Digital Twin Simulation: Engineering Smarter, Faster, and More Reliable Products

Why Digital Twins are Necessary and Important

Imagine being able to predict equipment failures before they happen, optimize system performance in real time, and reduce expensive physical testing. This is the power of digital twins. A digital twin is a virtual replica of a physical asset, enabling real-time monitoring, simulation, and optimization.

With increasing system complexity and the demand for faster, more efficient product development, digital twin technology is revolutionizing engineering across industries.

Example of a Digital Twin

How Digital Twins Help Address Major Challenges

Downtime and Maintenance: Unplanned machine downtime can disrupt workflows, delay production schedules, and lead to significant revenue losses. Unexpected failures not only impact profitability but also strain resources and damage customer trust. Digital twins help predict failures and optimize maintenance schedules, ensuring uninterrupted operations.

Fault Detection and Prediction: Late fault detection can result in system failures, escalating repair costs, and potential damage to other components. At the same time, excessive false alarms can interrupt production and reduce efficiency. Digital twins enable precise fault detection, balancing accuracy with minimal disruption.

Controls Optimization: Misconfigured control systems can lead to production stoppages and hardware degradation. Control software developed in lab conditions may not perform reliably in real-world applications. Digital twins allow engineers to test and optimize control strategies in a virtual environment before deployment, improving safety margins and efficiency.

What-if Scenarios: Physical testing of all possible operating conditions is expensive and time-consuming. Relying solely on sensor data and human intervention can introduce inconsistencies. Digital twins enable engineers to simulate countless real-world scenarios quickly, reducing reliance on costly prototype testing.

How Simulation is Making an Impact

GT-SUITE provides a comprehensive platform to develop digital twins, integrating robust multi-physics simulation with cutting-edge data science. By seamlessly connecting with a customer’s data collection system in a cloud-based environment, GT-SUITE enhances asset performance, minimizes downtime, and improves decision-making. Let’s explore how digital twins solve critical challenges and how you can build one to unlock the full potential of your engineering systems.

Conceptual Visualization of Digital Twin for Fleet Optimization

“How to Guide” for Building a Digital Twin

A digital twin functions by continuously updating a virtual replica of a physical asset, system, or process with real-time data. This enables simulation, analysis, optimization, and monitoring of the physical counterpart.

Digital Twin Orchestration Workflow

Here’s how to build one:

Step 1: Data Collection; Data can be collected from sensors on the physical asset (e.g., an engine, machine, or compressor), measuring parameters such as temperature, pressure, vibration, speed and more. Additionally, historical test data and field data from previous operations can be leveraged.

Data Collection

Step 2: Data Integration; All collected data—whether from sensors, past tests, or field operations—is aggregated, cleaned, and processed to ensure accuracy and usability for simulation and analysis.

Data Cleaning

Step 3: Virtual Model Creation;

- Physics-based modeling: GT-SUITE enables the creation of high-fidelity digital twins using physics-based models calibrated with real-world measured data, ensuring precise system behavior predictions.
Virtual Model Building
- Data-driven machine learning models: GT-SUITE’s Machine Learning Assistant utilizes datasets from sensors, testing, field operations, and design of experiments to create fast-running mathematical models (metamodels). These models leverage real-time sensor data for enhanced predictive accuracy and can also integrate synthetic data from physics-based simulations, reducing reliance on physical testing while improving reliability.

Data Driven Machine Learning Model

Step 4: Real-Time Interaction; The virtual model continuously updates via live data streams and sensor feedback which enables real-time monitoring, fault detection, and predictive analysis. This ensures that engineering teams can proactively optimize system performance, diagnose failures, and improve reliability.

Real-Time Integration of Virtual Model and Physical Asset

Case Studies: Real-World Applications of Digital Twins

Case Study 1: Fuel Cell Fault Simulation and Detection for On-Board Diagnostics Using Real-Time Digital Twins

As electrified powertrains become more complex, developing reliable On-board diagnostic (OBD) systems is crucial for ensuring compliance with evolving regulations. However, the lack of prototype hardware makes traditional validation methods impractical. To address this, real-time digital twins enable virtual testing through model-in-the-loop (MiL), software-in-the-loop (SiL), and hardware-in-the-loop (HiL) simulations.

Three Step Procedure for Hardware-in-the-Loop Simulation

This approach involves creating high-fidelity models to identify and analyze failure modes in key fuel cell components such as the compressor, recirculation pump, humidifier, and cooling system. The process begins with fuel cell stack calibration using measured polarization curves and predictive loss models. This is followed by balance of plant modelling that integrates the stack with sub-systems like the anode recirculation loop, the cathode system including humidifier, intercoolers, compressor, and turbine, and motor controls as well as the cooling systems. To enhance efficiency, the model is optimized using 0D and map-based approaches, ensuring fast simulation without sacrificing accuracy. Fault scenarios are then defined, specifying key variables to be monitored by the control system. In the HiL phase, the fast-running fuel cell model developed in GT-SUITE is integrated with MATLAB Simulink for real-time execution. The model’s performance is assessed by running real-time simulations, comparing speed and accuracy, and introducing faults to evaluate system response alongside sensor data. By leveraging real-time digital twins, engineers can efficiently develop and validate OBD systems, ensuring robust fault detection while reducing reliance on physical prototypes. This accelerates regulatory compliance and enhances the reliability of fuel cell powertrains.

Click here to access the complete webinar.

 Case Study 2: Enhancing Cabin Thermal Management with Digital Twin Technology

A major automotive company leveraged digital twin technology to optimize cabin thermal management, improving energy efficiency and driver comfort in electric trucks. The challenge was to balance the battery thermal system and cabin climate control while maintaining efficient energy use.

Cabin Comfort Model in GT-TAITherm

To achieve this, the company calibrated high-fidelity models, dividing the cabin into flow volumes and creating surface mesh models for precise simulations. A co-simulation was then conducted by integrating GT-SUITE’s fluid solver with GT-TAITherm, leveraging key parameters. The model was rigorously validated against multiple real-world test datasets, ensuring accuracy and reliability.

Building on this foundation, the company aims to take its digital twin implementation to the next level by developing fast-running models for SIL and HIL applications and integrating internet of things (IoT) connectivity for real-time data exchange and enhanced predictive capabilities. These advancements will enable smarter automation, deeper system insights, and more responsive thermal management strategies, bringing them closer to a fully realized digital twin ecosystem.

Click here to access more digital twin related presentations.

Learn More About our Digital Twin Solutions

Digital twins are transforming engineering by enabling predictive maintenance, optimizing system performance, and accelerating product development. By combining physics-based modeling with data-driven machine learning, GT-SUITE provides a powerful platform for creating and deploying digital twins across industries.

Whether you are working on fuel cell powertrains, vehicle thermal management, or other complex systems, leveraging digital twins can drive efficiency, reduce costs, and improve reliability.

Ready to take your engineering processes to the next level? Start building your digital twin with GT-SUITE today! If you’d like to learn more about how GT-SUITE‘s capabilities, contact us!

Simulation for the HVACR Industry: How to Leverage Machine Learning

The HVACR Industry is Evolving

As we step into 2025, major trends in systems simulation are emerging. The heating, ventilation, air conditioning, and refrigeration (HVACR) industry is seeing accelerated growth in the adoption of simulation throughout the design and development process. This industry is looking to further modernize, appeal to consumers, and demand energy-efficient, sustainable and smart solutions. HVACR original equipment manufacturers (OEMs) are addressing these requests by implementing engineering processes that are supported by machine learning, Internet-of-Things (IoT), and AI advancements.

How Systems Simulation Modeling & Machine Learning can Assist

The HVACR industry is being driven by new regulations that have been enacted to combat climate change as well as to increase efficiency of current and future vapor compression systems. These trends include:

A big emphasis on using low global warming potential (GWP) refrigerants
Heat pumps becoming more popular within the industry

To achieve practical viability and adoption of heat pumps and low GWP refrigerants in HVAC systems within the next decade, thousands of prototypes and experiments need to be performed. Fast, accurate and robust modeling and simulation can also aid in this endeavor and accelerate this process. There also needs to be extensive collaboration between industry, academia, and policy makers to comprehensively address these goals. An important tool available to industry, academia and policy makers is the huge amount of data already available from different sources. This data can be leveraged using machine learning tools to aid and enhance modeling as well as providing new physical insight into HVAC systems.

