## 2 Health Feature Parameters
### Health Features Extraction  
The data-driven estimation method establishes a mapping relationship with maximum battery capacity using multidimensional health feature data. Therefore, how to select health features is crucial for data-driven methods to accurately describe trends in battery capacity \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Battery** & **Charging Current (A)** & **Rated Capacity (Ah)** & **Temperature (\({}^{\circ}\)C)** & **Number of Cycles** \\ \hline B0005 & 1.5 & 2 & 24 & 168 \\ B0006 & 1.5 & 2 & 24 & 168 \\ Cell1 & 1.48 & 0.74 & 40 & 78 \\ Cell2 & 1.48 & 0.74 & 40 & 73 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The parameters and operating conditions of the selected batteries. Figure 1: Battery capacity decline curve: (**a**) Battery B0005 and B0006 in the NASA dataset; (**b**) Battery Cell and Cell2 in the Oxford dataset. degradation. The operating phases of lithium-ion batteries mainly involve the charging and discharging phases. During the charging phase, a fixed constant current and constant voltage mode is commonly implemented. Compared to the irregularity of the discharging phase under actual operating conditions, it is easier to extract stable and effective health feature data under the charging phase [35]. Characterization of the charging phase is relatively easy to analyze and extract because the charging process is usually carried out in a controlled constant voltage and constant current mode. Because, in practice, the discharge phase of Li-ion batteries may be affected by a variety of factors, resulting in irregularities in discharge data. For example, factors such as environmental conditions and usage behavior can lead to more complexity in extracting regular data during the discharge phase. In real life, when operating an electric vehicle artificially, the driver's behaviors such as acceleration, deceleration, and braking will lead to fluctuations in the battery discharge current and voltage; the vehicle load (e.g., the number of passengers and the weight of cargo) will also affect the battery discharge rate and voltage output. The combined effect of these external factors leads to the fact that the discharge data of electric vehicles under actual operating conditions tend to show a high degree of irregularity. Conversely, constant voltage and constant current charge states make it easier to extract stable and effective health characteristics data. To demonstrate the irregularity of the discharge data more intuitively, as shown in Figure 2, which shows the variation curve of the battery pack voltage of an electric vehicle under actual operating conditions. The discharge voltages show a rather chaotic trend in actual operation. These irregularities can create difficulties for our modeling and prediction. Therefore, the feature data of the charging voltage curve are analyzed in this paper. For example, the charging voltage curve of the Cell1 battery changes with battery degradation as shown in Figure 3a. In the initial and final stages, the curve is characterized by steepness. Nevertheless, the curve is relatively flat and is known as the plateau voltage in the intermediate state. The plateau voltage duration gradually shortens as the charging and discharging cycles increase, which aligns with the decreasing maximum capacity of the battery. Consequently, equal voltage difference charging time (EVDCT) can be used as a key feature parameter for assessing battery status. The incremental capacity analysis (ICA) method elucidates how the decrease in maximum battery capacity is related to changes in charging voltage. The ICA method calculates the difference dQ/dV between capacity Q and voltage V to accentuate incremental capacity related to distinct voltage intervals during charging. Figure 3b displays the change in the capacity increment curve, with the highest peak decreasing gradually with an increasing number of cycles, reflecting the trend of maximum battery capacity.