Everything You Wanted to Know About Sodium-Ion Batteries

From Lithium-Ion to Sodium-Ion Batteries: A New Era in Battery Technology

As the demand for energy storage continues to rise, sodium-ion batteries (NIBs) are gaining momentum as a compelling alternative to lithium-ion batteries (LIBs). Leveraging more abundant and cost-effective materials, NIBs are especially well-suited for low-speed electric vehicles—where range is less critical and affordability is key—as well as for renewable energy systems and other large-scale applications. Here’s why sodium-ion technology is drawing increasing interest:

1. Cost-effective & abundant materials

Sodium is far more abundant and significantly cheaper than lithium. This makes NIBs highly attractive for large-scale use, particularly where affordability and raw material availability are key concerns, such as in grid storage and renewable integration.

2. Environmentally friendly

Sodium is easier to extract and process, which results in a smaller environmental footprint. NIBs align well with global efforts to transition toward cleaner, more sustainable energy technologies.

3. Safety & stability

NIBs offer better thermal stability than LIBs. They can safely be discharged to zero volts and use thermally stable sodium salts that generate fewer hazardous byproducts. Their slower heating rates and delayed self-heating under stress conditions make them a safer option, particularly in high-temperature or abusive environments.

4. Cold climate performance

Unlike LIBs, Na-ion cells consistently maintain performance in cold temperatures. They are less prone to electrolyte freezing and capacity loss, making them ideal for use in harsh environments.

How do Sodium-Ion Batteries Work?

Sodium-ion (NIBs) operate on electrochemical principles similar to LIBs. During charging, Na⁺ ions move from the cathode to the anode; during discharge, they travel back to the cathode. This process closely resembles the ion movement in LIBs, as illustrated in Figure 1. The materials, however, differ. A typical Na-ion battery includes:

• Cathode: Common materials include layered metal oxides, polyanionic compounds, or Prussian blue analogs.
• Anode: Hard carbon is widely used due to its structural stability and compatibility with sodium.
• Electrolyte: Sodium salts like NaPF₆ or NaClO₄ in carbonate solvents.
• Separator: Same as in LIBs—allows Na-ion transfer while preventing short circuits.
• Current collectors: Aluminum is used for both anode and cathode, lowering material costs compared to copper-based Li-ion systems.

What are the Key Challenges Facing Sodium-Ion Battery Technology?

While sodium-ion technology is promising, it still faces several technical challenges before achieving widespread commercialization:

• Lower energy density: NIBs typically offer energy densities between 100-160 Wh/kg, which is lower than that of LIBs. This makes them less ideal for high-performance electric vehicles or aerospace applications, in which compact size and high energy-to-weight ratios are required.
• Cycle life: Sodium ions are larger and heavier than lithium ions, which causes more mechanical stress during cycling and leads to faster material degradation. Improving cycle durability is essential for NIBs to compete in long-term applications.
• Scaling production: As NIBs are still in the early stages of mass production, improving process efficiency and developing industry standards are key to driving down costs and accelerating adoption.
• Lower operating voltage: NIBs generally operate at lower voltages, reducing energy output per cell, and often requiring more cells in series.

How to Model Sodium-Ion Batteries using GT-AutoLion

At Gamma Technologies, we are enabling the advancement of Na-ion technology through high-fidelity electrochemical simulation with GT-AutoLion. Using a robust pseudo-2D (P2D) framework, GT-AutoLion allows users to simulate Na-ion cells with detailed physics-based models that help optimize performance, thermal behavior, and safety.

To illustrate sodium-ion behavior, AutoLion-1D includes an example model based on NVPF/hard carbon chemistry. Figure 2 shows the calibration of this model against experimental data at different C-rates.

The Future of Sodium-Ion Battery Technology

While LIBs dominate the market, NIBs are emerging as a strong competitor, especially in applications where cost, resource availability, and safety are top priorities. With strengths in large-scale energy storage and reliable cold-weather performance, NIBs represent a promising alternative for the future.

Rapid progress in NIB research is improving their performance and durability. Physics-based simulation tools like GT-AutoLion are essential for bridging the gap between NIBs and LIBs by helping engineers design safer, more efficient, and higher-performing NIBs for real-world use.

Ready to shape the future of energy storage? With GT-AutoLion, you can refine your NIB designs and stay ahead in this fast-evolving market. We’re here to support your journey and help push the boundaries of innovation in energy storage.

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 and  contact us to see how Gamma Technologies can support your battery development goals.

References

[1] Yu, Dandan, et al. “Low‐Temperature and Fast‐Charge Sodium Metal Batteries.” Small 20.30 (2024): 2311810.
[2] Zhao, Lina, et al. “Engineering of sodium-ion batteries: Opportunities and challenges.” Engineering 24 (2023): 172-183.
[3] Iwan, Agnieszka, et al. “The Safety Engineering of Sodium-Ion Batteries Used as an Energy Storage System for the Military.” Energies 18.4 (2025): 978.

 

Fuel Cell System Modeling: Powering the Future of Hybrid Locomotives

Transitioning from Diesel to Hydrogen Locomotive Power: Modeling the Future of Rail Transport

Can a fuel cell-powered locomotive haul freight as reliably as diesel while significantly reducing emissions?

 

As the transportation sector accelerates toward net-zero goals, hydrogen fuel cells are emerging as a clean alternative to conventional diesel locomotives. In fact, the hydrogen fuel cell train market is projected to generate $653.6 billion in cumulative revenue by 2038, with unit sales growing at a compound annual growth rate (CAGR) exceeding 100%, clearly signaling global momentum.

Yet, designing an efficient and reliable hybrid fuel cell-battery system for rail applications is no easy task.

This is where GT-SUITE plays a critical role. It offers a unified simulation platform to model, analyze, and optimize the dynamic interaction between fuel cells, batteries, cooling systems, and traction power demands under realistic operating conditions.

Customer Spotlight: Wabtec Corporation

Wabtec Corporation, a respected leader in the global locomotive industry, presented their innovative fuel cell powertrain work at Gamma Technologies’ technical conference. This blog summarizes their study to help you better understand the modeling objective, fuel cell-battery control strategy, and key insights gained using GT-SUITE. To access the complete presentation, click here.

Optimizing the Power Split Between Fuel Cells and Batteries in Hybrid Locomotives

The primary objective of this simulation study was to optimize the power split between the PEM (Proton Exchange Membrane) fuel cell system and the traction battery to fulfil the power demand of the locomotive’s traction motor under realistic route conditions.

The traction battery acts as a secondary energy source, supporting peak load requirements and supplying power during low-demand phases.

A 1D simulation model of the hybrid locomotive powertrain was developed in GT-SUITE to simulate power distribution and energy flow across a representative rail route.

Simulation Framework for Hybrid Hydrogen Locomotives

The GT-SUITE model integrates multiple physical domains in a single simulation environment.

Key Inputs:

1. Throttle and dynamic braking data for selected routes
2. Ambient conditions (pressure and temperature) for each route
3. Battery specifications, including charge/discharge limits
4. Empirical model of fuel cell stack performance and control logic

Simulation Configuration:

• Route data: 5 rail routes selected based on typical duty cycles
• Power profiles: Time-dependent notch profiles for both throttling and dynamic braking
• Weather conditions: Summer and winter ambient profiles
• Fuel Cell Module Variants: 3 configurations delivering net power comparable to a diesel locomotive
• Battery Pack Options: 3 configurations plus one baseline case without a battery

 

Simulations for Route-Based Hydrogen Powertrain Performance

The following configurations were studied for a single route simulation:

• Fuel Cell Power Rating as a percentage of diesel engine equivalent: 80%, 100%, 120%
• Battery Power Rating as a percentage of diesel engine equivalent: 0%, 1.5%, 3%, and 6%

Simulation Outputs and KPIs

The model provided detailed insights into:

1. 1. Total hydrogen consumed
   2. Power supplied by the battery
   3. Power loss in the battery system
   4. Power generated by the fuel cell
   5. Power recovered through regenerative braking
   6. Power delivered to the traction motor
   7. Power loss at the traction motor

Key Insights from Fuel Cell Locomotive Modeling

• Increasing battery capacity helps shift the fuel cell’s operating region toward higher efficiency, improving route-specific hydrogen consumption.
• Power deficits decrease with larger battery configurations, as expected.
• A careful trade-off between fuel cell sizing and battery cost (initial and operational) can help determine optimal hybrid configurations.
• GT-SUITE enables estimation of fuel economy, power deficits, and component interactions, offering an efficient way to right-size the hybrid powertrain.
• The simulation framework is scalable to multiple routes and environmental conditions, making it highly adaptable for feasibility studies.

