# Battery Life Prediction for Ensuring Robust Operation of IoT Devices in Remote Metering
## 2 Related Work  
There has been limited machine learning research focused on estimating the remaining capacity of primary batteries using voltage. This is due to the fact that, as previously discussed, conventional primary batteries exhibit notably flat discharge curves with just a single voltage drop.  
Conversely, machine learning-based SOC prediction for secondary batteries has seen active development [13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. Early research employed a deep neural network (DNN) [13] as the foundational model, predicting SOC by considering three main attributes: instantaneous voltage, instantaneous current, and instantaneous temperature. In another study, a long short-term memory (LSTM) [14] architecture was used to examine various features for their correlation with SOC. Parameters such as voltage, current, and environmental factors (temperature, humidity, and wind speed) were compared against SOC. Given that voltage exhibited the strongest correlation, predictions were ultimately based solely on voltage. A convolutional neural network (CNN) [15], commonly applied in computer vision, was then introduced by applying temporal convolutions to relaxation voltage data collected over a 10-s window. Subsequently, a CNN-LSTM-DNN framework [16] was developed, combining the strengths of a DNN, LSTM, and CNN into a single model. In this approach, the CNN extracted temporal features, LSTM handled time series information, and DNN enhanced the estimation accuracy. As a result, the CNN-LSTM-DNN outperformed the basic LSTM, CNN, and CNN-LSTM models in SOC estimation for secondary batteries.  
Additionally, some researchers adopted a bidirectional gated recurrent unit (Bi-GRU) [17] to reduce computational overheads while also leveraging bidirectional data analysis. This model processed voltage, current, and temperature inputs. To address gradient loss, the Nesterov Accelerated Gradient (NAG) algorithm was used for estimating the remaining capacity. Building on the Bi-GRU concept, the CNN-bidirectional weighted gated recurrent unit (CNN-BWGRU) [18] was subsequently proposed, integrating a CNN layer to extract input features. It too utilized voltage, current, and temperature data, passing these features through the Bi-GRU for SOC prediction.  
Various Transformer-based models [19; 20; 21; 22] have been employed for SoC estimation. The Transformer architecture is renowned for its ability to process more comprehensive information than traditional neural networks. The Transformer with an immersion and invariance adaptive observer [19] utilizes two separate encoders to simultaneously encode information on current temperature and voltage temperature. The outputs are then concatenated and used as input for the decoder to estimate the SoC. Next, TTSNet [20] proposes a sequence-to-sequence network that employs a temporal Transformer, using voltage, current, and temperature as inputs to estimate the SoC. In addition, another Transformer-based online battery life estimation model [21] improves the estimation accuracy by incorporating vehicle speed as an additional input feature. Finally, SGEformer [22] predicts the battery lifespan by leveraging a variety of input data, including the number of charge cycles, voltage, current, and load, combined with a lookback window technique.  
Nevertheless, it is essential to note that these studies encompass data from both the charging and discharging processes, rendering them unsuitable for primary batteries, where no charging phase exists. Moreover, the discharge profiles of primary and secondary batteries differ significantly, necessitating distinct models. Consequently, attempting to estimate the SOC of primary batteries using a model trained exclusively on secondary battery data presents formidable challenges. To address these challenges in SOC estimation, we have introduced the MSD primary battery, incorporating machine learning techniques, particularly the C-SDFormer model, for SOC estimation tailored to primary batteries.