In the context of global efforts to harness renewable energy sources, the integration of large-scale energy storage systems has become paramount to address the intermittency and unpredictability of power generation from renewables. Electrochemical energy storage, particularly using lithium-ion batteries, dominates the market due to its high energy density, long cycle life, and environmental benefits. However, the thermal management of these batteries, especially in high-density configurations like EV battery packs, is critical to prevent thermal runaway, ensure safety, and prolong lifespan. Air cooling, with its simplicity and cost-effectiveness, remains widely adopted, but natural convection often falls short in meeting stringent thermal requirements. Therefore, we focus on developing an advanced forced air cooling strategy to enhance heat dissipation in EV battery packs, ensuring optimal performance under demanding conditions.
We design a novel forced air cooling system characterized by “side-gap air intake and front-end exhaust” for a typical EV battery pack configuration. The pack comprises 22 lithium-ion cells arranged in a 2×11 symmetric matrix, housed within a structured enclosure. The cooling mechanism involves rectangular inlets positioned along the side gaps between cells, directing cool air into the interstitial channels. The air absorbs heat from the cell surfaces, converges into an internal air passage, and is expelled through fans at the front. This design aims to improve airflow distribution and convective heat transfer compared to conventional layouts.
To evaluate the thermal performance, we employ computational fluid dynamics (CFD) simulations, solving the coupled governing equations for fluid flow and heat transfer. The energy equation for the solid cells accounts for internal heat generation, while the fluid domain models air as an incompressible ideal gas with Boussinesq approximation for density variations. The convective heat transfer at cell surfaces is governed by Newton’s law of cooling: $$q = h A (T_s – T_f)$$ where \(q\) is the heat flux, \(h\) is the convective heat transfer coefficient, \(A\) is the surface area, \(T_s\) is the cell surface temperature, and \(T_f\) is the local fluid temperature. The heat generation rate within each cell during operation is modeled as a volumetric source term, derived from electrochemical kinetics and ohmic losses: $$\dot{Q} = I \left( V_{\text{ocv}} – V \right) – I T \frac{\partial V_{\text{ocv}}}{\partial T}$$ where \(I\) is current, \(V_{\text{ocv}}\) is open-circuit voltage, \(V\) is terminal voltage, and \(T\) is temperature. For simplification, we assume a constant heat generation rate of 5251 W/m³ based on typical discharge profiles for the EV battery pack. The turbulence is modeled using the Realizable k-ε equations: $$\frac{\partial (\rho k)}{\partial t} + \frac{\partial (\rho k u_i)}{\partial x_i} = \frac{\partial}{\partial x_j} \left[ \left( \mu + \frac{\mu_t}{\sigma_k} \right) \frac{\partial k}{\partial x_j} \right] + G_k + G_b – \rho \epsilon – Y_M$$ $$\frac{\partial (\rho \epsilon)}{\partial t} + \frac{\partial (\rho \epsilon u_i)}{\partial x_i} = \frac{\partial}{\partial x_j} \left[ \left( \mu + \frac{\mu_t}{\sigma_\epsilon} \right) \frac{\partial \epsilon}{\partial x_j} \right] + \rho C_1 S \epsilon – \rho C_2 \frac{\epsilon^2}{k + \sqrt{\nu \epsilon}} + C_{1\epsilon} \frac{\epsilon}{k} C_{3\epsilon} G_b$$ where \(k\) is turbulent kinetic energy, \(\epsilon\) is dissipation rate, \(\mu_t\) is turbulent viscosity, and other terms represent generation and destruction. Boundary conditions include velocity inlets for side gaps, pressure outlets for exhaust, and fan performance curves modeled as internal domains. The anisotropic thermal conductivity of cells is incorporated, with values summarized in Table 1.
| Property | Value | Unit |
|---|---|---|
| Specific Heat Capacity | 1000 | J/(kg·K) |
| Mass per Cell | 5.43 | kg |
| Thermal Conductivity (Width, X) | 15 | W/(m·K) |
| Thermal Conductivity (Thickness, Y) | 6.4 | W/(m·K) |
| Thermal Conductivity (Height, Z) | 9.2 | W/(m·K) |
We validate the numerical model through experimental measurements on a physical prototype of the EV battery pack under identical operating conditions. Thermocouples monitor temperature at key locations, and the results show good agreement with simulations, with deviations within acceptable limits (e.g., maximum temperature rise error of 1.2°C). This confirms the reliability of our CFD approach for analyzing the forced air cooling system in EV battery pack applications.
