Leveraging CFD Modeling in BESS Applications
As power grids evolve and become more modern and complex, battery energy storage systems are being used more frequently. Commonly known as a BESS, this device is typically used for power grid energy storage as an operating reserve, for demand-side load management and for frequency control, as well as to minimize the risk of power outages. It is often used as an energy storage solution with intermittent renewable energy sources.
BESS facilities can range in size from a single “sea can” shipping container to a large warehouse-sized facility. They typically include batteries (lithium-ion or other chemistries), electrical components (inverters and power distribution equipment), and gas detectors and other sensors – along with the HVAC system, which serves the dual purpose of cooling the batteries and providing emergency ventilation.
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| Figure 1. A BESS enclosure: CFD models for the baseline design (top left), optimized design (top right) and thermal runaway analysis (bottom) | |
The HVAC system for BESS applications is challenging to design due to the high heat gain from the batteries (up to 320 BTUH per sq. ft.) with the additional constraint of having limited space in compact projects. BESS systems are also susceptible to thermal runaway, where an initiation event (such as a battery overcharging) can cause the batteries to overheat and, through a chain reaction, start releasing flammable gases. The HVAC system must therefore be able to not only cool the batteries during normal operation but also vent flammable gases during a thermal runaway event to reduce the risk of fire and explosion.
| Table 1. Simulation Inputs |
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A computational fluid dynamics (CFD) analysis can be used to address both aspects by first optimizing the system’s cooling performance and then using a transient (time-varying) analysis to verify ventilation performance during a thermal runaway event.
A CFD model of a compact (shipping-container style) BESS enclosure is shown in figure 1, highlighting the baseline and optimized designs for the cooling performance analysis as well as the model set-up for the thermal runaway analysis. The model inputs for both analyses are shown in table 1.
In normal operation, the target temperature for the batteries is 77°F or less, with less than a 9°F temperature differential from the top to the bottom of each battery rack. For the thermal runaway analysis, the flammable gas release rate and composition (a mixture of CO, CO2, H2 and various hydrocarbons) are taken from a UL 9540A test, which looks at the fire safety hazards related to this specific phenomenon. Three cells are assumed to be undergoing thermal runaway, and the temperature of the emitted gas mixture is assumed to be 324°F. The sensor and actuator control lag times are ignored, and the relief louver is opened at a lower explosive limit (LEL) of 10%.
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| Figure 2. Cooling performance optimization: velocity plot for the baseline design (top left), velocity plot for the optimized design (top right), battery temperature plot for the baseline design (bottom left) and battery temperature plot for the optimized design (bottom right) | |
Results for the cooling analysis are depicted in figure 2 and tables 2 and 3. The velocity plots show that there is short-circuiting of the supply air into the cooling unit intake, which reduces the effective capacity of the system and causes high battery temperatures. In the optimized design, the number and sizing of the supply air grilles are revised to increase throw and improve air distribution, and a blocking plate is added in the aisle between the batteries (like the cold-aisle concept from data centers). The revised design shows significant improvement in both the airflow patterns and the battery temperatures, which are now in line with the performance requirements.
| Table 2. Battery Temperatures for the Baseline Design |
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| Table 3: Battery Temperatures for the Optimized Design |
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Results for the thermal runaway ventilation scenario are shown in figures 3 and 4. During the gas build-up phase (relief louver closed), both sensor locations show similar data, with a 10-minute build-up time. During the venting phase (relief louver open), the concentrations at both sensors differ, with sensor 1 showing a much more rapid drop in the percent LEL and a lot more noise at sensor 2 with a much slower drop.
The difference in the percent LEL at the two sensor locations highlights the effects of air distribution inside the enclosure and that even in a reasonably well-mixed system, a single sensor is not sufficient to drive the emergency ventilation system. Both sensors, along with the volumetric plots in figure 4, show that the BESS is fully cleared of flammable gases at just over the 20-minute mark.
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| Figure 3. Flammable gas build-up and venting during a thermal runaway event | |
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| Figure 4. Flammable gas concentrations during the build-up and venting phases | |
While BESS enclosures are challenging to design due to their size and function, these projects also represent an excellent opportunity to leverage a CFD analysis. Key takeaways from this discussion include that CFD modeling can be used during design of the HVAC system in a BESS to do the following:
- Optimize cooling to achieve required temperatures during normal operation
- Benchmark the performance of the emergency ventilation system.
- Fine-tune LEL sensor locations for a fast and accurate system response
To learn more about Predict, or if you come across a project you believe is well suited for a CFD analysis, contact us at info@PredictCFD.com.