Applying Machine Learning to Thermal Model Simulations

We at Gamma Technologies presented a paper at the Purdue Herrick Conferences on machine learning under the model speedup umbrella titled, ‘Application of Feedforward Neural Networks to Simulate Battery Electric Vehicle Air Conditioning Systems.’

In this paper a representational model of the thermal systems of a battery electric vehicle (BEV) was built in GT-SUITE and transient drive cycle simulations were carried out to compare the speed and accuracy of a machine learning based metamodel compared to a physics-based solution. The air-conditioning circuit in the EV thermal model was replaced with a feed-forward neural network trained against physical data. The battery and cabin temperatures were tracked and compared during a heat-up (ambient temperature -10 C) and cooldown (ambient temperature 30 C) cycle.

We observed that the machine learning metamodel does a good job in capturing the battery and cabin temperatures but there is some mismatch between the evaporator and condenser heat transfer rates during the heat-up simulation. However, the speed of the metamodel is around 35% faster than the physics-based model with an RT factor of 0.17 compared to the already faster than real time physics-based solution which has an RT factor of 0.37.

With this work we were able to show that we can successfully integrate machine learning based metamodels into GT system models using in-built tools and use them as alternatives to physics-based solutions to address various simulation needs.

EV thermal system model with physics-based solution and feedforward neural net

Figure: EV thermal system model with physics-based solution (red) and feedforward neural net (green)

Link to Presentation

Learn More About our HVACR Simulation Solutions

See how GT-SUITE’s simulations solutions impact the HVACR industry here.

GT-SUITE doesn’t take hours or days to run complex simulations. HVACR engineers can run simulations in a matter of seconds or minutes, which allows for a more iterative design cycle in addition to the ability to test more product possibilities.

Firms such as Trane have successfully unleashed the power of GT-SUITE and simulation on HVACR scroll compressor designs. Learn how a simple sensitivity analysis enabled Trane engineers to solve a challenging performance shortfall problem that resulted in an estimated savings of between $50,000 and $40 million in product development costs.

Gamma Technologies and GT-SUITE: Pioneering the Future of Simulation

Unveiling the Power of GT-SUITE

This year, Gamma Technologies celebrated a significant milestone: its 30th anniversary. Since its inception in 1994, Gamma Technologies has been at the forefront of engineering simulation, revolutionizing how industries approach design and innovation. At the heart of this transformation is GT-SUITE, the company’s flagship systems simulation software that has become a cornerstone in various fields, from automotive to aerospace, HVACR, energy, and beyond.

Gamma Technologies grew its prowess in the automotive industry with GT-POWER, the industry standard engine performance simulation tool used by most engine manufacturers and vehicle original equipment manufacturers (OEMs). GT has continuously expanded its simulation capabilities to meet consumer demands with extensive developments in batteries, electric motors, and more with products such as GT-AutoLion, GT-PowerForge, GT-FEMAG, and others. GT continues to accelerate in agnostic powertrain and systems development worldwide.

SOURCE: AFDC (n.d.a). National Academies of Sciences, Engineering, and Medicine. 2021. Assessment of Technologies for Improving Light-Duty Vehicle Fuel Economy—2025-2035. Washington, DC: The National Academies Press. https://doi.org/10.17226/26092.

GT-SUITE is more than just a simulation tool. It’s a comprehensive, multi-domain platform that empowers engineers to model, simulate, and analyze complex systems. With capabilities spanning mechanical, electrical, fluid, and thermal domains, GT-SUITE offers a holistic approach to understanding how different systems interact. This versatility is essential in today’s engineering landscape, where the integration of various technologies and systems is more crucial than ever.

In the transportation industries (automotive, on-and-off highway vehicles) GT-SUITE has made a substantial impact. The software allows for the creation of detailed simulations of vehicle systems, from powertrains to suspension systems.

Almost every vehicle on the road has components simulated and designed with one of Gamma Technologies’ simulations. Most major automotive original equipment manufacturers (OEMs) have used GT-SUITE for engine and vehicle development.

By providing a virtual environment to simulate and optimize designs, GT-SUITE helps manufacturers improve performance, reduce costs, and shorten turnaround times. To accelerate development time, GT’s XiL (X-in-the-Loop) modeling capabilities (method that combines virtual testing with real-world elements to validate components of an Electronic Control Unit or ECU) integrate seamlessly with industry tools, ensuring a streamlined and efficient product design cycle.

The ability to simulate real-world scenarios and interactions is particularly valuable for developing advanced technologies such as electric and hybrid vehicles, where precise predictions and optimization are critical.

Expanding Horizons: Aerospace, HVACR, Marine, Energy, and Beyond

The influence of GT-SUITE extends beyond automotive engineering.

In the HVACR (heating, ventilation, air conditioning, and refrigeration) industry, Gamma Technologies’ comprehensive set of validated 0D/1D/3D multi-physics component libraries have enabled HVACR engineers to tackle challenges in development such as sustainability and efficiency, decarbonization, new refrigerants, and system complexity and controls. GT-SUITE’s combined with GT-TAITherm can model human comfort which allows the user to have an additional target besides traditional temperature and humidity. The human comfort model has localized comfort zones that can be used to determine if cabin insulation or HVAC settings need to be modified. Well-known brands such as Carrier, Copeland, Daikin, Sanden, Trane, Tecumseh, Rheem, and others have found tremendous benefits utilizing GT-SUITE. Learn more about these case studies here.

In aerospace, the software supports the design and analysis of complex systems like propulsion and avionics. Engineers from organizations such as NASA, Roush, SAFRAN, and others have used GT-SUITE to ensure that aircraft systems are both efficient and reliable, contributing to advancements in performance and safety. Some of the applications that Gamma Technologies’ simulation solutions have assisted in include cryogenic systems, propulsion system modeling, environmental controls systems (ECS), fuel cell simulation, thermal management, e-propulsion batteries, flight dynamics and controls, multi-body dynamics, landing gear development, and fuel tank modeling.

GT is proud to say that our solutions have already been used to support the future of urban air mobility by providing simulations for electric aircraft and electric vertical take-off and landing (eVTOL) vehicles (including air taxis) development.

In the energy and oil & gas sectors, GT-SUITE aids in the development of innovative solutions for power generation and renewable energy. Our customers are already choosing GT for upstream, midstream, and downstream applications to optimize production. The ability to simulate energy systems helps companies enhance efficiency and sustainability, addressing some of the most pressing challenges in energy production and consumption.

It might be surprising that the marine industry is aggressively moving towards a sustainable future as well. GT is proud to have partnered and work with organizations such as the Maritime Battery Forum, WIN GD, Yanmar R&D, and Toshiba. These firms have leveraged GT-SUITE’s solutions to simulate engine and drivetrain development for ship modeling and create digital twins for electrified motors.

Gamma Technologies has been on the forefront of implementing AI (artificial intelligence) & ML (machine learning) technologies. These tools elevate simulation capabilities by allowing thousands of variables to be considered and help designers best engineer superior products. AI and ML enhance simulations by creating accurate and dynamic metamodels (mathematical models) that can adapt to complex, real-world scenarios in real-time. These technologies also streamline the analysis of vast data sets, leading to more precise predictions and informed decision-making.

To learn more about our machine learning capabilities, read this two-part blog series on enhancing model accuracy by replacing GT’s lookup maps and optimizing neural networks.

A Legacy of Innovation

As Gamma Technologies celebrated its 30-year milestone, it’s clear that its impact on the engineering world is profound. GT-SUITE’s ability to provide detailed, multi-domain simulations has empowered engineers across industries to tackle complex problems and push the boundaries of what’s possible. This dedication has kept GT-SUITE at the cutting edge of simulation technology, ensuring that it meets the ever-changing demands of its diverse user base.

Looking Ahead

As we look to the future, Gamma Technologies is well-positioned to continue its legacy of pioneering simulation technology. With GT-SUITE leading the way, the company is set to drive further advancements in engineering and design, helping industries navigate the complexities of modern technology and innovate for a better tomorrow.

Learn More About Gamma Technologies’ Simulation Solutions

To learn more about our simulation capabilities, visit our website. Learn more about GT-SUITE here. Contact us here to speak to a GT expert!

Top 10 Gamma Technologies Blogs of 2023!

From calculating EV range to heat pump design, there is a blog for every simulation!

As we kick off 2024, let’s look back at the best blogs of 2023! Since the inception of Gamma Technologies, GT-SUITE has optimized system simulation solutions for manufacturers! In no order, these are the top 10 blogs written in 2023 that highlight the vast application use cases and technical capabilities GT-SUITE can deliver!

