Category: GT-AutoLion

Soldier overseeing military drone while data streams in the background|Mapping of predicted anode potential|Virtual sensor estimating lithium plating|Virtual sensor estimating lithium plating risk in real time

Simulating Lithium–Sulfur Batteries for UAV Missions with GT-AutoLionSulfur

As industries push for lighter and more energy-efficient solutions, lithium–sulfur (Li–S) batteries are gaining increasing attention. Their appeal is clear: they offer the potential for much higher energy per weight than conventional lithium-ion batteries while using sulfur, an abundant and low-cost material. But while the promise is real, so are the challenges. Unlike lithium-ion batteries, […]

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Electric Car EV charging station people charging in car port

Lithium Plating Detection: How Virtual Sensors Enable Smarter Fast Charging

Fast Charging Pushes Li-ion Batteries Toward a Hidden Risk Fast charging has become a defining requirement for modern electric vehicles and battery-powered systems. Users expect shorter charging times, consistent performance, and reliable operation under all conditions. For simulation, battery control, and engineering teams, this shift raises a shared challenge: predicting and managing internal battery risk […]

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||Digital Twin Results in Live Analysis Dashboard|GT-Cloud for Digital Twin Execution|Daily SOH estimation for device leveraging GT-Play|GT-Autilion Digital Twin Models hosted on GT-Play|Digital Twin Cloud Setup|An innovative smart sensor box mounted on a sleek metal pole in a modern urban park

Virtual Validation of Lithium-Ion Battery Management Systems

Battery Management Systems (BMSs) play a critical role in ensuring lithium-ion battery packs operate safely, efficiently, and reliably across all operating conditions. As battery systems become more complex and application demands continue to increase, validating BMS algorithms has become significantly more challenging. Building on the previous blog’s overview of BMS architecture for lithium-ion batteries (LIBs), […]

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Part 3 — Integrating Physics‑Based Digital Twins into an IoT and Cloud Environments

Part 1 introduced the operational challenges associated with battery-powered IoT monitoring devices. Part 2 described how GT‑SUITE and GT‑AutoLion models capture the system physics and battery aging mechanisms. This final part explains how these models integrate into an IoT and cloud architecture, allowing each digital twin to operate as an automated, scalable component of the […]

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|An innovative smart sensor box mounted on a sleek metal pole in a modern urban park

Part 2 — Modeling Physical Digital Twins: Battery Behavior and Aging with GT‑SUITE and GT‑AutoLion

In Part 1, we explored why solar-charged, battery-powered IoT monitoring devices require reliable prediction of availability, SOH, and RUL, and why telemetry, statistical methods, or machine learning alone are not sufficient. In this second part, we focus on the modeling foundation: how GT‑SUITE and GT‑AutoLion provide the physics‑based structure that allows each digital twin to […]

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An innovative smart sensor box mounted on a sleek metal pole in a modern urban park

Part 1 — Exploring the Power of Physics-Based Digital Twins

From asset monitoring stations in remote locations to industrial sensors tracking equipment health, many modern systems rely on small, self-powered IoT devices operating far from human reach. These devices are typically equipped with a solar panel, a battery, and a communication module. Their job is simple: collect data, stay powered, and reliably transmit information to […]

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Machine Deep learning algorithms

Combining Physics and Machine Learning to Predict Battery Aging with Confidence

Battery technology is evolving rapidly to meet the growing demands of electric vehicles, large-scale energy storage systems, and portable electronics. A major challenge lies in reliably predicting long-term battery performance within practical development timelines. Because batteries degrade gradually during both use and storage, conventional testing methods take a long time to produce accurate lifetime estimates. […]

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Engineering Insights Blog|BMS|BMS|Tilt-Rotor Animation|Operational Window|VTOL|Simulation Result Summary Table|Flight Mission Profile|Simulation Results|Series Hybrid Tilt-Rotor Model Example in GT-SUITE|Comparison of steady and transient model response|IceFraction Animation|fuel cell bus|Schematic of water recirculation|System Workflow of a Series Hybrid Tilt-Rotor in GT-SUITE_01||Ice fraction near cathode catalyst layer||Routes Selected for System Level Simulation|2D flowfield in CAD

A Year of Engineering Insights: Our Top 7 Blogs You Shouldn’t Miss

From smarter thermal systems and next-generation batteries to digital twins, fuel cells, and advanced air mobility, we explored how engineering simulation is reshaping engineering decisions across industries. If you’re working at the intersection of innovation, performance, and efficiency, these seven blogs capture the most impactful ideas we shared in 2025, each addressing real-world engineering challenges […]

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