CFD-Driven Design of an Air-Cooling System for Lithium-Ion Battery Packs in a Formula Student Car

Abstract

In the high-performance environment of Formula Student Car racing, effective battery thermal management is crucial for safety, reliability, and performance. This work presents the design and validation of a lightweight, air-based Battery Cooling System (BCS) developed for a Formula Student vehicle. The system addresses the significant thermal loads generated by 528 Molicel P45B lithium-ion cells, arranged in a constrained U-shaped module layout. Using Computational Fluid Dynamics (CFD), the airflow geometry was optimized to deliver uniform cooling across all modules while minimizing aerodynamic drag. Simulations evaluated the system’s performance under various ambient temperatures (25 °C and 30 °C) and airflow velocities (from 16 m/s to 18 m/s), identifying the impact of duct geometry, internal air guides, and airflow distribution on thermal regulation. Results showed that, at nominal ambient temperature (25 °C), all monitored cells stayed below the 60 °C threshold required by FS regulations. At elevated ambient conditions (30 °C), regions above 60 °C appeared within the pack, revealing non-uniform cooling and reduced safety margin. These findings suggest that, while the system complies with current rules, additional design refinements are needed to enhance robustness under harsher conditions. Additionally, these results are specific to a Formula Student application under competition constraints and are not intended to be generalized to production EVs.

1. Introduction

In high-performance electric racing, such as the Formula Student (FS) competition, efficient battery thermal management is essential for optimizing vehicle performance, ensuring safety, and meeting the rigorous demands of competition. FS, a global engineering competition, challenges teams to design, build, and race formula-style electric cars. The intense acceleration, rapid deceleration, and extended race durations inherent in this context impose substantial thermal loads on the vehicle’s energy storage systems. Without effective cooling strategies, batteries can overheat, reducing efficiency, compromising safety, and accelerating wear [1,2].

Lithium-ion batteries, the preferred energy storage medium for FS cars, are highly sensitive to temperature variations. Studies show that optimal battery performance is maintained within a narrow temperature range, typically between 25 and 40 °C, with temperatures exceeding 60 °C causing significant efficiency losses and accelerating chemical degradation [1,2]. Moreover, uneven temperature distribution within a battery pack can exacerbate these issues, leading to imbalances, decreased capacity utilization, and increased risk of thermal runaway [1,3,4]. In the most extreme cases, uncontrolled heat generation can lead to catastrophic failure, making thermal management critical not only for performance but also for safety [5].

Recent reviews report that, despite the lower heat removal capacity compared with liquid systems, air cooling remains attractive where simplicity, weight, and cost are critical. When airflow is properly distributed, maximum temperature and spatial gradients can be kept within safe bounds; the main limitation is cooling non-uniformity, which accelerates degradation. The same reviews also note the growing adoption of hybrid strategies (air combined with phase change materials or localized liquid cooling) in high-load scenarios [6,7,8].

Experimental and CFD studies demonstrate that intake/manifold geometry, splitter and baffle placement, and the position of inlets and outlets are decisive in determining both peak temperature and cooling uniformity, with a direct impact on fan power. Optimized duct shapes and airflow guides have been shown to reduce hot spots and inter-module ΔT while avoiding excessive pressure losses [9,10,11]. These works demonstrate that geometric refinement, rather than simply increasing flow rate, is essential to robust cooling performance.

For Formula Student applications, the design space is further limited by strict packaging and safety rules, which constrain intake area and prevent rearrangement of the pack layout. FS-oriented studies confirm that airflow distribution becomes the dominant driver of performance under these constraints, highlighting the importance of refining ducts and guides within the available space [12,13].

The novelty of the present work lies in applying detailed CFD analysis to an air-cooled pack developed under Formula Student geometric restrictions, quantifying its performance margins and identifying conditions where design refinements are most needed.