Why Use GT-SUITE for Hydrogen Train and Rail Electrification Projects?

GT-SUITE provides a holistic modeling platform for virtual prototyping and optimization of fuel cell-electric locomotives. Its capabilities include:

• Electrochemical modeling of hydrogen PEM fuel cells
• Battery system dynamics, including thermal and aging effects
• Mechanical and thermal subsystem modeling, such as cooling circuits and lubrication systems
• Full system-level simulation of train powertrains, including control strategies and energy management

This allows engineers to virtually test fuel cell vs battery performance and make informed design decisions before committing to physical prototypes.

Conclusion: Advancing Clean Rail Transportation with Simulation

Simulation accelerates innovation. With GT-SUITE, engineers can explore the full design space of hybrid hydrogen locomotives, optimizing component sizes, control logic, and energy flow management. This empowers rail operators to confidently pursue clean transportation technologies and reduce reliance on fossil fuels. Learn how simulation supports cleaner rail strategies in our blog “How to Model Fuel Reformers with Simulation“, and watch the “GT Webinar – Fuel Cell Fault Simulation and Detection for OBD Using Real-Time Digital Twins” to see how digital twins enable predictive maintenance and regulatory compliance or contact us to see how Gamma Technologies can support your fuel cell development goals.

How Multi-scale Models Are Enhancing Battery Performance and Design

Beyond Experimentation: Predicting Battery Performance with Multi-Scale Models

In today’s electrified world, designing better batteries goes far beyond trial-and-error testing. Engineers and researchers are increasingly turning to simulation to accelerate innovation and reduce development costs. Lithium-ion batteries power modern energy storage systems, from electric vehicles to grid storage. As demand grows for higher performance, longer lifespan, and improved safety, accurate battery modeling becomes increasingly important. A promising path forward involves uniting atomic-scale simulations with continuum models (multi-scale modeling) to enhance the fidelity of performance predictions.

Enhancing Battery Design Through Atomic and Continuum Model Integration

Multi-scale modeling approach accelerates the design of new battery materials, reducing reliance on time-consuming and expensive experimental testing. It enables:

• Faster material development: Atomic-scale simulations allow rapid exploration of new electrolytes and additives.
• Better battery optimization: Engineers can fine-tune battery performance for specific applications, such as high-power EV batteries or high-energy grid storage solutions.
• Improved safety and longevity: Accurate predictions help optimize electrolyte formulations, reducing risks associated with lithium plating and thermal runaway.

The Role of the Electrolyte in Battery Simulation

Achieving high predictability in battery modeling requires a deep understanding of each cell component, particularly the electrolyte, which is essential for lithium-ion transport between the electrodes. The electrolyte significantly impacts overall battery performance by influencing internal resistance, voltage behavior, and degradation over time. However, key electrolyte properties, such as ionic conductivity, lithium transference number, and diffusivity, are difficult to measure and highly sensitive to variables like temperature, concentration, and interactions with electrode materials.

Understanding Atomic-Scale Simulations in Battery Modeling

Continuum models—mathematical approaches that simulate battery behavior at the cell or system level by treating materials as continuous substances—such as the pseudo-two-dimensional (P2D) model, rely heavily on these properties to simulate ion transport and electrochemical behavior. Inaccurate values may lead to significant errors in predicting concentration gradients, voltage losses, and overall battery performance. Atomic-scale simulations—methods that simulate battery behavior at the molecular (or atomic) level by modeling individual particles or molecules—help estimate electrolyte properties across a wide range of conditions, improving the accuracy of continuum models and reducing reliance on experimental measurements.

Combining Atomic-Scale and Continuum Models for Better Insight into Battery Performance

To generate accurate electrolyte property data across various conditions, Gamma Technologies has collaborated with Compular, a company specializing in atomic-scale simulations. By integrating Compular’s molecular dynamics (MD) simulations with our GT-AutoLion continuum model, it is possible to directly calculate fundamental electrolyte properties from atomic-level simulations.

To integrate Compular’s simulation with Gamma Technologies’ (GT) GT-AutoLion, an electrolyte with an additive (LiPF6 in EC:PC:EMC, 1:3:8 by volume, with 2% FEC) is simulated across varying salt concentrations and temperatures to study the performance of both high energy and high power-dense cells.

GT-AutoLion utilizes a physics-based P2D model to simulate battery charging and discharging. It divides the battery into three main regions—anode, cathode, and separator—discretizing them to capture critical electrochemical interactions (Figure 1). However, to ensure the accuracy of these models, precise electrolyte data is necessary. This is where MD simulations come into play.

Compular Lab models electrolytes at the molecular level using MD simulations, simulating systems with about 5000 atoms. These atoms move according to the laws of physics, and we track their motion over time to observe how they interact. The simulations run at specific temperatures and salt concentrations, and for each condition, we record around 15 nanoseconds of ion movement.

To extract useful data from these simulations, Compular’s CHAMPION, a software tool, analyzes how ions move together. It calculates what’s called Onsager coefficients, which describes how different ions affect each other’s motion (see Figure 2). From these, we derive four key transport properties:

• Ionic conductivity: how efficiently ions carry electric charge through the electrolyte
• Salt diffusivity: how fast ions spread out in the electrolyte
• Transference number: the fraction of the current carried by the cation
• Thermodynamic factor: how ion–ion interactions influence diffusion and concentration behavior

Typically, calculating these properties requires long simulations because random motion (or “noise”) from non-interacting ions makes it harder to get accurate results. But CHAMPION improves efficiency by focusing only on the meaningful interactions between nearby ions, reducing the required simulation time by about 90% to approximately 10 nanoseconds.

The Workflow: From Atomic Interactions to Battery Performance

Here’s how the combined modeling approach works:

1. Molecular Dynamics Simulations: Compular’s lab tool runs MD simulations to extract crucial electrolyte properties like conductivity and diffusivity. These simulations provide insights into how electrolyte composition affects battery behavior, particularly under different temperatures and salt concentrations.
2. Integrating Data into GT-AutoLion: A Python script transfers the extracted electrolyte data into the GT-AutoLion simulation framework. The data is structured into a reference object (XYZMap) that incorporates temperature/concentration-dependent electrolyte properties.
3. Simulating Battery Performance: Using these electrolyte properties, GT-AutoLion predicts voltage vs. capacity curves for both energy-dense and power-dense cells. This allows for comparative analysis under varying temperatures and charging rates.

Simulation Findings: Electrolyte Properties and Their Impact on Battery Performance

When we tested these models, we found some interesting trends.

MD simulations reveal the following (Figure 3):

• Ionic conductivity and salt diffusivity decrease at lower temperatures, affecting overall battery efficiency.
• There is an optimal salt concentration (~1 M) that maximizes conductivity.
• Transport properties vary significantly with electrolyte composition and temperature, influencing battery performance.

Electrochemical Battery Modeling using GT-AutoLion

By incorporating atomic-scale insights, GT-AutoLion enables:

• Accurate predictions of voltage vs. capacity for power-dense and energy-dense cells.
• Insights into how electrolyte behavior impacts capacity, especially under high C-rates and low temperatures.
• A better understanding of trade-offs between power and energy density in different applications.

Conclusion: The Future of Battery Simulation

The progression of battery technology will rely heavily on the integration of multiscale simulation techniques that connect molecular-level behavior with system-level performance. With enhanced prediction accuracy and faster innovation, the future of lithium-ion is brighter than ever. If you are interested to read more about battery modeling you can read blogs on using simulation for battery engineering and watch webinar on “Machine Learning for Fast, Integrated Battery Modeling” or  contact us to see how Gamma Technologies can support your battery development goals.

Using Toshiba’s Battery Electrochemical Models to Make System-level Decisions Faster

Eliminate the Guess Work in Battery Modeling Selections 

System-level design engineers have a difficult task. They deal with challenging questions such as: What Lithium-ion cells work best for a particular application; How many cells should be placed in series and parallel; or What type of motor and drive technology should be used? They have to make these decisions with little information about the actual components and before any prototypes. Often, important decisions with long-lasting consequences are made with “rules of thumb” and shortcuts such as; let’s use the component-level data from the previous generation.