Shout-outs to our colleagues for their contributions!

Learn more about our simulation solutions!

If you’d like to learn more about how Gamma Technologies can be used to solve your engineering challenges, contact us here!

Wishing you a healthy & prosperous 2024!

Using Simulation for Battery Engineering: 15 Technical Blogs to Enjoy

At Gamma Technologies, our GT-SUITE and GT-AutoLion simulations provide battery engineers and designers robust solutions for modeling and predicting battery performance throughout its lifecycle.

Enjoy reading our battery-focused technical blogs to learn more about:

Since the inception of GT-SUITE, Gamma Technologies has recognized the transformation of automotive and non-automotive industries. Our solutions are powertrain and industry agnostic, and we are looking to guide customers and partners towards a sustainable world.

Learn more about our battery simulation solutions!

If you’d like to learn more about how GT-SUITE and GT-AutoLion can be used to solve your battery pack design challenges, contact us here!

This blog was initially published June 10th, 2022

Mitigating the Domino Effect of Battery Thermal Runaway with Simulation

What Happens During Battery Thermal Runaway?

As the world continues to move towards a more sustainable future, so does the popularity of electric vehicles. While the benefits of electric vehicles are many, one of the key challenges is ensuring that the battery packs used in these vehicles are safe and reliable. In the context of battery packs, thermal runaway stands out as an inherent hazard that can evoke profoundly negative media attention and public concern.

During a thermal runaway event, undesired exothermic side reactions occur. These reactions are the response of the battery components exposure to extreme operating conditions, including but not limited to: high operating temperatures, fast charging, cell fractures by external objects, and internal short circuits.

Just like the domino effect, a single cell entering thermal runaway can easily spread to the surrounding cells and cause fires and explosions in the whole pack. This is known as thermal runaway propagation.

How to Avoid Battery Thermal Runaway

The question arises, how can you can safeguard your cell from entering thermal runaway? Some common triggers for thermal runaway include excessive heating, electrical faults such as short circuits and nail penetration or even a faulty cell. Therefore, the goal should not be to never have a cell enter thermal runaway but rather to design a battery pack that can withstand a cell entering thermal runaway without causing the thermal runaway to propagate to the rest of the pack.

To effectively tackle this challenge, it is critical to develop precise models capable of predicting and mitigating thermal runaway propagation in battery packs. Well-designed battery pack models ensure appropriate cooling systems and safety features are engineered to minimize the risk of thermal runaway. Additionally, these models can be used to develop early warning systems that can detect when a pack is starting to overheat.

Experimental Costs of Testing Thermal Runaway

Experimentally analyzing thermal runaway propagation in lithium-ion battery cells and packs is ideal, but requires significant resources of both time and money. The process will need designing and constructing different test scenarios and equipment, not to mention the experimental condition variations and the safety risks associated with intentionally inducing such events. Just to test a simple lithium-ion battery pack prototype for thermal runaway propagation could cost nearly $100k per test scenario. What’s more is that thermal runaway propagation is an inherently complex event. It can be influenced by a wide range of factors, such as overcharging, overheating, internal shorting, and nail penetration. It is nearly impossible to replicate all the real-world scenarios in a laboratory setting. In the meantime, cell manufacturers may experience major delays in the release schedule of their final product, whether it is an electric vehicle, an electrical vertical takeoff and landing vehicles (eVTOLs), or others due to the fact that physical testing is often conducted at the late stages of the development cycle.

History of Simulating Thermal Runaway Propagation with GT-SUITE

Cell and pack manufacturers have diligently turned their attention to computer simulation and modeling techniques to analyze thermal runaway propagation. Many cell manufacturers look to 3D computer aided engineering (CAE) simulation to avoid the challenges associated with experimental physical testing of thermal runaway propagation. The components for modeling thermal runaway propagation include:

Pre-runaway battery model
Thermal runaway trigger
Cell-level thermal runaway model
Heat transfer model

Using 3D CAE serves as an exemplary simulation and modeling technique and is known to provide intricate details regarding thermal runaway propagation. However, this method is known for its considerable time requirement and model renderings that are challenging to implement as well as the difficultly to test numerous “what if” scenarios on.

In a previous blog, we demonstrated how the simulation platform GT-SUITE was employed to model the propagation effect of thermal runaway in a small battery module. GT-SUITE provides a 1D CAE solution that offers faster running models than the common 3D CAE models. We showcased how an equivalent circuit model can be used as the pre-runaway battery model. Simple external heating such as the thermal runaway trigger, a rule-based model for the cell-level thermal runaway model, and 1D thermal networks were used for the heat transfer model. Since then, GT-SUITE has been prolifically used by many battery pack designers not only to predict lithium-ion battery performance metrics but also to simulate the thermal runaway propagation and gain invaluable insights into the behavior of their battery packs under different conditions.

Cell thermal runaway events vary greatly based on the events leading to thermal runaway. For instance, how quickly the cells were heated to a runaway state will affect the mass of vent gases evolved and their composition. This becomes important as the commonly used rule-based models are not able to capture these detailed values. Within GT-SUITE’s battery modeling platform GT-AutoLion, the latest GT-SUITE development includes a unique 1D&3D multi-physics model for thermal runaway propagation. This modeling approach not only provides fast-running models but also demonstrates strong physics.

Cell-level Thermal Runaway Propagation Enabled by P2D Electrochemical Modeling Together with Chemical Reactions

The first step for modeling a thermal runaway propagation is to have a pre-runaway model. More commonly, equivalent circuit models (ECMs) have been used as pre-runaway models to predict the performance of lithium-ion batteries. However, there are multiple shortcomings with this approach as they cannot fully capture the complex electrochemical reactions occurring within a battery cell.

To address these limitations, we will use GT-AutoLion, which is based on a pseudo-two-dimensional (P2D) electrochemical modeling, to calibrate the pre-runaway lithium-ion battery performance, voltage, and heat generation during a normal operation leading up to a thermal runaway event. Using Gamma Technologies’ physics-based modeling, GT-SUITE users will now have access to more meaningful results while running different thermal runaway propagation scenarios.

p2d electrochemical model of li-ion cell simulation

P2D electrochemical model in GT-AutoLion and measured results correlation graph

In addition to the above capabilities, GT-AutoLion can have user-defined chemical side reactions for thermal runaway propagation modeling. In a use-case based on an article by Feng et al., we modeled the thermal runaway reactions based on couple of reactions happening in a lithium-ion battery cell:

Solid electrolyte interphase (SEI) decomposition
Anode – electrolyte interface
Separator melting
Cathode decomposition (2 reactions)
Electrolyte vaporization and degradation

The cell-level thermal runaway model we developed by utilizing the new capabilities of GT-AutoLion shows an excellent match with the findings documented in the literature. Below are some of the results that indicate the temperature rise and reactant concentrations for the cell entering the thermal runaway.

cell-level electrochemical-thermal coupled modeling simulation

Comparing the cell-level electrochemical-thermal coupled modeling results by GT-AutoLion and experimental results by Feng et al. (a) temperature evolution over time, (b) changes in normalized concentration of reactants over time.

Simulating a Module-Level Thermal Runaway Propagation

Using a simple battery module consisting of 20 cells in a series, with fins in between the cells, that are connected to a cold plate to provide cooling. The GEM3D tool in GT-SUITE was used to convert the CAD components to a finite element mesh for the cells, fins, and cold plate material. The model also had a flow volume that represented the air inside the module around the battery cells and was further connected to a burner where combustion reactions were defined. This would potentially be the combustion of chemicals that are released upon cells entering the thermal runaway.

Components of modeling thermal runaway propagation

Using GT solutions, we have a strong and fast-running model in which any cell in the module can be selected as the “trigger” cell by applying an external heat until a certain trigger temperature is reached. For this example, thermal runaway was initiated in the center cell (through simple heating and vent gases which were combusted in the burner).

Thermal Runaway Case Studies

Two case studies, using GT solutions, were carried out to observe the battery pack behavior during thermal runaway propagation: (i) without any coolant flow in the cold plate and (ii) with coolant flow of 2kg/s in at 60 °C. Building this model took just a few hours from start to finish.

The 12-minute thermal runaway simulation took about 2 hours to calculate, including thermal, electrical, chemical, and flow physics.

