Existing deep learning methods for lithium-ion battery State of Health (SOH) prediction rely almost exclusively on discharge voltage–current curves, ignoring electrochemical impedance spectroscopy (EIS) data that directly reflects internal degradation mechanisms. Fusing these two modalities is non-trivial: discharge curves are high-dimensional temporal sequences residing on a continuous dynamical manifold, while impedance features are low-dimensional static snapshots with fundamentally different statistical distributions. However, naive concatenation introduces modal conflicts rather than complementary gains. We propose the Hybrid Sensing Synergy Architecture (HSSA), which combines a Mamba backbone (O(L) complexity) for discharge curve modeling with a Q-former module that aligns impedance features into the temporal representation space via learnable query tokens and cross-attention. A prepend fusion strategy injects the aligned queries as prefix tokens, enabling the backbone to condition on internal electrochemical context from the first time step. On the NASA battery dataset, HSSA achieves MAE of 0.887 (large-scale, 11 batteries, a 9.8% improvement over unimodal Mamba), 1.457 (medium-scale, five batteries, a 28.0% improvement), and 2.705 (small-scale, four batteries, an 8.7% improvement), demonstrating consistent improvements across all data regimes. On out-of-sample battery B28, HSSA achieves 65.3% improvement. Ablation studies confirm that Q-former alignment is essential and prepend fusion significantly outperforms concatenation-based alternatives.
Meng et al. (2026) studied this question.