In the development of electric vehicles (EVs), thermal management of the EV battery pack is critical to ensure safety, performance, and longevity. During charging and discharging cycles, the EV battery pack generates substantial heat, leading to temperature rise and uneven thermal distribution among cells. This can accelerate degradation, reduce efficiency, and even cause thermal runaway. Therefore, effective cooling systems are essential to maintain the EV battery pack within an optimal temperature range of 20–50 °C and a maximum temperature difference below 5 °C. Among various cooling methods, liquid cooling stands out due to its high thermal conductivity and efficiency, making it a preferred choice for modern EV battery packs. In this study, I explore the design of liquid cooling plate structures for an EV battery pack, comparing different configurations under constrained liquid mass flow rates. I analyze key parameters influencing cooling performance and propose an optimized design to meet thermal management requirements. Throughout this work, the term “EV battery pack” is emphasized to highlight its centrality in electric vehicle systems.
To begin, I established a fundamental model for battery cell heat generation. The heat generation rate \( q \) in a lithium-ion cell is calculated using the Bernardi model, which accounts for irreversible and reversible heat effects. The formula is given by:
$$ q = \frac{1}{V_b} \left( I^2 R_{\text{cell}} – I T \frac{dE_0}{dT} \right) $$
where \( V_b \) is the cell volume in m³, \( I \) is the current in A, \( R_{\text{cell}} \) is the internal resistance in Ω, \( T \) is the temperature in °C, and \( \frac{dE_0}{dT} \) is the temperature coefficient in V/K, often negligible at approximately 0.0005 V/K. For the EV battery pack, this model helps predict thermal behavior during operation.
The equivalent specific heat capacity \( C_p \) and average density \( \rho \) of the battery cell are derived from its layered composition. These are expressed as:
$$ C_p = \frac{\sum_{i=1}^n C_i m_i}{m} $$
$$ \rho = \frac{m}{v} = \frac{\sum_{i=1}^n m_i}{\sum_{i=1}^n v_i} $$
where \( C_i \) is the specific heat capacity of each layer in J/(kg·K), \( m_i \) is the mass of each layer in kg, \( m \) is the total mass in kg, and \( v_i \) is the volume of each layer in m³. These parameters are essential for thermal simulations of the EV battery pack.
I validated the cell model through experiments on a 190 Ah ternary lithium-ion cell, typical for EV battery packs. The thermal properties are summarized in Table 1.
| Component | Density ρ (kg/m³) | Specific Heat Cp (J/(kg·K)) | Heat Generation Rate q (W/m³) |
|---|---|---|---|
| Cell Core | 2478.5 | 1100 | 2988.5 (0.5C), 11978.5 (1C), 47962.8 (2C) |
Discharge tests were conducted at 0.5C, 1C, and 2C rates in a controlled environment at 25°C. Temperature data from experiments were compared with simulation results from Fluent. The comparison showed close agreement, with maximum errors below 6.3%, confirming the model’s accuracy for the EV battery pack. For instance, at 2C discharge, the experimental peak temperature was 56.83°C, while simulation yielded 55.11°C, both exceeding the safe limit and underscoring the need for liquid cooling in the EV battery pack.
Next, I designed five liquid cooling plate structures for the EV battery pack, focusing on configurations that enhance heat dissipation while maintaining uniform temperature distribution. All designs were simulated under identical liquid mass flow rates to ensure fair comparison. The cooling plate material is aluminum alloy, and the coolant is a 50% ethylene glycol-water solution. Their properties are listed in Table 2.
| Material | Density (kg/m³) | Specific Heat (J/(kg·K)) | Thermal Conductivity (W/(m·K)) | Viscosity (kg/(m·s)) |
|---|---|---|---|---|
| Aluminum Alloy | 2719 | 871 | 202.4 | – |
| 50% Ethylene Glycol-Water | 1071.1 | 3300 | 0.384 | 0.017 (at 25°C) |
The five cooling structures, illustrated schematically, include: (a) transverse parallel (referred to as horizontal parallel), (b) S-shaped with multiple bends, (c) alternative S-shaped variant, (d) stepped configuration, and (e) longitudinal parallel. Each design integrates cooling plates between cells in the EV battery pack to maximize contact area. The simulation setup involves coupling between cell surfaces and cooling plates, with adiabatic conditions on external surfaces. The coolant inlet is a velocity inlet, and the outlet is a pressure outlet, with initial temperatures set at 25°C. To standardize comparisons, the coolant volume in all structures is kept equal, ensuring the same mass flow rate across designs.
