# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 2 Lithium-Ion Battery Data Explanation and Health Factor Selection
### Selection of Health Factors  
Since SOH cannot be measured directly, and the raw data of the battery such as voltage and current are generated during the battery charging and discharging process, but they will be affected by the operation of the battery itself, causing these data to be disturbed. Therefore, using them to predict battery SOH is not accurate. In contrast, HF (health factor) is extracted from these voltage and current data [31]. By properly selecting HF as the model input, the above-mentioned interference factors can be effectively weakened or eliminated, and the inherent characteristics and changing trends of the battery can be more realistically revealed, thereby improving the reliability and accuracy of the prediction.  
In practice, lithium-ion batteries are more stable when charging than when discharging. To this end, the current-time curve, voltage-time curve, and temperature-time curve of the B0005 battery under different cycles of charging and discharging are compared, as shown in Figure 2a-f. To clearly illustrate the state of charge and discharge of each battery over time, the curves in the figure are presented in a gradual gradient from light to dark. The charging process of lithium-ion batteries is generally divided into a constant current charging stage (CC) and a constant voltage charging stage (CV). The battery is charged with constant current to the voltage charging limit, then it enters constant voltage charging to maintain a constant charging current. The charging time of the battery is essentially determined by the available capacity. When the battery health state (SOH) decreases, the reduction in its actual available capacity will directly shorten the duration of the constant current charging stage. It is worth noting that in the case of capacity decay, in order to achieve a fully charged state, the subsequent constant voltage charging stage may require additional extended operation time. As shown in Figure 2a-f, the constant current charging time of the battery and the time when the cut-off voltage and temperature reach the peak value show a trend of gradually shortening when charging under different cycle periods, and the time when the charging temperature reaches the peak value is gradually advanced, showing a certain regularity and stability. At the same time, the current-time, voltage-time, and temperature-time curves during discharge are affected by various factors and have  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Battery** & **Voltage Range** & **Charge** & **Discharge** & **Temperature** \\
**Number** & **(V)** & **Current (A)** & **Current (A)** & **(\({}^{\circ}\)C)** \\ \hline B0005 & 2.7–4.2 & 1.5 & 2 & 24 \\ B0006 & 2.5–4.2 & 1.5 & 2 & 24 \\ B0007 & 2.2–4.2 & 1.5 & 2 & 24 \\ B0018 & 2.5–4.2 & 1.5 & 2 & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Relevant battery parameters.  
Figure 1: Capacity degradation curve.  
poor stability [32]. Taking all factors into consideration, we selected five health factors, namely equal pressure rise charging time, constant current charging time, constant voltage charging time, corresponding time of temperature peak during charging, and constant current charging time ratio, as the characteristics of this SOH characterization. In addition, the above five health factors are marked as HF1, HF2, HF3, HF4, and HF5 in order to facilitate subsequent research and description.  
In this study, we extracted five health factors (HFS) of the B0005 battery, and in order to make the data distribution more reasonable, improve the robustness of the algorithm, and accelerate convergence, we normalized it. As shown in Figure 3, there is information overlap in the five extracted health factors [33,34]. It can be seen that in order to reduce the computing cost, normalization is necessary.  
Figure 2: Battery charge and discharge status.