pybamm

Fast and flexible physics-based battery models in Python

Curated index of open-source electrochemical battery models (DFN/P2D, SPM, SPMe) with reproducibility-focused reviews.

Code and data for the paper "Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods" by Brosa Planella et al. (2021).

An automated Twitter Bot that Tweets random Battery Simulations and replies to requested Battery Simulations.

A copier template for battery modeling projects using PyBaMM

C/C++ solvers used by PyBaMM, and their Python bindings

pybamm.org website source code

A comprehensive simulation platform integrating vehicle dynamics, environment emulation, body controls, and battery management for holistic testing and validation of automated vehicles.

Battery toolbox for PathSim

Sandbox to develop, test and compare Kalman Fitler-enabled estimation techniques for state of charge of a sample lithium-ion battery, utilizing transient signals to predict state across points in time.

Physics-based electrochemical modeling of lithium-ion cells in Python: a from-scratch DFN/SPM solver, parameter estimation, and thermal, degradation, and design studies, validated against PyBaMM.

High-cycle thermal-mechanical fatigue simulation and optimization framework for FGM-SMA lattices using PyBaMM/LAMMPS-driven multi-physics modeling.

Phase 1 battery fleet study across three different battery chemistries (residential systems) include the analysis: telemetry quality, SOH forecasting, anomaly screening, PyBaMM reference models, and synthetic dispatch.

PyBaMM sensitivity analysis of electrolyte conductivity and thickness in a PILBCP-inspired solid polymer electrolyte battery.

Thevenin ECM parameter identification (NASA PCoE) + early cycle-life prediction from 100 cycles (MIT-Stanford, Severson 2019 protocol) + PyBaMM physics ruler + third-party identification audit. Measurable is not identifiable.

从零复现 Severson et al. 2019（Nature Energy）电池循环寿命预测：官方 test_ind 口径 EN6 中位百分比误差 5.9%（95% bootstrap CI 5.1~8.9，论文点估计 9.1%），并拆解出 33.6%→5.9% 的完整误差归因链。原创拓展：NASA 数据 34 颗标签审计、零样本跨数据集迁移、域间隙四实验拆账、KM 生存分析。附 13 篇攻略文档、可复跑脚本、测试与 CI，每个结论都有数据与复核记录。

NFPP Sodium-Ion Energy Storage Evaluation for Distribution Networks

Observability-aware battery fault diagnostics with calibrated abstention, external simulation, and measured-cell evaluation.

Localization of The PyBaMM Documentation

[WORK IN PROGRESS] an attempt to run PyBaMM simulations in the browser via Pyodide 🌐