Average results of 20 splits are listed in Table 8. As shown in Tables 8 and in the multi-class battery classification task, the proposed RLR model still presents the best performance. The four metrics are all higher than considered benchmarks, which are 87.6%, 70.8%, 73.4%, and 72.1%, respectively.
The 5-fold averaged cross validation results for two classification tasks are presented in Fig. 9. It is observable that the α value of 0.6 generates the highest accuracy in binary battery classification, and the α value of 0.9 produces the best results for multi-class battery classification.
In Fig. 12, results from one random experiment show that only one long-lived battery is erroneously classified into the short-lived group. Notably, this misclassified battery has a lifetime of 561 cycles, closely approaching the predefined threshold of 550 cycles. Thus, the classifier is more prone to misclassifying it as a short-lived battery.
This performance improvement could be interpreted by the utilization of more spatial and temporal information from the raw battery data as more cycles are considered. On the other hand, in the range after first 20 cycles, the classification performance does not have much improvement but even gets a bit impaired with a larger cycle range considered.
Another setting considers , which is a multi-class classification task grouping batteries into lifetime. Given a training dataset , the goal of modeling is to learn the nonlinear mapping from the early-cycle raw battery data to the battery lifetime group, which is expressed in (1). (1)
This can be attributed to the model being well trained based on sufficient data from the same type of LFP batteries in this dataset, without influences from other battery types. However, after transferring to the smaller Datasets II and III, the classification accuracy notably decreases.
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