Take PyBaMM battery simulation from notebooks to production workflows
Ionworks gives battery R&D teams the data infrastructure, parameterization tools, and simulation management that turns open-source PyBaMM models into repeatable engineering processes.
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Who this page is for
This page is for battery engineers, electrochemical modelers, and R&D team leads who use PyBaMM software (or are evaluating it) and need a path from individual Python notebooks to shared, reproducible simulation workflows. If your team has outgrown ad hoc scripts but doesn’t want to build internal tooling from scratch, start here.
PyBaMM alone vs. PyBaMM with Ionworks
PyBaMM is a powerful open-source simulation engine. It solves electrochemical models, supports flexible experiment definitions, and gives researchers full control in Python.
What PyBaMM does not provide is the connective tissue around simulation: structured test data storage, version-controlled parameterized models, team-wide access to results, or managed studies that stay reproducible across projects and personnel changes. Ionworks adds that layer.
Teams keep writing custom PyBaMM code when the problem calls for it. Ionworks handles the rest: data, parameters, simulation management, and shared visibility.
Why teams start with PyBaMM
PyBaMM is an open-source physics-based battery modeling package in Python. It provides a framework for writing and solving systems of differential equations, a library of battery models and parameters, and specialized tools for simulating battery-specific experiments and visualizing results.
The project supports flexible model definitions and fast battery simulations across SPM, SPMe, DFN, and other electrochemical model types.
For individual researchers and small projects, PyBaMM is a strong foundation. Teams choose PyBaMM because the modeling depth is real, and the open-source ecosystem around it is active.
Where notebook-based PyBaMM workflows break down
The challenge is not what PyBaMM can model. It is what happens when a team of five or fifteen people needs to run, compare, and trust those models week after week.
Battery parameter estimation is the real bottleneck
Physics-based battery models depend on accurate parameterization, and many parameters are unknown or difficult to measure directly. Teams accumulate parameter sets with unclear provenance.
Test data is fragmented across lab systems
Battery R&D teams generate data across BioLogic, Maccor, Neware, Arbin, and other cycler platforms. Fragmented storage becomes a modeling problem.
Scaling simulations creates overhead notebooks can’t absorb
Running a structured study that compares dozens of protocol variations and lets a colleague reproduce results six months later requires infrastructure notebooks were never designed to provide.
Python flexibility is powerful, but not always team-friendly
For a team that needs to run standard simulations weekly, flexibility becomes a coordination cost. A web interface extends access to engineers and managers.
What battery teams need beyond open source
The missing piece is not a better model. It is structured management around the model: organized data, validated parameters, traceable simulations, and reproducible investigations.
A system of record for battery test data
Structured battery test data management for cell measurements, time-series cycling data, and cell metadata.
Repeatable model fitting and validation
Create, version, and share parameterized models tied to specific cell specifications and validated parameter sets.
Scalable simulation for engineering decisions
Managed simulation runs driven by real protocols, with support for parameter sweeps, scenario comparison, and result reuse.
Optimization built on physics-based models
Explore design parameters and operating tradeoffs against engineering constraints, using validated physics-based battery models.
How Ionworks extends PyBaMM for teams
Ionworks Studio is a battery modeling platform built on PyBaMM’s modeling foundation. It adds data management, parameterization tracking, and simulation coordination that turns models into shared, reproducible engineering processes.
Measure
Teams upload data from Maccor, Neware, BioLogic, and other cycler formats into one shared system of record. Data is organized around cell instances and measurements.
Train
A parameterized model combines a specific electrochemical model, a validated parameter set, and a cell specification. Once used in a simulation it becomes immutable.
Predict
Simulations are configured by selecting a parameterized model and defining an experiment protocol. Identical completed simulations are reused rather than rerun.
Optimize
Ionworks supports battery design optimization and tradeoff exploration. Teams define objective functions tied to real performance targets and sweep design parameters.
Example questions teams can answer
What is the fastest charge rate that avoids lithium plating across a temperature range?
A validated electrochemical model can predict plating onset across conditions that would take months to test exhaustively.
How do we set charge and discharge limits for a new cell chemistry?
Parameterized models fitted to early test data let teams simulate operating envelopes before committing to long cycling studies.
What are the energy-power tradeoffs for different electrode designs?
Sweeping design parameters in Ionworks Studio surfaces tradeoffs across the design space in hours, not weeks.
Which charge protocol minimizes degradation for a target duty cycle?
Protocol-driven simulations using real cycler files let teams compare protocol variants against the same validated model.
Built by the team behind PyBaMM
Ionworks is built by the team that created and maintains PyBaMM. Ionworks Studio uses PyBaMM as its simulation engine, so teams get the same electrochemical model fidelity with the coordination and traceability their workflow requires.
Physics-based battery modeling is useful only when the models are parameterized against real data, the parameter sets are traceable, and the simulation results are reproducible. Ionworks provides that combination as battery R&D software rather than leaving each team to assemble it internally.
Frequently asked questions
What is PyBaMM used for?
PyBaMM is an open-source Python library for physics-based battery modeling. It lets researchers define, parameterize, and solve electrochemical models to simulate battery behavior.
Is PyBaMM enough for a battery R&D team?
For individual work, yes. For team-scale workflows, it lacks structured data management, versioned models, and shared access. Ionworks Studio adds those capabilities.
What is a parameterized model?
A parameterized model bundles an electrochemical model type, a validated set of parameters, and a cell specification into a single, ready-to-run configuration.
Why use a GUI if my team already uses Python?
Python handles custom model development. A shared web interface handles routine work: configuring simulations, comparing results, and giving non-coding team members access.
How does Ionworks relate to PyBaMM?
Ionworks is built by the team that created PyBaMM and uses PyBaMM as its simulation engine. It wraps the solver with data management, parameterization tracking, and a web interface.
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