# State-of-Charge Prediction Model for Ni-Cd Batteries Considering Temperature and Noise
## 4 Analysis of SOC Prediction Results of Ni-Cd Battery Using APSO-GRNN  
### Test Platform and Experimental Method  
A Ni-Cd battery installed on a 380A high-speed locomotive was used as the test object, and Table 1 shows the detailed battery parameters. The nickel-cadmium battery was tested in a constant-temperature test chamber using the BTS-3000n power battery detection cabinet as the charging and discharging experimental platform. The charging and discharging voltage and current were adjusted to meet the experimental conditions and the acquisition module was used to measure them. The voltage measurement accuracy was \(\pm\)0.1 mV, the current measurement accuracy was \(\pm\)0.01 mA, and the battery discharge capacity was calculated accordingly. Multiple batteries were subjected to repeated experiments to verify the validity of the experimental data. The voltage, current, and other electrical data were transmitted to the host computer and stored in the database. Figure 4 shows the experimental platform used in the study. The working condition method is widely considered the most scientific battery testing method. It involves splitting, cutting, simplifying, and statistically analyzing the power distribution of the battery to obtain a dataset that can be conveniently tested using charging and discharging equipment. This method can simulate the actual use of the battery and is suitable for evaluating the effectiveness of battery SOC prediction models. Figure 4: Experimental platform. Figure 3: APSO-GRNN model structure diagram. In this study, we employed three different battery test conditions, a dynamic stress test (DST), a federal urban driving schedule (FUDS), and cyclic pulse conditions, to charge and discharge the battery. The DST and FUDS testing procedures outlined in the battery test manual are widely used and enable the formulation of battery charge and discharge strategies based on actual battery usage. Consequently, they comprehensively reflect feature vectors in battery usage and are suitable for training and testing SOC prediction models. Meanwhile, the pulse-discharge conditions reflect the electrical characteristics of the battery at different temperatures. A power battery cabinet was used to simulate these three test conditions, and the resulting data, including the discharge voltage, current, and temperature, were collected and used as the training data for the prediction model. The experimental steps are outlined in Table 2.  
The experiments were carried out in a constant temperature test chamber at 20 \({}^{\circ}\)C for the DST and FUDS and at 0 \({}^{\circ}\)C, 10 \({}^{\circ}\)C, 30 \({}^{\circ}\)C, and 40 \({}^{\circ}\)C for the pulse-discharge experiments. The sampling period T was set to 1 s. Before the experiments, the discharge parameters such as the voltage, current, and power were loaded into the battery test cabinet. The changes in the battery voltage and current under these three working conditions are depicted in Figure 5.  
Figure 5b,d illustrate the current under the DST and FUDS working conditions, respectively. These conditions involve three distinct states: discharging, standing, and charging. They provide a comprehensive description of the actual working state of the battery and can be used as training data for the model. Figure 5e,f demonstrate that transitioning from resting to discharging induced a noticeable voltage rebound. Therefore, the model must possess the ability to focus on correlated data, prioritizing the historical data that are most relevant to the test point. This correlation-focused ability enhances the accuracy of the model's predictions. \begin{table}
\begin{tabular}{c c} \hline
**Item** & **Parameter** \\ \hline Nominal capacity & 190 Ah \\ Initial weight & 6.1 kg \\ Initial voltage & 1.35 V \\ Resistance & 1.35 \(\Omega\) \\ Initial density & 1.23 g/cm\({}^{3}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Detailed battery parameters.