A set of scripts for constructing diffractograms taken in the operando mode during charge-discharge of the electrode material.

High Dimensional Regression Coefficient Analysis for Functional Data

Master's thesis project consisting in the development of a pipeline to segment and render tomography data of lithium-ion batteries during abuse testing.

Projeto de PCAER do IPS

A Python module for identifying the location of knee/elbow points and onsets in battery capacity/internal resistance degradation curves.

This repository provides a model deployment framework (MDF) for real-time lithium-ion battery model utilization in CAN-capable test benches. It can be used for the investigation of advanced battery management strategies in short- and long-term experimental studies.

Lithiation of amorphous silicon protocol

Herramienta para analizar los datos de descarga de una batería de Litio Samsung INR21700 o similares obtenidos con una Carga Electrónica Rigol DL3021

This app is an ASE-base workflow used to reproduce a rational initial SEI morphology at the atomic scale by stochastically placing the crystal grains of the inorganic salts formed during the SEI's reaction.

A NLEIS toolbox for impedance.py that provides RC level nonlinear equivalent circuit modeling (nECM) and analysis

Unofficial reproduction of: A transferable lithium-ion battery remaining useful life prediction method from cycle-consistency of degradation trend(2022)

Efficiency, thermal, and sizing models for drone motors, controllers, and batteries.

Deep learning of lithium-ion battery SOH using the DeTransformer model learns the aging characteristics of the battery and then makes predictions about the battery SOH in order to monitor the health of batteries in electric vehicles.

Timeseries of lithium-ion battery packs

Grid-scale li-ion battery optimisation for wholesale market arbitrage, using pytorch implementation of dqn, double dueling dqn and a noisy network dqn.

The project focused on "Battery Remaining Useful Life (RUL) Prediction using a Data-Driven Approach with a Hybrid Deep Model combining Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM)." This repository aims to revolutionize battery health estimation by leveraging the power of deep learning to predict the remaining useful life