# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 1 Introduction  
Internet of Things (IoT) devices in remote metering applications are typically installed in locations where continuous power supply is challenging, or where human access is limited [1, 2]. These devices require a stable and long-lasting power source to operate reliably for several years, and potentially up to a decade [34]. Due to these requirements, primary batteries are predominantly used, as they provide the necessary longevity and stability without the need for frequent replacement or maintenance, making them ideal for applications where regular access is restricted [5, 6]. However, due to the flat discharge curve characteristics of primary batteries [7], it is difficult to predict the battery life, and this often leads to unexpected fully discharge. Such unexpected power loss can have a significant impact on remote metering devices and cause property damage [8, 9, 10]. To mitigate this risk, backup batteries [11] are currently used, or batteries are replaced in advance. However, these approaches are expensive and lead to a significant waste of resources, making them unsustainable for wide-ranging or long-term applications. In this paper, we propose a battery life prediction method to ensure the robust operation of IoT devices in remote metering applications, as shown in Figure 1. The robust battery life prediction process is divided into two stages. As the first stage, we focuses on predicting the state of charge (SOC), facilitating real-time remote monitoring of battery conditions in metering devices. The SOC is estimated using temperature, voltage and communication quantity data. This SOC inference is performed on a server, where the meter device periodically transmits its measured parameters. The high cost of SOC measurement equipment poses a significant challenge in practical IoT devices deployed for remote metering applications. Therefore, we employed alternative parameters for real-time prediction instead of utilizing SOC directly, enabling efficient and cost-effective monitoring. In the second stage, a hardware-based alert system is integrated into IoT devices to issue warnings before complete discharge, leveraging a custom-designed Multi-Stage Discharge battery architecture. In the first stage, we developed the CNN-Series Decomposition Transformer (C-SDFormer) model, which is capable of accurately predicting the SOC of primary batteries. As mentioned earlier, three inputs are employed for SOC prediction. Among these, instances of communication induce transient voltage fluctuations, which represent significant factors  
Figure 1: Battery life prediction method for IoT devices in remote metering applications. for SOC prediction due to the inherent characteristics of IoT devices in remote metering applications. We incorporated a transformer layer into the model architecture to effectively capture and analyze voltage variations, leveraging its attention mechanism to focus on voltage fluctuation data. In addition, we combined a convolutional neural network (CNN) with series decomposition techniques to enhance the model's ability to handle complex patterns of battery discharge behavior. This led to the development of the C-SDFormer model, which enables real-time discharge capacity prediction, providing a more accurate and robust approach to battery health monitoring in remote metering applications. Environmental factors, including the installation location of IoT devices and other external influences, degrade the accuracy of SOC prediction during the first stage [12]. To address these challenges, a Multi-Stage Discharge (MSD) primary battery was developed for the second stage to ensure a hardware-based low battery alert before the battery is fully discharged. Specifically, the MSD primary battery was developed by mixing two electrolytes, which results in two distinct voltage drops during discharge. A low battery alert is triggered within the voltage range corresponding to the first of these voltage drops.