This blog will focus on two of these challenges:

• What Lithium-ion cells work best for a particular application?
• How many cells should be placed in series and parallel?

To help engineers answer these questions, we’ve partnered with Toshiba Corporation. Toshiba provides the SCiB™ Lithium-ion cells that use lithium titanium oxide (LTO) anodes for superior safety, long life, rapid charging, and excellent performance at low temperatures. Within the SCiB™ product lineup, Toshiba provides a full spectrum of cells ranging from high-power cells (2.9 Ah, 10 Ah), high-energy cells (20 Ah, 23 Ah), and combination cells (20 Ah-HP). 

For cell selection and battery sizing, a system-level engineer might start by using models and data from batteries used in previous generations of similar products. Additionally, if specification sheets for Lithium-ion cells are available, they can be used to calibrate electrochemical models. However, Toshiba, a long-time user of Gamma Technologies’ (GT) GT-AutoLion, wanted to provide more than just a specification sheet and provide their clients with encrypted GT-AutoLion models. 

Mission – Tugboat

Tugboats are key for the navigation of large and bulky vessels in the narrow water channels of typical ports. Our today’s mission is around a battery-electric tugboat that is responsible for the towage of large vessels and consist of a series of phases:

• Transit phase: transit from the tugboat pier to the calling vessel
• Towing phase: towage of the vessel from the dock and out of the port
• Return phase: returning to the tugboat pier and charging station

We will take the view of an electrical system engineer at a tugboat building company, going through the cell selection and pack sizing process with these models provided by Toshiba. The system we are modeling is a battery-electric propulsion tugboat operating in a port requiring idling, transit, and towing maneuvers for multiple large vessels in a single-day of operation.

To illustrate the mission, we’ve overlaid arrows over a Google Maps screenshot of the Hamburg harbor, along with the target speed vs. distance profile we’ve applied to the model (Figure 1).

 

This mission is repeated four times over the course of a 15 hour workday, meaning that the tugboat will have 3 hours and 45 minutes to accomplish the mission and recharge the battery before being sent on the next mission.

Tugboat Simulation Model

The system-level model of the tugboat consists of the following components:

• 42m boat hull with a displacement of 280t
• Propulsion:
  • Two 2.4 m azimuth thruster
  • Two 2.7 MW Permanent magnet synchronous machines
  • Maximum bollard pull of 58 tons
• Genset:
  • Diesel genset with 1000/1260 kW @ 50/60 Hz
• A battery that needs to be selected and sized (will be described in the text below)

These components are arranged according to the single-line diagram below (Figure 2). In this system design we assume a DC link between the components. In case of an AC link design, additional switchboards would need to be integrated. In the image, “ESS” represents the electrical storage system (battery), “Shore Supply” is the onshore power supply (for cold ironing and battery charging), and “Hotel Load” stands for the onboard electrical power consumers.

 

The model, built in the simulation platform GT-SUITE, is shown below (Figure 3). Please note the yellow links represent electrical connections and the black links represent mechanical connections. Additional thermal components and connections can be integrated to include thermal dependencies in the model and the warm-up of components.

Please note, that the four bulk carriers displayed in the image will be towed individually throughout a 15-hour workday. Each bulk carrier is being modeled as a passive load weighing nearly 47,000 tons.

GT-AutoLion Model

The Toshiba-provided model of the SCiB cell is built using GT-AutoLion, which follows the principles of the pseudo two-dimensional model (P2D) for lithium-ion batteries. The P2D model is based on the work of and captures the electrochemical reactions occurring inside the cell to capture terminal current, terminal voltage, power, heat generation, and concentration gradients of Lithium throughout the cell. As shown in the figure below, the model discretizes the lithium-ion using the finite control volume approach (Figure 4). The cathode, anode, and separator are discretized in the “thickness” direction; additionally, in each control volume of the cathode and anode, a spherical representation of active materials is used and is discretized in the radial direction.

In addition to the P2D model, GT-AutoLion has built upon the original work from Doyle, Newman, and Fuller to also include capabilities to capture Li-ion degradation, swelling, and thermal runaway.

The performance of the cell model was calibrated by Toshiba between -30°C and 70°C, and the voltage results of putting the cell model through constant current discharge tests are shown below (Figure 5).

Integrated Model

As GT-AutoLion is available as a model template in GT-SUITE, the model integration into system-level is very simple. The physics-based battery model received from Toshiba is simply linked to the electrical domain, which allowed us to replace the existing electrical-equivalent battery model, as shown in the image below (Figure 6).

The supervisory controls were defined to operate the tugboat in “battery-electric” mode until the battery state of charge falls below 20% and to switch on the genset to continue the maneuver in “diesel-electric” mode, while keeping the battery state of charge at almost constant level.

The results shown below are displayed for a single maneuver (one 3 hour and 45-minute interval) with a battery that was sized to have 250, 20 Ah SCiB™ cells placed in series and 170 placed in parallel (250S/170P) (Figure 7).

Battery Sizing Results

The next image compares results between two different battery sizes, 250S/170P is shown in light grey and 250S/142P is shown in black (Figure 8). Notice how the state of charge decreases slower in the battery with more parallel cells, which allows the tugboat to complete more of its mission before needing to turn the genset on. Ultimately, this will mean that as battery size increases, less fuel is required to accomplish a single mission.

How Simulation Accelerates the Development of eVTOL Aircraft for Taxi Services

Unlocking the Potential of eVTOL Aircraft for Taxi Services: Advancing On-Demand Transportation Safely and Efficiently

The world is rapidly advancing towards an integrated and accessible on-demand transportation network. Electric Vertical Takeoff and Landing (eVTOL) vehicles have emerged as the ideal solution for the near future, offering faster and more efficient travel options. However, ensuring the safety and reliability of these innovative aircraft is paramount. This blog explores how simulation models play a vital role in evaluating eVTOL designs, identifying potential issues early, and paving the way for reliable and sustainable air mobility solutions.

Understanding the Aging Challenges of Li-ion Batteries in eVTOL Aircraft

Limited durability of lithium-ion batteries poses a significant obstacle in developing long-lasting eVTOL aircraft. Lithium-ion batteries experience capacity decline, increased impedance, and decreased power output over time. A deep understanding of battery aging is crucial to predict lifespan accurately and optimize battery management systems (BMS) for longevity and reliability. Let’s learn how addressing these challenges is vital for ensuring the success of eVTOL technology.

How Simulation Plays a Role in Electric Aircraft Designs

Simulation platforms such as GT-SUITE offer practical, efficient, and reliable solutions for studying various aspects of electric aircraft design. This comprehensive simulation suite allows for in-depth exploration of critical areas such as aerodynamics, flight control, mission definition, propulsion, and battery pack systems (see Figure 1 below). With GT-SUITE, engineers can comprehensively assess and optimize each subsystem, ensuring optimal performance and safety throughout the eVTOL aircraft’s operation. Check out our blog on eVTOL design and this study co-authored with Advanced Rotorcraft Technology on a comprehensive simulation for eVTOL aircraft. 

Real-World Aging Scenarios: Using Simulation for Battery Degradation Prediction

To ensure the economic viability of an eVTOL taxi service, maximizing the number of trips during peak traffic hours is crucial. However, it is essential to consider the limitations imposed by the battery pack. In a recent case study, we discussed the evolution of an eVTOL’s range over a span of four years, exploring a scenario where ten trips were scheduled each weekday between 6 AM and 10 AM in the morning, and from 4 PM to 8 PM in the evening. To maintain optimal performance, a 10-minute recharge was performed between each trip, with a full recharge between the morning and evening shifts. The proposed aircraft utilization was inspired by the UberAir Vehicle Requirements and Mission study. The vehicle requirements and mission have been developed through extensive analyses of current and predicted demand, understanding the capabilities of enabling technologies, and focusing on creating the optimal rider experience. Extensive analyses of current and predicted demand, understanding the capabilities of these technologies, and focusing on creating the optimal rider experience. 

By leveraging the advanced capabilities of another simulation platform GT-AutoLion, we were able to extrapolate the battery power demand during a representative mission flight from GT-SUITE and simulate the long-term degradation of the battery pack in a real-world scenario. In Figure 2, the evolution of the state of charge (SOC) of the eVTOL battery pack during the morning shift is plotted. 

