In recent years, the rapid adoption of electric vehicles (EVs) has underscored the critical role of battery thermal management systems (BTMS) in ensuring safety, performance, and longevity. The EV battery pack, typically composed of lithium-ion cells, generates significant heat during charging and discharging cycles. Without efficient cooling, this heat can lead to temperature non-uniformity, accelerated degradation, and even thermal runaway, posing severe risks. As an researcher focused on thermal management solutions, I have investigated various cooling strategies to address these challenges. Among them, air cooling is cost-effective but limited by poor heat conductivity, while liquid cooling offers better performance but adds complexity and weight. Phase change materials, though promising, face issues like leakage and performance decay over time. To overcome these limitations, I propose a novel air-liquid hybrid cooling BTMS designed to enhance heat dissipation and temperature uniformity for EV battery packs. This study integrates computational fluid dynamics (CFD) simulations with battery heat generation models to evaluate the system’s performance, emphasizing the impact of key parameters like water flow rate and air velocity. The findings aim to provide practical insights for optimizing EV battery pack thermal management in real-world applications.
The EV battery pack is the heart of an electric vehicle, and its thermal behavior directly influences driving range, power output, and safety. Lithium-ion batteries, favored for their high energy density, are sensitive to temperature variations. Operating outside an optimal range (typically 20°C to 40°C) can reduce efficiency and lifespan. During high-rate discharges, the EV battery pack can produce substantial heat due to internal resistance and electrochemical reactions. For instance, at a 4C discharge rate, heat generation can exceed 70,000 W/m³, as shown in prior studies. Traditional cooling methods struggle to manage such loads effectively. Air cooling systems, while simple, often result in large temperature gradients across the EV battery pack, leading to localized hotspots. Liquid cooling systems, using channels or cold plates, improve heat transfer but increase mass and complexity. Hybrid approaches, combining multiple cooling mechanisms, offer a balanced solution by leveraging the strengths of each method. In this work, I explore a hybrid system that integrates air cooling with a liquid-cooled microchannel cold plate featuring herringbone fins, targeting enhanced thermal uniformity and reduced peak temperatures for cylindrical 18650 cells in an EV battery pack.
To model the thermal dynamics, I consider a battery module comprising 16 cylindrical 18650 lithium-ion cells arranged in a 4×4 configuration. Each cell is connected to a herringbone fin microchannel cold plate made of aluminum, which serves as both a structural support and heat exchanger. The cold plate includes microchannels with a cross-section of 4 mm × 1.5 mm, and batteries are attached via sleeves. The overall dimensions of the simulation domain are 109 mm × 109 mm × 70 mm, with an aluminum shell of 2 mm thickness. The spacing between cells is set to 25 mm horizontally and vertically, ensuring adequate airflow. The EV battery pack’s geometric design prioritizes compactness while facilitating cooling from both air and liquid streams. Key parameters for the battery cells are summarized in Table 1, based on standard specifications for 18650 cells used in EV applications.
| Parameter | Value |
|---|---|
| Length (mm) | 65 |
| Diameter (mm) | 18 |
| Rated Voltage (V) | 3.2 |
| Rated Capacity (Ah) | 1.35 |
| Equivalent Density (kg/m³) | 2018 |
| Equivalent Specific Heat Capacity (J/(kg·K)) | 1282 |
| Radial Thermal Conductivity (W/(m·K)) | 0.9 |
| Axial Thermal Conductivity (W/(m·K)) | 2.7 |
The heat generation within the EV battery pack is modeled using the Bernardi equation, which accounts for irreversible and reversible heat effects. This approach assumes uniform heat generation within each cell and constant material properties. The heat generation rate \( Q \) (in W/m³) is expressed as:
$$ Q = \frac{I^2 r + I T \left( \frac{\partial U_{\text{OCV}}}{\partial T} \right)}{V_{\text{battery}}} $$
where \( I \) is the discharge current (A), \( r \) is the internal resistance (Ω), \( T \) is the battery temperature (K), \( U_{\text{OCV}} \) is the open-circuit voltage (V), and \( V_{\text{battery}} \) is the battery volume (m³). For simulation purposes, heat generation values at different discharge rates are derived from experimental data, as shown in Table 2. These values reflect the thermal load on the EV battery pack under varying operating conditions, crucial for designing effective cooling systems.
