State of Health Estimation for Lithium-Ion Batteries in Energy Storage Systems: A Multi-Scale Spatiotemporal Deep Learning Approach

Abstract

Accurate state of health (SOH) estimation of lithium-ion batteries is essential for ensuring the safe and efficient operation of electric transportation and grid-scale energy storage systems (ESS). However, extracting reliable degradation information from Battery Management System (BMS) data remains challenging due to measurement noise, operating variations, and nonlinear characteristics of charging signals. To address this issue, a multi-scale spatiotemporal deep learning framework based on a multi-scale convolutional neural network and bidirectional long short-term memory (MS-CNN-BiLSTM) is proposed. A degradation-aware sliding-window strategy is first designed to extract aging-related features from charging signals, while parallel multi-scale convolution branches with different receptive fields are employed to capture temporal characteristics at multiple scales. Subsequently, an attention-enhanced BiLSTM module is introduced to aggregate long-term degradation dependencies and adaptively capture informative temporal representations. The proposed framework is evaluated using lithium-ion batteries with different chemistries, including the NASA LCO and MOLICEL NCM datasets. Experimental results demonstrate that the proposed method achieves accurate SOH estimation with MAE values as low as 0.0026 and R2 values above 0.979. Furthermore, the model maintains consistent estimation performance across different battery chemistries with relatively low computational complexity, suggesting its potential suitability for embedded BMS applications.