The challenges associated with battery cooling are compounded by the increasing energy demands of electric racing vehicles. To enhance mileage and power delivery, battery packs are becoming more densely packed and feature higher energy density cells. This trend increases internal heat generation, particularly during the high charge and discharge cycles characteristic of racing [14]. Heat generation in lithium-ion cells arises from electrochemical reactions, ohmic resistance, and entropy changes, all of which intensify under the high currents required for race performance [14]. Without an effective cooling system, this heat accumulation can cause localized hotspots, accelerate thermal degradation, and, in extreme cases, lead to thermal runaway [15].

Various cooling strategies have been developed to mitigate these thermal challenges. Liquid cooling and phase change material systems are effective due to their high thermal conductivity and capacity to handle extreme heat loads. However, these systems are often unsuitable for FS applications because they add significant weight, increase system complexity, and require additional maintenance [16]. Air cooling, by contrast, offers a lightweight and cost-effective solution that aligns with the competition’s principles of efficiency and simplicity. Although air has a lower heat capacity and thermal conductivity compared to liquids, advancements in duct design and airflow management have demonstrated its potential to effectively regulate battery temperatures in high-performance environments [16,17].

Recent studies have emphasized the importance of duct geometry, placement, and airflow optimization in achieving uniform cooling across battery packs. CFD tools have proven instrumental in this regard, enabling precise simulations of airflow patterns and temperature distributions. By iteratively refining duct designs based on simulation results, engineers can ensure that cooling systems deliver sufficient airflow to critical areas while minimizing aerodynamic drag [18].

At the cell level, heat generation can be represented either via the Bernardi formulation or by an equivalent uniform volumetric heat-source term. The Bernardi model is widely recognized as the most representative [19,20,21], while the uniform heat-source approach is also widely applied in BTMS studies due to its simplicity and ability to capture pack-scale trends [22,23,24,25]. For this reason, the latter approach was adopted. Ideally, validation against experimental or literature benchmarks should be performed if data is available. Previous works have highlighted that beyond global correlations, spatial validation of velocity and temperature distributions is a recommended best practice [26,27], which frames the validation strategy of the present study.

The scope of this work was focused on the design, simulation, and validation of an air duct system to ensure effective heat dissipation. Using CFD simulations, the cooling system was optimized to provide uniform airflow across the battery modules, minimize temperature gradients, and maintain compatibility with the overall vehicle architecture. The air-cooling system was developed to align with FS principles of lightweight construction, aerodynamic efficiency, and adherence to safety standards.

The contributions of this paper are as follows: (i) the development of an air-cooled battery system tailored for a Formula Student (FS) electric car; (ii) a design incorporating custom air duct intakes strategically positioned to channel airflow over the battery cells while minimizing aerodynamic drag; and (iii) the validation of the system through CFD simulations under various operating conditions, ensuring compliance with FS regulations that require monitored battery cell temperatures to remain below 60 °C. This approach aligns with the competition’s emphasis on innovation, efficiency, and adherence to stringent safety standards.

The results of this work contribute to the ongoing advancement of battery thermal management systems for electric racing applications. The present study addresses a Formula Student-specific cooling problem under strict packaging and rules; therefore, the findings are not intended as a general solution for commercial EVs.

2. Battery Cooling System

The thermal management of lithium-ion batteries is a critical aspect of high-performance electric vehicles, especially in the FS competition. These vehicles are subjected to intense operational conditions, including rapid accelerations, sustained power outputs, and frequent thermal cycling, which result in significant heat generation within the battery pack. To ensure safe and reliable operation, FS regulations mandate that at least 30% of the battery cells must be monitored, and all monitored cells must remain below a maximum temperature of 60 °C [28]. This temperature threshold is critical to prevent chemical degradation, maintain efficiency, and reduce the risk of thermal runaway.

The primary objective of this study was to design an air-cooling system capable of maintaining battery cell temperatures within these limits under all anticipated operating conditions. The system was developed to integrate seamlessly with a pre-defined battery layout, which consisted of 528 Li-ion cells arranged in six U-shaped modules. This configuration, while outside the scope of the design work presented here, posed specific thermal challenges that influenced the cooling strategy.

