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Learn about advanced lithium-ion battery simulation through a conference talk that showcases JuliaSimBatteries, a powerful tool integrating electrochemical, thermal, and degradation physics modeling. Explore how the Doyle Fuller Newman (DFN) model enables lifetime predictions of batteries with fast charging capabilities that are 150,000 times faster than real-time simulations. Discover the scalability features that allow modeling from single cells to thousands of battery packs using electrochemical models. Understand how Scientific Machine Learning (SciML) uncovers hidden governing laws from data, particularly in degradation and low-temperature behavior. Master parameter estimation and optimization tools for characterizing material properties and designing batteries. Gain insights into solving challenging problems including pack modeling, uncertainty quantification, fast charging simulations, degradation analysis, physics discovery, and rapid lifetime predictions. Learn why this Julia-based solution performs 100 times faster than comparable battery modeling tools while maintaining physical accuracy and robust solving capabilities.