Cooling performance is evaluated based on maximum temperature, maximum temperature difference among cells, temperature standard deviation (reflecting overall uniformity), pressure drop across the coolant inlet and outlet, and a heat exchange factor \( \beta \). The heat exchange factor is defined as:
$$ \beta = \frac{Q}{\Delta P} $$
where \( Q \) is the heat removed in W, and \( \Delta P \) is the pressure drop in Pa. A higher \( \beta \) indicates better cooling efficiency per unit pump power, which is crucial for energy-efficient thermal management in EV battery packs.
Simulations were run with coolant mass flow rates varying from 5 to 25 kg/min. Results for each design are summarized in Table 3, highlighting key metrics at a flow rate of 15 kg/min.
| Structure | Max Temp (°C) | Max ΔT (°C) | Temp Std Dev (°C) | Pressure Drop (Pa) | Heat Exchange Factor β |
|---|---|---|---|---|---|
| Horizontal Parallel (a) | 32.63 | 4.77 | 1.38 | 721.63 | 1225.84 |
| S-shaped (b) | 34.50 | 5.20 | 1.25 | 2540.10 | 980.50 |
| Alternative S-shaped (c) | 35.80 | 6.10 | 1.60 | 2680.30 | 920.75 |
| Stepped (d) | 36.50 | 6.50 | 1.75 | 450.20 | 1500.20 |
| Longitudinal Parallel (e) | 37.20 | 7.00 | 1.90 | 400.80 | 1550.60 |
From the data, the horizontal parallel structure (a) demonstrates the best overall cooling for the EV battery pack, with the lowest maximum temperature and temperature difference, alongside moderate pressure drop and heat exchange factor. The S-shaped structures (b and c) show higher pressure drops due to increased flow path length and bends, reducing efficiency. The stepped and longitudinal parallel designs (d and e) have lower pressure drops but poorer cooling performance, resulting in higher temperatures. Thus, structure (a) is selected for further parameter optimization in the EV battery pack.
To delve deeper, I analyze the impact of key parameters on the cooling performance of the horizontal parallel structure. The parameters include coolant inlet inner diameter, cooling plate wall thickness, and coolant temperature. For each analysis, the mass flow rate is fixed at 15 kg/min, and other conditions are held constant to isolate effects.
First, the inlet inner diameter is varied from 28 mm to 44 mm. The results, plotted in Figure 1, show that at 36 mm, the maximum temperature, temperature difference, and standard deviation reach minima, indicating optimal cooling and uniformity for the EV battery pack. Pressure drop decreases with diameter but stabilizes beyond 36 mm, while the heat exchange factor increases. This suggests 36 mm as the ideal diameter, balancing cooling performance and energy consumption in the EV battery pack system.
The relationship between diameter and pressure drop can be described by the Darcy-Weisbach equation:
$$ \Delta P = f \frac{L}{D} \frac{\rho v^2}{2} $$
where \( f \) is the friction factor, \( L \) is the flow length, \( D \) is the diameter, \( \rho \) is density, and \( v \) is velocity. For the EV battery pack, larger diameters reduce velocity and pressure drop, but excessive sizes may hinder compact packaging.
Second, wall thickness is examined from 1.5 mm to 2.5 mm. As shown in Figure 2, cooling performance peaks at 2.0 mm, with minimal temperature metrics. Pressure drop slightly decreases with thickness due to reduced flow restriction, and the heat exchange factor increases. A thickness of 2.0 mm is chosen for the EV battery pack cooling plates, offering structural integrity and thermal efficiency.
