Changelog

We’ve just launched a redesigned Simulation Dashboard, making it easier than ever to explore and visualize your simulation results. Whether you’re comparing dozens of runs or diving deep into one, the new dashboard adapts to your workflow.

Key Features:

• Resizable & Reorderable Plots: Customize your layout to focus on what matters most.

• Multiple Dashboards per Dataset: Create different views for different questions—no need to rerun simulations.

• Smart Filtering: Set filters for each plot so new simulations that match your criteria are automatically included—no manual updates needed.

This upgrade builds on all the existing plotting functionality you’re used to:

• Flexible Plotting: Visualize time series variables or aggregated metrics with ease.

• Overlays Made Easy: Compare multiple simulations on a single plot with automatic labeling.

Whether you're running 1 or 100 simulations, your data just got a whole lot easier to navigate.

After parameterizing a model, one of the most important steps is validating how well it generalizes to real-world data. Ionworks now makes this step effortless with built-in support for parameter validation in the parameterization pipeline.

Key Features:

• Easy Validation Setup: Pick your model and parameter set, upload one or more validation datasets, and let Ionworks handle the rest.

• Automated Model Runs: The selected model is automatically run against the validation data.

• Fit Quality Report: Quickly see how well the model fits each dataset with clear, visual feedback—no manual scripting required.

This helps you catch overfitting, compare parameter sets, and build confidence in your model before moving on to design or optimization.

Running and managing parameterizations just got a major upgrade. Ionworks now supports a standard configuration format and a command-line interface (CLI) to make large-scale, reproducible parameterization easier than ever.

Key Features:

• Batch Parameterization: Run large sets of parameterizations across multiple datasets with a single config file.

• Experiment Management: Swap models, cost functions, optimizers, and hyperparameters without rewriting code—perfect for rapid iteration.

• Cloud Execution: Launch parameterizations on the cloud seamlessly, with full support for parallel runs.

Configurations can be defined flexibly in JSON, YAML, or Python dictionaries, and executed either:

• Directly from Python, or

• From the Command Line Interface (CLI)

This new tooling makes it easy to scale up your workflows and bring consistency to even the most complex parameterization experiments.

Clean, standardized data is the foundation of good modeling—but getting there can be a pain. Ionworks now includes robust data processing tools and a new Data Loader to streamline this entire workflow.

Key Features:

• Flexible Ingestion: Load raw data from any cycler format—whether it's on your local machine or in the cloud (e.g., AWS S3).

• Targeted Loading: Select just the parts of the data you need, such as specific step numbers, time ranges, or cycles.

• Quick Visualization: Generate basic plots of the raw or processed data in one command.

• PyBaMM Experiment Generation: Automatically build a PyBaMM Experiment object that reproduces the uploaded data—ideal for model validation or parameter fitting.

This makes it easier than ever to go from raw experimental data to simulation-ready input in minutes—not hours.

Here’s what’s new:

• More Flexible Simulations – You can now adjust chemistry, capacity, and initial SoC before running a simulation.

• Expanded Output Data – See total time, throughput capacity, and throughput energy for your simulated protocol.

• Improved Parsing Capabilities – We now support:
  - Drive cycles ("waveforms")
  - Temperature cutoffs ("thermocouple")
  - CCCV steps
  - Capacity-based termination

We’ve revamped how you can add new models to Ionworks Studio

• Start from anywhere – create a model using an existing one, choose from a library of parameter values, or upload your own.

• Automated parameter extraction – required parameters are detected and grouped by component for clarity.

• Full parameter control – edit any parameter, including arbitrary Python functions, to fine-tune your models.

This update makes it easier to compare different parameter sets and analyze the sensitivity of key values, helping you refine your models and extract deeper insights.

Watch the video below for an example investigating the effect of reaction rate and diffusivity on the cell voltage

We have developed a specialized model for phase-separating materials such as LFP, LCO, LTO, and graphite. This advancement enables researchers and engineers to simulate and analyze LFP battery performance with enhanced accuracy and efficiency.

Key Features of the Ionworks LFP Model

• Accurate Electrochemical Simulations: Our model captures the unique electrochemical properties of LFP, providing precise simulations of charge/discharge cycles, voltage profiles, and capacity fade over time.

• Thermal Behavior Analysis: Understand the thermal characteristics of LFP cells under various operating conditions to ensure safety and optimize thermal management systems.

• Customizable Parameters: Adjust variables such as particle size, electrode thickness, and electrolyte composition to tailor the model to specific LFP battery designs.

Applications and Benefits

Incorporating the LFP model into the Ionworks platform allows for:

• Enhanced Battery Design: Optimize cell and pack configurations for applications requiring high safety and longevity.

• Performance Prediction: Accurately forecast battery behavior under different load conditions and usage scenarios.

• Accelerated Development Cycles: Reduce reliance on extensive physical prototyping by leveraging simulation data, thereby saving time and resources.

For more detailed information check out our blog post on Battery Design

Get in touch for access to the model!

We’re excited to introduce the Battery Cycler Protocol Simulator, a powerful tool designed to help battery researchers and engineers verify cycler protocol files before running real experiments. By simulating test sequences, the tool detects common errors—such as incorrect current or voltage limits, misconfigured loops, or unexpected step transitions—before they cause failed tests. This initial beta release supports Maccor and Neware protocol files, with support for Arbin, BioLogic, and Novonix coming soon.

Key Features:

• Protocol Simulation: Run a full simulation of your cycler protocol before execution.

• Error Detection: Identify incorrect voltage/current limits, step sequence errors, and looping mistakes.

• Visual Step Execution Flow: See how your protocol will run in a step-by-step breakdown.

• Web-Based Platform: No installation required; access it anywhere.

This release is just the beginning! We’re working on expanded protocol support, advanced loop validation, and API integration to make protocol verification even more seamless.