{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"PyBaMM is a Python framework for simulating battery behavior by solving systems of differential equations, providing pre-built battery models, parameters, and tools for running battery-specific experiments and visualizing results.","skillfed_tags":["battery-simulation","electrochemistry","scientific-computing"],"use_cases":["Simulate constant-current discharge profiles for lithium-ion cells to predict capacity and voltage curves.","Model complex multi-step charge/discharge experiments (e.g., CCCV charging) to validate battery management strategies.","Explore the effect of parameter changes (electrode thickness, porosity, conductivity) on battery performance without physical prototyping.","Develop and test custom battery models by writing differential equations within the PyBaMM framework.","Generate publication-ready plots and data exports for battery performance analysis and research papers."],"what_it_does":"PyBaMM (Python Battery Mathematical Modelling) is an open-source framework for simulating battery electrochemistry by solving differential equations. It combines a general-purpose solver framework with a library of pre-built battery models (such as the Doyle-Fuller-Newman model), parameter sets, and experiment definitions. Users can run simple constant-current discharge simulations with default settings or define complex multi-step experiments (discharge, rest, charge cycles) with custom physics, geometry, discretization methods, and solver parameters.\n\nThe package is designed for battery research and development, enabling exploration of how design choices and modeling assumptions affect performance under different operating scenarios. It depends on scientific Python libraries (numpy, scipy, sympy, pandas, xarray) for computation and visualization, and includes optional support for JAX-based solvers. The project is actively maintained, fiscally sponsored by NumFOCUS, and follows a CalVer versioning scheme with documented breaking-change policies.","worth_installing":"Yes. PyBaMM is a mature, actively maintained framework (Production/Stable status, recent release) with low installation friction, permissive licensing, and no known vulnerabilities. Install it if you need to simulate battery electrochemistry, validate battery designs computationally, or conduct battery modeling research. The large dependency tree (13 runtime packages) is standard for scientific Python and poses no unusual risk."},"id":"pybamm","links":{"html":"https://skillfed.io/packages/pybamm","md":"https://skillfed.io/packages/pybamm.md","pypi":"https://pypi.org/project/pybamm/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-22","license_spdx":null,"license_treatment":"permissive","name":"pybamm","python_support":"supports_current","summary":"Python Battery Mathematical Modelling"},"popularity":{"monthly_downloads":106144,"position":12667,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.7.1.0"}