The FS vehicle’s battery pack comprises 528 Molicel P45B cells from company Molicel (Taipei, Taiwan, ROC), divided into six U-shaped modules. Each module consists of four parallels of 22 cells arranged in two layers, as shown in Figure 1. This layout optimizes space within the vehicle chassis while providing robust electrical connectivity. Although the configuration was pre-defined, its impact on thermal management posed unique challenges that guided the development of the air-cooling system.

To establish the thermal demands of the battery pack, simulations were conducted by the team using OpenLap software version 01.00. These simulations emulated the car’s performance during the endurance race, which represents the most challenging thermal conditions in FS. The analysis was based on five different track layouts, each designed to simulate typical endurance scenarios.

The OpenLap simulation relied on a comprehensive set of vehicle parameters, including aerodynamic coefficients, weight distribution, gear ratios, and powertrain characteristics, to closely replicate the car’s real-world performance. These simulations provided critical insights into the vehicle’s energy usage and motor demands under race conditions. While the motor was limited to a peak power of 60 kW, the endurance race demonstrated an average power of 34 kW.

Research on the Molicel P45B cells revealed that, during discharge, approximately 6% of the energy is dissipated as heat [29]. This value was obtained considering an average output on continuous discharge testing as shown in Figure 2.

Using the discharge efficiency of 94% for Molicel P45B cells, the heat dissipation rate for the battery was calculated. The total thermal power generation was determined as follows:

$$P_{heat}= P_{motor} (1-{\eta }_{cell})$$

(1)

substituting the values, returns a heat power of 2.04 kW produced in the battery pack. This value represents the heat that needs to be dissipated across all 528 cells during operation. Dividing the total thermal load by the number of cells provides the heat generated per individual, which is equal to 3.86 W.

These values formed the baseline for designing the air-cooling system, which was developed to ensure uniform temperature distribution and compliance with the FS regulation.

The air duct system was designed to channel airflow from the lateral side of the main hoop of the chassis directly to the battery modules, with each inlet serving three modules as shown in Figure 3. The entrance to the battery measured 263 mm in length and 46 mm in width, while the overall air intake had a height of 81 mm. The primary objective was to ensure uniform airflow across the modules, maintaining effective cooling while adhering to the pre-defined external constraints of the vehicle design.

Several intermediate geometries were tested during the optimization process, including variations in splitter position and duct curvature. As their performance trends were consistent, with progressive improvements in airflow distribution, only the final configuration is reported in detail, as seen in Figure 4. The intermediate iterations are not shown for brevity, but they were used to guide the refinements that led to the final design.

Another challenge identified during the design process was the presence of free spaces around the modules, which allowed air to bypass the cells rather than flow directly through them. To address this, the free spaces were reduced, creating additional airflow resistance. This adjustment forced the air through the modules and over the battery cells, significantly improving convective heat transfer and reducing temperature gradients across the modules. Additionally, small air directors were added near the modules to channel airflow directly against the cells, Figure 5, further enhancing heat dissipation.

The final design successfully combined these refinements, resulting in a BCS capable of delivering uniform airflow across all modules. Several intermediate geometries of the airflow duct were simulated during the design process. These were part of the present study and not adopted from external sources. Their performance trends were consistent, and only the final geometry is presented in detail to focus on the discussion.

3. Numerical Model

3.1. Computational Domain

The preparation of the battery geometry was a critical step to ensure the accuracy of the thermal and airflow simulations. This process involved isolating the regions of interest and defining the flow volume where air would move around the battery cells. These steps were essential for effective meshing and boundary condition application, providing a reliable foundation for the simulations.

