Academic Battery & Cell Tools

Python Battery Mathematical Modelling. Physics-based battery simulation framework implementing SPM, SPMe, and Doyle-Fuller-Newman models. Widely used for cell performance and degradation studies.

Python Battery Optimisation and Parameterisation. Fits PyBaMM electrochemical and equivalent circuit models to experimental data using Bayesian and deterministic methods. Published in JOSS 2025.

Battery Modelling Toolbox for continuum electrochemical simulation in MATLAB and Julia. Supports 1D–3D DFN models, full thermal coupling, and geometries including cylindrical, pouch, and coin cells.

Multiphase Porous Electrode Theory. Implements Cahn-Hilliard phase-field models for phase-separating electrode materials (e.g. LFP). Handles non-equilibrium thermodynamics beyond classical Newman porous electrode theory.

Lithium-Ion Open-Source Simulator with Battery Algorithms. Finite volume P2D model in MATLAB for cell and pack simulation. Supports thermal dynamics, drive cycles, and MPC-based charge control.

The original Newman electrochemical battery model, now wrapped with a Python API for hierarchical control and visualisation. The foundational reference model against which newer frameworks are validated.

Porous Electrode Theory lithium-ion simulation in Julia. Millisecond-scale P2D solves using automatic differentiation and continuous callback for CC-CV simulation. Benchmarked against LIONSIMBA and PyBaMM.

Fast implementation of the Doyle-Fuller-Newman electrochemical-thermal model in MATLAB. Widely cited as a compact, high-performance P2D reference implementation and used as a benchmark in comparative studies of open-source DFN codes.

Co-simulation Battery Modelling for Accelerated Parameter Optimisation. DFN model in MATLAB with integrated closed-loop parameter identification using particle swarm optimisation. Benchmarked against LIONSIMBA and PyBaMM; published JOSS 2025.

Cell Inhomogeneous Degradation Electrochemical Model. Full 3D electrochemical-thermal-degradation simulation in Python using FEniCS/FEniCSx. Resolves spatial inhomogeneity across the full cell domain, including real cell geometries.

Ultra-high-capacity 3D battery modelling simulator using fully coupled thermal-electrochemical physics. Designed for large-scale pack simulation — a single run can couple tens of thousands of DFN models. Developed under £2m Faraday Institution funding; now licensed to About:Energy.

Python Equivalent Circuit Network modelling framework. Distributed 3D electro-thermal ECN model for cylindrical, prismatic, and pouch cells with ageing, composite electrode hysteresis, and tab design effects. Compatible with PyBOP for parameterisation.

Battery Performance and Cost model. Bottom-up Excel-based tool for designing Li-ion packs for HEV, PHEV, and BEV applications and estimating manufacturing cost at production scale. The only publicly available peer-reviewed bottom-up battery cost model; widely used by government agencies, OEMs, and researchers.

Order-N Electronic Total Energy Package. Large-scale atomistic quantum-mechanical simulation using density functional theory. Free to academics worldwide; commercially licensed for industry. Extended by Faraday Institution to model battery interfaces and electrochemical systems.

Small LCO pouch cells tested at 40 °C using CCCV charging and Artemis urban drive cycle discharging, with characterisation tests every 100 cycles. Supports degradation modelling and BMS algorithm development.

124 commercial LFP/graphite 18650 cells cycled under diverse fast-charging protocols at 30 °C. Cycle life spans 150–2,300 cycles. The benchmark dataset for ML-based cycle life prediction and charge optimisation.

Collection of NMC/NCA cycling datasets from Stanford's Energy Control Lab, including UDDS drive-cycle ageing (INR21700-M50T, 28 months), eVTOL high C-rate datasets, and parallel module experiments covering cell-to-cell variation.

Center for Advanced Life Cycle Engineering dataset covering cylindrical, pouch, and prismatic cells with chemistries including LCO, LFP, and NMC. Includes full and partial cycling, drive profiles, OCV, and EIS measurements.

279 Samsung INR21700-50E NMC cells aged across 71 conditions combining calendar and cycle degradation. Uses full-factorial and Latin hypercube DoE plus model-based optimal experimental design. Published Scientific Data 2024.

55 18650 cells across 6 batches, cycled to end-of-life at 4C discharge under CC-CV charging. Widely used for SOH estimation and RUL prediction benchmarking; cited in over 100 publications. Accompanied by a preprocessing code library.

The first public multi-institution battery cycling data repository. Aggregates datasets from SNL, CALCE, HNEI, University of Michigan, and others into a standardised format with immediate browser visualisation of capacity fade, efficiency, and full charge/discharge curves.

Advanced Vehicle Testing Activity battery pack testing data. Real-world EV battery degradation from fleet vehicles including Nissan Leaf and Chevrolet Volt, tested at regular mileage intervals. Includes static capacity and HPPC tests across the full lifespan. Covers AC Level 2 vs DC fast charging effects.

86 commercial 18650 cells (NCA, NMC, LFP) cycled across temperature, depth of discharge, and C-rate conditions to 80% capacity. EIS measured every 3% capacity fade. Complements the Preger 2020 publication on chemistry-dependent degradation.

Python package for EIS data analysis. Plots Nyquist and Bode spectra, fits equivalent circuit models, validates spectra with Kramers-Kronig relations, and reads Biologic, VersaStudio, and ZView file formats.

Efficient numerical impedance computation for any PyBaMM model using automatic differentiation. Enables fast physics-based EIS at any operating point; integrates with PyBOP for model parameterisation from impedance data.

Battery cycling data processing library that parses Arbin cycler files and enables pandas-based data manipulation. Supports incremental capacity (dQ/dV) analysis, ICA/DVA, and OCV relaxation point extraction.

