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ai-batt-os

an-introduction-to-statistical-learning

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

battery-parameter-spaces

Battery fast-charging parameter spaces

battery_aging

NASA Li-ion Battery Aging Datasets

batterydatasetimplementation

Implementation of a model that predicts the SoH of batteries using the NASA Battery Dataset

batterysocmodel

This model is used to estimate the SOC of battery based on NN and Kalman filter.

bayesianoptimization

A Python implementation of global optimization with gaussian processes.

capstoneproject

crazytreestomcat

Config files for my GitHub profile.

forecasting-battery-capacity

Research project at the University of Cambridge on developing techniques to forecast battery state of health from features in the electrochemical cycling data

forecasting-battery-life

Forecasting Remaining Useful Life (RUL) using NASA Li-Ion Battery Dataset

gaussian-processes-regression-tutorial

An Intuitive Tutorial to Gaussian Processes Regression

github-slideshow

A robot powered training repository :robot:

gp-structure-search

gpy

Gaussian processes framework in python

huotarim-xgboost-li-ion-batteries

XGBoost regression for predicting the state-of-health for litium-ion batteries

iot_bms

Internet of Technology based Battery Management System We created circuit which monitored Charging, and Discharging at constant load of Li-ion battery. For this current and voltage and temperature sensor was used. Using this data, we created Battery characteristic data. This helped us determine State of Charge (SOC), State of Health (SOH). Overcharge protection circuit was placed to prevent battery from overcharging. SOC, Voltage, Current, and Temperature were uploaded in real-time to Firebase. Website was created which displayed battery and its parameters in real-time.

joyful-pandas

Pandas中文教程

li-ionsocalgorithm_gradientboosting

The available capacity of a battery, called the state of charge, is a fundamental characteristic for energy storage applications or electric vehicles. In order to model the state of charge of a lithium-ion battery using data-driven techniques, complex algorithms should be used so the dynamic behaviours of the battery are captured. An ensemble decision tree called gradient boosting tree was fitted with the information extracted from different experiments based on dynamic and constant discharge profiles at different temperatures and implemented in a laboratory. Despite the capacity and and the battery’s state of health being below its theoretical life expectancy, results of the model showed an adequate behaviour. The model’s performance was validated using previously unused data.

liiondefdet-

Appearance Surface Defect Detection on Cylindrical Lithium-Ion Battery Using Deep Residual Networks with Transfer Learning

lstm_battery_rul_prediction_

machine-learning-pipeline-for-battery-state-of-health-estimation

machine-learning-session

nasa_battery_project

Prediction of battery state from charging/discharing profile

nasa_battery_soh_pred

ntu-machine-learning

**大学李宏毅老师机器学习

onedrive-sdk-python

OneDrive SDK for Python! https://dev.onedrive.com

phd-thesis

I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth

predicting-battery-lifetime

Predicting battery lifetime. cs229 final project, Fall 2018

prediction-of-lithium-ion-batteries-soh

Artificial Neural Network (ANN) has been used to estimate state-of-health (SOH) of lithium-ion batteriess. The batteries were stored at different storage temperature (35°C and 60°C) and conditions (fully-discharged and fully-charged) and their capacity was recorded for the duration of 10 months at one-month intervals.