Li-ion Battery Simulation
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 phiscio-chemical 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.
Aging Prediction with GT-AutoLion
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.
Safety Prediction with GT-AutoLion
With GT-AutoLion, users can replace expensive and dangerous cell-level and pack-level safety tests in a virtual testing environment. 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 left).
Process Integration with GT-AutoLion
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 enable electrochemical models enables parameter identification and streamlines model calibration to experimental data. Finally, models can even be encrypted for suppliers and OEMs to freely share models.
- Optimize material composition and cell design to meet power, energy, and durability requirements for a given application
- Predictive aging models of active material isolation, SEI layer growth, and Lithium plating allow a physics-based model of capacity loss and resistance growth
- Comprehensive electrochemical materials database that includes popular electrochemical materials characterized at a wide range of temperatures and Lithium concentrations
- Fast evaluation of various battery chemistries and cell designs in system-level environment
- Bring GT-AutoLion electrochemical models into 3D CAE with GT-AutoLion-3D
- Reduce testing time and cost with predictive physics-based models
- Predict how cells will age in real-world applications
- Integrate fresh or aged GT-AutoLion battery models into GT system-level models to predict how system-level performance metrics evolve over time
- Design and optimize fast charging strategies
- Analyze cell expansion, stress, and strain with advanced active material swelling model
- Battery Aging Blog Series
- Webinar: How to Perform Battery Aging Analysis with GT-AutoLion
- Webinar: Key Considerations to Understand and Maximize Battery Life
- Blog: Building a Detailed, Highly-Predictive Battery Model from a Cell Spec Sheet
- Blog: Lithium-ion Battery Modeling for the Automotive Engineer