The preparation process began with a thorough feature repair of the battery model. Ensuring that all geometrical features were clean, properly connected, and free of inconsistencies was critical to avoid potential errors during meshing and simulation. Furthermore, to optimize computational efficiency, the simulation leveraged the symmetrical nature of the battery pack. Only half of the geometry was modeled, with a symmetry plane applied to represent the mirrored half as shown in Figure 6. This approach significantly reduced computation time and resource requirements while maintaining the accuracy of airflow and thermal interaction simulations across the entire battery geometry. This simplification was made to reduce computational costs. Furthermore, the asymmetry in the flow is minimal due to the nature of Formula Student tracks, which are generally flat and include symmetric sections such as figure-eight layouts.

3.2. Meshing Strategy

The meshing process for the BCS was carried out using Fluent Meshing to ensure a high-quality representation of the geometry and reliable resolution of thermal and flow phenomena. A surface mesh was first generated with curvature sizing, providing finer resolution in critical regions such as the air duct walls, battery module surfaces, and cylindrical cells. This ensured that areas with strong curvature were captured with sufficient detail.

Inflation layers were applied on all cell surfaces to resolve the steep velocity and temperature gradients in the boundary layer. These layers provided accurate prediction of near-wall convective heat transfer, which is critical for evaluating the system’s thermal performance. Figure 7 illustrates the local refinement and inflation layers applied in the most critical regions, highlighting how the near-wall mesh resolution was enforced. The bulk flow region was meshed with a hex-core strategy, offering an efficient balance between accuracy and computational cost. The use of a symmetry plane reduced the domain size by half, further decreasing computational effort without compromising accuracy. The relatively high number of elements is mainly a consequence of the complex geometry of the module, particularly the narrow gaps left in the connector slots, which required local refinement to capture flow and heat transfer reliably.

Mesh quality was assessed through standard indicators. Average skewness was 0.25, 0.24, and 0.21 for G1–G3, respectively, while average orthogonal quality improved from 0.48 in G1 to 0.55 in G3. The maximum aspect ratio remained within acceptable limits. Localized outliers were detected but represented less than 0.00001% of the total cell count and were confined to non-critical regions, thus not affecting the global solution. Near-wall resolution was targeted $y^{+}\approx 1$, allowing the viscous sublayer to be directly resolved. These values fall within recommended limits for internal flow simulations.

To verify mesh independence, three grid levels (G1–G3) were compared under reference operating conditions of 18 m/s inlet velocity and 25 °C ambient temperature.

Table 1. Mesh statistics and independence test results.

Grid Number	Elements	Avg. Skewness	Avg. Orth. Quality	${\mathit{T}}_{\mathit{m}\mathit{á}\mathit{x}}$ [°C]	${\mathit{T}}_{\mathit{avg}}$ [°C]
1	$2.27\times {10}^7$	0.25	0.48	57.83	45.88
2	$3.58\times {10}^7$	0.24	0.51	57.15	45.42
3	$4.54\times {10}^7$	0.21	0.55	56.91	45.21

summarizes the mesh characteristics and the thermal results. The maximum and average cell temperatures predicted by G2 and G3 differed by less than 1%, confirming that the solution was mesh independent. Consequently, the fine grid (G3) was adopted for all subsequent simulations.

Convergence was monitored using both residuals and engineering quantities. Residuals were reduced below ${10}^{-3}$ for continuity, momentum, and turbulence equations, and below ${10}^{-5}$ for the energy equation. Monitored variables (domain-averaged temperature, maximum cell temperature, and pressure drop) stabilized within 0.1% over 200 consecutive iterations.

3.3. Governing Equations and Turbulence Model

Airflow and heat transfer were solved with a steady, pressure-based RANS formulation coupled to the energy equation, assuming air as an incompressible Newtonian fluid with temperature-dependent properties (see

Table 2. Air and material properties assigned in Fluent.

Parameter	Air (25/30 °C)	Molicel P45B	S275JR Steel	Simona PVC
$\rho$ (kg/m3)	1.184/1.164	2887.16	7800	1440
Cp (J/kg·K)	1007/1007	888	470	1076
k (W/m·K)	0.02551/0.02588	0.83/11.55 (radial/axial)	42.5	0.143
$\mu \times {10}^{-5}$ (kg/m·s)	1.849/1.872	-	-	-

). Compressibility was neglected because the maximum inlet velocity (18 m/s) corresponds to a Mach number ≈ 0.05, well below the conventional threshold of 0.3. Solver settings followed a coupled pressure–velocity algorithm with second-order spatial discretization.