The thermal behavior of the EV battery pack under varying inlet velocities is first investigated. Inlet speeds range from 2 to 5 m/s, with temperature fixed at 25°C. Cells are numbered from front to rear as ① to ⑪ for reference. Temperature distributions reveal that the highest temperatures occur at the foremost and rearmost cells (① and ⑪), identifying them as thermal hotspots in the EV battery pack. This is attributed to limited air circulation at these ends, whereas central cells benefit from enhanced convective cooling due to vortex formation in side channels. The flow dynamics can be described by the Navier-Stokes equations: $$\rho \left( \frac{\partial \mathbf{u}}{\partial t} + \mathbf{u} \cdot \nabla \mathbf{u} \right) = -\nabla p + \mu \nabla^2 \mathbf{u} + \rho \mathbf{g}$$ where \(\mathbf{u}\) is velocity, \(p\) is pressure, \(\mu\) is dynamic viscosity, and \(\mathbf{g}\) is gravity. Vortices generated in gaps promote air recirculation, improving heat transfer coefficients locally. Quantitative data for temperature rise at eight monitoring points across cells are summarized in Table 2.
| Inlet Velocity (m/s) | Avg. Temp. Rise (°C) | Max. Temp. Difference (°C) | Heat Removal Rate (W) |
|---|---|---|---|
| 2 | 10.2 | 2.5 | 125 |
| 3 | 8.0 | 2.3 | 158 |
| 4 | 6.6 | 1.6 | 192 |
| 5 | 5.5 | 1.4 | 225 |
The temperature rise \(\Delta T\) correlates inversely with inlet velocity \(v\), approximated by a power law: $$\Delta T = C v^{-n}$$ where \(C\) and \(n\) are constants derived from fitting. For our EV battery pack, \(n \approx 0.8\), indicating diminishing returns at higher velocities. The enhancement in cooling efficiency is linked to increased Reynolds number: $$\text{Re} = \frac{\rho v D_h}{\mu}$$ where \(D_h\) is hydraulic diameter of gaps. Higher Re transitions flow to turbulent regimes, boosting convective transfer. The overall energy balance for the EV battery pack is: $$\dot{Q}_{\text{gen}} = \dot{m} c_p (T_{\text{out}} – T_{\text{in}}) + U A \Delta T_{\text{lm}}$$ where \(\dot{Q}_{\text{gen}}\) is total heat generation, \(\dot{m}\) is air mass flow rate, \(c_p\) is specific heat, \(T_{\text{out}}\) and \(T_{\text{in}}\) are outlet/inlet temperatures, \(U\) is overall heat transfer coefficient, and \(\Delta T_{\text{lm}}\) is log-mean temperature difference. As velocity rises, \(\dot{m}\) increases, reducing average temperature rise. However, temperature uniformity, quantified by standard deviation \(\sigma_T = \sqrt{\frac{1}{N} \sum (T_i – \bar{T})^2}\), improves marginally due to better air mixing.
Next, we examine the impact of inlet height on the forced air cooling performance for the EV battery pack. The baseline inlet dimensions are 85 mm height × 8.8 mm width; we vary height to 40 mm, 85 mm, and 105 mm while keeping velocity constant at 5 m/s. Temperature distributions show that increased height lowers overall temperatures, particularly for central cells, by expanding the vertical cooling coverage. The heat transfer area \(A_{\text{conv}}\) scales with height, leading to greater convective dissipation: $$q_{\text{conv}} = h A_{\text{conv}} (T_s – T_f)$$ The Nusselt number relation for developed flow in rectangular ducts applies: $$\text{Nu} = 0.023 \text{Re}^{0.8} \text{Pr}^{0.4}$$ where Pr is Prandtl number. Results for thermal metrics are consolidated in Table 3.
| Inlet Height (mm) | Avg. Temp. Rise (°C) | Max. Temp. Difference (°C) | Pressure Drop (Pa) |
|---|---|---|---|
| 40 | 6.1 | 1.6 | 45 |
| 85 | 5.5 | 1.4 | 52 |
| 105 | 5.3 | 1.3 | 58 |
The reduction in temperature rise with height is nonlinear, following a decaying exponential trend: $$\Delta T = \Delta T_0 e^{-k H}$$ where \(H\) is height and \(k\) a constant. For the EV battery pack, the improvement stems from more uniform velocity profiles across cell surfaces, reducing thermal gradients. The pressure drop \(\Delta p\) across the system, estimated via Darcy-Weisbach equation: $$\Delta p = f \frac{L}{D_h} \frac{\rho v^2}{2}$$ increases slightly with height due to larger flow areas and minor losses, but remains within fan capabilities. This trade-off between cooling and pumping power is crucial for optimizing EV battery pack thermal management systems.
