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an-optimization-for-soc-of-battery-storages-with-deep-neural-network

CapaSmart Project Soft Part

arduino

open-source electronics prototyping platform

battery-kalman

A simple and naive battery modelisation + Kalman filter for state of charge (SoC) estimation

battery-state-of-charge

This project calculates the SoC by using current integration method which measure the current passing through current sensor while charging or discharging and then integrating it in a time. The system based on Atmega32 microcontroller with software architecture in the picture attached with this project. EEPROM is included in the system to save the previous value of SoC after restarting the system and calculate the new state based on the previous one. Timer (CTC Mode) and interrupt are used to return the number of milliseconds passed since the MCU began running the current program.

battery_soc_estimation

Battery state of charge estimation using kalman filter in Matlab

batterysocmodel

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

cocalc-docker

Docker setup for running CoCalc as downloadable software on your own computer

deep-learning

deeplearningimplementations

Implementation of recent Deep Learning papers

deeplogo

A brand logo detection system using tensorflow object detection API.

dl_class

Notes for deeplearning class

equiv-circ-model

Equivalent circuit model for a battery cell

implementing-can-protocol-using-3-lpc1768-mbed-boards-with-mpc2551-transcievers

kalman-filter-battery-soc

an experiment in SoC calculation using Kalman filters

logo-detector

A custom model trained using Tensorflow's object detection API to detect company logos in images/videos.

logo_detection

Logo detection and brand recognition

nasa_battery_project

Prediction of battery state from charging/discharing profile

nleis-battery-manuscript

This repository contains all of the code for reproducing the work found in our inital manuscript on nonlinear EIS (NLEIS) for lithium-ion batteries.

nn-grocery-shelves

Recognition of product positions on shelf images using neural networks

openfast

Main repository for the NREL-supported OpenFAST whole-turbine simulation code. Documentation is avaiable at http://openfast.readthedocs.io

productner

Algorithms to categorize products and do named entity recognition on words in product descriptions

ptejaswini.github.io

smart-battery-management-system

Using machine learning to estimate the state of charge of lithium ion batteries for electric vehicles

soc-estimation-of-lithium-ion-batteries

Various machine learning algorithms have been used to estimate state-of-charge (SOC) of calendar-aged lithium-ion pouch cells. Calendar life data was generated by applying galvanostatic charge/discharge cycle loads at different storage temperature (35°C and 60°C) and conditions (fully-discharged and fully-charged). The data was obtained at various C-rates for duration of 10 months at one-month intervals. The wininng model, Random Forest (RF), has achieved a R2 score of 99.98% and a mean absolute error (MAE) of 0.14% over test data, confirming the ability of RF to capture input-output dependency. The model will be employed to estimate the SOC of calendar-aged lithium-ion batteries which is essential for the reliable operation of electic vehicles (EVs).

soc-estimation-svr

Example of Machine Learning application: State of Charge estimation of a battery using SVR