The model results shown in the figures below indicate that when there is no coolant flow in the cold plate, case (i), every cell entered the thermal runaway, one after another. Starting from the center cell and propagating to neighboring cells until all the cells reached high temperatures of 600 to 700 °C. If this were a physical test, the pack would have needed to be re-designed and re-tested! But since no real battery modules were destroyed in this virtual environment, this simulation could now be repeated under different conditions.

Consider the scenario wherein a battery pack is equipped with a coolant flow configuration as delineated in case (ii). As indicated in the figures below, it can be observed that certain cells, primarily the adjacent cells positioned in the middle of the pack, may still undergo thermal runaway. Yet such an occurrence was confined to the limited number of cells located at the center, meaning that the battery pack would not be set on fire.

Thermal Runaway Modeling Results

All 20 cells enter thermal runaway

4 cells enter thermal runaway

To see a full tutorial of building models for thermal runaway propagation using GT-SUITE and GT-AutoLion, watch this video here!

Click the image to watch the video!

Learn More About our Battery Thermal Runaway Solutions

Lithium-ion batteries can experience thermal runaway from a variety of trigger events. Propagation of a thermal runaway event to other cells in the battery pack should be avoided for a safe pack design, but repeated physical testing is expensive and poses significant challenges. GT-SUITE offers a fast-running simulation approach to model this event, combining the electrical, chemical, thermal, and flow domains into a single model. This innovative 1D & 3D multiphysics model enables accurate prediction of the cell heat release under different operating conditions which allows different thermal runaway mitigation strategies to be simulated.

If you’d like to learn more or are interested in trying GT-SUITE and GT-AutoLion to virtually test a battery pack for thermal runaway propagation, view this webpage here. To speak with a GT expert, contact us here!

Top 10 Gamma Technologies Blogs of 2022!

From battery thermal runaway to fleet route optimization, there is a blog for every simulation!

Since the inception of GT-SUITE, Gamma Technologies has offered state-of-the-art simulation solutions for manufacturers. Our simulation solutions help guide customers and partners toward highly optimized products.

In no order, these are the top 10 blogs of 2022!

Other Gamma Technologies Blogs to check out in 2022!

Shout-outs to our colleagues for their contributions!

Learn more about our simulation solutions!

If you’d like to learn more about how Gamma Technologies can be used to solve your engineering challenges, contact us here!

Have a great holiday season and wishing you a healthy & prosperous 2023!

How Simulation Is Used To Design ICE vs. Battery Electric Vehicle Thermal Management Systems

Understanding Vehicle Thermal Management

Vehicle electrification across the transportation industry is being driven by demands for reducing emissions and increasing fuel economy. However, engineering these electrified vehicles comes with a new set of challenges for thermal management of the powertrain and cabin. In this blog I will discuss some of these new challenges for battery electric vehicle thermal management and how it compares to combustion engine vehicles. But first, I’ll discuss some common traits between thermal management of both vehicle types.

Similarities Between ICE vs. Battery Electric Vehicles Thermal Management Systems

The goals for thermal management system design remain the same regardless of the powertrain: to keep the powertrain components in their desired temperature range, and to provide a comfortable cabin for the occupants. The optimal design should balance energy usage, system cost, and reliability. In cold environments, the thermal management system should enable fast warmup of the vehicle. Both battery electric vehicles (BEV) and internal combustion engine (ICE) vehicles are less efficient at cold temperatures. In warm environments, excess heat from the powertrain needs to be rejected to the environment to prevent damage to the components. In addition, the cabin temperature needs to be controlled for a comfortable driving experience.

Similar types of components are used between combustion engine vehicles and battery electric vehicles. A single-phase coolant loop would likely use an ethylene glycol and water mixture for the working fluid, with a pump, liquid-to-air heat exchanger, and control valve to manage the coolant flow. A cooling fan is used to enhance the air flow through the heat exchanger at low vehicle speeds. Previously mechanically driven pumps and fans were standard, but recently electrically driven components are used for greater system control. A two-phase refrigeration system is necessary for providing additional cooling below the environment temperature.

The integration of other systems is also an important consideration for transient analysis and controls. Different thermal strategies may be needed depending on the powertrain demands, component temperatures, and environment temperatures. For both a combustion engine and battery electric vehicle, a system that performs well at steady state conditions may not be sufficient to manage temperatures for transient driving cycles. The heat produced by powertrain components at ideal operating temperatures will be different than the heat generated at warmer or colder temperatures, and de-rating of the powertrain may be necessary to prevent component damage. In both types of vehicles, the demands for heating or cooling the cabin will impact the cooling circuit temperatures.

Differences Between ICE vs. Battery Electric Vehicles Thermal Management Systems

The most obvious difference between the combustion engine vehicle and the battery electric vehicle is the heat source. In the electric vehicle, the primary waste heat to the coolant is from the motor, power electronics, and battery. If this waste heat is not sufficient, an auxiliary heater or two-phase system can be used to add heat and bring the components up to their operating temperature. Whereas in the combustion engine, the primary heat source is from the combustion process. Additional heat is added to the coolant from the engine and transmission oil caused by friction in those components.

Energy Usage for BEV

Energy Usage for ICE Systems

These differences in the heat sources lead to differences in the operating temperatures of the components. The combustion engine operates at high temperatures, which allows the coolant to be used to warm the cabin in cold environments or rejected to the environment at higher temperatures. In more complicated combustion engine cooling systems, a separate lower temperature loop maybe used to provide coolant for a charge air cooler or water-cooled condenser. This separate coolant loop also would be operating at above ambient temperatures and could reject heat to the environment using a coolant to air heat exchanger. In the battery electric vehicle, the motor and power electronics can operate at higher temperatures, but the ideal battery temperature range is between 20 °C and 40 °C. This would require a refrigeration system to provide additional cooling for the battery because the ambient air may not be enough in warm environments.

Operating Temperatures for Vehicle Components

The differences in temperature requirements and operating conditions among the components in the BEV increase the complexity of its cooling system. Additional cooling is only required for the battery, so a separate cooling loop could be utilized for the battery linked to the refrigeration system. Cooling this smaller loop below ambient rather than the full cooling loop would require less energy to run the compressor, which increases the vehicle range. The requirement to heat the battery in cold environments would require either an auxiliary heater, operating the refrigeration system in a heat pump mode, utilizing waste heat from the motor and power electronics, or some combination of these strategies. To achieve these goals using a single system, multiple pumps and valves are necessary. More complex controls to route the coolant and optimize the pump speeds are required for efficient operation. In contrast, the combustion engine cooling system can typically be satisfied with a single coolant loop unless a charge-air-cooler requires additional cooling at a lower temperature.

Combustion Engine Cooling (Green) and Refrigeration (Magenta) Systems

BEV Motor Cooling (Green), Battery cooling (Teal), and Refrigeration (Magenta) Systems

How Simulation Is Used For Thermal Management System Designs

With the increased interaction between the vehicle systems in a BEV, an integrated system simulation is necessary for optimal design. Over a transient driving cycle, the thermal management of the battery and cabin need to be energy efficient to maximize the vehicle range. During a fast-charging event, the battery temperature needs to be carefully managed to prevent unnecessary cell aging. For a rapid acceleration or towing event, the motor and inverters need to be properly cooled to prevent component damage. GT-SUITE is the optimal simulation platform to manage these simulation needs by providing:

Industry leading sub-system models
GT-SUITE simulations are recognized across the automotive industry for their accuracy and flexibility. Our publications page highlights customer use cases for every vehicle system across the electrical, mechanical, thermal, fluid, chemical, and controls domains.
Detailed component models and real-time capability
GT-SUITE provides detailed simulations for individual components that will greatly enhance the model capabilities. For the battery and motor, the temperature distributions over a driving cycle or fast-charging event in a 3D finite element model can predict hot spots and the effects of different cooling strategies. Electro-chemical models of the battery can predict the cell aging over a vehicle life cycle.  In addition, the 3D cabin comfort model linked to GT-TAITherm can accurately predict occupant comfort over a wide range of vehicle conditions. These detailed models can be reduced to a real-time capable model for software or hardware in the loop simulations.
Robust model integration
GT-SUITE is designed to properly model the interaction between vehicle systems in an integrated model. For example, the heat generated within the motor and battery can be added as a source term in the thermal component models, with individual component temperatures used to calculate the correct performance within the electrical and mechanical system models. By building these sub-system models in the same tool, it is easy to model the interaction between them and change the simulation parameters for different analyses.

