The rapid development of renewable energy and smart grids has heightened the demand for efficient energy storage solutions. Among these, container-type energy storage system has emerged as a critical technology due to their modularity, scalability, and adaptability. Central to these systems is the energy storage battery, which requires precise thermal management to ensure performance, longevity, and safety. This study investigates the thermal behavior of lithium-ion batteries within containerized energy storage system, focusing on optimizing airflow distribution and temperature uniformity using computational fluid dynamics (CFD). Key findings, methodologies, and innovations are summarized below.
1. Thermal Challenges in Energy Storage Batteries
Lithium-ion batteries, the core of modern energy storage system, exhibit significant sensitivity to temperature fluctuations. Key thermal characteristics include:
- Capacity and Lifespan Degradation: Elevated temperatures accelerate electrolyte decomposition and electrode aging, reducing capacity by ~1% per °C above 25°C. Below 0°C, capacity drops exponentially due to increased internal resistance.
- Thermal Runaway Risk: Temperatures exceeding 80°C trigger exothermic reactions, leading to thermal runaway with internal temperature spikes up to 520°C.
- Temperature Uniformity: A maximum temperature difference of ≤5°C between battery cells is critical to prevent performance imbalances.
To address these challenges, this study proposes a hybrid cooling strategy combining forced air convection and structural optimizations.
2. Numerical Modeling and Validation
2.1 Governing Equations
The thermal-fluid dynamics of the system are governed by the conservation laws:
Mass Conservation:∇⋅(ρu)=0∇⋅(ρu)=0
Momentum Conservation:ρ(u⋅∇u)=−∇p+μ∇2u+Fρ(u⋅∇u)=−∇p+μ∇2u+F
Energy Conservation:ρCp(u⋅∇T)=k∇2T+qvρCp(u⋅∇T)=k∇2T+qv
where qvqv represents the volumetric heat generation rate of the battery, calculated as:qv=1Vb(I2Rb+IT∂U∂T)qv=Vb1(I2Rb+IT∂T∂U)
2.2 Grid Independence and Validation
Mesh sensitivity analysis ensured computational accuracy. For the battery box, a grid size of 2.8 million elements achieved temperature convergence within 1%. Cooling ducts required 2.4 million elements, while the full container model utilized 96 million elements. Experimental validation against published data showed a maximum error of 6% in temperature predictions.
3. Battery Box Thermal Optimization
3.1 Design Parameters and Boundary Conditions
The battery box housed 48 cells and DC-DC converters, with inlet airflow at 0.83 m/s and ambient temperature of 20°C. Key thermal metrics included:
- Cell surface temperature ≤35°C.
- DC-DC component temperature ≤85°C.
- Maximum intra-cell temperature difference ≤5°C.
3.2 Optimization Strategies
Four structural modifications were evaluated (Table 1):
| Parameter | Tested Variations | Optimal Value |
|---|---|---|
| Wall openings | 0–2 openings, sizes 75–410 mm | 1 opening, 410×75 mm |
| Inlet position | Front/middle/back, top/bottom | Mid-height, centered |
| Inlet size | 50×25 mm to 150×50 mm | 100×35 mm |
| Backplate distance (D₀) | 8–26 mm | 20 mm |
Key Results:
- Wall openings improved airflow circulation, reducing peak cell temperatures by 12%.
- Mid-height inlets minimized DC-DC component temperatures (345 K vs. 365 K for edge inlets).
- Smaller inlets (100×35 mm) enhanced cell temperature uniformity but had negligible impact on DC-DC cooling.
- A backplate distance of 20 mm balanced airflow distribution and pressure drop.
4. Cooling Duct Design and Airflow Uniformity
4.1 “Main Duct + Riser” Architecture
The cooling system employed a bifurcated duct design to serve 220 battery boxes. Airflow uniformity was quantified using:
- Non-uniformity Coefficient (S):
S=1m∑i=1m(vi−vˉvˉ)2×100%S=m1i=1∑m(vˉvi−vˉ)2×100%
- Deviation Coefficient (Lᵢ):
Li=vi−vˉvˉLi=vˉvi−vˉ
4.2 Optimization Techniques
- Main Duct: Tapered geometry and internal baffles reduced S from 87.25% to 4.79%.
- Risers: Angled deflectors (43.75°) at outlets improved airflow uniformity, limiting Lᵢ to ≤10%.
Performance Metrics:
| Parameter | Pre-Optimization | Post-Optimization |
|---|---|---|
| Main duct S | 87.25% | 4.79% |
| Riser Lᵢ (max) | 37.22% | 9.6% |
| Minimum inlet velocity | 0.61 m/s | 1.5 m/s |
5. Full-System Integration and Validation
5.1 Grid Fusion Methodology
The container model was partitioned into 11 subdomains for parallel meshing, with interfaces synchronized during CFD solving. This approach reduced meshing complexity by 40% while maintaining accuracy.
5.2 Thermal Performance
Post-optimization, the system achieved:
- Peak cell temperature: 306.5 K (33.4°C).
- DC-DC component temperature: 353.4 K (80.3°C).
- Maximum intra-cell ΔT: 4.93°C.
Critical Findings:
- Localized hotspots in initial designs resulted from airflow deflection at duct-cell interfaces.
- Rectifying vanes at duct outlets stabilized airflow direction, eliminating temperature gradients.
6. Conclusion and Future Directions
This study demonstrates that modular optimization of battery boxes and cooling ducts, coupled with CFD-guided design, significantly enhances the thermal performance of containerized energy storage system. Key innovations include tapered ducts, angled deflectors, and grid fusion techniques. Future work will explore:
- Genetic algorithm-driven baffle optimization.
- Active airflow control using dynamic meshing.
- Integration of phase-change materials for hybrid cooling.
Tables and Equations:
- Table 1 summarizes battery box optimization parameters.
- Equations for conservation laws and uniformity metrics provide a mathematical foundation for replication.
By prioritizing temperature uniformity and airflow efficiency, this research advances the reliability and safety of energy storage batteries in large-scale renewable energy applications.