GT-AutoLion
Thermal Battery Performance and Aging Simulation
Software Overview WHAT IS GT-AUTOLION?
GT-AutoLion is the industry-leading battery simulation software trusted by cell manufacturers and OEMs to design next-generation battery cells and optimize control strategies. At the same time, it enables accurate predictions of performance, degradation, and safety for any lithium-ion cell and beyond.
Built on a fast and reliable electrochemical, physics-based approach, GT-AutoLion delivers high-fidelity modeling of the internal processes within lithium-ion cells—empowering engineers with the insights needed to drive innovation and efficiency.
Tailored Solutions for Every Battery Modeling Need
From real-time battery modeling to micro-scale level design, GT-AutoLion offers a comprehensive suite of solutions to meet diverse battery simulation requirements—all within a unified simulation environment. Whether you need high-fidelity electrochemical modeling or fast predictive tools, our technology adapts to your specific needs.
On top of the Pseudo-2D model, GT-AutoLion also includes a swelling model capable of predicting stress, strain, and pressure in a cell as active material expands during lithiation. Finally, every installation includes a comprehensive electrochemical materials database, reducing the burden for laboratory testing of electrochemical properties.
BATTERY CELL DESIGN
DETAILED AND EFFICIENT CELL DESIGN CAPABILITIES
Optimizing li-ion cell design is critical to achieve the different energy storage application requirements. GT-AutoLion provides advanced simulation tools that enable engineers to evaluate and refine cell properties and structure before physical prototyping.
GT-AutoLion features a comprehensive, ready-to-use materials database, enabling users to quickly access and implement various chemistries without the need for extensive laboratory testing. By leveraging detailed electrochemical, thermal, and mechanical models, users can analyze key design parameters such as electrode porosity, separator thickness, tab location, and anode overhang.
BATTERY PERFORMANCE PREDICTION
PERFORMANCE CAN BE PREDICTED UNDER ANY LOAD AND FOR ANY APPLICATION
GT-AutoLion can be used to predict how various Li-ion chemistries and cell designs will perform before they are prototyped or even available for testing. With GT-AutoLion, a Li-ion battery’s performance can be predicted under any load, including constant current (voltage drop and temperature rise shown to the left) and more dynamic loads.
GT recognizes that Li-ion cells and batteries must function within broader systems. To support this, GT-AutoLion seamlessly integrates with GT’s system-level, battery pack, and Simulink models, allowing performance analysis in an integrated environment. A built-in battery characterization toolbox simplifies exporting electrical-equivalent models, while GT’s Design of Experiments, Design Optimizer, and distributed computing accelerates parameter identification and calibration. Additionally, encrypted models enable secure sharing among suppliers and OEMs.
BATTERY SWELLING AND DEFORMATION
GET INSIGHTS INTO THE MECHANICAL BEHAVIOR OF A LITHIUM ION CELL
Swelling and deformation in lithium-ion batteries (LIBs) arise from multiple mechanisms, including volume changes in electrode particles during lithium intercalation and de-intercalation, surface film growth due to side reactions, and elastic deformation of electrodes under external mechanical loads. These changes can affect both the electrochemical performance and structural mechanical properties of the battery.
The GT-AutoLion is a coupled electrochemical, thermal, and mechanical solution delivers powerful, in-depth insights into battery swelling and deformation, and enables a comprehensive understanding of these complex phenomena. With advanced modeling capabilities, it accurately captures particle-level stress and strain, tracks stress evolution throughout the battery’s lifecycle, analyzes strain and stress variations based on state of charge (SOC), and provides a detailed spatial visualization of stress and strain distribution.
BATTERY CONTROL STRATEGIES DESIGN
BATTERY MANAGEMENT SYSTEM CALIBRATION & VERIFICATION
GT-AutoLion empowers engineers to design smarter, more efficient BMS algorithms for State of Charge (SOC) estimation, State of Health (SOH) tracking, thermal management, cell balancing, and cell protection. Its variety of simulation solutions provide deep insights into real-world battery behavior, enabling predictive control strategies and optimized fast charging strategies to enhance performance, extend battery lifespan, and improve safety.
