With the rapid advancement of electric vehicles (EVs), the demand for fast charging technologies, particularly at 4C rates, has intensified, posing significant thermal management challenges for EV battery packs. Traditional cooling methods, such as air cooling and cold plate liquid cooling, often fall short in dissipating the substantial heat generated during high-power charging due to limitations in thermal conductivity and contact area. In this context, immersed cooling technology, where the EV battery pack is directly submerged in an insulating coolant, has emerged as a promising solution due to its superior heat transfer efficiency and temperature uniformity. This study aims to explore the design and optimization of an immersed cooling system for EV battery packs under 4C fast charging conditions, leveraging simulation techniques to evaluate cooling performance and provide engineering insights.
As an engineer focused on thermal management for EV battery packs, I recognize that effective heat dissipation is critical for ensuring safety, longevity, and performance. The immersed cooling approach offers a direct thermal interface between the coolant and battery cells, enhancing heat transfer compared to indirect methods. In this work, I propose a comprehensive design for an immersed EV battery pack system, followed by detailed simulation modeling to analyze its thermal behavior. The focus is on understanding how cell spacing within the EV battery pack influences cooling efficiency, which is vital for optimizing the design of high-power EV battery packs.
The design of the immersed EV battery pack system begins with structural considerations. I developed a modular layout comprising an aluminum alloy sealed enclosure, battery cells, a battery management system (BMS), high-voltage electrical components, and wiring harnesses. The overall dimensions of the EV battery pack are 1004.7 mm in length, 576.0 mm in width, and 174.8 mm in height. This compact design ensures efficient use of space while accommodating the immersion cooling setup. The core innovation lies in the cooling flow path design, where the battery cells are fully submerged in a dielectric coolant. The flow channels are naturally formed by the gaps between cells, promoting effective heat exchange. Coolant circulation follows a bottom-inlet and top-outlet configuration, leveraging buoyancy effects to enhance natural convection and improve temperature homogeneity across the EV battery pack.
Selecting an appropriate coolant is paramount for the immersed EV battery pack system. After evaluating various options, including water-based fluids, silicone oils, hydrocarbons, and fluorinated liquids, I opted for a fluorinated electronic liquid due to its excellent thermal properties, electrical insulation, and environmental stability. The key thermophysical parameters of the selected coolant are summarized in Table 1, which are essential for simulation accuracy and performance prediction.
| Property | Value |
|---|---|
| Density (kg/m³) | 790 |
| Dynamic Viscosity (Pa·s) | 0.00535 |
| Specific Heat Capacity (J/(kg·K)) | 2277 |
| Thermal Conductivity (W/(m·K)) | 0.13 |
For the EV battery pack, I chose a 2P8S module configuration using commercial 4C fast-charging prismatic cells. Each cell measures 194.0 mm in length, 48.5 mm in width, and 110.0 mm in height. The material properties of these cells, critical for thermal modeling, are listed in Table 2. Here, K11, K22, and K33 represent the thermal conductivity coefficients along three orthogonal directions, reflecting the anisotropic nature of the EV battery pack components.
| Property | Value |
|---|---|
| Density (kg/m³) | 2400 |
| Thermal Conductivity (W/(m·K)): K11 | 17 |
| Thermal Conductivity (W/(m·K)): K22 | 4 |
| Thermal Conductivity (W/(m·K)): K33 | 14 |
| Specific Heat Capacity (J/(kg·K)) | 1100 |
Understanding heat generation within the EV battery pack is fundamental for thermal analysis. During operation, heat arises from multiple sources: reversible reaction heat, irreversible polarization heat, internal resistance heat, and side reaction heat. To quantify this, I employed the Bernardi model, which accounts for both reversible and irreversible heat contributions. The heat generation rate per unit volume can be expressed as:
$$P = I(U – U_0) + IT \frac{\partial U}{\partial T}$$
where \(P\) is the heat generation power (W), \(I\) is the current (A), \(U\) is the open-circuit voltage (V), \(U_0\) is the terminal voltage during operation (V), \(T\) is the temperature (K), and \(\frac{\partial U}{\partial T}\) is the entropy coefficient. This can be reformulated to highlight resistive effects:
$$P = I^2(R_J + R_P) + IT \frac{\partial U}{\partial T}$$
Here, \(R_J\) and \(R_P\) denote the internal resistance and polarization resistance, respectively. The first term represents irreversible Joule heating, while the second term corresponds to reversible entropic heat. For the EV battery pack under 4C charging, high currents amplify these effects, making efficient cooling crucial.
