Modelling approaches to detecting and preventing thermal runaway risk in Li-ion BESS

NFPA 855

NFPA 855 is the International Fire Code and the Standard for the Installation of Stationary Energy Storage Systems. The standard has been set out by the National Fire Protection Association (NFPA) in the US to ensure that BESS can be operated safely. While not explicitly required, battery modelling can be used alongside physical testing and hazard mitigation analysis (HMA) to prove that the installation will meet safety requirements. HMA is required for the standard to limit fire propagation, mitigate explosion hazards, and ensure flammable and toxic gases do not pose a threat to the community in the vicinity of the installation.

NFPA 855 outlines the minimum standards for mitigating hazards associated with the design, construction, installation, commissioning, operation, maintenance and decommissioning of a BESS installation. The NFPA 855 standards also determine what size an installation can be based on these factors and their location to buildings, public ways and hazardous materials.

Related to thermal runaway, the NFPA 855 sets out the specification requirements for the equipment, ventilation, smoke detection, fire control and suppression, and emergency planning protocols. The NFPA 855 standard also analyses the consequences of thermal runaway, gas and smoke detection failures, and ventilation failures.

UL9540A testing

The UL9540A physical testing method evaluates the different fire safety hazards associated with propagating thermal runaway at the cell, module, and unit level. Cell-level testing determines if thermal runaway can be easily induced in a cell; module-level testing looks at the module design, rate of heat release rate, off-gassing, and debris hazards; and unit level testing looks at BESS design, rate of heat release rate, off-gassing, debris, and deflagration venting hazards inside the container, as well as the wall surface temperatures and heat flux.

These physical tests provide a lot of information at all levels of an installation, which can be used to make accurate models of a BESS to see how thermal runaway hazards might manifest and evolve/propagate once a thermal runaway event has started. The most recent version of UL9540A, published earlier this year, includes mandatory large-scale fire testing (LSFT), where a BESS unit is set on fire and allowed to burn.

The information gained from modelling and analytics

Computational modelling can be done at all levels. There are a lot of physics-based modelling approaches that cover the cell level, and what happens within the cell. While this is important for thermal runaway, as this is ultimately where any thermal runaway propagation starts, this section is dedicated to the larger-scale modelling methods that look at BESS safety in relation to thermal runaway—including the pre-cursor gas dynamics that come before a thermal runaway event, as well as the potential propagation of fires and explosions in BESS containers and installations once a thermal runaway event has started.

Modelling thermal runaway events within BESS containers is crucial for detecting the range of potential hazards in an installation, including toxic and flammable gas release, fires, and explosions. There are three broad areas of BESS modelling that helps to understand and prevent thermal runaway in a BESS installation:

Dispersion modelling

This simulates the spread of toxic and flammable gases released inside the BESS when thermal runaway occurs. While some analytics can detect these gases as an early warning method for detecting thermal runaway before it takes hold, the modelling side involving thermal runaway gases ensures that any installation is in a safe enough location from occupied areas if a thermal runaway event does occur. This is because hydrochloric and hydrofluoric acid can be dispersed during an event and be a health hazard if the installation is sited too close to residential areas.

Fire modelling

This simulates the thermal radiation effects of a fire and its propagation during a thermal runaway event. This modelling looks at the heat from batteries, jet fires from safety events, and the potential for secondary fires, and how all these factors not only impact the installation, but also any people and structures in the vicinity of the BESS.

Explosion modelling

This simulates the overpressures and propagation of pressure from flammable gases that are confined in BESS containers. The modelling determines how damaging any explosion is likely to be, and what the chances are of causing major structural damage and injury/death at the installation and surrounding areas.

Modelling BESS thermal runaway events is not just a compliance exercise for BESS installations; it is a critical way to manage safety and protect both people and other energy assets. In terms of specific modelling techniques, there are two classes of techniques that are widely used to model BESS thermal runaway events: computational fluid dynamics (CFD) and failure mode and effects analysis (FMEA).

CFD evaluates the impact of battery failures and how this generates thermal events, smoke and toxic gases inside the BESS and models how this impacts the local environment around the BESS. CFD can also be used to determine heat flux inside the BESS and how much of a risk one BESS container is to other equipment around it, the dispersion of gas and complex gas flow patterns inside the BESS, the propagation and exit points of pool and jet fires, and for determining how severe any thermal events will be for a specific installation. CFD can also be used as part of NFPA 68/NFPA 69 deflagration analysis that models how effective deflagration panels will be in an installation and what the potential deflagration hazards are.

On the other hand, FMEA is a failure mode modelling approach that identifies potential failure modes in different components that are likely to cause a fire inside the BESS. FMEA investigates how different components in the BESS could cause thermal runaway, and investigates the specific failure modes (and their severity) that could lead to thermal runaway events in each of these components. This approach was first adopted by the US Department of Defense and has since become a widely used tool on the commercial side of the industry. FMEA is also recommended as a safety analysis tool of the Energy Storage Management System (ESMS) in UL 9540 and NFPA 855.