The focus of this study was the ability to model the aging mechanism using GT-AutoLion. GT-AutoLion offers solutions that empower engineers to leverage cycle and calendar aging data, extracting valuable insights into battery degradation within more realistic scenarios. GT-AutoLion’s physics-based and postdictive degradation models can be calibrated to align with this data and subsequently applied to predict the aging behavior of Li-ion cells in various applications. These applications include li-plating, active material isolation, cathode electrolyte interphase (CEI) and solid electrolyte interphase (SEI) layer growth and cracking. These can all be modeled and investigated as aging mechanisms in Gamma Technologies’ software (see Figure 3). 

The GT-AutoLion aging simulation generates an external file (.cellstate) capturing the Li-ion cell’s state at each cycle during the aging process. Each cycle represents a mission flight of the eVTOL aircraft. This external file serves as valuable input for the system-level models, enabling accurate predictions of how the aged battery will impact the product performance (see Figure 4).  

By harnessing the power of this invaluable tool, we were able to project the anticipated range of the battery pack over a four-year operational period (see Figure 5).

 

Such insights provide a comprehensive understanding of the technology, aiding both technical exploration, and supporting the business development of the latest eVTOL advancements. 

With the ability to simulate and predict battery performance and aging, operators of eVTOL taxi services can make informed decisions about their operations, keeping an eye on profitability while maintaining reliable and sustainable service. By accurately estimating the range evolution over the course of four years, they can strategize the optimal scheduling of trips, maximizing efficiency during peak traffic hours, and mitigating the impact of battery pack limitations. 

Learn More About our eVTOL Simulation Solutions 

The future of on-demand transportation is bright with eVTOL vehicles leading the way. Through the use of cutting-edge simulation models and advanced battery management solutions like GT-SUITE and GT-AutoLion, the safety, reliability, and performance of eVTOL aircraft is elevated to new heights. Embracing innovation and overcoming obstacles, the sky’s the limit for this exciting and sustainable mode of transportation! 

Watch this great video case study of coupling GT-SUITE and Advanced Rotorcraft Technology’s FLIGHTLAB simulation capabilities to provide an easy-to-use, holistic solution for simulation-supported system design during the early design stages of eVTOLs.   

If you’d like to learn more about how GT-SUITE and GT-AutoLion are used to solve eVTOL simulation challenges, contact us! 

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. 

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! 

How Will Electric and Hybrid Vehicle Development Be Impacted by the Softening of US Rules

Governmental Regulations Impacting Automotive OEMs

In recent news, new vehicle tailpipe governmental regulations in the United States have softened for original equipment manufacturers (OEMs) development of electric vehicles (EVs) and hybrids (HEVs). 

The Department of Energy has significantly slowed the phase-out of existing rules that give automakers extra fuel-economy credit for electric and hybrid vehicles they currently sell. The real-world impact of the complex regulations has helped U.S. automakers meet new federal standards for fleetwide fuel efficiency continuing to sell traditional, internal combustion engine (ICE) vehicles. 

The Role Simulation Plays in New Vehicle Development

With these changes, it’s now imperative for the engineering community to leverage simulation platforms such as GT-SUITE in today’s automotive development for several reasons: 

1. Cost Reduction: Developing new automotive technologies, especially in the context of EVs and hybrids, can be expensive. Simulation allows OEMs to test various designs and configurations virtually, reducing the need for physical prototypes and costly trial-and-error processes.
2. Time Efficiency: With simulation, OEMs can accelerate the development process. They can quickly assess the performance of different components and systems, identify potential issues, and iterate on designs much faster than with traditional methods. This agility is crucial in a competitive market where time-to-market can make a significant difference.
3. Regulatory Compliance: Although regulations may slow down, they are unlikely to disappear. OEMs still need to meet stringent emissions standards and fuel efficiency requirements. Simulation enables them to explore different powertrain configurations, optimize efficiency, and ensure compliance with current and future regulations.
4. Technology Exploration: Even as regulations ease, the demand for cleaner and more efficient vehicles continues to grow due to environmental concerns and consumer preferences. Simulation allows OEMs to experiment with emerging technologies, such as advanced battery chemistries or fuel cell systems, and stay ahead of the curve in the evolving automotive landscape.
5. Risk Mitigation: Investing in new technologies carries inherent risks. Simulation helps OEMs mitigate these risks by providing insights into potential challenges and performance limitations before committing to large-scale production. This allows them to make informed decisions and allocate resources more effectively.
6. Optimization and Innovation: Simulation enables OEMs to optimize the performance of electric powertrains, hybrid systems, and fuel cell technologies. By fine-tuning parameters such as energy efficiency, range, and power output, they can deliver vehicles that meet or exceed customer expectations while staying competitive in the market.

Learn More About Our Simulation Solutions

While phased-in regulations may temporarily ease the pressure on OEMs, simulation remains a crucial tool for innovation, efficiency, and competitiveness in the automotive industry. Especially in the context of evolving technologies such as electric powertrains and fuel cells. 

To learn more about GT-SUITE, visit our website here. Speak to GT expert today as well here and see how to incorporate simulation for your vehicle development needs.  

How to Analyze Noise, Vibration and Harshness in Electric Powertrains (e-NVH) using Simulation

What are the Sources of Noise and Vibration in Electric Drives?

The general shift towards electrification in the electric vehicle (EV) market and beyond has created a need for higher fidelity simulation of electric powertrains. One aspect of this trend that has been getting attention is the desire for detailed analysis of an electric motor’s noise, vibration, and harshness (NVH) characteristics early in the design stage. 

The sources of the characteristic high-pitch whine of electric motors are the interaction between different airgap field harmonics inside the machine, as well as the switching voltage inputs from the inverter. These elements generate force waves in the airgap, which can excite the structure of the motor and cause vibrations, particularly at specific resonant frequencies. Imperfect torque and stator load profiles cause further vibration of attached machinery components and the gearbox housing (e-axle). Unlike internal combustion engines, where the engine sound is often a prominent feature that we want to accentuate, with electric drive units, any sound that is produced is usually undesirable, so the goal is to minimize it. 

A Complete & Fully Integrated Workflow

To properly analyze how this noise and vibration is created and to mitigate it, a system-level simulation of the motor, the inverter and the mechanical components is necessary. To capture the NVH characteristics of an electric drive unit, a new workflow, that spans GT-FEMAG’s electromagnetic finite element analysis simulations and GT-SUITE’s electrical and mechanical transient simulations, was developed (noted in Figure 1 below).

Electrical Section 

The first step in this process is to use GT-FEMAG, a finite element electromagnetic modeling tool built for motor design to design a motor that meets speed and torque requirements for the traction motor. After the motor design has been finalized, GT-FEMAG can export a very high-fidelity model of the motor, used to populate the datasets of a new lookup-table–based permanent magnet synchronous motor (PMSM) template in GT-SUITE, that can capture the torque ripple and the spatial harmonics inside the machine. This motor template is coupled with a detailed 3-phase inverter and controlled with a closed loop feedback control (Figure 2). 

This simulation outputs the 3-phase currents in the motor windings, at multiple different speeds (see Figure 3 below). These currents will be used in the next step to calculate the motor forces, in the mechanical part of the workflow. 

Mechanical Section 

Moving on to the mechanical section of the workflow, using the ABC currents that were calculated previously, FEMAG evaluates the magnetic pressure as a function of space and time, which we can then use to predict the forces that are generated in the motor, as a function of rotor position and stator tooth, for each operating speed and torque combination (Figure 4). These forces will be the boundary conditions for the mechanical analysis in the next step. 

Next, these excitation loads are used in GT-SUITE as an input for a forced frequency analysis to get the structural steady–state response of the overall gearbox housing. By performing a Fourier transformation of the loads from the previous step, we can obtain the amplitudes of the applied loads at each frequency, resulting from the various speed and order combinations of the simulated drivetrain. With this information, it is possible to directly identify areas that end up in excessive surface vibration and react accordingly by modifying the system. Additionally, the surface vibration response can be used to perform an acoustic analysis using a rapid sound assessment method that will provide the sound pressure level at any location around that structure (see Figure 5 below).

An All-in-One Package for e-NVH Analysis  

This workflow can offer a very straightforward and convenient way to analyze the NVH performance of any electric powertrain. As a tightly linked system, contained fully within GT’s library of tools, it enables users to run many iterations easily and quickly and to optimize their designs based on many parameters, like the geometric characteristics of the motor, the switching frequency, or the modulation strategy of the inverter etc., and see how these changes affect the NVH performance. The high degree of integration between the electromagnetic, the electrical and the mechanical domains of this workflow provides a seamless user experience, without having to resort to multiple different simulation tools, as is typically the case. 

