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• Comment: In accordance with the Wikimedia Foundation's Terms of Use, I disclose that I have been paid by my employer for my contributions to this article. BobJoanathan63 (talk) 21:26, 4 August 2026 (UTC)

GT-AutoLion is proprietary lithium-ion battery simulation software developed by Gamma Technologies and integrated with the company's GT-SUITE simulation platform. It uses physics-based electrochemical models to simulate battery-cell performance, thermal behavior, degradation, and failure conditions. Cell models created in GT-AutoLion can also be incorporated into battery-pack and vehicle simulations.^[1]

AutoLion was developed by EC Power as a collection of simulation tools for lithium-ion cells and battery packs. A 2013 SAE technical paper described the software as using a thermally coupled battery model that calculated electrochemical and thermal behavior simultaneously. It also included a degradation model for predicting changes in cell performance over its operating life.^[2]

Gamma Technologies acquired the AutoLion software business from EC Power in February 2018. The company announced that the software would be offered under the name AutoLion-GT and integrated with GT-SUITE and GT-DRIVE+.^[3][4] The integrated product was released as GT-AutoLion with version 2019 of GT-SUITE.^[5]

GT-AutoLion includes a pseudo-two-dimensional electrochemical model based on the Doyle-Fuller-Newman model. It represents lithium transport through the electrolyte and electrode particles. The model also calculates charge conservation in the solid and electrolyte phases and reactions at the electrode-electrolyte interface. Thermal calculations can account for heat from electrochemical reactions, electrical resistance, and reversible processes.^[6]

GT-AutoLion's Design Optimizer can use a genetic algorithm to reduce the difference between simulated and measured results. The parameters selected for fitting depend on the test. Low-current measurements may be used to fit equilibrium behavior and cell capacity, while higher-current measurements may be used to fit resistance, reaction kinetics, and diffusion-related behavior.^[6][7]

Thermal models vary in complexity. Some studies treat the cell as a single thermal mass. Others divide it into several regions to calculate internal temperature differences. One cylindrical-cell model used separate nodes for the jelly-roll core, cell surface, and internal cavity.^[7][8]

A 2026 comparison published by Ionworks described GT-AutoLion and PyBaMM as using electrochemical models based on the pseudo-two-dimensional framework. The comparison characterized GT-AutoLion as a commercial tool integrated with GT-SUITE for system-level simulation, while PyBaMM was described as an open-source, Python-based package whose model equations and source code can be inspected and modified. Ionworks is the primary commercial sponsor of PyBaMM.^[9]

In a study of low-temperature battery performance, Rivoir and colleagues modeled a Panasonic NCR18650PF graphite-NCA cell. They used current, voltage, and surface-temperature measurements collected at -10, 0, and 25 degrees Celsius. A model previously calibrated only at 25 degrees Celsius failed to produce physically valid results at 0 and -10 degrees Celsius. The researchers added temperature-dependent corrections for electrode and electrolyte transport properties and recalibrated the model using measurements from all three temperatures.^[7]

During validation, the model's voltage root-mean-square error (RMSE) ranged from 0.0356 to 0.0509 V. Its temperature predictions were less accurate at -10 degrees Celsius, where the temperature RMSE was approximately 1.40 degrees Celsius.^[7]

The cell model was also incorporated into a battery-electric vehicle simulation. Over repeated Worldwide Harmonized Light Vehicles Test Procedure cycles, the calculated range increased from 266 km at -10 degrees Celsius to 290 km at 40 degrees Celsius. The range estimates were specific to the study's vehicle model and passive-cooling assumptions.^[7]

A three-node GT-AutoLion model developed by Hernandez Egea and colleagues represented the jelly-roll core, cell surface, and internal cavity of a cylindrical NMC 21700 cell. The researchers inserted a thermocouple through the cell's negative terminal and into its central cavity. Measurements were collected at ambient temperatures of 10, 25, and 45 degrees Celsius and at current rates between 0.3C and 3C.^[8]

At a 3C discharge rate, the measured difference between internal and surface temperature exceeded 16 degrees Celsius at an ambient temperature of 10 degrees Celsius. The difference was approximately 6 degrees Celsius at 45 degrees Celsius.^[8]

Across the reported calibration and validation cases, the model's voltage RMSE ranged from 1.7 to 7.0 mV. Its temperature RMSE ranged from 0.0069 to 0.1279 degrees Celsius. The reported quantitative validation covered discharge conditions. The model assumed uniform volumetric heat generation and was not validated for aged cells or abuse conditions.^[8]

Mazzeo and colleagues modeled laboratory-assembled graphite-NMC622 coin cells in GT-AutoLion. Static parameters were first calibrated against C/20 discharge measurements. Dynamic parameters were then fitted using C/10 and C/5 measurements at 26 and 45 degrees Celsius. These parameters included exchange-current density, contact resistance, and solid-phase diffusion coefficients.^[6]

Relative root-mean-square errors for the static and dynamic voltage models ranged from 0.959% to 1.95%. The lowest reported error was obtained for the C/10 test at 45 degrees Celsius, while the highest was obtained for the C/20 test at 26 degrees Celsius.^[6]

The study also calibrated degradation models against cycle-aging measurements. Two state-of-charge ranges were tested: 100% to 0% and 90% to 10%. The state-of-health model had a relative root-mean-square error of 5.64% for the 90% to 10% test and 18.4% for the 100% to 0% test.^[6]

The researchers did not have calendar-aging measurements. They stated that these measurements would be needed to separate capacity loss during storage from degradation caused by cycling.^[6]

GT-AutoLion simulations were used to develop BattBee, a reduced-order equivalent-circuit model for internal short circuits and thermal runaway. Kang, Tu, and Fang configured a 25 Ah graphite-NMC811 pouch cell in GT-AutoLion and used simulated drive-cycle data to calculate BattBee's electrical and thermal parameters.^[10]

Under normal operating conditions, BattBee produced an average voltage RMSE of 33 mV and a surface-temperature RMSE of 0.22 K when compared with the GT-AutoLion results. The reduced-order model used a simpler circuit structure than the GT-AutoLion model.^[10]

The researchers first simulated an internal short circuit in GT-AutoLion-3D. They then used GT-AutoLion-1D to simulate the subsequent thermal-runaway process. The resulting data were used to fit BattBee's short-circuit and heat-generation parameters.^[10]

BattBee and its fault-detection method were also evaluated using mechanical-indentation measurements from Sandia National Laboratories. The tests produced internal short circuits followed by thermal runaway events. Gamma Technologies provided equipment for the study and was one of its funders.^[10]

The accuracy of a GT-AutoLion model depends on the data and operating conditions used for calibration. In one study, a model calibrated at 25 degrees Celsius failed to produce physically valid results at 0 and -10 degree Celsius. It produced valid results at those temperatures after the researchers added temperature-dependent transport properties and recalibrated it using measurements from several temperatures.^[7]

Thermal predictions also depend on how the cell is represented. A single-mass model can calculate the cell's general temperature response but does not resolve differences between its core and surface. Models with several thermal regions can calculate these differences, although they require more parameters and measurements for calibrations.^[7][8]

Aging models require tests that separate capacity loss during cycling from capacity loss during storage. Cycle-aging measurements alone may not distinguish degradation under load from capacity loss during storage. Mazzeo and colleagues calibrated their aging model using cycle tests but did not have calendar-aging data. They identified calendar-aging measurements as necessary for distinguishing the modeled degradation mechanisms more clearly.^[6]