Closing Thoughts on Thermal Management System Design

The design of electric vehicles requires additional complexity for properly managing the battery, motor, power electronics, and cabin temperatures. The interaction between the single-phase and two-phase systems must be included to accurately predict the battery temperatures over a range of operating conditions. More complex controls are needed to create a robust and efficient system. Because of these complexities and enhanced interactions, simulation is necessary for system design. We will be expanding on these topics to discuss the component and system models in subsequent blog posts.

If you’d like to learn more or are interested in trying GT-SUITE to understand thermal management in ICE or xEV, Contact us!

Written by Brad Holcomb
This blog was originally published on May 26, 2021

How Simulation Can Increase Productivity in Electric Vehicle Thermal Management Design

What To Consider When Designing Thermal Management Systems in Electric Vehicles

Thermal management system design in the electrification era requires a complex approach to ensure vehicle performance and customer satisfaction. Electric vehicle’s (EV) system design and controls will influence the range of the vehicle, cabin comfort, and performance, and it is important to use a robust approach to understand how relationships and tradeoffs within these systems affect targets. Robust virtual analysis allows such studies to occur efficiently, from fast-running system-level models, to detailed thermal analysis of subsystems.

Simulation Can Assist EV Thermal Management Design Productivity

Critical systems in an EV, such as battery packs and integrated motors, require proper cooling to meet performance and range requirements. With the increasing scope of model capabilities, comes the need to be able to edit the boundary conditions and test cases quickly and efficiently for multiple party’s needs. GT-Play, a web-based interface for GT-SUITE, can directly assist with this using a central location for model download, analysis, and design decisions, with a select group of experts overseeing the model uses and capabilities.

Here’s a walkthrough of an integrated thermal model that has been uploaded to GT-Play for three different end users:

1. Vehicle Test Engineer: The test engineer needs an efficient way to compare experimental results with simulation results to validate the system-level model. This requires the ability to change test conditions and edit boundary parameters, but the engineer does not have experience with GT. To do this, they reached out to the modeling expert to build such a result within the GT-PLAY platform, as highlighted below:

Thermal Management Simulation case setup in GT-Play (Demo Setup)

Thermal Management Simulation case setup in GT-Play (Demo Results)

2. CAE Engineer: In this case, a design engineer needs to understand how their decisions with regards to a battery cold plate affect the thermal system performance. With the previous design built into a system model already, they communicated with the model expert and wanted to quantify the differences between the original design and the new layout. After reaching out to the model expert, they have received access to look at this study in GT-PLAY, highlighting the most important outputs. The general problem and setup are overviewed in the images below:

cae engineer thermal management simulation model

Battery cold plate thermal system model

3. Calibration Engineer: This engineer needs understanding of how different thermal control parameters will affect vehicle performance. Specifically, they need to understand how changing valve switching inputs will affect the cooling of critical components (battery, motors) versus how it will affect energy efficiency. Since testing this use case would be time intensive, the engineer reached out to the model expert to build this study virtually and gave them specific results they would need to decide. The controls that will be analyzed and reviewed are highlighted in the image below:

See How These Simulations Can Be Applied to EV Thermal Design in a Live Webinar

On September 7th, SAE and Gamma Technologies will offer a FREE, live webinar: ‘How to Increase Productivity in EV Design by Leveraging Thermal Simulation.’ In this webinar, we will discuss how critical systems, such as battery packs and integrated motors, can meet performance and range requirements through simulation. This starts with component selection and moving forward with detailed CFD analysis before being merged into a larger system using unique productivity tools.

This webinar will help you understand the process of model building, uploading, and analyzing in GT-PLAY for such a complex model. Also, you will further learn how these capabilities can be utilized within GT-SUITE through the three real-world use cases mentioned earlier.

Learn More about our Thermal Simulation Applications

View this curated page on our thermal simulation applications here.

Bio of Author:

Joseph Solomon is a Solutions Consultant at Gamma Technologies, focusing on electrification solutions. Joseph assists GT users in battery design, e-powertrain system analysis, thermal management, and controls development. In 2021, Joseph applied GT-AutoLion to complete his Masters in mechanical engineering at the University of Michigan, titled Investigating Lithium Ion battery performance with an electrochemical-mechanical model. Contact Joseph here!

Machine Learning Simulation: HVACR Industry

The HVACR Industry Is Evolving To Meet Climate Change Needs

Recently our colleagues from Gamma Technologies (GT) attended the 50th Herrick Conferences at Purdue University. These conferences occur bi-annually and cover a span of areas that are important to the heating, ventilation, air conditioning, and refrigeration (HVACR) industry including: pumps and compressors, refrigeration and air conditioning and high-performance building HVAC systems.

Some key takeaways from the conference were mostly points that the HVACR industry is being driven by new regulations that have been enacted to combat climate change as well as to increase efficiency of current and future vapor compression systems. These trends include:

A big emphasis on using low global warming potential (GWP) refrigerants that give the same performance as traditional refrigerants in vapor compression systems.
Heat pumps becoming more popular within the industry and can lead to a big boost in performance for many applications.

How Simulation System Modeling & Machine Learning Can Assist the HVACR Industry
To achieve practical viability and adoption of heat pumps and low GWP refrigerants in HVAC systems within the next decade, thousands of prototypes and experiments need to be performed. Fast, accurate and robust modeling and simulation can also aid in this endeavor and accelerate this process. There also needs to be extensive collaboration between industry, academia, and policy makers to comprehensively address these goals. An important tool available to industry, academia and policy makers is the huge amount of data already available from different sources. This data can be leveraged using machine learning tools to aid and enhance modeling as well as providing new physical insight into HVAC systems.

At the Herrick Conferences, many machine learning papers were presented. These were among 4 major categories. The paper numbers are provided in brackets. Reference these papers here (start at page 20).

Model Speedup: Transient Drive Cycle Simulation (2166), Screw Rotor profile for energy efficiency (1446), and Heat exchanger surrogate modeling (2396)
Developing Accurate Correlations: Friction factor and heat transfer correlations: (2120)
Physical Insight and Dimensionality Reduction: Reduced order model of unitary equipment (2386)
Fault Detection and Advanced Control: Fault classification and detection for AC systems: (2342 and 2351), Predictive Control in EV thermal systems (2484), Energy optimization of VCS (2411).

Read this blog on the growing roles simulation plays in this industry!

Applying Machine Learning to Thermal Model Simulations

We at GT presented a paper on machine learning under the model speedup umbrella titled, ‘Application of Feedforward Neural Networks to Simulate Battery Electric Vehicle Air Conditioning Systems’.

Figure: EV thermal system model with physics-based solution (red) and feedforward neural net (green)

Link to Paper

Link to Presentation

Learn More About our HVACR Simulation Solutions

See how GT-SUITE’s simulations solutions impact the HVACR industry here.

Accelerate Electric Aircraft Design Certification with Systems Simulation

Electric Aircraft Are the Future

The aviation industry has long imagined a future with electric aircraft. Development has started on smaller scales with aircraft such as electric vertical take-off and landing aircrafts (eVTOLs), short take-off and landing aircraft (e-STOLs), and others.

According to a May 2022 study by Aviation Today, e-aviation has potential of being a $200 billion-plus market. Recent industry forecasts predict the growth of the global electric and hybrid-electric aircraft propulsion system market as well as the global hydrogen aircraft market to reach $74.9 billion by 2035 and $144.5 billion by 2040 respectively.

Major trends of the development of e-aircrafts include:

Higher power density batteries
Increased safety standards to reduce battery thermal runaway
Shorter battery re-charge cycles with battery and hydrogen fuel-cell technology
Lighter, more efficient electric propulsion systems
Thermal management optimization

Integrating Simulation in E-Aircraft Development

It’s undeniable that e-aircrafts are making their impact into the next decade.

By integrating simulation into the design process, development and testing times can be drastically reduced. This allows engineers to arrive at optimal solutions.

Simulation tools such as GT-SUITE offers solutions to study many aspects of e-aircraft design (read this study on eVTOL design) from the overall vehicle system to battery pack electrical and thermal performance over flight missions.

Since safety is paramount for e-aircrafts, GT-SUITE can also predict thermal runaway of battery cells and packs. Engineers can study down to the battery cell level and use GT’s electrochemistry modeling capabilities to predict cycle life, which is crucial in understanding the economic feasibility of various e-aircraft types.

Ensuring Aircraft Design Certification with Simulation

Despite the technological and economic development with e-aircrafts, mass adoption still faces the pressures of regulation, Federal Aviation Administration (FAA) certification and rules governing airworthiness, traffic control technology and public concerns over noise and safety.

