Thermal management is a paramount concern for the safety, efficiency, and lifespan of electric vehicle (EV) battery packs. As lithium-ion batteries powering EVs operate under high discharge rates, they generate substantial heat due to internal electrochemical reactions and Joule heating. Inadequate heat dissipation can lead to elevated temperatures, significant temperature gradients within the pack, accelerated degradation, and in severe cases, thermal runaway. Air-cooling systems remain a prevalent choice for many EV applications due to their inherent advantages of structural simplicity, low cost, minimal maintenance, and high reliability. However, the primary challenges of traditional air-cooled EV battery pack designs are their relatively low heat transfer efficiency and poor temperature uniformity, especially under demanding operating conditions. This often results in localized hot spots that compromise the overall performance and safety of the EV battery pack.
To address these limitations, this study presents a comprehensive investigation into the synergistic optimization of flow channel architecture and a novel perforated guide plate design for an air-cooled EV battery pack. The core objective is to enhance convective heat transfer and improve airflow distribution, thereby reducing the maximum temperature and minimizing temperature differentials across the EV battery pack. I employ computational fluid dynamics (CFD) simulations to analyze the thermal and flow fields under various design configurations. The performance of two distinct flow channel layouts—double “Z”-type and double “U”-type—is evaluated. Subsequently, the optimized channel structure is integrated with a micro-perforated guide plate, and the effects of plate configuration, hole size, and inlet air velocity are rigorously examined. The findings demonstrate that a carefully engineered combination of flow path and guide plate can significantly boost the cooling performance of an EV battery pack, making air-cooling a more viable solution for modern thermal management demands.
The foundation of this analysis is a three-dimensional numerical model of a representative EV battery pack module. The module consists of 12 prismatic lithium-ion cells arranged in a 12-series, 1-parallel (12s1p) configuration, simulating a common building block for larger EV battery pack systems. Each cell is assumed to have dimensions representative of high-capacity cells used in EVs, with a volume $$V_b$$. A critical component of the proposed design is the introduction of a thin, perforated guide plate attached to the broad side of each cell using a layer of thermal interface material (e.g., thermally conductive silicone) to minimize contact resistance. The guide plate serves to disrupt and redistribute the cooling airflow, promoting better heat exchange from the cell surfaces. The entire assembly is housed within a duct that defines the airflow channels. Two primary duct structures are modeled: the double “Z”-type, where the inlet and outlet are on opposite sides, and the double “U”-type, where the inlet and outlet are on the same side. The cooling air enters from designated inlets, passes through the perforations in the guide plates and the gaps between cells, and exits from the outlets.
The thermal behavior of the EV battery pack is governed by the heat generation within the cells and the coupled heat transfer to the cooling air. The volumetric heat generation rate $$q$$ (in W/m³) inside a lithium-ion cell during discharge is calculated using the Bernardi equation, a simplified yet widely accepted model that accounts for reversible and irreversible heating:
$$q = \frac{I}{V_b} \left[ (E_0 – U_t) – T \frac{dE_0}{dT} \right]$$
Here, $$I$$ is the discharge current (A), $$V_b$$ is the cell volume (m³), $$E_0$$ is the open-circuit voltage (V), $$U_t$$ is the terminal voltage (V), $$T$$ is the absolute temperature (K), and $$dE_0/dT$$ is the entropy coefficient (V/K). For a 1C discharge rate of a typical high-capacity EV battery cell, the heat generation rate is a significant value that drives the thermal load. The material properties for the cell and cooling air used in the simulations are summarized in Table 1.
| Parameter | Cell | Air |
|---|---|---|
| Density, $$\rho$$ (kg/m³) | 2155 | 1.165 |
| Specific Heat Capacity, $$c_p$$ (J/(kg·K)) | 1030 | 1005 |
| Thermal Conductivity, $$k$$ (W/(m·K)) | Anisotropic: (1.6, 22.2, 22.2) | 0.0267 |
| Dynamic Viscosity, $$\mu$$ (kg/(m·s)) | — | 1.86 × 10-5 |
The CFD simulations are performed using a pressure-based transient solver in Ansys Fluent. The airflow is modeled as an incompressible fluid with constant properties. The Realizable $$k$$-$$\epsilon$$ turbulence model is adopted to capture the turbulent flow characteristics within the EV battery pack. The governing equations solved for the fluid domain (air) are the conservation of mass, momentum, and energy:
Continuity equation:
$$\frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \vec{v}) = 0$$
Momentum equation (Navier-Stokes):
$$\frac{\partial}{\partial t} (\rho \vec{v}) + \nabla \cdot (\rho \vec{v} \vec{v}) = -\nabla p + \nabla \cdot (\mu_{eff} \nabla \vec{v})$$
where $$\mu_{eff}$$ is the effective viscosity (laminar + turbulent).