Battery Evaluation and Early Prediction software. Parses cycling data from Arbin, Biologic, Maccor, and Neware cyclers; validates data integrity; extracts features for cycle life ML models; and can automate cycler experiment setup.

Mechanistic degradation diagnosis tool implementing the Dubarry model. Quantifies loss of lithium inventory (LLI), loss of active material (LAM), and kinetic hindrance via electrode voltage signature matching. Used by 125+ organisations worldwide. Free for academic use.

Open-source ML platform for battery degradation modelling. Unifies data preprocessing, feature extraction, and model training (from sklearn to CNN/LSTM) across multiple public datasets. Bridges the battery science and machine learning communities.

Battery Lifetime Analysis and Simulation Toolsuite. Library of validated degradation models for commercial Li-ion cells (NMC, NCA, LFP) in Python and MATLAB. Simulates calendar and cycle ageing under realistic EV and stationary storage duty cycles.

Open-source implementation of NREL's AI-Batt toolkit. Uses bi-level optimisation and symbolic regression to autonomously identify algebraic models for Li-ion battery calendar ageing capacity fade.

Python Battery Electrode Potential calculator. Automates OCV decomposition to determine individual electrode OCP curves and stoichiometric cycling ranges from full-cell OCV measurements, without cell teardown. Includes a GUI and curated OCP database. Published Batteries 2025.

Probabilistic Inference on Noisy Time Series. General-purpose Bayesian optimisation and sampling library developed for electrochemical and physiological time-series models. Implements MCMC, nested sampling, and derivative-free optimisers. Underpins parameter inference in PyBOP.

Python Processing for Battery Experiments. Ingests and standardises data from multiple battery cycler manufacturers into a common format. Includes parsers for common cyclers and optimised data storage to minimise file sizes and processing time.

Machine-readable interactive registry of operando sample environments available across UK central facilities including ISIS and Diamond Light Source. Searchable by technique, beamline, cell configuration, temperature, and pressure. Helps researchers avoid duplicating hardware already available at national facilities.

Open standard and file format for physics-based Li-ion battery model parameters. v1.1 (March 2025) adds SPMe support, composite electrodes (graphite/silicon blends), hysteresis models, and degradation state specification. Designed to enable parameter hand-off between academia, cell suppliers, OEMs, and software tools including PyBaMM.

PostgreSQL database of DFN-type continuum battery model parameters drawn from the published literature. Queryable via Python (Google Colab notebooks provided). Accompanies the Wang et al. 2022 parameterisation review in Progress in Energy.

DFT-computed property database covering hundreds of thousands of inorganic materials. Includes band structures, formation energies, elastic properties, and intercalation voltages for battery electrode and electrolyte candidates.

FAIR-compliant ontology and linked data schema for battery data and models. Provides a semantic vocabulary for electrochemical parameters and experimental metadata, enabling interoperability between simulation tools including BattMo and PyBaMM.

Web platform for battery parameterisation and virtual cell testing. Provides access to pre-validated characterisation data and electrochemical models for hundreds of commercial cells, including degradation across lifespan. Integrates DandeLiion simulation technology. Launched October 2025; Faraday Battery Challenge-funded.

Excel-based cell modelling workbook for rapid energy density assessment of experimental cell designs. Covers four cell formats and five chemistries (Li-ion, Na-ion, lithium-sulfur, all-solid-state, hybrid solid-state). Updated May 2025. Intended for researchers assessing low-TRL materials in commercial cell contexts.

The first public multi-institution battery cycling data repository. Aggregates datasets from SNL, CALCE, HNEI, University of Michigan, and others with immediate browser visualisation of capacity fade, efficiency, and charge/discharge curves. Data downloadable; scripts available on GitHub.

Browser-based frontend for BattMo P2D simulations. Adjust cell parameters and experimental protocols through a Streamlit interface without installing MATLAB or Julia. Runs physics-based simulations on-demand via a backend API.

Web portal hosting the MIT-Stanford fast-charge cycling datasets. Provides browser-based dataset access and visualisation alongside Python API endpoints. Hosts multiple battery cycling datasets with documentation.

Interactive web-based demonstration of the 'Alawa degradation mode diagnosis toolbox. Explore LLI, LAM, and kinetic hindrance signatures without local installation. Full toolbox requires academic licence agreement.

The world's first universal open-source BMS hardware and software development platform, now in its second generation. 15+ years of Fraunhofer IISB R&D embedded in hardware and firmware. TÜV road-homologated on an NMC/graphite EV; used in a 100 kWh stationary system. BSD 3-Clause software, CC BY 4.0 hardware.

Techno-economic optimisation platform for distributed energy systems including battery storage. Sizes battery systems and determines optimal dispatch to minimise lifecycle energy costs, maximise resilience during grid outages, or reduce emissions. Available as a web tool, REST API, and open-source Julia code.

Curated index of open-source battery modelling and characterisation tools produced by Faraday Institution research programmes, including PyBaMM, PyBOP, BEEP, and related software. Updated as projects reach release.

Community-curated GitHub repository listing open battery datasets categorised by type: degradation, performance, materials, and special-application datasets from NASA, Oxford, MIT, CALCE, HNEI, Idaho National Laboratory, and others.

Structured overview of the open-source battery software ecosystem: simulation frameworks, cycling data tools, EIS analysis packages, and ML libraries. Includes comparison of PyBaMM, BEEP, impedance.py, cellpy, and others.

The authoritative index of all open-source tools produced under Faraday Institution research programmes. Currently lists PyBaMM, DandeLiion, ONETEP, PyBOP, PyECN, BPX, PyProBE, CAMS, and the Operando Cell Registry. The primary reference point for Faraday-funded software outputs.