In recent years, the rapid development of renewable energy technologies has underscored the critical role of energy storage battery in stabilizing power grids and balancing supply-demand dynamics. Among various energy storage solutions, lithium-ion batteries have emerged as a dominant technology due to their high energy density, cost-effectiveness, and long cycle life. However, thermal management remains a significant challenge for energy storage battery, as excessive heat generated during operation can degrade performance, reduce lifespan, and even pose safety risks. This study focuses on optimizing liquid cooling structures for lithium iron phosphate (LiFePO₄) energy storage battery, leveraging computational fluid dynamics (CFD) simulations to evaluate cooling efficiency and pressure drop characteristics.
1. Introduction
The integration of energy storage battery into renewable energy systems is pivotal for mitigating the intermittency of solar and wind power. Lithium-ion batteries, particularly LiFePO₄ variants, are widely adopted due to their thermal stability and environmental adaptability. However, electrochemical reactions during charge-discharge cycles generate substantial heat, elevating battery temperatures beyond the optimal range of 20–40°C. Prolonged exposure to high temperatures accelerates capacity fade and raises the risk of thermal runaway. Liquid cooling systems, characterized by high heat transfer efficiency and uniform temperature distribution, have become a cornerstone for thermal management in energy storage battery.
This work investigates serpentine-channel liquid cooling plates, comparing transverse and longitudinal layouts to identify optimal configurations. Additionally, the impact of coolant flow rate on cooling performance and pressure loss is analyzed. The findings aim to guide the design of efficient thermal management systems for energy storage battery.
2. Model Establishment
2.1 Battery and Cooling Plate Parameters
The LiFePO₄ energy storage battery under study has the specifications listed in Table 1. A single repeating unit is modeled to simplify calculations, with the cooling plate positioned adjacent to the battery to absorb heat via coolant flow.
Table 1: Technical parameters of the LiFePO₄ energy storage battery
| Parameter | Value/Description |
|---|---|
| Cathode Material | LiFePO₄ |
| Anode Material | Graphite |
| Electrolyte | LiPF₆ |
| Capacity | 24 Ah |
| Nominal Voltage | 3.2 V |
| Density | 2100 kg/m³ |
| Specific Heat Capacity | 3660 J/(kg·K) |
| Thermal Conductivity (x,y,z) | 21.60, 21.60, 2.11 W/(m·K) |
| Dimensions | 204 × 174 × 72 mm |
Two serpentine channel configurations are examined:
- Transverse Layout: Total channel length = 1776 mm.
- Longitudinal Layout: Total channel length = 1898 mm.
2.2 Governing Equations
The thermal behavior of the energy storage battery and coolant flow is governed by conservation laws. The Bernardi model calculates the battery’s heat generation rate:q=1Vs[I2R−ITdE0dT]q=Vs1[I2R−ITdTdE0]
where VsVs is the battery volume, II is current, RR is internal resistance, TT is temperature, and dE0dTdTdE0 is the temperature coefficient of open-circuit voltage.
The coolant flow adheres to the Navier-Stokes equations:∂v∂t+(v⋅∇)v=−∇pρ+ν∇2v+g∂t∂v+(v⋅∇)v=−ρ∇p+ν∇2v+g∇⋅(ρv)=0∇⋅(ρv)=0
Energy conservation for the coolant and battery is expressed as:∂∂t(ρwcpTw)+∇⋅(kw∇Tw+ρwcpTwv)=0(Coolant)∂t∂(ρwcpTw)+∇⋅(kw∇Tw+ρwcpTwv)=0(Coolant)∂∂t(ρbcpTb)+∇⋅(kb∇Tb)=Q(Battery)∂t∂(ρbcpTb)+∇⋅(kb∇Tb)=Q(Battery)
Here, ρρ, cpcp, kk, and vv denote density, specific heat, thermal conductivity, and velocity, respectively. Subscripts ww and bb refer to the coolant and battery.
2.3 Boundary Conditions
- Initial temperature: 30°C.
- Inlet: Mass flow rate = 0.1 g/s.
- Outlet: Pressure = 0 Pa.
- Natural convection coefficient: 6 W/(m²·K).
3. Simulation and Validation
3.1 Grid Independence Test
Five meshing schemes (Table 2) were evaluated to ensure simulation accuracy. Pressure drop variations across schemes remained within 5%, confirming grid independence.
Table 2: Grid independence validation results
| Scheme | Grid Count | Pressure Drop (Pa) | Deviation (%) |
|---|---|---|---|
| 1 | 736,456 | 31.24 | – |
| 2 | 752,345 | 31.30 | 0.19 |
| 3 | 778,478 | 31.35 | 0.35 |
| 4 | 798,427 | 31.38 | 0.45 |
| 5 | 813,457 | 31.40 | 0.51 |
3.2 Pressure Distribution
For transverse and longitudinal serpentine channels, the pressure drop (ΔPΔP) was calculated as:ΔPtransverse=31.240 Pa,ΔPlongitudinal=31.923 PaΔPtransverse=31.240Pa,ΔPlongitudinal=31.923Pa
The 0.683 Pa higher ΔPΔP in the longitudinal layout stems from its 122 mm longer flow path, increasing frictional resistance.
3.3 Temperature Distribution
The longitudinal layout exhibited superior cooling uniformity, with a maximum battery temperature of 30.500°C compared to 30.593°C for the transverse layout. Coolant temperature rises were 0.436°C (longitudinal) and 0.411°C (transverse), reflecting longer heat absorption paths in the former.
4. Impact of Coolant Flow Rate
4.1 Pressure Drop vs. Flow Rate
Increasing the coolant flow rate amplifies pressure losses exponentially. For example, doubling the flow rate from 0.1 g/s to 0.2 g/s raises ΔPΔP by 58% in the longitudinal channel.
Table 3: Flow rate effects on pressure drop
| Flow Rate (g/s) | Transverse ΔPΔP (Pa) | Longitudinal ΔPΔP (Pa) |
|---|---|---|
| 0.1 | 31.24 | 31.92 |
| 0.2 | 49.37 | 50.51 |
| 0.3 | 72.85 | 74.89 |
| 0.4 | 101.20 | 104.30 |
4.2 Cooling Efficiency vs. Flow Rate
Higher flow rates enhance cooling but with diminishing returns. At 0.4 g/s, the battery temperature stabilizes near 30.2°C, only 0.3°C lower than at 0.1 g/s.
Table 4: Flow rate effects on battery temperature
| Flow Rate (g/s) | Max Battery Temperature (°C) |
|---|---|
| 0.1 | 30.59 |
| 0.2 | 30.45 |
| 0.3 | 30.32 |
| 0.4 | 30.21 |
5. Conclusion
This study systematically evaluates liquid cooling designs for LiFePO₄ energy storage battery, emphasizing the trade-offs between cooling efficiency and pressure loss. Key findings include:
- Longitudinal serpentine channels achieve better temperature uniformity (30.500°C vs. 30.593°C) but incur higher pressure drops.
- Increasing coolant flow rate improves cooling marginally while disproportionately raising energy consumption.
- Optimal thermal management requires balancing channel geometry, flow rate, and system energy consumption.
These insights provide a foundation for designing high-performance thermal management systems in energy storage battery, ensuring reliability and longevity in renewable energy applications. Future work w