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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
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.
Master's thesis project consisting in the development of a pipeline to segment and render tomography data of lithium-ion batteries during abuse testing.
This paper summarizes a deep learning-based approach with an LSTM trained on the widely used Oxford battery degradation dataset and the help of generative adversarial networks (GANS).
This research presents a detailed analysis of the current state of lithium extraction and refinement, covering various sources such as brine pools, hard rock, recycled electronics, and coal ash. It outlines the specific methods employed, the capital investment required, and the main challenges faced in each process.
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.
This repository contains code for estimating the State of Charge (SoC) of LG HG2 batteries using Fully Connected Network (FCN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) models along with optuna based hyperparameter tuning.
A robot car developed using Arduino that can operate in 3 modes - Manual, Automatic and Voice. The car is controlled wirelessly via Bluetooth with an android app developed using MIT App Inventor.