A self-attention-based CNN-Bi-LSTM model for accurate state-of-charge estimation of lithium-ion batteries

Abstract

In the quest for clean and efficient energy solutions, lithium-ion batteries have emerged at the forefront of technological innovation. Accurate state-of-charge (SOC) estimation across a broad temperature range is essential for extending battery longevity, and enduring effective management of overcharge and over-discharge conditions. However, prevailing challenges persist in achieving precise SOC estimates and generalizing across a wide temperature range, particularly at lower temperatures. Our comparative analysis reveals that, while a single-layer bidirectional LSTM model with a self-attention mechanism achieves remarkable SOC estimation accuracy at room temperature, the intricacies of SOC estimation at lower temperatures necessitate the incorporation of more hidden layers and more complex network architecture to capture intricate features influencing battery dynamics. Hence, we propose a deep learning model, based on convolutional neural networks integrating bidirectional long short-term memory and self-attention mechanism (CNN-Bi-LSTM-AM), specifically designed to tackle the challenges of achieving accurate SOC estimations across a wide temperature range. The proposed model demonstrates proficiency in capturing both spatial and temporal dependencies critical for lithium-ion battery SOC estimation. Furthermore, the integration of a self-attention mechanism enhances the model’s adeptness to discern pertinent features and patterns within the dataset, thereby improving its overall performance and robustness, even in sub-room temperature environments.