Energy equation:
$$\frac{\partial}{\partial t} (\rho h) + \nabla \cdot (\rho \vec{v} h) = \nabla \cdot (k \nabla T) + S_h$$
where $$h$$ is enthalpy and $$S_h$$ is the heat source term.
For the solid domains (cells and guide plates), the energy equation reduces to the heat conduction equation with a source term:
$$\rho_s c_{p,s} \frac{\partial T}{\partial t} = \nabla \cdot (k_s \nabla T) + q$$
where the subscript $$s$$ denotes solid properties and $$q$$ is the volumetric heat generation rate for the cells (zero for guide plates).
The boundary conditions include a velocity inlet, a pressure outlet, and convective heat transfer on the external surfaces of the EV battery pack housing. A critical step in ensuring simulation accuracy is the mesh independence study. The cell count was progressively increased until key output parameters, namely the maximum cell temperature and maximum temperature difference within the EV battery pack, showed variations of less than 1%. A similar procedure was followed for time-step independence. The final computational mesh for the double “Z”-type EV battery pack model contained approximately 9.4 million elements, and a time step of 15 seconds was used for the transient simulations of a 1-hour discharge cycle.
The first phase of the investigation focuses on selecting the optimal flow channel structure for the EV battery pack. The thermal performance of the double “Z”-type and double “U”-type ducts is compared under identical operating conditions: 1C discharge, 10 m/s inlet air velocity, and 20°C ambient temperature. The key metrics are the maximum temperature ($$T_{max}$$) and the maximum temperature difference ($$\Delta T_{max}$$) across all cells in the EV battery pack at the end of the discharge cycle. The simulation results, visualized through temperature contours, reveal distinct patterns. The double “Z”-type duct demonstrates superior airflow distribution, as the air is forced to traverse across the entire pack before exiting. This leads to more uniform cooling. In contrast, the double “U”-type duct shows a shorter flow path for air near the inlet/outlet side, resulting in uneven cooling and higher temperature gradients. The quantitative comparison is presented in Table 2.
| Flow Channel Type | Max Cell Temperature, $$T_{max}$$ (°C) | Min Cell Temperature, $$T_{min}$$ (°C) | Max Temperature Difference, $$\Delta T_{max}$$ (°C) |
|---|---|---|---|
| Double “Z”-type | 27.25 | 22.07 | 5.18 |
| Double “U”-type | 27.45 | 22.03 | 5.42 |
Although both designs keep temperatures within safe limits (below 60°C as per standards), the double “Z”-type configuration yields a lower $$T_{max}$$ and a significantly lower $$\Delta T_{max}$$. The temperature uniformity is crucial for preventing localized stress and balancing cell aging in an EV battery pack. Therefore, the double “Z”-type structure is selected as the baseline for further optimization with the guide plate.
The second phase evaluates the impact of integrating the perforated guide plate into the double “Z”-type EV battery pack. A baseline case without any guide plate is simulated for comparison. The presence of the guide plate drastically alters the flow field. In the case without a guide plate, airflow tends to bypass through the least resistant paths, creating recirculation zones and vortices, particularly in the central and outlet regions of the pack. This leads to poor heat removal from cells situated in these low-flow areas. The addition of the guide plate, with its array of small holes, acts as a flow distributor. It creates a pressure drop that helps equalize the airflow through different channels along the pack’s length, mitigating vortex formation and ensuring that cooling air is more effectively directed over each cell’s surface. The thermal benefit is substantial. For the same operating conditions, the EV battery pack with the guide plate exhibits a $$T_{max}$$ reduction of over 2°C and a $$\Delta T_{max}$$ reduction of nearly 2°C compared to the pack without it. This underscores the guide plate’s role in enhancing the thermal homogeneity of the EV battery pack.
