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attention-based-cnn-bilstm-battery-soh-prediction2

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

autoencoder-cnn-lstm--to-prediction-battery-rul

battery_rul_early_prediction

This is the code for battery RUL early prediction

cnn-astlstm2

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

data-driven-prediction-of-battery-cycle-life-before-capacity-degradation

Code for Nature energy manuscript

nasa_li-ion_battery_soh_prediction_with_mvip-trans2

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 battery signals from both local and global views. In this way, the learning ability of both local features and long period contexts will be enhanced.

rul-prediction-battery

Application of Deep Learning in RUL Prediction of Batteries

soh_estimation_of_lithium-ion_battery2

State of health (SOH) prediction for Lithium-ion batteries using regression and LSTM

sohbenchmark2

A code library and benchmark study on SOH estimation of lithium-ion battery

thorough-pytorch

PyTorch入门教程，在线阅读地址：https://datawhalechina.github.io/thorough-pytorch/

time_series_forecasting

transformer_time_series

Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting (NeurIPS 2019)