JuliaHub is thrilled to announce a game-changing advancement in lithium-ion battery simulation: JuliaSim Batteries. This newly released tool extends state-of-the-art cell models with electrochemical, thermal, and degradation physics to the pack, enabling highly accurate simulations.
Overcoming Challenges in Conventional Battery Simulation
"Accurate battery models involve solving several coupled partial differential equations (PDEs), posing serious computational challenges," states Dr. Marc D. Berliner, Lead Developer of JuliaSim Batteries. "JuliaSim Batteries elevates your workflow with its bleeding-edge differential equation solvers integrated with Scientific Machine Learning (SciML)."
Commenting on the launch, Dr. Michael Tiller, Senior Director of Product Management, said, "JuliaSim Batteries brings predictive electrochemical models within reach, accelerating innovation in battery design, manufacturing, and research. This empowers engineers at OEMs and component suppliers to run analyses on both individual cells and/or battery packs containing thousands of cells. Utilizing the pseudo-2D Doyle-Fuller-Newman model, JuliaSim Batteries predicts battery lifetimes with CC-CV fast charging 150,000x faster than real-time."
"JuliaSim Batteries is the first system to allow for realistic physics at the scale of battery packs," said Dr. Chris Rackauckas, VP of Modeling and Simulation at JuliaHub. "We're excited to see how properties like accurate thermal predictions and degradation for large-scale packs will improve how engineers design electric vehicles and aircraft."
Seamless Integration with SciML
JuliaSim Batteries boasts state-of-the-art differential equation solvers integrated with SciML, enabling users to harness the predictive power of electrochemical models for large-scale battery packs. The solution offers sophisticated state of health (SOH) and state of charge (SOC) estimation, along with estimation of critical thermal measurements, enhancing battery safety and performance monitoring. Selecting the perfect battery fit for your application can be complex and challenging owing to the intricacies of diverse battery chemistries and form factors. JuliaSim Batteries empowers engineers and scientists to design and analyze batteries starting at the cell to find the right battery for their use case. Ultimately, this leads to better predictability, improved battery performance, and selecting the right battery fit given the complexities of diverse chemistries.
Key Capabilities
JuliaSIm Batteries includes key capabilities such as simulating manufacturing defects, fast charging, forecasting lifetime health and degradation, and comparing cell chemistries.
Several electrochemical models are offered for cells, modules, and packs, such as the Doyle-Fuller-Newman Model (DFN), the Single-Particle Model with electrolyte (SPMe), and the Single-Particle Model (SPM). From uncovering hidden physics to understanding degradation and low-temperature behavior, JuliaSim Batteries revolutionizes battery simulation.
Join our webinar series
To learn more about JuliaSim Batteries and gain practical insights into battery simulation, modeling, performance, predictive analysis, and optimization, we invite you to our webinar series, where Dr. Marc D. Berliner and Dr. Ranjan Anantharaman will showcase the capabilities of JuliaSim Batteries.
Register here:
Contact us to learn more about JuliaSim Batteries.
