Revolutionize Battery Design with AI-Enhanced Simulation
What's Inside This Document
Breakthrough Battery Modeling Technology: Discover how Dyad (formerly JuliaSim) Batteries is transforming lithium-ion battery development with advanced electrochemical simulation that's 150,000x faster than real-time. This technical overview reveals how engineers are predicting battery lifetimes, optimizing fast-charging performance, and scaling from single cells to massive battery packs.
Unprecedented Performance Claims
Lightning-Fast Simulations
150,000x faster than real-time battery lifetime predictions
Complete lifetime estimation in under one minute using the DFN model
300 differential equations solved efficiently with state-of-the-art solvers
Seamless scaling from single cell to thousands of connected cells
Advanced Physics Integration
Electrochemical modeling with Doyle-Fuller-Newman (DFN) precision
Thermal management for real-world operating conditions
Degradation physics including SEI capacity fade models
Scientific Machine Learning to discover hidden governing laws from data
What Makes This Revolutionary
Complete Battery Ecosystem Modeling
Explore how Dyad Batteries handles the full spectrum of battery simulation challenges:
Cell-level behavior with single particle models
Module integration for complex battery architectures
Pack optimization for electric vehicles and energy storage
Fast-charging analysis under extreme operating conditions
AI-Powered Discovery
Learn how Scientific Machine Learning (SciML) combines physics-based models with data to:
Uncover hidden degradation mechanisms
Optimize low-temperature behavior
Predict real-world performance variations
Reduce experimental testing time and costs
Technical Capabilities Covered
Model Library Options
✅ Doyle-Fuller-Newman (DFN) - High-fidelity pseudo-2D electrochemical modeling
✅ Single-Particle with Electrolyte (SPMe) - Efficient cell-level degradation analysis
✅ Single-Particle Model (SPM) - Rapid simulations for pack-level studies
Advanced Features
✅ Uncertainty Quantification - Understand parametric uncertainty with Model Optimizer
✅ Pack Modeling - Scale to thousands of connected cells
✅ Degradation Prediction - SEI capacity fade and lifetime modeling
✅ Fast Charging Analysis - Robust simulations under extreme conditions
Industry Applications
Perfect for engineers working on:
Electric Vehicle battery pack optimization
Energy Storage Systems for grid applications
Consumer Electronics battery performance
Battery Manufacturing process optimization
Scientific Machine Learning Integration
Discover how to combine traditional electrochemical physics with modern AI techniques to:
Extract insights from experimental data
Accelerate model calibration processes
Predict performance under untested conditions
Reduce development cycles from months to weeks
Ready to accelerate your battery development? This document provides the complete technical specifications and capabilities for next-generation battery simulation.
