In recent years, the global demand for green electricity has surged, driven by the urgent need to reduce carbon emissions and mitigate climate change. Energy storage systems, particularly those utilizing lithium-ion batteries, have become pivotal in harnessing renewable energy sources like solar and wind. However, the thermal stability of energy storage lithium batteries remains a critical challenge. High operating temperatures can accelerate side reactions, degrade battery materials, and even trigger thermal runaway, posing significant safety risks. To address this, we focus on maintaining the optimal operating temperature range of 20–35°C for battery packs, as exceeding this range can lead to capacity loss and reduced lifespan. This study employs a numerical simulation approach, calibrated with experimental data, to explore key factors influencing the thermal characteristics of energy storage lithium batteries, including channel aspect ratios, cooling plate layouts, and inlet water flow rates. Our goal is to enhance cooling efficiency while minimizing energy consumption in liquid cooling systems.
The thermal management of energy storage lithium batteries is essential for ensuring safety and longevity. Lithium-ion batteries, known for their high energy density and long cycle life, are widely adopted in large-scale industrial and commercial energy storage systems. Nevertheless, their poor thermal stability under high-temperature conditions necessitates effective cooling strategies. Traditional air cooling methods often fall short due to limited heat dissipation capacity, leading to poor temperature uniformity. In contrast, liquid cooling systems, especially indirect cooling via cold plates, offer superior performance. This research delves into optimizing liquid cooling designs by examining three pivotal aspects: the aspect ratio of flow channels, the configuration of cooling plates, and the impact of water flow rates. Through detailed numerical analysis, we aim to provide insights that can improve the thermal management of energy storage lithium batteries, thereby supporting the reliable operation of energy storage stations.
To model the thermal behavior of energy storage lithium batteries, we developed a comprehensive numerical framework based on fundamental physical principles. The battery pack consists of multiple cells arranged in a 1P52S configuration, each with a capacity of 280 Ah and dimensions of 173.8 mm × 71.6 mm × 207.2 mm. Key components, such as thermal silica gel and polycarbonate plates, are incorporated to simulate real-world conditions. The liquid cooling plate features a serpentine flow channel design with parallel paths, and we investigate three aspect ratios (W/H = 7, 5, 3) to assess their effects on heat transfer and fluid dynamics. The governing equations include the energy conservation equation for the battery, the Navier-Stokes equations for fluid flow, and the continuity equation for mass conservation. These are expressed as follows:
$$ \rho C_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + Q $$
where \( \rho \) is density, \( C_p \) is specific heat capacity, \( k \) is thermal conductivity, \( T \) is temperature, \( t \) is time, and \( Q \) is the heat generation rate. For the fluid flow, we use:
$$ \frac{\partial}{\partial t} (\rho \vec{v}) + \nabla \cdot (\rho \vec{v} \vec{v}) = -\nabla P + \mu \nabla^2 \vec{v} + \vec{F} $$
and the continuity equation:
$$ \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \vec{v}) = 0 $$
Here, \( \vec{v} \) is the velocity vector, \( P \) is pressure, \( \mu \) is dynamic viscosity, and \( \vec{F} \) represents external forces. The heat generation in energy storage lithium batteries comprises irreversible and reversible components, modeled using the Bernardi equation:
$$ q_v = \frac{1}{V_b} \left( I^2 R + I T \frac{dE_{OC}}{dT} \right) $$
where \( I \) is current, \( R \) is internal resistance, \( V_b \) is battery volume, and \( \frac{dE_{OC}}{dT} \) is the temperature coefficient of the open-circuit voltage. The internal resistance varies with the state of charge (SOC), as detailed in Table 1, which summarizes the 10-second pulse discharge internal resistance across different temperatures and SOC levels for energy storage lithium batteries.