A single representative hydraulic diameter cannot be defined for this duct due to its irregular cross-sections, narrow gaps and abrupt area changes. In similar air-cooled BTMS and automotive duct studies under comparable velocities and length scales, the internal flow is consistently treated as turbulent; we adopt the same modeling choice here. Representative reviews and applications employ turbulence models for irregular ducted flows in BTMS contexts [2,5,14].

Turbulence was modeled with the shear-stress transport (SST) $k$-ω model because it combines accurate near-wall behavior with robustness in separated regions and under adverse pressure gradients, features expected in the present geometry. This rationale aligns with established SST guidance and with recent BTMS applications reporting reliable prediction of separation and convective heat transfer in constrained internal passages [1,4,30].

3.4. Simulation Setup

The setup for the BCS simulations was meticulously configured to evaluate the system’s thermal and airflow performance under operational conditions. This process involved defining materials, boundary conditions, turbulence models, and solver parameters to ensure an accurate representation of real-world behavior. Special attention was given to capturing the interaction between airflow and heat dissipation within the battery pack, as these factors are critical to maintaining cell temperatures below the operational threshold of 60 °C. By simulating different ambient temperatures, of 25 °C and 30 °C, and airflow conditions, the setup provided valuable insights into the cooling system’s efficiency and effectiveness.

3.4.1. Materials Assignment

The accurate assignment of material properties was a critical step in the simulation setup to ensure precise representation of the battery cooling system’s behavior. Each material within the system, from the battery casing to the airflow region, was carefully defined based on its physical and thermal characteristics.

The battery casing, constructed from S275JR steel, was assigned to its specific thermal and structural properties. These properties were crucial for modeling the casing’s role in conducting and dissipating heat generated within the battery pack. Similarly, the module housing, made of PVC-CAW from Simona AG (from Kirn, Germany), was assigned its thermal attributes to capture the interaction between the modules and surrounding airflow accurately.

For the battery cells, bibliographic research on lithium-ion cells, specifically Molicel P45B, provided the necessary thermal data [31,32]. These parameters allowed for the simulation of heat generation during operation, reflecting the cells’ real-world discharge conditions.

The airflow, modeled as a fluid region, was assigned properties corresponding to air at ambient temperatures of 25 °C and 30 °C [33]. These conditions were chosen to evaluate the cooling system under varying environmental scenarios, simulating realistic race conditions. The thermal conductivity (k), specific heat capacity (Cp), density ($\rho$) and dynamic viscosity ($\mu$) of the air were integral to capture its role as a cooling medium, ensuring accurate predictions of convective heat transfer.

The material properties of each component, summarized in Table 2, underline the fidelity of the simulation. By defining these properties, the model effectively captured the thermal interactions and airflow dynamics within the battery system, enabling robust evaluation and optimization of the cooling design.

3.4.2. Boundary and Thermal Conditions

The boundary conditions for the battery cooling simulation were carefully designed to ensure accurate airflow and thermal analysis. At the air intake, a velocity inlet boundary condition was defined based on previously conducted wind tunnel testing performed during the broader scope of this project. These tests determined an average velocity range between 17.87 m/s and 18.14 m/s. To account for potential variations, three inlet velocities of 18 m/s, 17 m/s, and 16 m/s were tested. The temperature of the air at the inlet was set at two values: 25 °C for initial tests and 30 °C for subsequent evaluations.

