JuliaSimBatteries: high-performance lithium-ion battery simulations

JuliaSimBatteries is an advanced lithium-ion battery simulation tool integrating sophisticated electrochemical, thermal, and degradation physics. Utilizing the Doyle Fuller Newman (DFN) model, it can predict a battery's entire lifetime with fast charging 150,000 times faster than real time. The number of connected batteries is scalable from one cell to packs of thousands using electrochemical models. Scientific Machine Learning (SciML) enables the discovery of hidden governing laws from data, such as degradation and low-temperature behavior. Characterize material properties and propose battery designs using the parameter estimation and optimization tools in JuliaSim.

Graphical user interface

Overview of JuliaSimBatteries

Building accurate models are essential for understanding, optimizing, and designing batteries. Physically accurate battery models are computationally expensive and difficult to solve robustly. JuliaSimBatteries is more than 100 times faster than other battery modeling tools while solving the same physics, thanks to the speed of the Julia programming language. Bring your battery workflow to the next level to solve challenging problems:

  • Pack modelingJuliaSimBatteries is performant and enables the predictive power of electrochemical models for large-scale battery packs.
  • Uncertainty quantification – Uncertainty is inherent in battery modeling. JuliaSimBatteries helps mitigate and understand the root causes of uncertainty with JuliaSim Model Optimizer.
  • Fast charging – Built for robust and efficient simulations, even at the extreme operating conditions of fast-charge.
  • Degradation – Predict battery lifetime and health with SEI capacity fade models.
  • Discover hidden physics – Combine physics from our battery models and your data to discover hidden governing laws using SciML tools.
  • Lifetime prediction – Estimate a battery's entire lifetime with fast charging in under a minute with the DFN model.

Lifetime simulation

JuliaSimBatteries offers several electrochemical models for use at any scale:

  • Doyle-Fuller-Newman Model (DFN) – The DFN model is an advanced pseudo-2D battery model. It accurately represents electrochemical processes within a lithium-ion battery, including diffusion, reactions, and concentration gradients in both electrodes. This model offers high-fidelity simulations, enabling analysis and optimization of battery performance under various operating conditions.

  • Single Particle Model with electrolyte (SPMe) – The SPMe model is a popular model to understand lithium-ion battery behavior. It simplifies the battery into a single particle for each electrode, considering electrolyte dynamics within the cell. This model allows for efficient simulations and provides valuable insights into cell-level behavior and degradation mechanisms.

  • Single Particle Model (SPM) – The SPM model is a simplified version of the SPMe model, commonly used by technical battery engineers for quick and computationally efficient simulations. It represents the battery as single particles in both electrodes without considering the electrolyte dynamics. While less detailed than the DFN or SPMe models, the SPM model is effective for initial assessments, rapid battery analysis, and large-scale pack simulations.