Simulate battery charge and discharge

Open your own project in Codex or Claude Code, with permission to read and write files and run commands. An ordinary web chat may not be able to install a Skill.

• Codex or Claude Code with permission to read and write project files and run local commands. A chat page without file tools cannot install this skill.
• Python 3.12, PyBaMM 26.9.0.0, and pybammsolvers 0.10.0 for the upstream reviewed snapshot. Check the solver and project environment before installation.
• An appropriate parameter set and cell assumptions. The bundled simulations and CSV comparisons run locally after dependencies are available, without an API key.

These requests run in your assistant's host environment. This page provides guidance; it does not run AI tasks or collect your project files.

Copy this request into your project conversation. Ask the assistant to review the original Skill and explain any required setup before using it.

I am using Codex or Claude Code with filesystem and shell tools. Install PyBaMM from https://github.com/K-Dense-AI/scientific-agent-skills at commit 593377bb048752abb0152deab9fbb1083a6087df, folder skills/pybamm. Read SKILL.md and retrieve the complete skill with its references, scripts, and assets when present, not just the instruction file. Preserve the original repository LICENSE.md and K-Dense Inc. attribution. Use the current project's skill directory supported by this host, following existing project configuration. Do not overwrite an existing skill; explain conflicts first. Check dependencies and explain proposed environment changes before installing them. Tell me the installation path and how to invoke it, then wait for my files and task.

Once installation is confirmed, replace the example paths and details with your own files, then send this request in the same project.

Use the installed pybamm skill for a local teaching run of the bundled Chen2020 isothermal SPM protocol: initial SOC 0.8, temperature 298.15 K, discharge at 0.5C for 600 s, rest for 120 s, then charge at 0.5C for 600 s. Explain the parameter-set source and how nominal capacity determines current. Keep the original protocol and any measurement files unchanged, and save curves, parameters, and reports in a new output folder. Compare baseline results with tighter solver tolerances and a refined mesh. Label the result as a reference simulation, not validation of my own cell or a battery lifetime prediction; do not control laboratory hardware.

• CSV curves for voltage, current, and net discharge capacity, plus runs using tighter solver tolerances and a refined mesh.
• Parameter and protocol provenance, package versions, numerical-check reports, and measurement residuals when matching cycling data is supplied.

Ask your assistant where each output was saved and how it was checked. Compare the result with your original inputs before you use or share it.

• Source and documentation checked on 2026-10-20; complete runtime workflows have not been tested here.
• The bundled helper covers isothermal single-cell SPM/DFN experiments. It does not provide arbitrary battery lifetime predictions, pack control, or a hardware operating envelope.
• Numerical convergence does not establish experimental accuracy. Parameter sets, initial state, temperature, and current must match the cell and measurement before interpreting residuals.
• The bundled example excludes aging, mechanics, and lithium plating; adding such physics requires a separate, validated model.

A third-party open-source selection by AimatraLab. The Skill belongs to its original author; source links go directly to their repository.

Original author

K-Dense Inc.

Repository

https://github.com/K-Dense-AI/scientific-agent-skills

Selected version

1.1

Fixed commit

593377bb048752abb0152deab9fbb1083a6087df

License

MIT

Documentation checked

2026-10-20