Certification is estimated to cost $1 million for a primary category aircraft (three seats or less), $25 million for a general aviation aircraft, and upwards of $100 million for a commercial aircraft.

GT-SUITE can help reduce certification costs and replace some testing requirements by being able to run large, fully detailed simulations within minutes, no matter an e-aircraft’s component sizing.

e-Aircraft Projects Using Multi-Physics Simulation

See how GT-SUITE ensure aircraft systems performance and safety certification here. This presentation provides a breakdown of three studies GT conducted with: Airbus A³ ,NASA, and Advanced Rotorcraft Technology (ART).

eVTOL pack thermal performance and state of charge (SOC) over real flight missions with Airbus A³

At the 2019 American Institute of Aeronautics and Astronautics (AIAA) P&E conference, GT presented a joint paper with Airbus A³ to study eVTOL pack thermal performance and SOC over real flight missions, compared to physical tests. The study also looked at battery SOH over different mission cycling and thermal management strategies.

Airbus A3 2019: eVTOL Battery Pack Thermal, SOC, & SOH study

Airbus A³ 2019: eVTOL Battery Pack Thermal, SOC, & SOH study

Battery Pack Thermal Runaway Simulation with NASA

GT modeled a thermal runaway simulation of a NASA Orion module battery pack presented at the 2020 Thermal & Fluids Analysis Workshop (TFAWS). This study looked at lithium batteries combined with electrochemical and thermal modeling techniques and tested assumptions within the battery pack. GT’s multi-physics simulation addressed various battery pack design challenges. These included: battery selection, development of the right pack geometry, performance, degradation, and overall safety.

Each 1D thermal runaway design takes about 15 minutes and enables users to experiment with battery thermal runaway uncertainties. On average, these models run roughly 2-4 times faster than real-time with just a laptop PC; resulting in a 30-minute simulation taking up to 7-15 minutes to run. Solely relying on 3D-CFD models won’t provide complete variability analysis.

Battery Thermal Runaway Simulation

Click here to watch this full presentation!

Learn more about our battery thermal simulation capabilities here!

Complete ‘rotor to battery’ simulation solution in collaboration with Advanced Rotorcraft Technology

At the 2022 Vertical Flight Society (VFS) Forum 78, GT collaborated with FLIGHTLAB – Advanced Rotorcraft Technology (ART) to highlight a complete ‘rotor to battery cell’ solution to address strict worthiness requirement for eVTOL design and development.

eVTOL electric propulsion system

Our multi-physics simulation-led approach analyzes the combined airframe and drive system to improve drive systems for electric and hybrid-electric aircraft. These efforts meet Urban Air Mobility (UAM) configurations.

Fusion of 1D system modeling with 3D detailed analysis – Courtesy of Advanced Rotorcraft

Read the full study here!

Learn more about e-aircraft simulation

Multi-physics Libraries with GT-SUITE

If you’d like to learn more about how GT-SUITE can be used to solve your e-aircraft simulation challenges, contact us here!

How a Catastrophic Ship Fire Reminded us Why Battery Thermal Runaway Simulation is Important

A few weeks ago, a 656-foot-long ship known as the Felicity Ace caught fire in the North Atlantic as it was transporting its cargo, including various automotive luxury brands, from Germany to Rhode Island. The fire broke out in the ship’s hold and continued to spread. Thankfully, all 22 crew members safely abandoned the vessel.

Of concern was the large number of electric vehicles on board this ship. Experts are still speculating if the cause of the fire was due to EV batteries. Nonetheless, unexpected battery fires are always a concern. The batteries complicated efforts in extinguishing the blaze said SMIT Salvage, the Dutch experts contracted to salvage the vessel. But as the ship eventually cooled down near a safe area off the Azores and began being towed, it “lost stability and sank,” according to the Portuguese Navy.

Why Battery Pack Safety for Electrified Vehicles (EVs) is Important

For years, there have been concerns over lithium-ion battery safety due to highly publicized thermal runaway events. This recent event brought to light the importance of battery safety and the need to use state-of-the-art engineering to mitigate the risk of thermal runaway.

Battery engineers are tasked with this challenging problem: to package a set of lithium-ion (Li-ion) cells as tightly as possible and minimize the amount of non-cell weight in the battery. In order to achieve this, engineers need to maintain proper temperature levels of cells, protect against premature cell degradation, and ensure safe operations.

How to Cost-Effectively Mitigate Battery Thermal Runaway

Historically, battery thermal runaway has been evaluated using physical testing—an expensive and dangerous endeavor.

With physical testing, costly prototype versions of the battery packs are assembled and thermal runaway is intentionally induced on a selected li-ion cell (either with a nail or by heating it to extreme temperatures).

A palatable, alternative solution is using simulation. The use of simulation throughout the design and development process allows for extensive testing, immediate results and analysis, and actionable feedback to ensure battery packs are engineered for optimal safety.

Here at Gamma Technologies, we offer GT-SUITE and GT-AutoLion, the ideal simulation design platforms to run virtual thermal runaway propagation tests. To learn more about our thermal runaway simulation capabilities, read this April 2021 blog written by GT’s own Joe Wimmer.

If you would like to learn more or are interested in trying GT-SUITE to virtually test a battery pack for thermal runaway propagation, Contact us!

Robust Battery Pack Simulation by Statistical Variation Analysis

Simulating Battery Packs – Not All Cells are the Same

When simulating a large battery module, typically we assume that all the cells in the module are going to be the same. However, that is not always the case. Factors such as the capacities and resistances of the cell can vary from cell-to-cell. This brings up the question – How can we model the variance of different cells within the module?

In v2021 of GT-SUITE, we added some new features to help model this cell-to-cell difference. One new feature is a new statistical variance analysis tool.

This new tool opens a wizard which walks users through choosing an object to select and vary the mean and standard deviation for the attribute variation. This will create unique parameters for each part associated with the object. To put it simply, each cell uses part overrides to define different capacities and resistances for each cell in a battery module.

Another new feature is in our design of experiments setup. The Monte Carlo method has been added as a new DOE distribution method in DOE Setup. This allows users to vary any parameter according to a normal distributed mean or standard deviation.

Let’s take a look at this example below.

In this example, we have a battery module with 444 cylindrical cells – 74 in series and 6 in parallel. We were told that the cell capacity was 3.2 Ah. Additionally, we were given distributions of the capacity and the resistance of 200 cells of the same type. The distributions followed a standard normal distribution and included the mean and standard deviation of the distribution.

With the statistical variance tool, we can add unique parameters for the capacity and resistance multiplier for each of the 444 cells with our simple wizard. Once that is done, we can open up DOE Setup and select how many experiments we want to run to see how the distribution of capacity and resistance affect our module. GT’s Monte Carlo solution enables normal distribution to be setup for the capacity and resistance multiplier parameters

After running the model, we can look at the responses for each individual cell in the module including the state of charge and total energy dissipated. The boundary conditions for this model include discharging at a 2C rate and using a simple thermal model of a 1-D convective boundary condition at an ambient temperature of 25^oC and a convective heat transfer coefficient of 10 W/m²K. The total energy dissipated varies by around 3% and the SOC varies by around 1% within the various cells in our module.

Those these results may seem small, but depending on the ambient temperature, discharging/charging protocols, or other factors, they can have a great effect on the battery module. In some instances, we might be able to see that one section of the battery module is heating much faster than another, meaning that some cells might need to be replaced faster than others or need to be designed to allow for more cooling compared to other areas of the module.

With these new tools and with GT-AutoLion, users can take large battery modules like these and analyze the responses of each individual cell and extrapolate to various C-rates and temperatures with physics-based modeling – allowing the user to gain more insight into their battery module, evaluate the battery degradation, and improve their BMS in the process.

Written by Vivek Pisharodi

Simulating Battery Thermal Runaway Propagation with GT-SUITE

Learn how to use a model to evaluate battery pack safety during thermal runaway events

Designers of battery packs are tasked with a very challenging problem: to package a set of cells as tightly as possible and minimize the amount of non-cell weight in the battery while maintaining proper temperature levels of cells, protecting against premature cell degradation, and ensuring safe operation. The last item on the list is, of course, the most important: ensuring that the battery pack is safe under any circumstance.