Moreover, GT-AutoLion & GT-SUITE offer the possibility to validate controller design by leveraging cell and pack battery models in Model-in-the-Loop (MiL), Software-in-the-loop (SiL) simulations, and Hardware-in-the-loop (HiL) simulations.
BATTERY PACK THERMAL MANAGEMENT
UNDERSTAND THE OPTIMAL THERMAL MANAGEMENT SOLUTION FOR YOUR APPLICATION
Electrical-equivalent and electrochemical battery models can be coupled to advanced thermo-fluids systems for cell-level and pack-level safety tests. GT-AutoLion plus GT-SUITE will provide thermal management engineers with a powerful tool to optimize the colling strategy of a battery pack and engineer solutions to mitigate thermal runaway propagation.
Physics-based or event controlled thermal runaway models can be easily integrated into the workflow for a realistic electrochemical, chemical, mechanical, and thermo-fluid simulation.
AGING PREDICTION
PREDICT HOW CELLS WILL DEGRADE IN ANY USE CASE
GT-AutoLion helps predict how Lithium-ion cells of any chemistry will degrade in any use case, including calendar aging, cycle aging, and mixed aging scenarios. GT-AutoLion includes an extensive list of available Li-ion degradation mechanisms, including active material isolation, SEI and cathodic film growth, electrolyte dry-out, and Lithium-plating (validation and visualization of these models shown to the left). These mechanisms enable users to predict not only capacity fade, but also resistance growth of a Li-ion cell as it ages. These models can be used to reduce testing time and cost, predict how batteries age in real-world scenarios, predict how aged batteries affect system performance, and calibrate and optimize fast charging strategies.
Features Highlight
- Seamless transition from 0D, P2D, P3D, and P4D modeling for Lithium-ion cells
- Built-in library of cathode, anode, and electrolyte material properties
- Advanced swelling model to integrate stress, strain, and performance prediction in one tool
- Direct integration with GT system-level models
- Direct co-simulation with Simulink and other tools for BMS control validation
- Built-in degradation mechanisms for physics-based Li-ion battery aging
- Integration with GT’s Design of Experiments (DOE) and Optimization features for fast model calibration
- Integration with GT’s thermo-fluids domain for thermal management of battery packs
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- Accelerate Innovation with Advanced Cell Design Tools
- Predict Performance with Physics-Based Battery Modeling and Seamless System Integration
- Predict Swelling and Deformation Through Integrated Electrochemical-Mechanical Modeling
- Predict Battery Aging for Real World Applications
- Physics-Based BMS Design Insights
- Use of Multiphysics Simulation for Optimal Battery Pack Thermal Managment
+ Anode Overhang of Li-Ion Cells
This use case demonstrates how GT-AutoLion-4D supports virtual studies of anode overhang in Li-ion cells, validated by experimental lab tests at IAV. The simulation approach enables detailed analysis of overhang impact on voltage relaxation, diffusion, and local 3D effects.
+ 3D Simulation Approach
This use case highlights a 3D simulation approach to optimize large-format Li-ion cell design. By using multi-tab current collectors, the method reduces voltage losses and improves active material utilization—achieving energy densities close to lab-scale benchmarks. It also introduces the first quantitative link between current uniformity and usable energy, enabling smarter, high-capacity battery designs.
+ Cell Calibration
In this case study, the Mercedes-Benz Research and Development India team, exploit the capabilities of GT-AutoLion to fully calibrate a Li-ion cell for performance prediction. The calibrated model has been insightful for the team in regard of cell selection for their specific application.
+ Fast Charging
In this case study, BorgWarner demonstrates the performance prediction capabilities of GT-AutoLion in fast charging condition. The team successfully validated the simulation outputs with experimental test and was able to get insights in the pack fast charging capabilities under different ambient temperature scenarios.
If interested in fast charging prediction simulation and insights, give a look at this blog.
