (°C) | Cooling Efficiency Improvement (%) |
|---|---|---|---|---|
| 2 | 30.84 | 25.15 | 5.69 | Base |
| 3 | 29.67 | 25.12 | 4.55 | 20.0 |
| 4 | 28.95 | 25.10 | 3.85 | 32.3 |
| 5 | 28.50 | 25.09 | 3.41 | 40.1 |
| 6 | 28.22 | 25.08 | 3.14 | 44.8 |
| 7 | 28.05 | 25.07 | 2.98 | 47.6 |
| 8 | 27.94 | 25.06 | 2.88 | 49.4 |
The cooling performance of the EV battery pack is also influenced by the spacing and arrangement of baffles. I test different ratios of baffle distances \( L_1 \) and \( L_2 \), where \( L_1 \) and \( L_2 \) represent the gaps between baffles and pack walls. Symmetric arrangements with \( L_1:L_2 = 1:1 \) yield the best results, minimizing temperature differences and hotspots. As the ratio deviates from symmetry (e.g., 2:1, 3:1, 4:1), cooling efficiency degrades, leading to higher maximum temperatures and reduced uniformity. This is quantified in Table 4, which compares thermal outcomes for various baffle configurations at a fixed inlet velocity of 4 m/s. The symmetric setup promotes balanced airflow distribution, crucial for maintaining consistent temperatures across the EV battery pack. These findings underscore the importance of geometric optimization in turbulence-based cooling systems for EV battery packs.
| Baffle Spacing Ratio \( L_1:L_2 \) | Maximum Temperature (°C) | Temperature Difference (°C) | Uniformity Index (Scale 1-10) |
|---|---|---|---|
| 1:1 | 28.95 | 3.85 | 9.2 |
| 2:1 | 29.34 | 4.24 | 8.5 |
| 3:1 | 29.78 | 4.68 | 7.8 |
| 4:1 | 30.15 | 5.05 | 7.1 |
To provide a comprehensive analysis, I also evaluate the transient thermal behavior of the EV battery pack during discharge cycles. The temperature evolution over time for different inlet velocities is modeled using the heat generation equations, and results indicate that temperatures stabilize after approximately 600 seconds of discharge, reaching a steady state where heat generation equals convective cooling. This is expressed mathematically as:
$$ \frac{dT}{dt} = 0 \quad \text{when} \quad q = h A (T – T_f) $$
where \( A \) is the surface area of the EV battery pack. The steady-state temperature \( T_s \) can be approximated by:
$$ T_s = T_f + \frac{q}{h A} $$
This relationship highlights the role of convective heat transfer in controlling the thermal state of the EV battery pack. Higher inlet velocities increase \( h \), thereby reducing \( T_s \), but as velocity rises, the increase in \( h \) becomes less pronounced due to turbulent saturation effects. For the EV battery pack with turbulence structures, the convective heat transfer coefficient \( h \) is enhanced by up to 35% compared to traditional parallel arrangements, as derived from CFD simulations using the correlation:
$$ h = \frac{Nu \cdot k}{D_h} $$
where \( Nu \) is the Nusselt number, \( k \) is air thermal conductivity, and \( D_h \) is the hydraulic diameter of flow passages. The Nusselt number for turbulent flow in the EV battery pack is estimated as:
$$ Nu = 0.023 Re^{0.8} Pr^{0.4} $$
with Reynolds number \( Re = \frac{\rho u D_h}{\mu} \) and Prandtl number \( Pr = \frac{C_p \mu}{k} \). These equations help quantify the cooling enhancement achieved through turbulence induction in the EV battery pack.
In terms of practical implications for EV battery pack design, the optimized system with symmetric baffles and an inlet air velocity of 4 m/s maintains a temperature difference below 5°C, which is within the optimal range for lithium-ion battery operation (typically 20°C to 40°C with ΔT < 5°C). This ensures high discharge efficiency and prolongs battery life. Additionally, the airflow power consumption for cooling the EV battery pack can be estimated using the pressure drop \( \Delta p \) across the pack:
$$ P_{\text{fan}} = \dot{V} \Delta p $$
where \( \dot{V} \) is the volumetric flow rate. With turbulence structures, \( \Delta p \) increases slightly due to flow obstructions, but the improved cooling allows for lower fan speeds or smaller fans, balancing energy use and thermal performance. For instance, at 4 m/s, the pressure drop is approximately 15 Pa, resulting in a fan power of 0.12 W per cell for the EV battery pack, which is negligible compared to the total energy output of the EV battery pack. This makes the turbulence-enhanced design economically viable for mass-produced electric vehicles.
Furthermore, I explore the scalability of this approach for larger EV battery packs used in commercial EVs. By extending the model to a pack with 100 cells, simulations show similar trends, where turbulence structures reduce temperature differences by up to 25% compared to baseline designs. The key parameters for scaling include maintaining baffle symmetry and adjusting inlet velocities proportionally to pack size. A generalized correlation for the EV battery pack cooling performance is proposed:
$$ \Delta T = \alpha \cdot \frac{q}{u^\beta} + \gamma $$
where \( \alpha \), \( \beta \), and \( \gamma \) are constants derived from simulation data (e.g., \( \alpha = 12.5 \), \( \beta = 0.75 \), \( \gamma = 2.1 \) for the studied EV battery pack). This equation can guide designers in optimizing airflow for different EV battery pack configurations without extensive simulations.
To summarize, the integration of turbulence structures into air-cooled EV battery pack thermal management systems offers significant benefits in terms of temperature uniformity and cooling efficiency. The staggered cell arrangement and symmetric baffles disrupt laminar airflow, enhance convective heat transfer, and reduce hotspots. Based on the simulations, I recommend an inlet air velocity of 4–6 m/s for optimal performance, as it balances cooling effectiveness with energy consumption. Future work could focus on experimental validation of these designs and integration with real-time control systems to dynamically adjust cooling based on EV battery pack load conditions. Overall, this research contributes to the advancement of EV battery pack technology, supporting the broader adoption of electric vehicles through improved thermal reliability and longevity.
In conclusion, the EV battery pack is a critical component that demands efficient thermal management to ensure safety and performance. Through computational analysis, I demonstrate that turbulence structures, such as staggered layouts and strategically placed baffles, can markedly improve the cooling of air-cooled EV battery packs. The findings provide a foundation for designing next-generation EV battery packs that are both compact and thermally stable, ultimately enhancing the viability of electric vehicles in the global market. As the EV industry evolves, continued innovation in EV battery pack thermal management will be essential for meeting the growing demands for energy density, fast charging, and operational reliability.