Learn More About our e-Powertrain NVH Solutions 

The full workflow is presented in more detail by GT’s experts in this 30-minute SAE webinar. If you’d like to learn more or are interested in trying GT-FEMAG and GT-SUITE for e-powertrain and NVH simulation, contact us!  

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!   

1. Decreasing Battery System Simulation Runtime using Distributed Computing
2. Calculating Electric Vehicle Range with Simulation
3. Engine Manufacturers Leverage Simulation to Engineer Ahead of Increasing Regulations
4. Enhancing Model Accuracy by Replacing Lookup Maps with Machine Learning Models (Machine Learning Blog Part 1)
5. Optimizing Neural Networks for Modeling and Simulation (Machine Learning Blog Part 2)
6. Mitigating the Domino Effect of Battery Thermal Runaway with Simulation
7. Designing Thermally Secured Electric Motors with Simulation
8. Understanding Fuel Cell Systems Simulation for Vehicle Integration
9. Addressing Heat Pump Challenges, from Home to Industry with Simulation
10. Simulating Predictive Cruise Control for a Heavy-Duty Truck: Quickly and Easily

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: 

1. Calculating electric vehicle (EV) range
2. Decreasing battery system simulation runtime
3. Vehicle modeling: ICEV & BEV correlation procedure 
4. Reducing battery charging time while maximizing battery life 
5. Reducing battery testing time and costs 
6. Predicting system performance with aged li-ion batteries 
7. Predicting lithium-ion cell swelling, strain, and stress 
8. Lithium-ion battery modeling automotive engineers 
9. Non-automotive li-ion applications: aircraft, ships, power tools, cell phones and others 
10. Battery thermal runaway propagation 
11. Fuel cell system modeling 
12. Virtual calibration of fast charging strategies 
13. Parametric battery pack modeling for all existing cooling concepts 
14. Robust battery pack simulation by statistical variation analysis 
15. Sensitivity analysis: ranking the importance of battery model parameters 

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

Designing Thermally Secured Electric Motors with Simulation

Component-Level Design & Analysis for Motor Thermal Security

Most of today’s traction motors in battery electric vehicles (BEVs) are permanent magnet synchronous machines (PMSMs) that use interior permanent magnet (IPM) rotors with rare-earth magnets embedded in the rotor (automotive companies are starting to explore or even build other technologies, but that is a topic for another blog).  These magnets generate heat and tend to demagnetize if they reach critical temperatures; moreover, because they are embedded in the rotor, cooling these magnets can be challenging. 

In the design process of a traction motor, a variety of stator and rotor cooling options should be studied, and simulation gives motor designers the ability to study trade-offs of different cooling strategies without having to build and test physical prototypes (saving both time and money). Traditionally, steady-state, component-level simulations that couple finite-element approaches for both electromagnetics and thermal conduction and convection are used to ensure the thermal security of the motor.   

For an example of this, see the model results below that utilize both GT-FEMAG and GT-SUITE. These simulation solutions from Gamma Technologies were used to couple the electromagnetic and thermal finite element solutions to study different stator cooling topologies for a traction motor. The results below include the trade-offs of structure temperature (windings and magnets), coolant temperature rise, and coolant pressure drop. 

System-Level BEV Design & Analysis

System-level engineering of BEVs, on the other hand, requires an understanding of the global energy management puzzle and temperature distribution of its components (such as batteries, motors, inverters, and the occupants) to predict detrimental hot spots and occupant comfort in either hot or cold ambient temperatures during transient events. For more on this topic, see a blog written by my colleague, Brad Holcomb. 

In the case of automotive applications, the most common transient analyses performed are drive cycle tests that can be 30 minutes, or longer. For these long, transient simulations, the traditional finite element model of the motor (introduced earlier) would be too slow to be integrated into system-level simulation for hot spot prediction. 

The challenge is how can system-level engineers have an accurate, fast-running representation of a traction motor capable of hot spot prediction that can be integrated into a system-level model? In other words, how can we blur the lines between component-level and system-level simulation to engineer better electric vehicles? 

Blurring the Lines Between Component-Level & System-Level Simulation with GT-SUITE and GT-FEMAG 

With GT-FEMAG and GT-SUITE, Gamma Technologies offers an innovative way to have physics-based models of these coupled electromagnetic-thermal models to be used in system-level simulation.

First, the electromagnetic solver of GT-FEMAG is integrated into GT-SUITE as a seamless pre-processor that can automatically generate either map-based versions of motors with detailed component losses (for example, winding, iron, or magnet losses) or equivalent circuit models of motors (commonly referred to as “Ld, Lq” models) for the transient solver of GT to use in system-level simulations. 

 

Second, the thermal solver of GT-SUITE has a generalized, physics-based, and one-click switch that automatically converts 3D finite element models into 1D lumped thermal network models. 

 

These two capabilities enable users to quickly traverse different modeling fidelities between fully detailed electromagnetic and thermal finite element and fast-running and accurate 1D models.

 

Electric Motor Simulation Demonstration & Results

In a demonstration model we created, we combined these technologies to be able to run back-to-back Worldwide Harmonized Light-Duty Vehicle Test Cycles (WLTC) on an electro-thermal motor model by imposing speed and torque on the motor for one hour of simulation. To give the model more transient warm-up behavior, we connected the motor to a simplified cooling system that includes a thermostat, a pump, and a heat exchanger. We also modeled three different ambient and initial soak temperatures of the system, modeling the warmup of the motor at –10 °C, 0 °C, and 10 °C ambient conditions. 

The simulations each only took 70 seconds to complete (over 50 times faster than real-time), but captured the transient warmup of the various components in the motor, including the windings and magnets in the rotor: 

 

Below is an animation showing the transient coolant temperature through the stator for the -10 °C ambient case over the course of the simulation (please note contour scale is non-linear to help visualize results).

Transient stator coolant temperature through 2 WLTCs at -10 °C ambient conditions 

Model Integration 

Because the standalone electro-thermal motor model was processed over 50 times faster than real-time, we can bring it directly into a system-level model for integrated simulations. These simulations allowed us to have a deeper understanding of the energy management and trade-offs between different cooling strategies for the entire system, including the motor, inverter, battery, and cabin.

Learn More About our Electric Motor Simulation Solutions

If you’d like to learn more or are interested in trying GT-FEMAG or GT-SUITE for component-level or system-level simulation of electric vehicles, contact us!

 

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:

1. Pre-runaway battery model
2. Thermal runaway trigger
3. Cell-level thermal runaway model
4. 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.

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:

1. Solid electrolyte interphase (SEI) decomposition
2. Anode – electrolyte interface
3. Separator melting
4. Cathode decomposition (2 reactions)
5. 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.

 

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.

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.

 

 

 

 

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

 

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!

How to Perform Battery Electric Vehicle Range Testing Using Simulation

Streamlining BEV Drive Cycles 

Welcome to the second blog of this two-part series on how simulation platforms such as GT-SUITE can streamline the various drive cycles of battery electric vehicles (BEVs) to determine the range and adjustment factors.  

Read part one to learn more about how electric vehicle (EV) range guidelines are currently determined here.  

Building Vehicle Thermal Simulation Models with GT-SUITE 

As noted in part one of this series, the BEV range test procedure outlined in the SAE J1634 standard involves varying test conditions and drive cycles to estimate the final range. Simulation platforms such as GT-SUITE offers engineers the chance to streamline the entire EV range estimation process.

 Using GT-SUITE, the first step in BEV range testing simulation is creating a model of the vehicle thermal management system. The model needs to represent a system-level thermal management circuit of the electric vehicle and contains an integrated model of the following circuits:  

1. High-Temperature (HT) – Cooling circuit 
2. Low-Temperature (LT) – Cooling circuit  
3. Indirect refrigerant circuit 
4. Cabin air circuit 
5. Under-hood air circuit 

This thermal management model is then integrated with a vehicle model, representing the full vehicle and electric powertrain. Multiple control elements are implemented to regulate and adjust inputs to the system components such as the electric pump/fan/compressor, controlled valves, and others. 

Automating BEV drive cycles with GT-Automation  

Once the thermal management model is integrated in the vehicle model, there are multiple cycles that need to be simulated to obtain the adjustment factor for a single vehicle configuration. This is where GT-SUITE’s built-in app, GT-Automation, can be leveraged to efficiently evaluate the 5-cycle testing process. 

