Fast and flexible physics-based battery models in Python

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).

Create reduced-order state-space models for lithium-ion batteries utilising realisation algorithms.

cideMOD solves DFN physicochemical equations by Finite Element methods using FEniCS library. It enables doing physics-based battery simulations with a wide variety of use cases, from different drive cycles to studies of the SEI growth under storage conditions. Thermal and degradation models can be used to obtain more realistic predictions.

Physics-Informed Neural Network SurrogaTe for Rapidly Identifying Parameters in Energy Systems

Supporting material for the review of continuum models by Brosa Planella et al (2022).

A copier template for battery modeling projects using PyBaMM

Tinkering on neural networks for battery modeling and agent-based operating adventures

Redox flow battery electrochemical cycling models in Python

Battery Systems Modeling in Python