Undesired exothermic reactions heat up a battery cell during battery thermal runaway simulation

1C Discharge

The most common challenge to ensuring battery safety is thermal runaway. Thermal runaway is a phenomenon that occasionally occurs in Lithium-ion (Li-ion) cells when extreme temperatures are reached. During thermal runaway, undesired exothermic side reactions heat up the cell, and as the cell heats up, the rate at which the undesired reactions occur accelerates, eventually causing a catastrophic loop of events that concludes with a destroyed Li-ion cell and a lot of heat released. This loop of events is summarized in the image below.

There are many potential causes of thermal runaway. For example, if a cell is heated to extreme temperatures, thermal runaway can occur. If a cell is pierced by a nail or crushed, this can cause an internal short which eventually leads to thermal runaway. Other times, thermal runaway can occur for seemingly no reason at all – in these cases it is often manufacturing issues or even internal dendrite growth that lead to internal shorts inside the Li-ion cell.

With all these potential causes for thermal runaway, as a pack designer, how are you supposed to protect your cells from entering thermal runaway? The unfortunate answer: you can’t.

In fact, this is the wrong question for a pack designer to be asking. Because thermal runaway can occur for so many different reasons, and occasionally for no apparent reason, pack designers must assume that at some point a cell in a battery pack will enter thermal runaway. The correct question to be asking is, “Is my pack designed well enough to withstand one cell entering thermal runaway without starting a chain reaction of neighboring cells entering thermal runaway?”

Thermal Runaway Propagation

Thermal Runaway Propagation is the key phenomenon to consider when designing a safe battery pack, this refers to the event of a single cell entering thermal runaway, releasing a large quantity of heat, and heating neighboring cells to the point of thermal runaway, essentially starting a chain reaction in which all cells in a battery pack are eventually destroyed.

There are various levels of success for this type of thermal runaway propagation scenario. There are the intuitive “pass” or “fail” results where a “pass” would mean that after a cell enters thermal runaway, it does not cause a chain reaction and a “fail” would mean that after a cell enters thermal runaway, it does cause a chain reaction. There is a less intuitive middle ground in these scenarios, too. For instance, maybe a chain reaction is set off, but the time delay between the first cell entering thermal runaway and the entire battery pack being destroyed is a long period of time, this may also be a “passing” result, depending on the application. If a cell is sensed to have entered thermal runaway while a vehicle is at highway speeds, does a family have enough time to stop and safely exit the vehicle before a fast-moving chain reaction is set off? If a cell enters thermal runaway during an EVTOL flight, is a pilot able to land before the chain reaction becomes unstable?

Without Simulation

Testing the design of a battery pack against thermal runaway propagation is an expensive and dangerous endeavor. First, expensive prototype versions of battery packs must be assembled, then a single cell is selected (either at random or with engineering discretion to determine which would be most likely to cause the undesired chain reaction), and finally thermal runaway is intentionally induced on the selected cell (either with a nail or by heating it to extreme temperatures). After that, it is up to the design of the pack to determine whether or not neighboring cells enter thermal runaway, and if they do, how fast.

This experimental setup has two major downsides. First, battery packs are expensive, and prototype versions of battery packs are even more expensive. To build these and intentionally destroy them can result in a high cost for battery safety testing. Second, this physical test is often done very late in the development cycle for the battery-powered product (e.g battery electric vehicle, EVTOL, electric bicycle). If a battery pack fails this test, it can be a major setback for the release schedule of the product, which can be detrimental to businesses.

With Simulation

Using simulation to run virtual thermal runaway propagation tests for Li-ion battery packs is a great way to avoid the costs and risks associated with experimental testing. In addition to that, single cells do not need to be picked out at random. Instead, multiple tests can be setup testing the “what if” scenario for every cell in a battery pack.

GT-SUITE is the ideal platform to run virtual thermal runaway propagation tests.

Modeling Thermal Runaway Propagation in GT-SUITE

In a paper published with NASA, who has extensive experimental data on thermal runaway of Li-ion cells, GT-SUITE was used to model the propagation effect of thermal runaway in a small battery module. The thermal runaway propagation model was built by converting CAD geometry and validated with experimental data.

Nominal Electrothermal Model of Battery Module

The study shows a number of test cases, including two of the battery modules during normal operation, which do not have cells entering thermal runaway. The animation below shows one of these tests, a battery module discharging at a C-rate of 1C. In the animation below, the blue – red contour animates local temperatures where blue is cool temperatures and red is hot temperatures. From the animation below, we can see the battery slowly warms up while being discharged at 1C.

1C Discharge

To take this electrothermal battery model and setup the thermal runaway propagation model, a few extra steps were required.

Cell-Level Experimental Thermal Runaway Tests

NASA has created specialized bomb calorimeters that impose thermal runaway on a single cell through a variety of causes (internal short, nail penetration, excessive heating). With this type of cell-level testing, NASA was able to measure the amount of energy released during a thermal runaway event. Some example results from their testing of cylindrical cells are shown below.

Alterations to Battery Model

The nominal battery model that was setup for the previous electrothermal model was upgraded to include a model of thermal runaway. This included the following changes:

The Trigger: If any jelly roll temperature rose above 180°C, the cell would immediately enter thermal runaway
The Heat Release: Once a cell entered thermal runaway, the cell would release energy in the form of heat (in this case 70 kJ)
- 40% of the heat released would be absorbed by the jelly roll in 1.5 seconds
- 60% of the heat released would be released as ejecta in 1.5 seconds
The Electrical Disconnection: Once a cell entered thermal runaway, it would no longer participate in the module, which means the neighboring cells, which are placed in parallel, would have more current flowing through them.

Module-Level Model of Thermal Runaway Propagation

Once these alterations were made to the battery model, any cell in the module can be selected as the “trigger” cell by applying an external heat until the trigger temperature of 180°C is reached.

In the first study, a cell in the corner of the module was selected to be the trigger cell. It was artificially heated to its runaway temperature of 180°C and it immediately entered thermal runaway. The animation below shows the results of the thermal runaway simulation with the corner cell (top of the image) selected as the trigger cell. Once again, blue cells are relatively cool and red cells are hot. From viewing the animation, we can see that the corner cell does not cause a chain reaction of neighboring cells entering thermal runaway.

Corner Cell Trigger

Since no real battery modules were destroyed in this simulation, this simulation can be repeated under different conditions. The next study conducted was to test how the module behaves when a cell in the center of the module enters thermal runaway. The image below shows how the module reacts when a cell in the center of the module is the trigger cell for thermal runaway. Once again, we can see that the thermal runaway event does not propagate to neighboring cells.

Middle Cell Trigger

The virtual thermal runaway propagation tests shown above both show “passing” results. The trigger cell self-heats to extremely high temperatures; however, the neighboring cells do not pass the 180°C threshold to enter thermal runaway.

In order to illustrate a “failing” test result, some changes were made to the module to make it more likely to propagate thermal runaway to neighboring cells. The busbar in the module was included, which increased the amount of heat conducted between neighboring cells. Additionally, the ratio of self-heat and heat released as ejecta was altered to be 30%-70% instead of the 40%-60% previously mentioned.

With these changes, the following results were observed. In this case, the trigger cell very quickly causes a chain reaction among neighboring cells and causes a much more catastrophic event than the previous two test cases presented.

failing battery thermal runaway simulation

Failing Test

Time-to-Results

Because time is one of the most important resources of battery pack designers, one of the key considerations when faced with a modeling challenge such as thermal runaway propagation is the total time that it takes to get results. The time that it takes to get results is the sum of the time it takes to build a model (“time-to-model”) and the time that it takes the model to run (“time-to-run”). With GT-SUITE, both time-to-model and time-to-run are minimized.

Time-to-Model

In the examples given above, the CAD geometry was converted into a GT model using GT’s built-in CAD geometry pre-processing tool GEM3D and models were further setup using GT’s integrated simulation environment in roughly half of a day.

Time-to-Run

In the examples given above, the models run roughly 2-4 times faster than real-time on a laptop PC, resulting in a 30-minute simulation taking 7-15 minutes to run. The finite element structure in this model consisted of 6,000 nodes and 13,000 elements.

This fast time-to run enables users to experiment with some of the uncertainty that comes with battery thermal runaway propagation. Which cell initiates thermal runaway? How much heat does it release? How is that heat released? How much material is ejected from it? All of these are sources of variability that can be explored with the help of fast-running models (look for a future blog on this specific topic!). This type of variability analysis would not be possible when using extremely detailed 3D CFD models.