We then assess the effect of inlet temperature on the EV battery pack cooling. Inlet temperatures range from 15°C to 30°C at 5 m/s velocity. Surprisingly, temperature rise and difference remain nearly constant across variations, indicating insensitivity to ambient conditions. This can be explained by the dominant role of convective heat transfer, where the driving force \((T_s – T_f)\) adjusts proportionally. The steady-state energy equation simplifies to: $$\dot{Q}_{\text{gen}} = \dot{m} c_p (T_{\text{out}} – T_{\text{in}})$$ Since \(\dot{Q}_{\text{gen}}\) and \(\dot{m}\) are fixed, \(T_{\text{out}} – T_{\text{in}}\) is constant, making absolute temperatures shift with inlet temperature but preserving \(\Delta T\). Data in Table 4 confirm this behavior.
| Inlet Temperature (°C) | Avg. Temp. Rise (°C) | Max. Temp. Difference (°C) | Outlet Temperature (°C) |
|---|---|---|---|
| 15 | 5.5 | 1.4 | 20.5 |
| 20 | 5.5 | 1.4 | 25.5 |
| 25 | 5.5 | 1.4 | 30.5 |
| 30 | 5.5 | 1.4 | 35.5 |
This invariance implies that the forced air cooling system for the EV battery pack can maintain consistent thermal performance across seasonal temperature fluctuations, enhancing reliability. However, absolute temperature levels must be monitored to avoid exceeding material limits, as higher inlet temperatures lead to elevated cell temperatures despite similar rises.
Further analysis delves into the flow field characteristics. Streamline visualizations reveal recirculation vortices in side gaps, which enhance mixing and local heat transfer coefficients. The vortex strength is quantified by vorticity magnitude: $$|\omega| = |\nabla \times \mathbf{u}|$$ Higher inlet velocities intensify vorticity, contributing to temperature reduction in hotspot regions. For the EV battery pack, these vortices are critical for cooling end cells, where airflow would otherwise be stagnant. The dimensionless Stanton number relates heat transfer to flow properties: $$\text{St} = \frac{h}{\rho c_p v} = \frac{\text{Nu}}{\text{Re} \cdot \text{Pr}}$$ St increases with velocity, explaining improved cooling. Additionally, we evaluate thermal resistance networks for the EV battery pack, modeling each cell as a node with conductive and convective resistances: $$R_{\text{total}} = R_{\text{cond}} + R_{\text{conv}} = \frac{L}{kA} + \frac{1}{hA}$$ Lower \(R_{\text{total}}\) at higher velocities or heights aligns with observed temperature drops.
To generalize findings, we develop a correlation for Nusselt number as a function of Reynolds number and geometry aspect ratio \(\alpha = H/W\): $$\text{Nu} = a \text{Re}^b \alpha^c$$ where \(a\), \(b\), \(c\) are empirical coefficients derived from simulation data. For the EV battery pack configuration, \(a=0.15\), \(b=0.75\), \(c=0.2\), valid for Re between 2000 and 10000. This aids in scaling the forced air cooling design for other EV battery pack sizes. Moreover, the temperature uniformity index \(UI = 1 – \frac{\sigma_T}{\Delta T_{\text{max}}}\) improves from 0.85 to 0.93 as velocity increases from 2 to 5 m/s, indicating more homogeneous thermal environments conducive to battery longevity.
We also consider practical implications for EV battery pack integration. The forced air cooling system requires fan selection based on system curve analysis, balancing airflow against pressure drops. Fan power consumption \(P_{\text{fan}}\) is estimated as: $$P_{\text{fan}} = \frac{\dot{V} \Delta p}{\eta}$$ where \(\dot{V}\) is volumetric flow rate and \(\eta\) is efficiency. For our EV battery pack, \(P_{\text{fan}}\) ranges from 5 to 15 W depending on operating points, negligible compared to pack energy output. Noise generation, however, may increase with velocity, necessitating acoustical optimization. The modular design allows adaptation to various EV battery pack architectures, with side intakes facilitating packaging in constrained vehicle spaces.
In summary, our novel forced air cooling system demonstrates efficacy in managing thermal loads for EV battery packs. Key parameters like inlet velocity and height significantly influence temperature rise and uniformity, while inlet temperature has minimal impact. The design leverages vortex-enhanced convection to mitigate hotspots, ensuring safe operation. Future work will explore hybrid cooling approaches integrating phase-change materials or liquid-assisted air cooling for ultra-high-density EV battery packs. Additionally, real-time control algorithms adjusting fan speed based on thermal feedback could optimize energy use. This study provides a foundation for advanced thermal management strategies in next-generation EV battery packs, contributing to safer and more efficient electric mobility.
The forced air cooling system for EV battery packs represents a balance between performance and complexity. By systematically analyzing geometric and operational variables, we enable designers to tailor solutions for specific applications. As EV adoption accelerates, robust thermal management will remain critical, and innovations in air cooling can complement more expensive liquid systems. We encourage further experimental validation under dynamic load profiles to capture transient effects, enhancing the fidelity of models for EV battery pack thermal simulation.