# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 5 Experimental Results and Discussion  
In this section, we conducted experiments on the SOC estimation capability up to the primary point and the prediction of the primary point using three MSD primary battery test sets (#1, #2, and #3). Specifically, we evaluated the efficiency of the C-SDFormer model through ablation studies. Additionally, we trained existing battery SOC estimation methods on MSD primary battery data and compared their performance on the same test set. To assess the contributions of individual components in the C-SDFormer model, we conducted an ablation study, evaluating four modifications: (i) without the CNN embedding layer (which effectively replaces it with a linear layer), (ii) without the CNN output layer, (iii) without the transformer layer, and (iv) without the series decomposition layer. The experimental results, summarized in Table 5, indicate that the complete C-SDFormer architecture outperforms all of these modified configurations across all evaluation metrics. Specifically, our ablation study reveals that the CNN embedding layer is the critical component: its removal resulted in the largest performance drop across MAE, RMSE, and R\({}^{2}\) metrics. This finding underscores its essential role in extracting robust, informative features from the raw input data. Without this layer, the model's ability to capture subtle variations in voltage, temperature, and communication count is markedly diminished, leading to poorer SOC estimation. Similarly, when the CNN output layer was omitted, we observed a notable decline in prediction accuracy. This layer is responsible for refining and consolidating the extracted features before they are passed to subsequent processing stages, and its absence reduces the model's capacity to form a cohesive representation of the input signal. Moreover, a configuration of the model that entirely excluded the transformer component--relying solely on CNN layers--showed significantly reduced performance across all evaluated metrics. This result demonstrates that the self-attention mechanism inherent to the transformer is indispensable for capturing the temporal dependencies and transient voltage fluctuations induced by communication. These transient events, although brief, are crucial for accurately predicting the discharge capacity, and the transformer effectively highlights these features through its attention weights. Finally, the removal of the series decomposition layer led to a considerable performance reduction (over 0.6% increase in MAE, 1% increase in RMSE, 0.5% increase in SMAPE, and a decrease of 0.01 in R\({}^{2}\)). This layer plays a pivotal role in segregating seasonal patterns from trend components, enabling the model to address the nonlinear dynamics present in the data more effectively. Its exclusion confirms that without this separation, the model struggles to differentiate between long-term trends and periodic variations, thereby compromising prediction accuracy. Collectively, these results highlight the complementary roles of each component in the C-SDFormer architecture. The ablation study not only validates the design choices made in our model but also demonstrates how each module contributes significantly to the overall robustness and accuracy of SOC prediction in real-world conditions. The previous experiments show that the structure of the proposed C-SDFormer model can successfully learn the characteristics of an MSD primary battery. Here, we performed a performance comparison with existing battery SoC estimation methods on the same dataset under identical conditions, and found that C-SDFormer was superior. Table 6 presents the performance comparison of SOC estimation for the test sets between the C-SDFormer and other methods. Transformer-based advanced models [19; 20; 21; 22] utilize various features such as current and SOC as input to specific layers during training to improve SOC estimation. However, these models were not applicable to our study because our dataset does not include these features as training inputs.