State-of-Health Prediction for Lithium-Ion Batteries Based on SIREN–Transformer

State-of-Health (SOH) is a critical indicator reflecting the degradation status of lithium-ion batteries (LIBs), which is essential for ensuring operational safety, prolonging lifespan, and optimizing battery management strategies. Consequently, accurate SOH prediction is paramount for effective battery health management and timely maintenance interventions. However, the primary difficulty no longer lies in achieving peak accuracy on a single benchmark but in maintaining consistent performance across heterogeneous scenarios. In real-world applications, battery usage patterns are often complex, highly dynamic, and nonstationary, which results in degradation trajectories that contain both subtle high-frequency fluctuations and long-term temporal trends. To address these challenges, this paper introduces a novel hybrid deep learning framework, termed the SIREN-Transformer model, which integrates the Sinusoidal Representation Network (SIREN) with the Transformer-based architecture. The SIREN module employs periodic activation functions to extract high-frequency features and capture subtle nonlinear patterns. Meanwhile, the Transformer component extracts long-range temporal relationships and contextual dependencies within the time-series data, enhancing the model’s capacity to generalize across diverse operational scenarios. The SIREN-Transformer model combines frequency-aware representation and long-range temporal modeling to accurately capture nonlinear, high-frequency, and complex degradation patterns in battery SOH prediction. Extensive experiments on multiple LIB datasets demonstrate the prediction accuracy and robustness, which validate the effectiveness of the proposed model for SOH prediction.