Cross-modal topology decodes battery faults from sparse voltage snapshots
Battery safety remains the primary bottleneck for mass electric vehicle (EV) adoption, yet field monitoring is hamstrung by a fundamental asymmetry: complex electrochemical faults must be diagnosed via sparse, low-frequency voltage measurements. Existing methods struggle to resolve the signal ambiguity between overlapping fault modes without hardware upgrades. Here, we demonstrate that these distinct fault fingerprints are not lost, but topologically folded within voltage snapshots. We introduce DeFault, a cross-modal diagnostic framework that mathematically unfolds one-dimensional voltage sequences into multi-dimensional phase-space topologies. DeFault employs a bidirectional cross-attention mechanism that acts as an autonomous, physics-aligned filter, explicitly decoding compounded fault modes that remain fundamentally invisible to sequence-based methods. Validated on a field dataset of 16.4 million data records from 99 in-service EVs, our method achieves an average accuracy of 0.96 and an F1 score of 0.84 for four fault types using only 500-second snapshots (spanning <100 mV). This work proves that high-fidelity, interpretable electrochemical diagnosis is achievable on legacy fleets without new sensors, providing a scalable solution for the battery safety crisis.