+ Battery Electric Boat
In collaboration with Toshiba, Gamma Technologies used GT-AutoLion and GT-SUITE to virtually size and evaluate SCiB™ Lithium-ion batteries for a battery-electric tugboat. By simulating real-world port operations, engineers optimized battery configurations before any physical prototypes—reducing development time, improving performance, and accelerating system-level design decisions. With encrypted, calibrated cell level models from Toshiba, engineers can confidently make early design choices and explore long-term battery behavior, aging, and system integration. Give a look at this use case detailed material and blog.
If you are interested in the Toshiba-supplied encrypted GT-AutoLion model of the 20Ah SCiB™ cell, fill out this request form here. Our team will carefully review all submissions.
+ Mechano-Electrochemical Model
Cell and pack designers currently rely on extensive electrochemical and mechanical testing to appropriately account for the volume change and the developed stresses. This mechano-electrochemical model predicts this volume change, which may reduce the required number of electrochemical and mechanical tests.
To learn more, read full blog here.
+ Electrothermal-Mechanical Model
In this case study, Accenture exploit GT-AutoLion the electrothermal-mechanical model to predict the swelling force experienced by a pouch cell and the thickness change over time. In this study, the mechanical model has been further coupled with degradation mechanisms that will affect the mechanical behavior of the cell.
+ Calibrate Battery Models with Experimental Data
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.
To learn more, read full blog here.
+ Predict Beyond Experimentation
In this case study, General Motors investigated the aging prediction capabilities of GT-AutoLion. GT-AutoLion can predict aging behavior outside the calibration range and can provide some aging details which is not available from test data. This has provided GM with useful insight on how to understand aging and offered the opportunity to reduce experimental testing.
+ Compare Degradation with Experimental Data
Ford Otosan, in this insightful study, uses a GT-AutoLion physics based electrochemical model to predict the degradation of a battery and compared the results with experimental data.
+ Extrapolate Data
Because of the great disparity between projected product lifetime and the product development cycle time, it’s not always feasible to rely solely on calendar aging or cycle aging data. To help address this issue, physics-based aging models can be calibrated to available data and then used to project, or extrapolate, the degradation of cells beyond the available data.
To learn more, read full blog here.
+ SOC Strategy Development.
Develop and validate high-accuracy State of Charge (SOC) algorithms using physics-based battery models. By simulating real-world conditions and edge cases. Integrate experimental data with advanced electrochemical modeling to enhance prediction reliability and streamline SOC strategy development.
+ Optimize Cell Balancing
Optimize cell balancing strategies with detailed insights from physics-based battery models. Simulate cell-to-cell variations and aging effects to design smarter balancing algorithms that improve performance, extend battery life, and ensure safety. Validate control approaches virtually to reduce development time and hardware dependencies.
+ High-Fidelity Battery Models to Define Safe Operating Limits
Generate maximum discharge current surface maps using high-fidelity battery models to define safe operating limits across temperature, State of Charge, and aging conditions. Visualize and quantify performance boundaries to inform BMS constraints, enhance safety margins, and enable aggressive yet reliable power delivery strategies.
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+ Battery Safety Management
In this case study, Kulr integrate GT-SUITE and GT-AutoLion in the holistic approach to battery safety management. The software is used for analysis informed design and ultimately to guide sensible design decision.
Read more about GT-SUITE and GT-AutoLion coupling to study thermal runaway propagation in this blog.
+Optimal Cooling Strategy for eVTOL
In this case study, a multiphysics system simulation to predict battery pack thermal performance and risk of thermal runaway is extensively analyzed with the objective to define the optimal cooling strategy for an eVTOL application.
Read in this blog about the challenges and solution for thermal managing in electric application.
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Learn more about GT-AutoLionBATTERY MODELING
Learn more about GT-AutoLionRELEVANT RESOURCES
- Fast Charging Li-ion Batteries at All Temperatures
- CAE Method for Linking Electrochemical Li-ion Models into Integrated System-Level Models of EVs
- Comparison of Li-ion battery degradation models for system design and control algorithm development
- Framework for Digital Twin Real-Time Battery System for MIL and HIL Simulations
- GT Webinar – Machine Learning for Fast, Integrated Battery Modeling
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