GT-Automation can be used to create a ProcessMap that allows configuring a workflow of individual processes that are executed in a specific order. It enables users to model the flow of interest to simulate all the required cycles without any manual intervention and extract the relevant energy consumption values into GT’s range calculation Excel tool.

As pointed out in part one of this series, the multi-cycle test (MCT) contains a mid-test constant speed section of varying durations that are dependent on the electric vehicle and its battery pack size. This is another use for GT-Automation to automate the determination of the duration of the mid-test steady state phase in MCT, thereby eliminating the iterative process of selecting the steady-state speed duration for every vehicle configuration.  

 

Calculating Range with GT’s Excel Tool

Lastly, engineers can use GT’s range calculation Excel tool which includes the required formulas from the SAE J1634 regulation embedded in the cells to estimate the adjustment factor and subsequently compute the label range.

Additionally, GTs range calculation Excel tool helps users manually tweak the energy consumption values in independent drive cycles to quickly understand the impact of different drive cycles and their dependencies on the adjustment factor and range. This further helps optimize control strategies and energy consumption for different vehicle configurations.  

Example Results for Two Vehicle Configurations 

The range of a typical passenger BEV is simulated according to the 2-cycle and 5-cycle methodology outlined in the SAE J1634 standard with different configurations. Utilizing the adjustment factors, both the vehicle configurations gained approximately 5-7% miles in the EPA’s certified range limits, making the 5-cycle testing a favorable option for the manufacturers.  

This, however, does not conclude that the 5-cycle testing option will always improve the adjustment factor and range for any vehicle configuration. It ultimately boils down to the efficiencies of different propulsion components and electrical load requirements at those additional 3 drive cycles that play a vital role in estimating the adjustment factor.  

Learn More About our Battery Simulation Capabilities 

If you’d like to learn more or are interested in trying GT for automating the 5-cycle test process flow for EV adjustment factor and range improvements, speak to a GT battery expert here. 

If you missed part 1 of this series, read more here.  

To learn more about more about battery simulation solutions, check out our battery modeling page and learn more about our hybrid and electric simulation solutions. Also, check our top 15 battery-related topics blogs in this list! 

Calculating Electric Vehicle Range with Simulation

How is Electric Vehicle Range Tested?

Range anxiety is one of the biggest concerns of consumers when it comes to looking to purchase an electric vehicle (EV). Because of this, manufacturers of EVs need to have accurate range predictions to build trust and quell range anxiety.

The determination of range for battery electric vehicles (BEV) has been historically tested using the 2-cycle test methodology from the SAE J1634 standard in North America. The 2-cycle test procedure, like the single-cycle test (SCT) and multi-cycle test (MCT), generally includes standalone or sometimes a combination of city and highway speed profiles.

The Five-Cycle Testing Guidelines for Electric Vehicles

The single-cycle test (SCT) is a full-deplete test, meaning that the vehicle is driven in a repeating city or highway drive cycle until the battery dies. This can take a long time and consumes significant resources, placing significant logistical strains on test facilities. Also, additional test cycles beyond the SCT are needed to better characterize the effects of temperature and accessory loads on EV range performance.

These constraints led the Environmental Protection Agency (EPA) to adopt new methodologies for testing and determining the range of BEVs called the multi-cycle test (MCT), short-multi cycle test (SMCT+), and 5-cycle test procedures. The MCT and SMCT are full-deplete tests and combine standard dynamic drive cycles (UDDS, HFEDS, or US06) with constant-speed driving phases. The goal of using the standard dynamic drive cycles is to determine the energy consumption associated with specific and established driving patterns. and the goal of the constant speed profiles is to rapidly discharge the battery energy consuming less time and resources compared to the SCT. The standard MCT procedure consists of four UDDS cycles and two HFEDS cycles in a specified sequence including mid-test and end-of-test constant speed “battery discharge phases” (CSC) which vary in duration depending on the vehicle and the size of its battery pack. The speed profile for MCT is shown in Fig1.

 

The SMCT includes AC energy consumption in its range determination by means of a shorter test as compared to the MCT. It accomplishes this by changing the order of the cycles and including a US06 cycle. SMCT is not a full-deplete test unlike MCT, and the remaining battery energy must be depleted separately in the case of the SMCT, which is often done by simply driving the vehicle at a steady-state speed (called SMCT+).

Lastly, the EPA 5-cycle procedure encompasses high vehicle speeds, aggressive vehicle accelerations, use of climate control system, and cold ambient conditions in addition to the standard City and Highway drive cycles used in SCT, MCT, or SMCT+. The 5-cycle test does a better job of reflecting typical driving conditions and styles. It produces energy consumption ratings that are more representative of a vehicle’s on-road range. Different testing options for 5-cycle EV certification are shown in Fig3.

Leveraging the Adjustment Factor to Improve EV label-range

Every original equipment manufacturer (OEM) is required to run at least 2-cycle to certify range for EVs in North America, but the drive cycles under consideration in 2-cycle tests are low-speed tests that aren’t truly representative of the real world. This forces the EPA to use an adjustment factor to yield a more realistic customer that experiences range. The default adjustment factor is 0.7, which reduces the raw range by 30% when an OEM opts to certify the range with just a 2-cycle methodology. For example, a car that achieves 500 miles of range during a 2-cycle test ends up with a 350-mile label range by using the default adjustment factor. However, the EPA allows manufacturers the option to run three additional drive cycles (US06, SCO3, and FTP cold drive cycle) and use those results to earn a more favorable adjustment factor. The adjustment factor can never be less than 0.7, in the case that the estimated adjustment factor from the 5-cycle test is less than 0.7 then a default adjustment factor of 0.7 can be applied.

Using GT-SUITE to predict the 5-cycle adjustment factor

GT-SUITE, a multi-physics simulation software, is used to automate the entire 5-cycle process outlined in SAE J1634 regulation to predict the adjustment factors for various vehicle configurations, leaving users with testing options to choose for EV range certification. In addition, GT will help users eliminate the iterative steady-state calculations involved in the MCT and manual extraction of energy consumption data required to estimate the adjustment factor by using a python script. The test condition and cycle information for the MCT and the standalone 5-cycle test are highlighted in Fig4.

Fig4: Range testing driving profile and test conditions

The BEV range test procedure outlined in SAE J1634 involves varying test conditions and drive cycles to estimate the final range. GT offers users the chance to streamline the entire EV range estimation process and takes it a step further to automate the required drive cycles to compute the adjustment factor.

Learn More About our Battery Simulation Capabilities

Stay tuned for Part 2 of this blog series, where we will discuss more about the implementation and automation of various 5-cycle test conditions in GT-SUITE to calculate the 5-cycle adjustment factor in EVs.

If you are interested in learning more about battery simulation, check out our battery modeling page and learn more about our hybrid and electric simulation solutions. Also, check our top 15 battery-related topics blogs in this list!

If you would like to reach out, email [email protected] or contact us here.

Decreasing Battery System Simulation Runtime using Distributed Computing

At Gamma Technologies, the goal of our battery suite simulation solutions, through GT-SUITE and GT-AutoLion, is to provide accurate, high-fidelity battery simulation capabilities for reliable prediction of real-world performance. In this blog, I investigate how battery simulation runtime can be saved running hundreds of design optimizations using distributed computing. Depending on the modeling requirements, some optimizations can benefit greatly from scaling the simulation runs using over a high performing computing (HPC) cluster to accelerate the turnaround time or greatly augment the design space.

With GT-AutoLion, you can take actual cells and use our unique, fully physical, pseudo-two-dimensional (P2D) models to predict cell performance of these cells. Different types of analysis are possible, such as: voltage, temperature rise, current, power, and several other metrics.

Additionally, GT-SUITE and GT-AutoLion can also create physics-based models to help predict the aging of a cell aging over time or over a certain number of cycles. Additionally, that cell can be placed in a system-level simulation to make for more meaningful aging predictions. GT battery simulations can provide insights such as the range of an electric vehicle or the number of years of operation for a power tool.

Simulating Electrochemical Models such as Cell Performance and Cell Aging

In an electrochemical model, parameters such as cell dimensions, cell chemistry, and various other material properties can be varied to match the experimental behavior of the cell.

With the use of GT-SUITE’s design optimizer,  these parameters can be varied to calibrate model behavior by minimizing the error between experimental and the simulated GT-AutoLion data.

