## 2 Experimental Data Analysis
### Data Acquisition  
Batteries generate a solid electrolyte interface (SEI) on their electrode surfaces when they undergo charging and discharging processes. This membrane grows rapidly, especially during the charge and discharge cycle of the new batteries [21]. As the number of cycles increases, the main reactants inside the cell are consumed, causing the creation of the SEI membrane to slow and eventually stabilize [22]. It should be noted that the formation of this layer leads to loss of battery capacity and irreversible expansion. Before the dynamic testing profiles, the battery first needs to be activated. To mitigate this initial effect on the experiment, a preload force of 500 N is applied to the new battery for charge and discharge  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **LFP Battery** \\ \hline Cathode & LiFePO4 \\ Anode & Graphite \\ Nominal capacity & 10 Ah \\ Nominal voltage & 3.2 V \\ Charge cutoff voltage/discharge voltage & 3.65 V/2.5 V \\ Size (H/W/L) & 135/68/13 mm \\ Brand & Dingshan New Energy Co., Ltd., Guiyang, China \\ Model code & 1368135-10 Ah \\ Production date & 13 January 2024 \\ \hline \end{tabular}
\end{table}
Table 1: Specific parameters of the pouch LFP batteries.  
cycling until both the battery's capacity and pressure profiles reach a state of equilibrium. The constant current and constant voltage (CCCV) profile is applied to the charging, and the LFP battery is discharged using the constant current profile. In charging, the battery is charged at 0.5 C constant current to 3.65 V, followed by constant voltage charging until the current drops below 0.5 A. The cell is then allowed to rest for 2 h. Subsequently, it is discharged at 0.5 C constant current (CC) until the voltage falls below 2.5 V. A 2 h rest period is maintained between each cycle. The battery activation experiment is carried out for a total of 10 cycles.  
To simulate EV driving conditions more comprehensively, this study employs experiments under four distinct operating conditions: the Supplemental Federal Test Procedure Driving Schedule (US06), the Federal Urban Driving Schedule (FUDS), the Dynamic Stress Test (DST), and the Beijing Dynamic Stress Test (BJDST). The experiments are conducted with an initial preload force of 500 N at 15 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, and 35 \({}^{\circ}\)C. Before testing at each temperature condition, the battery energy is recalibrated to minimize the interference from cycling-induced aging effects. Figure 2 shows the experimental procedure under a preload force of 500 N.  
The generalization performance of the model is further validated through experimental studies conducted at 25 \({}^{\circ}\)C, implementing preload forces of 1000 N and 1500 N, with each scenario encompassing four different operating conditions. An energy calibration procedure is also performed before each experimental set to eliminate potential aging-related effects on the data. The same experimental protocol is used for both battery samples in this study. To validate the transfer learning capability of the model, data from battery 1 and battery 2 are designated as the training and test sets, respectively. Furthermore, DST and BJDST profiles under various conditions are utilized for model training, while FUDS and US06 profiles are employed for model validation.  
Figure 1: The test platform.