State of Health (SoH) and Remaining Useful Life (RUL) prediction for Li-ion batteries based on Physics-Informed Neural Networks (PINN).

Predicting the remaining useful life (RUL) and state of health (SOH) of batteries using LSTMs

Attention-based CNN-BiLSTM for SOH prediction of lithium-ion batteries

2022-2 Capstone design, SOH Estimation Using Combined CNN-LSTM Model

这是我关于使用深度学习方法去评估锂电池健康状态（SOH）的一点点工作，对象是NASA的锂电池容量衰退数据集，分析了加入锂电池运行的可监测数据对SOH的影响

Algorithms for explaining machine learning models

A collection of research materials on explainable AI/ML

This research provides a prognostic framework for off-line SOH estimation of Li-ion battery. With a CNN-Transformer architecture, this program is capable of modeling the temporal correlations of ba…

State of charge (SOC), State of Health (SOH) Estimation using Machine learning algorithms such as Linear regression, random forest, support vector machine, ANN, CNN

Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

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

Code for paper "An end-to-end neural network framework for SOH estimation and RUL prediction of lithium battery"

The Matlab/Octave code contains codes of Whale Optimization Algorithm and Particle Swarm Optimization.

Python Whale Optimization Algorithm

锂电池SOH估计算法

TensorFlow Implementation of TCN (Temporal Convolutional Networks)

Remaining Useful Life (RUL) estimation of Lithium-ion batteries using deep LSTMs

PyTorch implementation of remaining useful life prediction with long-short term memories (LSTM), performing on NASA C-MAPSS data sets. Partially inspired by Zheng, S., Ristovski, K., Farahat, A., &…

LSTM built using Keras Python package to predict time series steps and sequences. Includes sin wave and stock market data

Prediction of battery lifetimes based on a Recurrent Neural Network (RNN) architecture. Data publicly available here: https://doi.org/10.1038/s41560-019-0356-8