To match data to constant current discharge, calendar aging, or cycle aging for instance, we can iterate hundreds of different designs on an HPC setup or through cloud computing.

The images below show the optimized GT-AutoLion results for constant-current discharge voltage curves, calendar aging and cycle aging data.

 

 

 

Leveraging the Cloud

With distributed computing, we can take a model which would normally be run locally on a machine and send it to a cluster using multiple cores. All that is required is additional solver licenses to increase the number of jobs. If a cluster is not readily available on-site, it is possible to access the cluster of a regional partner.

Likewise, a cloud server can be used to speedup simulation time. You can run long simulations or models which require high computing power. Cluster hours can be purchased from manufacturers such as AWS, Google Cloud, and Microsoft Azure to enable distributed computing.

See figures below that demonstrate these typical use cases.

 

 

 

Faster Runtimes of Complex Battery Simulation Models

One example of an intricate model includes a performance calibration exercise (included in installation of GT software). In this model, 600 designs are run using the design optimizer in GT-SUITE. The designs vary factors including the heat transfer coefficient, thicknesses of the cathode, anode, separator, and particle sizes of the active materials for 4 cases of varying constant-current discharges. For more information on why these factors were selected and to see this 600-design example model, GT’s own Ryan Dudgeon has written a blog post that explains more.

One design where all four cases are run locally on a standard work machine (with one logical core active) takes about 10 seconds to finish. During optimization, where we run 600 of these designs, this can increase the total runtime to roughly an hour.

However, with the aid of a distributed cluster we can run 5 designs in parallel with 5 solvers to decrease the runtime by 14 minutes. Additionally, we can use cloud computing to run even faster, which allows the computing resources to be fully elastic and allows for unlimited parallelization. The same number of 5 designs can be run in about half the time, saving 29 min. Increasing the number of designs in parallel up to 25 with cloud computing, decreased the runtime to just over 15 minutes. A 46-minute time savings!

It’s also interesting to look at an application where the speed increase brings more value, such as aging calibration models. The aging of a cell can be modeled as either calendar aging or cycle aging. For more information on how simulation can be used to predict aging, refer to this blog by my colleague Joe Wimmer.

Calendar aging involves taking a cell and measuring the loss in capacity over time. Running an optimization on a cell to calibrate calendar aging can take quite a bit of time if we are aging for months or years or looking at aging for different temperatures.

The calendar aging calibration model I explored ran for 360 days at 2 different temperatures. As shown in the table below, distributed computing can improve the simulation runtime of the model by nearly 2 and a half hours!

Another typical example is cycle aging calibration. Our cycle aging calibration example model undergoes a constant-current-constant-voltage (CCCV) charge after a constant current discharge.

This cycling protocol is repeated 1000 times at 3 different temperatures. Of course, we are not limited to just CCCV charging. Several different charging profiles such as boost charging and pulse charging can also be implemented, as mentioned in this blog. Since there is a varied load applied to the cell, we can’t run the model with large timesteps like with the calendar aging model, which has no load applied to the cell.

Running all 3 cases, (a total of 3000 cycles for 3 different temperatures), takes roughly 16 and a half minutes. Because of this, running the whole optimization containing 600 designs locally takes nearly 1 whole week! However, with the help of distributed computing, we were able to reduce the total runtime by a whole business week!

Learn More About Our Battery Simulation Solutions

These models can all be found in the GT installation as part of our GT-AutoLion calibration tutorials.

If you are interested in learning more about how you can implement distributed computing to improve your simulation speeds, you can reach out to [email protected] or 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!  

1. Simulating Your Way to HVACR Innovation 
2. How a Catastrophic Ship Fire Reminded us Why Battery Thermal Runaway Simulation is Important 
3. Reducing Costs & Increasing Efficiency in Power Converter Design  
4. Using Simulation to Model Closed-Cycle Argon Hydrogen Engines 
5. Sensitivity Analysis: How to Rank the Importance of Battery Model Parameters Using Simulation 
6. Accelerate Electric Aircraft Design Certification with Systems Simulation 
7. Vehicle Modeling and Simulation: ICEV & BEV Correlation Procedure 
8. How to Automate Real World Vehicle Route Generation Using Simulation 
9. A Look Inside Large-Scale Electrochemical Storage Systems Simulation 
10. Simulating a NASA Hydrogen Powered Rocket 

Other Gamma Technologies Blogs to check out in 2022! 

1. Using Simulation for Battery Engineering: 12 Technical Blogs to Enjoy 
2. Machine Learning Simulation: HVACR Industry 
3. Fast, Accurate Full Vehicle Thermal Management Simulation with GT-SUITE and TAITherm 
4. Using Simulation To Predict Battery Aging for Real World Applications 
5. How Simulation Can Increase Productivity in Electric Vehicle Thermal Management Design 
6. Using Simulation to Optimize Driving Routes and Vehicle Emissions 
7. How Simulation Is Used To Design ICE vs. Battery Electric Vehicle Thermal Management Systems  
8. Are Your Vehicle Passengers Comfortable? How to Validate An Accurate, Thermal Cabin Management Simulation Solution  

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!   

A Look Inside Large-Scale Electrochemical Storage Systems Simulation

Why Are Redox Flow Batteries Important?

Renewable electricity produced by solar and wind energy is taking an ever-increasing share of the total electricity generated. However, the fluctuating nature of these renewable energy sources makes grid management challenging without a reliable energy storage system. Electricity production from solar and wind generators is often curtailed when the supply exceeds the demand during the day[i]. Large-scale electrochemical storage systems are expected to play a critical role in managing grid demand fluctuations.

Among the various types of electrochemical storage systems, such as lithium-ion and lead-acid batteries, one that’s well suited for grid energy storage are vanadium redox flow batteries (VRFB). VRFBs are considered due to their fast response rate, long charging/discharging cycle lives, and non-flammable aqueous electrolytes [ii].

Modeling and Simulation of Redox Flow Batteries

Simulation platforms such as GT-SUITE can be used to model different aspects of VRFBs both at the cell level and systems level using components from different physical domains covering fuel cells, battery modeling, fluid flow, thermal management, control, and chemistry applications. Simulations can be used to perform a variety of virtual experiments to assess the performance of VRFBs with different design and operating parameters such as size of tanks and stack, electrolyte flow rate, vanadium concentration, and temperature. In this blog, we will show the effect of a few of these variables on battery performance.

Figure 1 shows the main components and operating principles of VRFBs. The anolyte tank stores the solution consisting of V²⁺ and V³⁺ ions, and the catholyte tank stores the solution consisting of VO²⁺ (V⁴⁺) and VO₂⁺ (V⁵⁺) ions. Pumps are used to circulate the electrolyte solution through the electrochemical cell consisting of carbon-based positive and negative electrodes separated by a proton exchange membrane.

With GT-SUITE, we have built a model of VRFBs as shown in Figure 2. This model accurately calculates the cell voltage by considering different physical and chemical processes such as: 

• Varying concentrations of different vanadium ions within the electrodes and tanks 
• Activation losses using the Butler-Volmer equation with proper dependence on vanadium ion concentrations and cell temperature 
• Ohmic losses in the electrolyte (using Bruggeman correction), Nafion membrane (empirical relationship with water content and temperature as parameters), and current collectors 

Results of Simulating Redox Flow Batteries 

We used this model to study how different operating parameters affect battery performance. Figure 3 shows the net voltage and open cell voltage (OCV) during the charging and discharging cycle for two different temperatures. The battery performs better at 313K than 283K primarily due to lower activation losses (i.e., faster reactions at electrodes) at higher temperatures.  

Figure 4 shows the voltage for two different volumetric flow rates. A higher flow rate leads to slightly better battery performance because vanadium ion concentrations in the electrodes are rapidly replenished by the flow from tanks. 

Finally, total vanadium concentration is varied between 1200 mol/m3 and 1600 mol/m3 by keeping all other parameters the same. As shown in figure 5, higher vanadium concentration allows the battery to be charged and discharged for longer durations (i.e., higher capacity).  

Learn How to Simulate a Variety of Electrochemical Devices  

Explore the domain libraries and capabilities GT-SUITE has to offer to model a variety of electrochemical devices such as fuel cells, electrolyzers, and batteries. 

Contact us to learn more. 