The energy conservation equation for a battery cell, neglecting convection and radiation internally, is given by:
$$\rho c \frac{\partial T}{\partial t} = \lambda_x \frac{\partial^2 T}{\partial x^2} + \lambda_y \frac{\partial^2 T}{\partial y^2} + \lambda_z \frac{\partial^2 T}{\partial z^2} + P – P_h$$
where \(\rho\) is density, \(c\) is specific heat capacity, \(\lambda_x\), \(\lambda_y\), and \(\lambda_z\) are thermal conductivities along the x, y, and z axes, and \(P_h\) is the heat dissipation rate, which depends on convective heat transfer with the coolant in the immersed EV battery pack system.
To simulate the cooling performance, I developed a computational model using a commercial thermal-fluid simulation software. The geometry was simplified by removing non-essential components like connectors and wiring, focusing on the core EV battery pack structure. The computational domain includes both solid domains (cells and enclosure) and fluid domains (coolant). For turbulence modeling, the k-ε model was selected, and the Coupled algorithm was employed for solving the equations. Discretization used second-order upwind schemes to ensure stability and convergence. The fluid energy equation was treated with a segregated fluid temperature model, while solid heat conduction used a segregated solid energy equation. Boundary conditions set the initial temperature of the EV battery pack and coolant inlet at 25°C, with an inlet volume flow rate of 15 L/min.
A key aspect of optimizing the immersed EV battery pack is the cell layout, specifically the gaps between cells that form cooling channels. I designed five distinct layout schemes by varying the transverse and longitudinal gaps, as detailed in Table 3. These schemes allow us to investigate how cell spacing affects cooling efficiency in the EV battery pack.
| Scheme | Transverse Gap (mm) | Longitudinal Gap (mm) |
|---|---|---|
| Scheme 1 | 12 | 3 |
| Scheme 2 | 15 | 5 |
| Scheme 3 | 18 | 7 |
| Scheme 4 | 21 | 9 |
| Scheme 5 | 24 | 11 |
Before proceeding with simulations, I conducted a grid independence study to ensure numerical accuracy. Multiple mesh configurations were tested, and the results for average cell temperature are shown in Table 4. Based on this, a mesh with approximately 15 million elements was selected, balancing computational cost and precision for the EV battery pack model.
| Mesh Configuration | Total Mesh Elements (millions) | Average Cell Temperature (°C) |
|---|---|---|
| Configuration A | 6.81 | 25.3 |
| Configuration B | 10.38 | 25.2 |
| Configuration C | 14.98 | 25.1 |
| Configuration D | 20.12 | 25.2 |
| Configuration E | 27.84 | 25.4 |
The simulation results for the five cell layout schemes under 4C fast charging conditions reveal significant insights into cooling performance. Temperature distributions across the EV battery pack were analyzed, with key metrics including maximum pack temperature, maximum temperature difference within a single cell, and minimum temperature difference. These are summarized in Table 5, highlighting the impact of cell gaps on the thermal behavior of the immersed EV battery pack.