At the outlet, a pressure outlet condition was applied with a gauge pressure of 0 Pa, representing ambient atmospheric conditions. Additionally, a target mass flow rate was imposed at the outlet to ensure that the mass flow rate matched the air entering the system, reflecting the effects of fans used to maintain airflow consistency. The volumetric flow rate ($\dot{v}$) was calculated using the following equation:

$$\dot{v}=V A$$

(2)

where V is the velocity at the inlet, and A is the cross-sectional area of the inlet. Substituting an inlet velocity of 18 m/s and an inlet area of 0.004 m², the volumetric flow rate was computed as 0.0720 m³/s. The mass flow rate ($\dot{m}$) was then calculated by the following equation:

$$\dot{m}=\rho \dot{v}$$

(3)

The resulting mass flow rate was 0.0852 kg/s. For inlet velocities of 17 m/s and 16 m/s, the mass flow rates were 0.0805 kg/s and 0.0758 kg/s, respectively. These values were used as targets at the outlet to simulate the fan-driven airflow through the battery pack.

The accurate modeling of the battery cooling system required detailed calculations for the thermal coefficients and heat generation rates to replicate the real-world operational behavior of the system. Each battery cell was treated as a volumetric heat source, and the heat transfer between the air and cell surfaces was modeled using empirical correlations for convective heat transfer. These parameters were critical for simulating thermal interactions and airflow dynamics within the cooling system.

The heat generation of the Molicel P45B cells was determined based on the average thermal energy dissipated during discharge. Each cell produced 3.86 W. Using the cell’s physical dimensions, with a diameter (d) of 21 mm and a height (he) of 70 mm, the volume of a single cell, $V_{cell}$, was calculated using the following equation:

$$V_{cell}= \pi {\left({\displaystyle \frac{d}{2}}\right)}^2 he$$

(4)

substituting the values the volume of a cell is equal to $2.4245\times {10}^{-5}$ m³.

Thus, the source term applied to each cell, representing thermal power generation in $\mathrm{W}/{\mathrm{m}}^3$, was calculated and then computed according to the following equation:

$$Q_{cell}={\displaystyle \frac{P_{heat \left(per cell\right)}}{V_{cell}}}$$

(5)

which returned a thermal power generation per cubic meter of $1.592\times {10}^5$ W/m³. The thermal source term, representing heat generation within each cell, was applied uniformly across all 264 cells in the model. This setup ensured that the simulation accurately reflected the consistent heat flux produced by each cell during operation.

These boundary conditions were critical for replicating realistic operating conditions and ensuring the accuracy and reliability of the simulation results.

3.5. Model Validation

The mesh-independence protocol followed here (three grid levels, monitored thermal/pressure metrics) is consistent with recent best practices for internal duct CFD validation [27]. Experimental validation was not yet possible because, at the time of writing, the Formula Student team is still in the process of building the battery pack. To assess the reliability of the numerical model, the predictions were therefore compared with established correlations and with data reported in the literature.

For single cylindrical cells, the predicted convective heat transfer coefficients were found to be consistent with the Churchill–Bernstein correlation for external flow around cylinders [24], with deviations within the typical ±20% scatter reported in the literature. At pack level, the computed pressure drop across the duct was of the same order of magnitude as those reported in similar air-cooled BTMS studies [1,2,5,9,18]. Moreover, the distribution of cell temperatures and the magnitude of maximum–average differences align with recent Formula Student applications [5,13,16,17].

These comparisons indicate that the model captures the dominant flow and heat transfer mechanisms with sufficient accuracy for design purposes, supporting the credibility of the results presented in Section 4.

4. Results and Discussion

The performance of the BCS was evaluated through detailed CFD simulations under varying operational conditions. The results presented in this chapter focus on the system’s ability to regulate battery cell temperatures and ensure compliance with safety requirements, particularly maintaining temperatures below the critical threshold of 60 °C. The analysis includes the thermal response of the cells, airflow behavior, and the system’s overall heat transfer efficiency at different inlet velocities and ambient temperatures.

To provide a comprehensive evaluation, the results are divided into two main sections. The first examines the system’s performance at nominal ambient conditions (25 °C), while the second addresses elevated ambient temperatures (30 °C) to assess the cooling system’s robustness under more challenging thermal loads. For each condition, numerical data is supplemented with contour plots to visualize temperature distribution and airflow patterns, offering critical insights into the system’s behavior and potential areas for optimization.

