Table 2 Comparison of the performance of the different ML methods
From: A comprehensive study on battery electric modeling approaches based on machine learning
ML method | RMSE | MAE | Tr. time | Test. time | Memory | Npar/ |
|---|---|---|---|---|---|---|
in [mV] | in [mV] | in [s] | in [s] | in [MB] | in [-] | |
MLR | 48.0 | 37.7 | 0.02 | <0.01 | 15.4 | 6 |
SVR | 18.3 | 10.1 | 1676 | 51.0 | 2.82 | 28048 |
K-NN | 37.9 | 22.3 | 0.50 | 7.10 | 17.2 | 2.2 Mio. |
Decision Tree | 14.6 | 8.34 | 1.89 | 0.01 | 2.56 | 29998 |
Random Forest | 11.7 | 6.48 | 24.4 | 0.22 | 11.5 | 0.6 Mio. |
AdaBoost | 10.8 | 5.98 | 61.7 | 0.59 | 29.8 | 1.2 Mio. |
GBR | 10.4 | 5.29 | 6.92 | 3.29 | 31.3 | 43681 |
FFNN | 5.57 | 3.51 | 69.7 | 0.15 | 3.51 | 5041 |
MergeFFNN | 7.63 | 4.75 | 78.2 | 0.22 | 3.54 | 8011 |
MergeLSTM | 8.45 | 5.66 | 1307 | 2.89 | 4.15 | 1201 |
LSTM | 8.24 | 5.46 | 1793 | 4.87 | 4.15 | 1821 |
MergeCNN | 7.39 | 4.73 | 194 | 0.68 | 3.76 | 5651 |
CNN | 7.99 | 5.21 | 273 | 0.80 | 3.75 | 7511 |