State of health (SOH) prediction for Lithium-ion batteries using regression and LSTM
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Updated
Jun 30, 2024 - Jupyter Notebook
State of health (SOH) prediction for Lithium-ion batteries using regression and LSTM
Sunwoda Electronic Co., Ltd, and Tsinghua Berkeley Shenzhen Institute (TBSI) generate the TBSI Sunwoda Battery Dataset. We open-source this dataset to inspire more data-driven novel material verification, battery management research and applications.
A comprehensive simulation platform integrating vehicle dynamics, environment emulation, body controls, and battery management for holistic testing and validation of automated vehicles.
An awesome list of papers on remaining useful life (RUL) prediction from arXiv
Repository of my master’s thesis "Development and evaluation of a model for predicting the state of health of traction batteries based on artificial neural networks"
translation for paper Machine learning pipeline for battery state-of-health estimation
Sandbox to develop, test and implement estimation techniques for state of charge/health of a sample lithium-ion battery, utilizing transient signals to predict state at a certain point in time.
Code and models for estimating the State of Charge (SoC) and of battery cells. Utilizing advanced deep learning techniques.
Master Thesis in Data Science and Engineering
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