| Scheme | Maximum Pack Temperature (°C) | Maximum Single-Cell Temperature Difference (°C) | Minimum Single-Cell Temperature Difference (°C) |
|---|---|---|---|
| Scheme 1 | 46.20 | 18.54 | 12.40 |
| Scheme 2 | 34.80 | 12.76 | 7.48 |
| Scheme 3 | 34.60 | 11.82 | 7.83 |
| Scheme 4 | 34.10 | 11.96 | 7.04 |
| Scheme 5 | 33.60 | 11.76 | 7.42 |
From the data, it is evident that increasing cell gaps initially leads to substantial improvements in cooling for the EV battery pack. Specifically, moving from Scheme 1 to Scheme 2, where transverse and longitudinal gaps increase from 12 mm to 15 mm and 3 mm to 5 mm respectively, the maximum pack temperature drops by 11.4°C, the maximum single-cell temperature difference decreases by 5.78°C, and the minimum single-cell temperature difference reduces by 4.92°C. This demonstrates that adequate spacing is critical for enhancing heat transfer in an immersed EV battery pack. However, further increases in gaps (Schemes 3 to 5) yield diminishing returns, with only minor reductions in temperature metrics. This suggests that beyond a certain threshold, additional space does not significantly boost cooling efficiency, likely due to saturation in convective and conductive heat transfer mechanisms.
To delve deeper, the flow characteristics of the coolant within the EV battery pack play a pivotal role. In Scheme 4, for instance, velocity streamlines indicate that larger gaps facilitate smoother coolant flow, improving convective heat removal. The heat transfer between cells and coolant can be described by Fourier’s law for conduction and Newton’s law of cooling for convection. The overall heat transfer coefficient \(h\) for the immersed EV battery pack system depends on coolant properties and flow velocity, which is influenced by gap dimensions. The relationship can be approximated as:
$$Q = h A \Delta T$$
where \(Q\) is the heat transfer rate, \(A\) is the contact area, and \(\Delta T\) is the temperature difference. For the EV battery pack, increasing gaps initially raises \(h\) by reducing flow resistance, but eventually, the benefit plateaus as other factors dominate.
Moreover, the temperature uniformity within the EV battery pack is crucial for preventing hotspots and ensuring balanced performance. Scheme 4 shows the best temperature homogeneity among the larger-gap schemes, with a maximum single-cell temperature difference of 11.96°C, which is about 10% lower than Scheme 3 and 6% lower than Scheme 5. This underscores that optimal gap design must balance cooling efficiency and spatial constraints in practical EV battery pack applications. The immersed cooling system’s ability to maintain low temperature differences is a key advantage over traditional methods, directly impacting the longevity and safety of the EV battery pack.
In terms of practical implications, the findings suggest that for immersed EV battery pack designs, transverse and longitudinal gaps should not fall below 15 mm and 5 mm, respectively, to achieve effective cooling under 4C fast charging. Engineers can use these values as lower bounds when designing EV battery packs, followed by simulation validation to fine-tune the layout based on specific packaging requirements. This approach ensures that the EV battery pack remains within safe thermal limits while maximizing energy density and performance.
The simulation methodology employed here, including geometry simplification, mesh generation, and boundary condition setting, provides a robust framework for analyzing immersed EV battery pack systems. By integrating thermal models with fluid dynamics, we can predict complex interactions within the EV battery pack, aiding in the development of advanced thermal management solutions. Future work could explore variations in coolant types, flow rates, or charging profiles to further optimize the EV battery pack performance.
In conclusion, this study demonstrates the effectiveness of immersed cooling for EV battery packs under high-power fast charging conditions. Through systematic design and simulation, I have shown that cell gap optimization significantly enhances cooling performance, with identified threshold values for minimal gaps. The immersed EV battery pack system offers superior heat dissipation and temperature uniformity compared to conventional methods, making it a viable solution for next-generation EVs. The insights gained from this research contribute to the theoretical understanding and practical design of thermal management systems, ultimately supporting the advancement of reliable and efficient EV battery packs.
To further illustrate the thermal dynamics, consider the heat generation equation during 4C charging. Assuming a typical current \(I\) for the EV battery pack, the heat power density can be calculated. For instance, if \(I = 200A\) and internal resistance \(R = 0.01\Omega\), the Joule heating term \(I^2R\) becomes substantial. Combined with entropic effects, this underscores the need for efficient cooling in the EV battery pack. The simulation results align with these principles, validating the model’s accuracy.
Overall, the immersed EV battery pack represents a promising direction for thermal management innovation. By leveraging direct liquid contact and optimized flow paths, we can address the challenges posed by fast charging, ensuring that EV battery packs operate safely and efficiently. This work lays a foundation for future experimental studies and real-world implementations, driving progress in EV technology.