| SOC/T (°C) | -20 | -10 | 0 | 10 | 25 | 35 | 45 | 55 |
|---|---|---|---|---|---|---|---|---|
| 5 | 5.34 | 3.66 | 1.73 | 1.24 | 0.57 | 0.46 | 0.34 | 0.23 |
| 10 | 5.05 | 3.47 | 1.63 | 1.18 | 0.53 | 0.43 | 0.33 | 0.22 |
| 15 | 4.75 | 3.29 | 1.56 | 1.13 | 0.51 | 0.42 | 0.32 | 0.23 |
| 20 | 4.46 | 3.11 | 1.50 | 1.09 | 0.49 | 0.40 | 0.32 | 0.23 |
| 25 | 4.17 | 2.93 | 1.45 | 1.06 | 0.47 | 0.39 | 0.31 | 0.23 |
| 30 | 3.88 | 2.76 | 1.41 | 1.03 | 0.46 | 0.39 | 0.31 | 0.23 |
| 35 | 3.64 | 2.59 | 1.38 | 1.00 | 0.45 | 0.38 | 0.30 | 0.23 |
| 40 | 3.43 | 2.46 | 1.34 | 0.98 | 0.44 | 0.37 | 0.30 | 0.23 |
| 45 | 3.24 | 2.33 | 1.32 | 0.96 | 0.43 | 0.37 | 0.30 | 0.23 |
| 50 | 3.14 | 2.24 | 1.29 | 0.94 | 0.43 | 0.36 | 0.29 | 0.23 |
| 55 | 3.12 | 2.20 | 1.27 | 0.93 | 0.42 | 0.36 | 0.29 | 0.23 |
| 60 | 3.09 | 2.18 | 1.25 | 0.92 | 0.42 | 0.36 | 0.29 | 0.23 |
| 65 | 3.05 | 2.15 | 1.24 | 0.91 | 0.43 | 0.37 | 0.30 | 0.24 |
| 70 | 3.01 | 2.13 | 1.23 | 0.91 | 0.42 | 0.36 | 0.30 | 0.23 |
| 75 | 2.97 | 2.10 | 1.22 | 0.90 | 0.41 | 0.35 | 0.29 | 0.23 |
| 80 | 2.93 | 2.07 | 1.21 | 0.89 | 0.41 | 0.35 | 0.29 | 0.23 |
| 85 | 2.88 | 2.04 | 1.19 | 0.87 | 0.40 | 0.34 | 0.29 | 0.23 |
| 90 | 2.83 | 2.00 | 1.17 | 0.86 | 0.39 | 0.34 | 0.28 | 0.23 |
| 95 | 2.79 | 1.97 | 1.16 | 0.85 | 0.39 | 0.33 | 0.28 | 0.23 |
| 100 | 2.77 | 1.97 | 1.17 | 0.86 | 0.39 | 0.34 | 0.29 | 0.23 |
The computational domain includes the physical model of the battery pack, an air domain, and a liquid domain. Boundary conditions are set with an ambient temperature of 25°C, and the initial temperature of all components matches the environment. The outer walls of the battery pack adhere to Newton’s law of cooling:
$$ -\lambda_{XT} \frac{\partial T_{XT}}{\partial n} = h (T_{xt} – T_{amb}) $$
where \( \lambda_{XT} \) is the thermal conductivity of the enclosure, \( h \) is the convective heat transfer coefficient (set to 5 W/(m²·K)), \( T_{xt} \) is the wall temperature, and \( T_{amb} \) is the ambient temperature. For the liquid cooling system, the inlet is defined as a mass flow inlet at 0.1791 kg/s (equivalent to 10 L/min for water at 22°C), and the outlet is a pressure outlet at atmospheric pressure. Gravitational effects are considered, and the simulation runs for 6480 seconds, corresponding to a 0.5C discharge rate until 90% SOC is depleted. To validate the model, we compared simulation results with experimental data from single-cell tests at 1C and 0.5C discharge rates, showing a maximum error of 2.9%, which confirms the model’s accuracy for energy storage lithium batteries.
Our investigation into the aspect ratio of flow channels reveals that while the temperature of energy storage lithium batteries is minimally affected, the pressure drop and energy consumption of the liquid cooling system vary significantly. For aspect ratios of W/H = 7, 5, and 3, with a constant height of 6 mm, we observed that smaller aspect ratios (e.g., W/H = 3) lead to a slight reduction in the maximum cell temperature—only 0.68% lower than W/H = 7. However, the pressure drop decreases substantially by 27.2% when comparing W/H = 3 to W/H = 5. This is attributed to the combined effects of convective heat transfer coefficient and heat exchange surface area. The energy consumption of the liquid cooling system, calculated as:
$$ Q_{ec} = \int_0^t \nabla P \cdot V_{in} dt $$
where \( \nabla P \) is the total pressure drop and \( V_{in} \) is the inlet volume flow rate, also shows a notable reduction with smaller aspect ratios. For instance, transitioning from W/H = 3 to W/H = 5 increases energy consumption by 37.3%, whereas moving from W/H = 5 to W/H = 7 results in only a 6.7% increase. These findings highlight that optimizing the aspect ratio can enhance the efficiency of thermal management for energy storage lithium batteries without compromising cooling performance.
