Thermal Battery Performance and Aging Simulation

Software Overview WHAT IS GT-AUTOLION?

GT-AutoLion is the industry-leading lithium-ion battery simulation software used by cell designers and OEMs to predict performance, degradation, and safety for any Lithium-ion cell. It predictively models the electrochemical processes within Lithium-ion cells using a fast and reliable, electrochemical, physics-based approach.


GT-AutoLion can be used to predict how various Li-ion chemistries and cell designs will perform before Li-ion cells 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-AutoLion uses the physiochemical Pseudo-2D model pioneered by Doyle, Fuller, and Newman to predict performance.

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.


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 cracking, SEI and cathodic film growth, 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.

Predicting battery cell degradation


With GT-AutoLion, users can create a virtual testing environment so that they minimize expensive and dangerous cell-level and pack-level safety tests. This includes cell-level and pack-level external short tests as well as thermal runaway propagation tests, where one cell enters thermal runaway and pass/fail criteria is determined by whether or not neighboring cells also enter thermal runaway (virtual thermal runaway propagation test shown to the right).


GT understands that Li-ion cells and batteries are not developed in a vacuum and it is important for these models to be able to be used by a large number of stakeholders inside and outside of battery teams.  To accomplish that, GT includes a streamlined workflow to integrate GT-AutoLion models of Li-ion batteries into GT system-level models (model of a battery electric vehicle shown to the left), GT battery pack models, or Simulink models.  GT-AutoLion includes a built-in battery characterization toolbox to easily export electrical-equivalent battery models.  Access to GT’s Design of Experiments, Design Optimizer, and distributed computing enables ultra-fast parameter identification and streamlines model calibration to experimental data. Finally, models can even be encrypted for suppliers and OEMs to freely share models.

Advanced Features

  • Seamless transition from 0D, P2D, and P3D 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
  • 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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With the seamless workflows available between GT-SUITE’s advanced system-level modeling and GT-AutoLion’s accurate Li-ion battery modeling, engineers understand how system-level decisions (such as vehicle topology decisions, thermal topology decisions, and controls decisions) can affect the long-term degradation of not only the battery but the performance of the entire system.

To learn more, read full blog here.

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.

Effect of swelling on performance of Lithium-ion cell.

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.

GT-AutoLion aging models Calibrated to experimental 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.

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Learn more about GT-AutoLionBATTERY MODELING

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