Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) designed to model sequential data. Unlike traditional RNNs, LSTMs address the vanishing gradient problem by using specialized memory cells that can store and access information over long time periods. LSTMs consist of gates—input, forget, and output gates—that control the flow of information, allowing them to learn dependencies and patterns in sequences for tasks like time series forecasting, natural language processing, and speech recognition.

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Battery Degradation Temporal Modeling Using LSTM Networks

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Accurate modeling of battery capacity degradation is an important component for both battery manufacturers and energy management systems. In this paper, we develop a battery degradation model using deep learning algorithms. The model is trained with the real data collected from battery storage solutions installed and operated for behind-the-meter customers. In the dataset, battery operation data are recorded at a small scale (five minutes) and battery capacity is measured at every six months. In order to improve the training performance, we apply two preprocessing techniques, namely subsampling and feature extraction on operation data, and also interpolating between capacity measurements at times for which battery operation features are available. We integrate both cyclic and calendar aging processes in a unified framework by extracting the corresponding features from operation data. The proposed model uses LSTM units followed by a fully-connected network to process weekly battery operation features and predicts the capacity degradation. The experimental results show that our method can accurately predict the capacity fading and significantly outperforms baseline models including persistence and autoregressive (AR) models.

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