Third, coolant temperature is varied from 15°C to 25°C. Lower temperatures reduce the maximum temperature linearly, as per Newton’s law of cooling:
$$ Q = h A (T_{\text{battery}} – T_{\text{coolant}}) $$
where \( h \) is the heat transfer coefficient, \( A \) is the area, and \( T \) are temperatures. However, temperature uniformity worsens with cooler coolant, as seen in increased temperature differences and standard deviation. The heat exchange factor improves slightly due to larger temperature gradients. For the EV battery pack, a coolant temperature around 25°C is recommended to balance cooling effectiveness with energy costs and uniformity.
Based on this analysis, the optimized design for the EV battery pack uses a horizontal parallel cooling structure with an inlet inner diameter of 36 mm, wall thickness of 2.0 mm, and coolant temperature of 25°C. Simulations at a 2C discharge rate confirm that this setup maintains the EV battery pack at a maximum temperature of 32.63°C, a maximum temperature difference of 4.77°C, a pressure drop of 721.63 Pa, and a heat exchange factor of 1225.84. These meet the thermal management targets for the EV battery pack, ensuring safe and efficient operation.
In conclusion, this study demonstrates the importance of tailored liquid cooling designs for EV battery packs. By constraining mass flow rates, I compared multiple cooling plate structures and identified the horizontal parallel configuration as superior for the EV battery pack. Parameter analysis revealed optimal values for inlet diameter, wall thickness, and coolant temperature, contributing to enhanced thermal performance. Future work could explore advanced materials or hybrid cooling systems to further improve EV battery pack efficiency. The insights here aid in developing robust thermal management solutions for next-generation electric vehicles, where the EV battery pack remains a core component.
To further elaborate, the thermal management of an EV battery pack involves complex heat transfer phenomena. The heat generation rate in an EV battery pack depends on discharge rates, as modeled earlier. For instance, at high discharge rates like 2C, the EV battery pack produces significant heat, necessitating efficient cooling. The liquid cooling system must dissipate this heat while minimizing energy consumption. The heat exchange factor \( \beta \) serves as a key metric, defined more precisely as:
$$ \beta = \frac{\dot{m} C_p (T_{\text{out}} – T_{\text{in}})}{\Delta P} $$
where \( \dot{m} \) is the mass flow rate in kg/s, \( C_p \) is the coolant specific heat, and \( T_{\text{out}} \) and \( T_{\text{in}} \) are outlet and inlet temperatures. This factor highlights the trade-off between cooling capacity and pump power in an EV battery pack.
In addition, the temperature uniformity within the EV battery pack is critical for longevity. Uneven temperatures can lead to cell imbalance, reducing overall capacity. The standard deviation of temperatures across the EV battery pack is calculated as:
$$ \sigma = \sqrt{\frac{1}{N} \sum_{i=1}^N (T_i – \bar{T})^2} $$
where \( N \) is the number of cells, \( T_i \) is each cell’s temperature, and \( \bar{T} \) is the average temperature. A lower \( \sigma \) indicates better uniformity, which the horizontal parallel structure achieves effectively for the EV battery pack.
Moreover, the pressure drop in the cooling system affects the pump selection and energy use. For the EV battery pack, a lower pressure drop is desirable to reduce parasitic losses. The designs with fewer bends, like the horizontal parallel, offer advantages in this regard. The overall energy efficiency of the EV battery pack thermal system can be evaluated by combining cooling performance and pump work.
This study underscores that liquid cooling is vital for modern EV battery packs, especially under high-load conditions. By optimizing design parameters, we can enhance both performance and sustainability. The EV battery pack is central to electric vehicle adoption, and effective thermal management ensures reliability and safety. As EV technology evolves, continued innovation in cooling strategies will be essential for advancing EV battery pack capabilities.