Dual-Branch Bidirectional Long Short-Term Memory Network for Battery State of Health Estimation Under Incomplete Data

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for battery management systems (BMS), as it directly affects system safety, operational reliability, and remaining useful life prediction. Traditional SOH estimation methods generally rely on complete charge–discharge cycle data, which limits their practicality in real-time and online applications. To address this limitation, this paper proposes a novel voltage-charge increment curve-based dual-branch bidirectional long short-term memory network (VCIC-DB-BiLSTM) for battery SOH estimation under incomplete data. First, non-uniformly sampled battery current-voltage data are processed into standardized sequences with equal voltage intervals via voltage-charge increment curves based on ampere-hour integration and cubic spline interpolation. Subsequently, sliding window segmentation is applied to extract fixed-length curve segments from continuous voltage intervals as input features, while the corresponding complete voltage interval curves are used as labels. Finally, the VCIC-DB-BiLSTM network is designed, which uses a dual-branch structure to integrate feature extraction from both patch-processed and raw data, combined with bidirectional sequential modeling. Experimental validation on four benchmark datasets, CALCE, Oxford, XJTU, and TJU, demonstrates that the proposed method achieves competitive performance in SOH estimation under incomplete discharge data conditions, confirming its effectiveness and practical applicability.