| Discharge Rate | Heat Generation (W/m³) |
|---|---|
| 1C | 5318 |
| 2C | 19452 |
| 3C | 42400 |
| 4C | 74163 |
The numerical model for the EV battery pack thermal management system is built using Ansys Fluent, incorporating a three-dimensional fluid-solid coupling approach. The CFD simulations solve the governing equations for continuity, momentum, and energy, under the following assumptions: the cooling fluids (water and air) are incompressible; fluid properties are temperature-independent; and radiation heat transfer is considered. The continuity equation ensures mass conservation:
$$ \frac{\partial \rho}{\partial t} + \frac{\partial (\rho u)}{\partial x} + \frac{\partial (\rho v)}{\partial y} + \frac{\partial (\rho w)}{\partial z} = 0 $$
where \( \rho \) is density (kg/m³), \( t \) is time (s), and \( u \), \( v \), \( w \) are velocity components (m/s). The momentum equations describe fluid motion:
$$ \frac{\partial (\rho u)}{\partial t} + \text{div}(\rho u \mathbf{U}) = -\frac{\partial p_F}{\partial x} + \frac{\partial \tau_{xx}}{\partial x} + \frac{\partial \tau_{yx}}{\partial y} + \frac{\partial \tau_{zx}}{\partial z} + F_x $$
$$ \frac{\partial (\rho v)}{\partial t} + \text{div}(\rho v \mathbf{U}) = -\frac{\partial p_F}{\partial y} + \frac{\partial \tau_{xy}}{\partial x} + \frac{\partial \tau_{yy}}{\partial y} + \frac{\partial \tau_{zy}}{\partial z} + F_y $$
$$ \frac{\partial (\rho w)}{\partial t} + \text{div}(\rho w \mathbf{U}) = -\frac{\partial p_F}{\partial z} + \frac{\partial \tau_{xz}}{\partial x} + \frac{\partial \tau_{yz}}{\partial y} + \frac{\partial \tau_{zz}}{\partial z} + F_z $$
Here, \( p_F \) is pressure (Pa), \( \tau \) denotes viscous stress components (Pa), and \( F \) represents body forces (N). The energy equation accounts for heat transfer:
$$ \frac{\partial (\rho T)}{\partial t} + \text{div}(\rho \mathbf{U} T) = \text{div}\left( \frac{k_T}{c_p} \text{grad} T \right) + S_T $$
where \( c_p \) is specific heat capacity (J/(kg·K)), \( k_T \) is thermal conductivity (W/(m·K)), and \( S_T \) is the heat source term (W/m³). Given the turbulent nature of airflow in the EV battery pack, the RNG \( k\)-\( \varepsilon \) turbulence model is employed for its accuracy in handling complex flows. The turbulent kinetic energy \( k \) and dissipation rate \( \varepsilon \) are modeled as:
$$ \frac{\partial (\rho k)}{\partial t} + \frac{\partial (\rho u_i k)}{\partial x_i} = \frac{\partial}{\partial x_j} \left( \alpha_k \mu_{\text{eff}} \frac{\partial k}{\partial x_j} \right) + G_k + G_b – \rho \varepsilon $$
$$ \frac{\partial (\rho \varepsilon)}{\partial t} + \frac{\partial (\rho u_i \varepsilon)}{\partial x_i} = \frac{\partial}{\partial x_j} \left( \alpha_\varepsilon \mu_{\text{eff}} \frac{\partial \varepsilon}{\partial x_j} \right) + C_{1\varepsilon} \frac{\varepsilon}{k} (G_k + C_{3\varepsilon} G_b) – C_{2\varepsilon} \rho \frac{\varepsilon^2}{k} – R_\varepsilon $$
with \( R_\varepsilon = \frac{C_\mu \rho \eta^3 (1 – \eta / \eta_0)}{1 + \beta \eta^3} \frac{\varepsilon^3}{k} \), where \( \eta = S \frac{k}{\varepsilon} \). Material properties for the EV battery pack components are listed in Table 3, ensuring realistic simulation inputs.
| Material | Density (kg/m³) | Specific Heat Capacity (J/(kg·K)) | Thermal Conductivity (W/(m·K)) |
|---|---|---|---|
| Water | 998.21 | 4128 | 0.6 |
| Air | 1.22 | 1006.43 | 0.0242 |
| Aluminum | 2719 | 871 | 202.4 |
Boundary conditions are set to mimic real operating scenarios for the EV battery pack. The initial temperature for all components is 298.15 K. Water inlet and air inlet are defined as velocity inlets with speeds \( v_{\text{water}} \) and \( v_{\text{air}} \), respectively, while outlets are pressure outlets at 0 Pa. Convective heat transfer with the environment is applied at external surfaces, with a coefficient of 10 W/(m²·K). The battery cells are assigned as heat sources based on Table 2, and fluid-solid interfaces are coupled for heat exchange. The SIMPLE algorithm handles pressure-velocity coupling, with second-order upwind discretization for momentum and energy. Residuals for continuity and velocity are set to 10⁻⁴, and for energy to 10⁻⁶, ensuring convergence. A mesh independence test is conducted to validate the simulation accuracy. As shown in Figure 4 (referenced from simulations), key metrics like maximum temperature and pressure drop stabilize beyond 3 million cells. Therefore, a mesh with approximately 3,001,362 elements is selected for all simulations, balancing computational cost and precision for the EV battery pack model.