Conclusion

When designing a battery module or a battery pack, the battery’s response to a cell entering thermal runaway needs to be studied to analyze whether or not the cell causes a chain reaction of cells entering thermal runaway, known as thermal runaway propagation. This can be done experimentally by building prototype modules and packs and imposing thermal runaway on a trigger cell; however, this can be extremely expensive and if the pack fails the test, can be a substantial setback in the development of the battery.

With GT-SUITE, these thermal runaway propagation tests can be done virtually. This provides a number of advantages, including the large cost advantage and the ability to run any number of hypothetical thermal runaway propagation tests.

If you’d like to learn more or are interested in trying GT-SUITE to virtually test a battery pack for thermal runaway propagation, Contact us!

Written by Joe Wimmer and Jon Harrison

How to Reduce Battery Charging Time While Maximizing Battery Life

In the age of electrification, promising technologies like battery electric vehicles and electric aircrafts are coming into the forefront of societal advancements. However, one major hurdle in electrification is speeding up the vehicle battery charging time. Re-fueling conventional vehicles and aircrafts generally takes minutes, whereas electric vehicles and aircrafts can take hours. For airlines, this down time can be very expensive, and a long charging period has been shown to reduce the likelihood of consumers purchasing pure electric vehicles.

To combat this, battery engineers are exploring options to reduce the amount of time required to charge a battery. Unfortunately, fast charging Li-ion batteries can cause premature battery degradation by initiating lithium plating, so an aging cost for fast charging must also be considered. Because of this, the system manufacturer (i.e automotive OEM, aircraft OEM, power tool OEM, or consumer electronics OEM) has to strike a balance between decreasing time required to charge a battery and the expected life of the battery (which can have a great effect on brand perception).

There are various charging protocols that both improve the battery life and shorten the charging time, when compared to traditional charging protocols. The effects of these charging protocols vary from cell to cell and need to be tested individually to fully understand their effects on cell charging time and degradation rate.

Testing and optimizing charging protocols is extremely resource intensive because it intentionally degrades lithium-ion cells, which can be expensive and time consuming. GT-AutoLion helps reduce this cost by supplementing experimental tests with virtual tests.

What is Lithium Plating?

One of the key contributors to battery degradation that comes with fast charging is lithium plating. Lithium plating is the reduction of lithium ions into lithium solid. It is caused when the potential in the anode falls below zero volts and cycling lithium-ions (Li⁺) reacts with electrons (e^–) to form lithium metal (Li⁺ + e^– -> Li). This lithium metal is deposited into the anode, lowering the porosity of the anode. Because Lithium-ions are consumed in this reaction, it decreases the capacity of the cell. Additionally, because it lowers the porosity of the anode, it increases the resistance of the cell.

Lithium plating occurs most frequently when Li-ion cells are charged with very high currents, especially at low temperatures.

Figure 1, taken from a paper using GT-AutoLion, shows how GT-AutoLion can be used to match experimental data of capacity fade with its built-in model for lithium plating. With this model, GT-AutoLion allows engineers to virtually test various charging strategies and their effect on both charging time and cell degradation.

Figure 1. Overview of lithium plating in a cell

Example Charging Protocols

A charging protocol is an algorithm which defines the charging methodology of a cell. Each charging protocol has different implementation costs and unique implications on charging time and cell degradation. Figure 2 summarizes three of the most common charging protocols.

The most common charging protocol is a constant-current-constant-voltage (CCCV) charge. During a CCCV charge, the cell is charged with constant current until a certain max voltage is reached. After, the cell discharges while maintaining the voltage at the previous max voltage, as shown in Figure 2 (left). A CCCV protocol is considered to be the simplest, safest, and most widely-used protocol to implement.

In boost charging (BC), the cell is charged with a constant boost current that is significantly higher than the subsequent constant current charge. The cell then discharges while maintaining a constant voltage. The BC protocol is shown in Figure 2 (middle). Implementing a BC protocol can decrease charging time without potentially losing cycle life.

Pulse charging (PC) is another charging protocol that can also be used. During PC, the current alternates between a high current and a low current and the voltage increases until an upper cutoff voltage is reached, as shown in Figure 2 (right). Pulse charging can reduce resistance due to diffusion, which reduces charging time and aging and improves the cycle life of a cell.

Fast Charge Strategy Development in Real-World Aging Simulation

While various charging patterns can be studied experimentally, these experimental tests often are not reflective of the real use case a battery may see in a vehicle, aircraft, power tool, or consumer electronic. As presented in a previous blog, GT-AutoLion and GT-SUITE can be used together to predict how a Li-ion battery will degrade over time while considering any use case such as various load profiles, drive cycles, and weather conditions. These analyses can also be upgraded to test the effect of the charging protocol on real-world charging time and battery degradation.

Conclusion

With GT-AutoLion and GT-SUITE, system manufacturers better understand the tradeoff between reducing the time required to charge a battery and maximizing the life of a battery. This tradeoff is imperative to understand because it has a profound effect on customer satisfaction and brand perception.

Written By: Vivek Pisharodi[/vc_column_text][/vc_column][/vc_row]

Digital Twin Simulation: Engineering Smarter, Faster, and More Reliable Products

Why Digital Twins are Necessary and Important

How Digital Twins Help Address Major Challenges

How Simulation is Making an Impact

“How to Guide” for Building a Digital Twin

Case Studies: Real-World Applications of Digital Twins

Learn More About our Digital Twin Solutions

Simulation for the HVACR Industry: How to Leverage Machine Learning

The HVACR Industry is Evolving

How Systems Simulation Modeling & Machine Learning can Assist

Applying Machine Learning to Thermal Model Simulations

Learn More About our HVACR Simulation Solutions

Gamma Technologies and GT-SUITE: Pioneering the Future of Simulation

Unveiling the Power of GT-SUITE

Expanding Horizons: Aerospace, HVACR, Marine, Energy, and Beyond

A Legacy of Innovation

Looking Ahead

Learn More About Gamma Technologies’ Simulation Solutions

Top 10 Gamma Technologies Blogs of 2023!

From calculating EV range to heat pump design, there is a blog for every simulation!

Learn more about our simulation solutions!

Using Simulation for Battery Engineering: 15 Technical Blogs to Enjoy

Mitigating the Domino Effect of Battery Thermal Runaway with Simulation

What Happens During Battery Thermal Runaway?

How to Avoid Battery Thermal Runaway

Experimental Costs of Testing Thermal Runaway

History of Simulating Thermal Runaway Propagation with GT-SUITE

Cell-level Thermal Runaway Propagation Enabled by P2D Electrochemical Modeling Together with Chemical Reactions

Simulating a Module-Level Thermal Runaway Propagation

Thermal Runaway Case Studies

Learn More About our Battery Thermal Runaway Solutions

Top 10 Gamma Technologies Blogs of 2022!

From battery thermal runaway to fleet route optimization, there is a blog for every simulation!

Other Gamma Technologies Blogs to check out in 2022!

Learn more about our simulation solutions!

How Simulation Is Used To Design ICE vs. Battery Electric Vehicle Thermal Management Systems

Understanding Vehicle Thermal Management

Similarities Between ICE vs. Battery Electric Vehicles Thermal Management Systems

Differences Between ICE vs. Battery Electric Vehicles Thermal Management Systems

How Simulation Is Used For Thermal Management System Designs

Closing Thoughts on Thermal Management System Design

How Simulation Can Increase Productivity in Electric Vehicle Thermal Management Design

Machine Learning Simulation: HVACR Industry

Accelerate Electric Aircraft Design Certification with Systems Simulation

How a Catastrophic Ship Fire Reminded us Why Battery Thermal Runaway Simulation is Important

Why Battery Pack Safety for Electrified Vehicles (EVs) is Important

How to Cost-Effectively Mitigate Battery Thermal Runaway

Robust Battery Pack Simulation by Statistical Variation Analysis

Simulating Battery Packs – Not All Cells are the Same

Simulating Battery Thermal Runaway Propagation with GT-SUITE

Learn how to use a model to evaluate battery pack safety during thermal runaway events

Thermal Runaway Propagation

Without Simulation

With Simulation

Modeling Thermal Runaway Propagation in GT-SUITE

Nominal Electrothermal Model of Battery Module

Cell-Level Experimental Thermal Runaway Tests

Alterations to Battery Model

Module-Level Model of Thermal Runaway Propagation

Time-to-Results

Time-to-Model

Time-to-Run

Conclusion

How to Reduce Battery Charging Time While Maximizing Battery Life

GT News