Having established the guide plate’s necessity, the study proceeds to optimize its geometric parameter: the hole size. The guide plate’s thickness and overall dimensions are fixed, while the square hole side length $$a$$ is varied. The design goal is to find a hole size that offers a good compromise between sufficient airflow (low flow resistance) and effective flow distribution/heat transfer augmentation. Four hole sizes are analyzed: 2 mm, 4 mm, 6 mm, and 8 mm. The performance is assessed not only by thermal metrics but also by flow parameters like pressure drop across the EV battery pack duct. The results are synthesized in Table 3.
| Hole Side Length, $$a$$ (mm) | Max Cell Temp, $$T_{max}$$ (°C) | Max Temp Diff, $$\Delta T_{max}$$ (°C) | Avg. Pressure Drop (Pa) | Flow Distribution Uniformity |
|---|---|---|---|---|
| 2 | 26.05 | 4.32 | > 3000 | Poor (very high velocity jets) |
| 4 | 26.38 | 4.55 | ~ 1500 | Moderate |
| 6 | 26.27 | 4.49 | ~ 650 | Good |
| 8 | 27.25 | 5.18 | ~ 360 | Good |
The 2 mm hole plate, while offering the lowest temperature, incurs an excessively high pressure drop, which would demand a powerful and energy-intensive blower, negatively affecting the overall efficiency of the EV. The 8 mm hole plate has a low pressure drop but provides less flow guiding effect, resulting in higher temperatures and gradients. The 6 mm hole plate emerges as the optimal choice, delivering excellent thermal performance (low $$T_{max}$$ and $$\Delta T_{max}$$) with a reasonable, manageable pressure drop. This design ensures effective thermal management for the EV battery pack without imposing undue parasitic energy costs on the vehicle’s systems.
The final optimization parameter is the inlet air velocity. The cooling capacity of an air-cooled EV battery pack is directly influenced by the mass flow rate of air. Simulations are conducted for the optimized design (double “Z”-type duct with 6 mm hole guide plate) at inlet velocities of 5 m/s, 10 m/s, 15 m/s, and 20 m/s. The relationship between cooling performance and fan power (proportional to pressure drop and flow rate) is a key trade-off. The results, plotted over the discharge cycle, show clear trends. Increasing the inlet velocity consistently lowers the maximum temperature in the EV battery pack. However, the rate of improvement diminishes at higher velocities due to the asymptotic nature of convective heat transfer. Furthermore, higher velocities dramatically increase the pressure drop and thus the required fan power. The data is summarized in Table 4.
| Inlet Air Velocity (m/s) | Max Cell Temp, $$T_{max}$$ (°C) | Max Temp Diff, $$\Delta T_{max}$$ (°C) | Estimated Relative Fan Power |
|---|---|---|---|
| 5 | 29.12 | 6.35 | 1.0 (Baseline) |
| 10 | 26.27 | 4.49 | ~ 5.5 |
| 15 | 25.10 | 3.85 | ~ 18 |
| 20 | 24.85 | 3.60 | ~ 42 |
While 20 m/s offers the best thermal numbers, the fan power requirement is over 40 times that of the 5 m/s case. For a practical EV battery pack thermal management system, energy efficiency is crucial. Therefore, an inlet velocity of 10 m/s is selected as the optimal operating point, providing a substantial improvement over natural convection or lower speeds with a moderate increase in auxiliary power consumption. This balance is essential for maximizing the driving range of the electric vehicle.
In conclusion, this detailed investigation demonstrates that the thermal performance of an air-cooled EV battery pack can be significantly enhanced through the synergistic optimization of flow channel geometry and auxiliary flow control structures. The double “Z”-type flow channel provides a foundational advantage in airflow distribution compared to the double “U”-type. The integration of a micro-perforated guide plate is a transformative addition, effectively breaking up unfavorable flow patterns and promoting uniform cooling across all cells in the EV battery pack. Optimizing the guide plate’s hole size to 6 mm strikes an optimal balance between thermal efficacy and system pressure drop. Finally, selecting an appropriate inlet air velocity (10 m/s in this study) ensures effective cooling without excessive parasitic energy draw. The proposed optimized design for the EV battery pack reduces the maximum temperature rise during a 1C discharge by several degrees Celsius and, more importantly, cuts the maximum internal temperature difference by over 30% compared to a conventional design without a guide plate. This leads to improved cell longevity, enhanced safety margins against thermal runaway, and more reliable performance of the EV battery pack under diverse operating conditions. The methodologies and findings presented here provide a valuable framework for designing efficient and cost-effective air-cooling solutions for next-generation electric vehicle energy storage systems.