The performance of the hybrid cooling system is compared against standalone air cooling and liquid cooling for the EV battery pack. Under a 3C discharge rate, temperature distributions are analyzed after the system reaches steady state. For air cooling alone, with an inlet air velocity of 0.30 m/s, the EV battery pack exhibits significant temperature non-uniformity, ranging from 298 K near the inlet to over 310 K at the outlet. This gradient stresses cells unevenly, potentially shortening pack life. In contrast, liquid cooling using the microchannel cold plate at a water flow rate of 0.05 m/s reduces the maximum temperature to around 300 K, but some cells away from the cold plate show higher temperatures. The hybrid system, combining both air and liquid cooling, achieves the best results: the peak temperature drops to approximately 299.5 K, and temperature uniformity improves markedly across the EV battery pack. This synergy allows air to cool exposed cell surfaces while liquid efficiently extracts heat through the cold plate, mitigating hotspots. The pressure distribution in the hybrid system reveals a minimal drop on the air side (under 10 Pa) but a more substantial drop in microchannels (around 200 Pa), indicating manageable pumping power requirements for the EV battery pack.
To optimize the hybrid cooling for the EV battery pack, I investigate the effects of water flow rate and air velocity. First, with a fixed air velocity of 0.30 m/s, water flow rates vary from 0.01 m/s to 0.10 m/s. As flow rate increases, the EV battery pack’s maximum temperature decreases, and temperature uniformity enhances. For instance, at 0.01 m/s, the peak temperature is 301.84 K, but at 0.10 m/s, it falls to 299.97 K—a reduction of 1.87 K. The cooling capacity improves because higher flow rates enhance convective heat transfer in the microchannels, removing more heat from the EV battery pack. However, diminishing returns are observed; increasing flow from 0.05 m/s to 0.10 m/s only lowers temperature by 0.27 K. This suggests an optimal range where further increases yield marginal benefits but may raise pumping costs. The time to reach steady state also shortens with higher flow rates, as the system dissipates heat faster from the EV battery pack.
Second, with a fixed water flow rate of 0.10 m/s, air velocity is varied from 0.10 m/s to 3.00 m/s. While higher air velocities reduce temperatures, the impact is less pronounced than for water flow. At 0.10 m/s air velocity, the EV battery pack’s maximum temperature is 302.30 K, dropping to 300.96 K at 1.00 m/s (a decrease of 1.34 K), and to 300.34 K at 3.00 m/s (only 0.62 K lower). Overall, increasing air velocity from 0.10 m/s to 3.00 m/s reduces the peak temperature by 1.96 K. This smaller effect highlights that air cooling alone is less efficient for the EV battery pack due to air’s low thermal conductivity. In the hybrid system, air primarily aids in cooling cell areas not in direct contact with the cold plate, improving uniformity rather than drastically lowering peak temperatures. Thus, when designing a BTMS for an EV battery pack, prioritizing water flow rate optimization is more effective for heat dissipation performance.
Further analysis involves parametric studies to generalize findings for the EV battery pack. I explore additional discharge rates (e.g., 2C and 4C) and different ambient temperatures to assess system robustness. At higher discharge rates, heat generation escalates, demanding more aggressive cooling. The hybrid system maintains temperatures within safe limits up to 4C, with peak temperatures staying below 305 K at optimal flow rates. This demonstrates its suitability for high-performance EV battery packs. Moreover, the herringbone fin design proves advantageous by increasing surface area for air-side heat exchange, reducing thermal resistance. A comparison of fin geometries could be a future direction to enhance the EV battery pack cooling further.
The implications of this research extend to real-world EV battery pack design. By implementing a hybrid cooling system, manufacturers can achieve better thermal management without excessive weight or complexity. For example, in an EV battery pack with hundreds of cells, scaling up the microchannel cold plate and integrating fans could maintain temperature uniformity across modules. Energy consumption is also a consideration; the hybrid system’s pumping and fan power should be minimized through control strategies that adjust flow rates based on thermal load. Simulations indicate that adaptive control, varying water flow and air velocity with discharge rate, can optimize energy use while keeping the EV battery pack within safe temperatures.
In conclusion, this study presents a comprehensive analysis of an air-liquid hybrid cooling thermal management system for enhancing the heat dissipation performance of EV battery packs. Through CFD simulations and battery heat modeling, I demonstrate that the hybrid approach surpasses standalone cooling methods in temperature uniformity and peak temperature reduction. Key parameters like water flow rate and air velocity are analyzed, revealing that water flow rate has a more significant impact on cooling efficiency for the EV battery pack. Specifically, increasing water flow rate from 0.01 m/s to 0.10 m/s lowers the maximum temperature by 1.87 K, whereas increasing air velocity from 0.10 m/s to 3.00 m/s reduces it by 1.96 K. Therefore, in optimizing BTMS for EV battery packs, designers should prioritize water flow rate adjustments. Future work could involve experimental validation, multi-objective optimization considering energy consumption, and integration with battery management systems for adaptive thermal control. This research contributes to advancing EV technology by ensuring safer and more efficient battery operation.