## 3 Dataset and Experimental Methods  
In this article, the lithium-ion cell degeneration datasets utilized, including current, voltage, temperature, and SoC values, were obtained from the NASA PCOE Research Center, including test data for a commercial Li-ion 18650 2 Ah battery [70] for repeated charge and discharge cycles. The battery was charged and discharged using a Constant-Current-Constant-Voltage (CC-CV) profile. NASA has posted two high-throughput datasets containing 62 cells in total on their website. A brief description of the datasets is provided here. The first one (Randomised Battery Usage Data Set) [71] hosted by NASA includes data from 28 Li cobalt oxide 18650 batteries having a nominal capacity of 2.2 Ah. These cells were continuously operated. There are 7 groups of 4 batteries in each group, all cycled at a given ambient temperature (room temperature, 40 \({}^{\circ}\)C). The second (Battery Data Set) [70] comprises 34 Li-ion 18650 batteries with 2 Ah of nominal capacity. It was the first publicly accessible dataset on batteries and made a significant impact on the field, providing insight into its influence. The cells were cycled over a range of ambient temperatures 4 \({}^{\circ}\)C, 24 \({}^{\circ}\)C, and 43 \({}^{\circ}\)C, charged using a common CC-CV protocol and with various discharge regimes [72]. To study the effect of operating temperature on the charge/discharge process of a battery, Figure 5 depicts the SoC for different running temperatures at (CC). Initially, the battery was charged with a constant-current profile until the voltage of the cell achieved its nominal voltage, then the battery was charged with a constant-voltage profile until the battery was completely charged. In the discharge phase, a variable-magnitude current profile was applied to completely discharge the battery, as shown in Figure 6a,c. The effect of temperature on the SoC during battery charge and discharge cycles was also taken into account in the simulation [73; 74; 75]. Figure 6d illustrates the surface temperature of the battery at room temperature. It is observed that the temperature increases throughout the first segment of the charge and ends with a temperature of about 26 \({}^{\circ}\)C. In this study, the experimentally measured SoC was compared with the SoC generated by the simulation. To estimate the simulated and measured SoC, a neural network was tested using both types of data. The training data consisted of 300,000 input voltage, current, and temperature values, and corresponding output SoC values, collected over several charge and discharge cycles. Figure 6a represents a variable current profile during the charging and discharging process, and Figure 6e displays the SoC of the battery as a function of time. It is essential to understand that the SoC is directly influenced by the current profile. When the current is positive (charging), the SoC increases, indicating that the battery is charging. Conversely, when the current is negative (discharging), the SoC decreases, indicating that the battery is discharging. Figure 6e thus reflects the variations in SoC in response to the variationsin current observed in Figure 6a. The models used to estimate the SoC take these current variations into account to provide an accurate estimate of the SoC, which is crucial for efficient energy management and extended lifespan of the battery under real-life conditions. The simulation aimed to train the proposed model at different current profiles to evaluate the robustness of the neural network. Since the experimental data incorporated the effect of temperature on the SoC, the latter was also added to the simulation. To evaluate the effects of various structural aspects of neural networks on the precision of SoC estimation, several tests were carried out. The acquired data were divided into three parts, the first representing 70% for training and the second, with 15%, for testing. Lastly, the final part was used for validation.