Table 1 Key contribution and RMSE of models for battery 05.
Author | Key contribution | Model | Battery 5 |
|---|---|---|---|
Developed a multi-channel LSTM model leveraging voltage, current, and temperature data, achieving RMSE of 0.0166 significantly enhancing RUL prediction accuracy for Li-ion batteries. | LSTM | 0.0983 | |
CNN-LSTM | 0.0174 | ||
ICC-LSTM | 0.0878 | ||
ICC-CNN-LSTM | 0.0166 | ||
RNN | 0.1051 | ||
Introduced the Hybrid Attention Forgetting Online Sequential Extreme Learning Machine (HA-FOSELM), achieving superior RUL prediction accuracy with RMSE of, optimized through the Hybrid Grey Wolf Optimizer and attention mechanism. | LSTM | 0.0135 | |
RVM | 0.0783 | ||
HA-FOSELM | 0.098 | ||
MC-RNN | 0.0255 | ||
RUL prediction result as baseline LSTM, SC-LSTM AND MC-LSTM | BL- LSTM | 0.0121 | |
SC-LSTM | 0.0245 | ||
MC-LSTM | 0.0168 | ||
Achieved RMSE of 0.0208 for LSTM in RUL predictions using a dataset, demonstrating the model’s comparative performance against MC-GRU, MC-SRU, and other methods. | MC-GRU | 0.0282 | |
MC-SRU | 0.0322 | ||
MC-LSTM | 0.0208 | ||
LSTM | 0.113 | ||
Reported RMSE of 0.0784 for PA-LSTM in RUL prediction, outperforming traditional RNN and RVM models. | RNN | 0.1047 | |
RVM | 0.0784 | ||
PA-LSTM | 0.0937 |