Applying data analytics to thermal runaway modelling

When it comes to modelling and monitoring thermal runaway in BESS, there are two main camps that companies sit in.

On one hand, you have the companies who are directly involved in computational-based modelling, looking at what could happen during a thermal runaway event in a specific BESS configuration, and using this data to ensure that any installation is safe. On the other hand, you have companies who are more on the analytics side who are not involved before the BESS has been installed. Instead, these analytics monitor the BESS in real-time to provide early warning alerts before a thermal runaway event occurs. Both are important and both serve different functions for preventing thermal runaway in BESS installations.

ACCURE

ACCURE has developed battery intelligence software that combines physics-based modelling with machine learning to predict when thermal runaway is going to occur in a BESS weeks before it happens. The analytics tracks over 20 different safety indicators to create a safety score that determines the risk of thermal runaway.

The physics models utilise thermodynamic and electrochemical principles to identify small anomalies, manufacturing defects, equipment failure, and precursor signs of internal short circuiting. The machine learning algorithms scan the large datasets of the model to zone in on the thermal runaway precursors, including voltage anomalies, impedance growth, and temperature hotspots.

The analytics are seen as an extra layer of safety above the BMS in a BESS, as the BMS gathers the data and sends it to the cloud, where the data is analysed and anomalies/precursor signs are found. The analytics can also analyse the ageing of the battery to better predict battery health over time, as poor battery health is more likely to generate defects that cause thermal runaway events.

TWAICE

TWAICE has developed multi-physics and cloud-based modelling/analytics platform for making sure that specific cell configurations are safe. TWAICE uses a combination of electrical, degradation, and thermal models to predict cell behaviour.

Electrical models that identify module imbalances and cells that showing precursors for overheating. Degradation models in the analytics assess the risk of propagation between cells during a thermal runaway event. Thermal modelling simulates how the cell responds to applied thermal loads and predicts the temperature rise based on entropic and Joule heating heat generation.

The models are used to both optimise performance and prevent cascading risks inside the BESS and can detect early signs of failure from a hot cell or weak battery string, and can be used to detect thermal, resistance and self-discharge anomalies, detect thermal runaway before it occurs, and identify any faulty cells before they affect the string.

PowerUP

PowerUP has developed an electrothermal modelling platform with machine learning called Battery Insight. The platform compares the current, voltage and temperature data collected from the BMS in both lab tests and the battery assets in the field to model the behaviour of the BESS and understand what is going on inside the battery. Like other platforms, it identifies subtle signals that provide insight into battery behaviour and the chance of thermal runaway.

The lab testing combines abusive and accelerated ageing tests to train the algorithms and make the platform more accurate at spotting data anomalies that suggest that thermal runaway could be imminent. This includes accelerated temperatures, over-voltage, under-voltage, over-current and cell imbalance. When the analytical platform has been integrated into a BESS, it yields state-of-safety indicators from the module to rack level that can alert BESS owners about potential hazards and thermal runaway up to 12 months before an event occurs.

Gexcon

Gexcon has an integrated software platform called the X-suite that can be used to simulate the potential effects of thermal runaway at all levels to satisfy regulatory compliance requirements. These platforms contain three main tools call EFFECTS, FLACS, and RISKCURVES.

EFFECTS is used to estimate the dispersion and release of toxic gases released during a thermal runaway event, including hydrogen fluoride, carbon monoxide, hydrogen chloride, hydrogen cyanide, and nitrogen dioxide gases, as well thermal and explosion risks. It’s a screening tool that can determine the concentration of gases in dispersing clouds, the heat radiation from BESS fires, and how overpressure of the gases inside the BESS could cause different types of explosions.

The data generated in EFFECTS can be inputted in FLACS, which is a CFD tool that can be used to identify how these gas, fire and explosion threats behave in more geometrically complex and constrained environments—including how terrain, layouts and ventilation affect gas dispersion and explosion dynamics. FLACS can also assess the container design to see how effective the mitigation measures are, including the effectiveness of ventilation and shielding systems in hazardous scenarios.

The data generated in both tools can also be fed into RISKCURVES for a more advanced quantitative risk assessment (QRA) of the BESS. This tool calculates the individual risks at specific locations, the individual risks each year, the potential societal risk based on the siting, and the potential consequences for exceeding certain thresholds of heat radiation, gas concentration, and internal pressure.

All the tools in the X-suite have been validated with experimental data and real-world incidents and has been designed to be a complete modelling workflow that can be used to bolster regulatory submissions and prove compliance with local and/or national guidelines.