By systematically analyzing these scenarios, the results aim to establish a clear understanding of the cooling system’s capabilities and limitations, highlighting the importance of airflow velocity in ensuring uniform cooling and maintaining thermal safety across all battery cells.

4.1. Thermal Performance

The BCS’s performance was evaluated under an ambient temperature of 25 °C for inlet velocities of 16 m/s, 17 m/s, and 18 m/s. Figure 8 presents the average temperature of the battery cells as a function of iteration number. For an inlet velocity of 18 m/s, the average cell temperature stabilized at 45.21 ± 0.04 °C, while at 17 m/s and 16 m/s, the average temperatures were slightly higher at 46.04 ± 0.04 °C and 46.95 ± 0.04 °C, respectively.

Figure 9 displays the maximum temperature of the cells for the same condition. At 18 m/s, the maximum temperature reached 56.91 ± 0.51 °C. For 17 m/s and 16 m/s, the maximum temperatures were 58.43 ± 0.53 °C and 59.67 ± 0.65 °C, respectively. All monitored cells therefore remained below the 60 °C regulatory threshold, though the margin decreased noticeably as airflow velocity was reduced.

To complement these numerical results, Figure 10 provides a contour visualization of the temperature distribution across the battery cells at 25 °C. This figure illustrates the spatial distribution of cooling effectiveness and highlights areas of localized heating. At 18 m/s (a), the temperature is more uniformly distributed, while at 16 m/s (c), localized hotspots appear near regions with reduced airflow. These contours show that higher inlet velocities led to more uniform cooling, whereas reduced airflow resulted in localized hot spots. This underlines the sensitivity of the system to airflow velocity.

The BCS’s performance was evaluated under an ambient temperature of 30 °C for inlet velocities of 16 m/s, 17 m/s, and 18 m/s. Figure 11 presents the average temperature of the battery cells as a function of iteration number. For an inlet velocity of 18 m/s, the average cell temperature stabilized at 50.04 ± 0.04 °C, while at 17 m/s and 16 m/s, the average temperatures were slightly higher at 50.84 ± 0.05 °C and 51.78 ± 0.05 °C, respectively.

Figure 12 displays the maximum temperature for these conditions. At 18 m/s, the maximum temperature reached 61.73 ± 0.51 °C. For 17 m/s and 16 m/s, the maximum temperatures were 62.88 ± 0.57 °C and 64.48 ± 0.47 °C, respectively. These results indicate that, under elevated ambient conditions, parts of the pack exceeded the 60 °C threshold. While the system therefore complies with the competition’s requirements, the occurrence of localized hot spots highlights that its thermal margin is reduced at higher temperatures, reinforcing the need for further design refinements to improve robustness.

To complement these numerical results, Figure 13 provides a contour visualization of the temperature distribution across the battery cells at 30 °C. This figure illustrates the spatial distribution of cooling effectiveness and highlights areas of localized heating with temperature up to 65 °C. At 18 m/s (a), the temperature is more uniformly distributed, while at 16 m/s (c), noticeable hot spots appear in regions with reduced airflow. These contours illustrate the development of localized hot spots as airflow decreases, reinforcing that the current cooling strategy loses robustness at elevated ambient temperatures.

4.2. Streamline Analysis

The streamline analysis provides insight into airflow behavior across the battery module at different inlet velocities. By examining the streamlines, it is possible to assess how air distribution affects cooling efficiency and potential stagnation zones.

Figure 14 presents an isometric view of the airflow streamlines for the three inlet speeds: (a) 18 m/s, (b) 17 m/s, and (c) 16 m/s. At 18 m/s, the airflow is more uniform, ensuring better heat dissipation across the battery module. At 17 m/s and 16 m/s, flow separation and recirculation became more evident, particularly at 16 m/s, where regions of reduced velocity appeared. These zones are associated with less efficient cooling and contribute to the temperature non-uniformities observed in the thermal results.