Citations

[i] California’s curtailments of solar electricity generation continue to increase. (n.d.). Retrieved October 28, 2022, from https://www.eia.gov/todayinenergy/detail.php?id=49276

[ii] Kebede, A. A., Kalogiannis, T., Van Mierlo, J., & Berecibar, M. (2022). A comprehensive review of stationary energy storage devices for large scale renewable energy sources grid integration. Renewable and Sustainable Energy Reviews, 159, 112213. https://doi.org/10.1016/j.rser.2022.112213 

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.

 

 

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.

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.

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:   

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:

 

 

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. 

Register today: https://hubs.ly/Q01jRcZb0  

 

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! 

Using Simulation To Predict Battery Aging for Real World Applications

What is The Warranty On a Lithium-ion Battery?

Over the years, lithium-ion technology has expanded into numerous applications, ranging from products as large as planes and ships to products as small as power tools and cell phones and everything in between.  Because of the inevitable degradation of lithium-ion cells, the lifespan of these products will likely be limited by the degradation of the Li-ion battery it uses.  In many instances, products may have special warranties for their battery system.  For instance, the Tesla Model S and Model X have a special battery warranty of 8 years, 150,000 miles; Dell Laptops offer 1 or 3 year battery warranties; and Makita offers 3 year warranties on most of the batteries in their power tools.

Determining how to warranty a battery is no easy task.  If the warranty is too short, there is a risk that less consumers will purchase your product; conversely, if the warranty is too long, there is a risk that there may be a significant amount of warranty claims down the road.  Mistakes in either direction are expensive.

To compound the difficulty of warrantying a battery pack, most of the companies selling products with Li-ion batteries in them do not manufacture the Li-ion cells. They simply buy cells from cell suppliers and package them in their system.  The engineers at these companies are focused on developing battery electric vehicles (BEVs), electric vertical take-off and landing (EVTOLs) aircrafts, power tools, or consumer electronics, not Li-ion cells.  Therefore, the people tasked with warrantying a battery are often not equipped with the proper information or knowledge about Li-ion technology to accurately predict battery lifetime. However, by utilizing a minimal amount of available data combined with physics-based simulation software, these engineers can predict battery lifetime, and therefore make more confident battery warranty decisions.

How is Battery Degradation Measured & How Simulation Can Predict Battery Aging 

To help cell buyers determine how to warranty a battery, cell suppliers often quantify battery degradation in two ways: calendar degradation and cycle degradation.

Calendar degradation measures how a cell degrades while it is exposed to zero current for an extended period of time.  These are sometimes referred to as “shelf life” tests because the cells can simply be placed on a shelf, forgotten about, and periodically tested.

Cycle degradation measures how a cell degrades while being cycled between fully charged and fully discharged over and over at constant currents.

Generally, cell suppliers will inform their customers about their cell’s degradation by including calendar and cycle life data in detailed cell documentation, where calendar and cycle aging are given in plots using capacity retention (% of Beginning of Life Capacity) on the Y-axis and cycle or calendar days on the X-axis.  As seen in the example images below, both tests can be run at different temperatures.

Neither of these tests are truly representative of what a Li-ion cell will experience in the real world, so an engineer responsible for determining the warranty of a battery in a system (such as a cell phone, hybrid vehicle, plane, etc.), may not find this data very helpful.

No BEV, EVTOL, or other product will be used in scenarios reflective of calendar or cycle aging tests.  BEVs will be used to commute back and forth to work, pick up groceries from supermarkets, and go on the occasional road trip.  EVTOLs will subject their batteries to demanding loads to transport people around large cities.  Power tools and consumer electronics may see extended periods of rest with occasional intense usage.  Additionally, due to weather patterns and varying usage and charging patterns between different consumers, the realistic demands that a battery may see in its lifetime can be very difficult to replicate in laboratory conditions.

Simulation tools like GT-AutoLion and GT-SUITE provide a unique solution that enables engineers to use the provided cycle and calendar aging to gain meaningful insights into battery aging under more realistic scenarios.

As shown in many technical papers, physics-based models of Li-ion battery performance and aging in GT-AutoLion can be calibrated to match experimental data, such as capacity fade and resistance growth during calendar and cycle aging.

Because the degradation models in GT-AutoLion are physics-based and postdictive, they can be calibrated to match this type of data and then used in other conditions to predict how Li-ion cells may age in any application.  In the case of a power tool supplier, these conditions can include typical usage patterns for various applications, including chain saws, drills, and even rotary tools.

Using Simulation to Predict Battery Degradation of Battery Electric Vehicles (BEV)

In the case of a complex system, like a battery electric vehicle (BEV), it’s important to capture interactions between the battery and other systems, such as thermal management systems, in order to accurately capture how the complete system affects battery life. For example, to predict the battery power demand of a BEV’s Li-ion battery during a drive cycle of a typical owner’s commute, a system-level model of the vehicle can be built using GT-DRIVE+. These drive cycles can then be repeatedly applied to a GT-AutoLion model, along with realistic rest times, in order to predict how a pack degrades in a real-world scenario.  On top of this, other variables can be studied to understand their effect on battery degradation, such as weather patterns and even charging patterns and strategies.

By combining the power of GT-DRIVE+ and GT-AutoLion, engineers have more meaningful aging predictions that can be quantified in terms of “Miles” or “Years of Operation” as opposed to “Cycles,” which then increases confidence when determining how to effectively warranty batteries.

In my next blog, I’ll discuss how GT-AutoLion predicts not only the capacity fade of a cell, but also predicts the performance of a system after a battery has begun degrading.

NOTE: This piece was originally published in June 2020.

Sensitivity Analysis: How to Rank the Importance of Battery Model Parameters Using Simulation

What is Sensitivity Analysis?

Sensitivity analysis is the study of how uncertainty in a model output (or response) can be attributed to different sources of uncertainty in the model inputs (or factors). These analyses are powerful tools for modeling and simulation activities, as they allow the modeler to identify the most and least influential parameters on the key predicted outputs of interest. Not only does knowing the important parameters allow the modeler to focus on a select few inputs for design work, but perhaps more importantly, identifying the negligible parameters allows a modeler to omit them from difficult optimization problems by setting them to constant values, thereby simplifying the optimization task.

The data science literature contains several sensitivity analysis methods. A general characteristic among them is a trade-off between number of required model evaluations and amount of information that can be extracted. For example, the so-called Sobol method can quantify the first-order, second-order, and even factor-factor interaction effects on a response of interest, but it often requires multiple thousands of model evaluations to extract this type of detailed information. As a result, we usually only perform the Sobol method on fast-executing metamodels.

 

The Elementary Effects Method & Use of Simulation 
In contrast, this study focuses on the the Elementary Effects method (sometimes called the Method of Morris), which has an advantage of being computationally efficient by requiring a relatively low number of model evaluations. However, the outputs of the method are not as detailed. As a result, it is good at providing a general ranking of many parameters, and for this reason it is particularly useful for identifying parameters with negligible influence that can be discarded from subsequent optimization or Design of Experiments studies.

This method was successfully applied to over 40 parameters of a GT-SUITE engine friction simulation model to reduce the complexity of an optimization, as described in this paper, A Global Sensitivity Analysis Approach for Engine Friction Modeling, by Oleg Krecker and Christoph Hiltner.

The full study will similarly apply the method to a battery model prior to an optimization task.

 

Why Engineers are Finding the Elementary Effects Method Beneficial 
As a computationally efficient method for sensitivity analysis that requires a relatively low number of model evaluations, the Elementary Effects method can rank all the parameters, identify the most important ones, and identify the negligible ones and is a useful modeling tool to gain insights to how a large number of parameters are affecting a model.

Applying the Elementary Effects method to a battery model having numerous parameters prior to optimization can aid in identifying parameters that have a negligible impact on the voltage and temperature results of interest, therefore reducing the optimization problem’s complexity by working with fewer parameters. To learn more about this in detail, we invite you to read the read the full technical case study, click here.

 

Background
The study, “Sensitivity Analysis and Factor Screening to Rank the Importance of 34 GT-AutoLion Battery Model Parameters,” highlights:

• A brief background on sensitivity analysis and their different types
• How GT-AutoLion‘s simulation battery model was used to characterize the voltage and temperature responses at four different operating conditions
• Why the Elementary Effects Method was the chosen sensitivity analysis method
• How 34 battery model parameters were tested and how the Elementary Effects Method ranks the importance of these parameters
• How the elementary effects method was applied in GT-SUITE using a Design of Experiments (DOE) study

To read the full study, click here.

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