Multimodal State of Health Prediction for Lithium-Ion Batteries via Mamba-Based Fusion of Discharge Curves and Impedance Spectra

Meng, Y; Sun, Q; Xu, J; Bian, A; Yang, Q; Wang, Z; Yang, Z; Zhi, M

AI Collection

Journal article

Multimodal State of Health Prediction for Lithium-Ion Batteries via Mamba-Based Fusion of Discharge Curves and Impedance Spectra

Abstract:: 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.

Publication status:: Published

Peer review status:: Peer reviewed

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APA Style

Meng, Y., Sun, Q., Xu, J., Bian, A., Yang, Q., Wang, Z., Yang, Z., & Zhi, M. (2026). Multimodal State of Health Prediction for Lithium-Ion Batteries via Mamba-Based Fusion of Discharge Curves and Impedance Spectra. Batteries, 12(6), 196.

MLA Style

Meng, Y, et al. “Multimodal State of Health Prediction for Lithium-Ion Batteries via Mamba-Based Fusion of Discharge Curves and Impedance Spectra.” Batteries, vol. 12, no. 6, 2026, p. 196.

Chicago Style

Meng, Y, Q Sun, J Xu, et al. 2026. “Multimodal State of Health Prediction for Lithium-Ion Batteries via Mamba-Based Fusion of Discharge Curves and Impedance Spectra.” Batteries 12 (6): 196.
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