Figure 15 provides a lateral view of the streamlines. At higher inlet velocities (18 m/s and 17 m/s), the flow remained more directed through the modules, whereas at 16 m/s, recirculation effects were more pronounced and the airflow distribution less uniform. These observations are consistent with the localized hot spots identified in the temperature contours at 25 °C and 30 °C.

These results emphasize the importance of maintaining adequate airflow velocity to ensure effective cooling performance and minimize the risk of overheating due to airflow stagnation. The BCS results reveal its ability to maintain the system within safe operating temperatures, with most of the cells remaining below 60 °C at various inlet speeds (16, 17, and 18 m/s) and ambient temperatures up to 25 °C. At 30 °C, the system remains effective for most cells but approaches its thermal limits, necessitating adjustments, such as motor power reduction, to ensure long-term reliability.

Additionally, constraints imposed by the vehicle’s design and the limited space available for air inlets affected the overall airflow distribution. These restrictions inevitably influenced the cooling uniformity, further reinforcing the importance of a well-designed battery enclosure to maximize airflow exposure.

The analysis underscores the need for consistent airflow and uniform cooling across the battery modules to prevent thermal imbalances and maintain operational efficiency. Future design improvements should focus on refining the battery pack layout, optimizing module spacing, and adjusting cooling duct positioning to enhance airflow dynamics and thermal performance. While the current system performs within safe limits, these refinements will be necessary to achieve a more effective and reliable cooling strategy.

5. Conclusions

This study developed and evaluated an air-based battery cooling system for a Formula Student car. At 25 °C ambient temperature, all cells remained below 60 °C, with average values between 45 and 47 °C and maximum values below the regulatory threshold. At 30 °C, localized regions of the pack exceeded 60 °C, with peak values up to 65 °C depending on inlet velocity. These results indicate that, although the system complies with competition requirements, its thermal margin decreases under more demanding ambient conditions.

The analysis also showed the decisive role of airflow velocity and geometry in determining thermal behavior. Higher inlet velocities promoted more uniform distribution, whereas reduced velocities led to recirculation zones and localized overheating. Importantly, the design was developed under significant constraints: the battery layout was pre-defined and could not be altered, and the available space for the air intake was limited by the chassis geometry. These restrictions reduced the design freedom for airflow optimization and contributed to the observed non-uniformities.

Despite the positive performance under nominal conditions, this study has several limitations. The CFD simulations assumed steady-state conditions, neglected radiation effects, and relied on constant thermophysical properties. These simplifications may underestimate transient gradients or local conduction effects. In addition, the absence of on-track experimental validation leaves some uncertainty regarding real-world performance under racing conditions.

Validation in this study relied on a structured mesh-independence test and comparisons with established correlations and published BTMS results, which provide a first-order check but do not replace geometry-matched, spatial validation. Following best practice [26,27], future work will include velocity- and temperature-field measurements once the FSUMinho battery is assembled. The modeling approach adopted, based on a uniform heat-source term, is consistent with recent EV/BTMS studies [21,22,23,24], while Bernardi-based formulations remain the reference for detailed cell-level analyses [19,20,21].

For future vehicle iterations, improvements in the battery module layout and cooling design will be a priority. The findings from this study indicate that the current battery case design imposes significant airflow restrictions, leading to localized thermal inefficiencies. To address this, the next design phase should focus on optimizing airflow paths, reducing pressure losses, and implementing structural modifications to enhance heat dissipation. Advanced CFD simulations should be utilized to test these design changes before physical prototyping.

Additionally, incorporating alternative cooling strategies, such as hybrid air–liquid cooling solutions, could provide enhanced thermal regulation under extreme conditions. Material selection for the battery casing should also be reconsidered to improve heat dissipation without adding excessive weight to the vehicle. Finally, experimental studies on different cooling configurations should be conducted to determine the most efficient design, ensuring a balance between performance, weight, and aerodynamic impact.