Next, we explore the impact of cooling plate layouts on the thermal behavior of energy storage lithium batteries. Comparing traditional bottom cooling with a novel bottom-plus-side cooling configuration, we find dramatic improvements in temperature control. With bottom cooling alone, the maximum temperature rise reaches 7.62°C, and the global temperature difference across cells is 8.80°C. In contrast, the bottom-plus-side cooling reduces the maximum temperature rise to 0.51°C—a decrease of 21.8%—and the temperature difference drops to 3.0°C, improving uniformity by 68.1%. This enhancement is due to increased heat transfer power; bottom-plus-side cooling achieves up to 333.85% higher heat transfer power in the initial 200 seconds and 107.52% at the end of discharge. The superior performance stems from the additional cooling surfaces that mitigate vertical temperature gradients, which are critical for the longevity and safety of energy storage lithium batteries. Table 2 summarizes the material properties used in the simulations, emphasizing the components relevant to energy storage lithium batteries.
| Component | Density (kg/m³) | Specific Heat (J/(kg·K)) | Thermal Conductivity (W/(m·K)) |
|---|---|---|---|
| Cell | 2152 | 1051.1 | \( \lambda_{th} = 1.04 \), \( \lambda_w, \lambda_h = 21.05 \) |
| Liquid Cooling Plate | 2680 | 880 | 237 |
| Thermal Silica Gel | 2420 | 967 | 2.1 |
| Coolant | 1065 | 3394 | 0.419 |
| PC Board | 1200 | 1340 | 0.194 |
Varying the inlet water flow rate from 5 L/min to 15 L/min demonstrates that moderate flow rates (10–12.5 L/min) offer the best balance for cooling energy storage lithium batteries. At 10 L/min, the maximum cell temperature remains below 32.7°C, and the temperature difference is under 1.8°C. Increasing the flow rate to 12.5 L/min further reduces the maximum temperature by 0.20°C, but beyond this point, improvements diminish. For example, at 15 L/min, the temperature reduction is negligible, while the pressure drop and energy consumption rise significantly. The pressure drop, influenced by flow resistance, follows the relation:
$$ \nabla P \propto \frac{\mu L v}{D_h^2} $$
where \( \mu \) is viscosity, \( L \) is channel length, \( v \) is velocity, and \( D_h \) is hydraulic diameter. The energy consumption, integrated over time, increases with higher flow rates due to greater friction losses. This underscores that excessive flow rates do not yield proportional benefits for energy storage lithium batteries and can strain the cooling system. Therefore, we recommend flow rates of 10–12.5 L/min for optimal thermal management of energy storage lithium batteries.
In a multi-factor analysis, we combine the optimal conditions: W/H = 3 aspect ratio, bottom-plus-side cooling layout, and 12.5 L/min flow rate. This configuration achieves the lowest maximum temperature rise of 0.4°C and the smallest global temperature difference of 2.9°C. Comparatively, the bottom-plus-side cooling alone results in a maximum temperature rise of 0.5°C and a temperature difference of 3.0°C, indicating that it plays a dominant role in the multi-factor setup. Other factors, such as aspect ratio and flow rate, contribute marginally. This synergy highlights the importance of integrated design approaches for energy storage lithium batteries, where cooling strategies can be tailored to maximize performance and efficiency. The heat transfer dynamics can be further analyzed using the Nusselt number correlation for turbulent flow:
$$ Nu = 0.023 Re^{0.8} Pr^{0.4} $$
where \( Re \) is Reynolds number and \( Pr \) is Prandtl number, which governs the convective heat transfer in the cooling channels of energy storage lithium batteries.
In conclusion, our numerical study on energy storage lithium batteries reveals that aspect ratio variations have minimal impact on temperature but significantly affect pressure drop and energy consumption. The bottom-plus-side cooling layout drastically improves temperature uniformity and reduces peak temperatures, while moderate water flow rates (10–12.5 L/min) provide efficient cooling without excessive energy use. The multi-factor optimization demonstrates that the cooling layout is the most influential factor, offering a practical pathway for enhancing the thermal management of energy storage lithium batteries. Future work could explore dynamic operating conditions and advanced materials to further optimize the performance and safety of energy storage lithium batteries in large-scale applications.