JuliaSim Batteries: Build Better Batteries with Experiments and 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.

Our software can simulate 100 days of cycling in 60 seconds

Physical prototyping can take weeks – our product can cut down your prototyping process by providing physics-backed machine learning libraries that can simulate the same battery performance - and give you the output of an experiment in under 60 seconds. This rapid experimentation can help your teams build the best cells and packs.

JuliaSim Batteries helps you reduce time and overhead to build better batteries 10x faster and at a much lower cost.


Product Screenshot

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.


  • Seamlessly scale single cells to packs of thousands for real-time simulation, optimization, and design
  • Design electrochemical cells and entire battery packs with thousands of cells with ease
  • Enhances physics-based precision with Scientific Machine Learning insights for the best of the worlds
  • Extreme speed and efficiency in computing helps you rapidly converge on the optimal decision, saving your company thousands of hours of time
  • Interpretability means that every step in the process can be observed and accounted for. Adjust any parameter to better understand the output


1: Optimize your Battery Chemistry

JuliaSim Batteries offers several electrochemical models in cells, modules, and packs

Battery Screenshot

How to set up a battery configuration:

  1. In the first panel at the top, select from a pre-existing battery model (from the dropdown).
  2. From the chemistry preset, select your battery chemistry.
  3. Choose whether this setup is for a single cell or packs of cells with the number in series and parallel.

You can also click the “Show Advanced Options” button to edit the battery components and chemistry even more.

2: Build Experiments

Experiments Screenshot

Setting up experiments:

The experiments panel lets you create a number of steps - each of which will be part of the overall experiment you are running with your battery.

  1. The first thing to set is the initial state of charge of the battery (a fraction from 0 - 1).
  2. Then, you’ll have a panel box for each step. Set the mode, the input value, and the time for this step. You can move each panel box around to change the order and also hit “repeat” to repeat a series of steps.

3: Run Predictive Analysis

Analysis Screenshot

Analyzing your experiment:

Now that you’ve set up an experiment, you can run the results to see how your battery performs.

  1. Select from a number of views and a number of plots
  2. Those plots will appear on the screen
  3. You can edit the time scale of the plots and change the number of cycles

Contact Us

Our team consists of battery experts and JuliaSim modeling experts. We’ll build a custom plan of action to bridge the gap between theory and testing, leading to an accelerated workflow, reduced risk, and increased productivity.

Email us at: sales@juliahub.com

Visit us at: https://juliahub.com/products/batteries

More Details of JuliaSimBatteries

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.

JuliaSim Batteries Simulation

JuliaSim Batteries is one piece of the JuliaSim ecosystem. To learn more about JuliaSim, click here.

Graphical user interface