跨模态拓扑从稀疏电压快照解码电池故障
Cross-modal topology decodes battery faults from sparse voltage snapshots
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中文总结 AI 辅助
本研究针对EV电池安全瓶颈，提出跨模态诊断框架DeFault，通过将电压序列展开为相空间拓扑解码故障，在99辆EV的1640万条数据上对四种故障类型平均准确率达0.96，无需新增传感器即可实现高可解释性故障诊断。
中文摘要 AI 辅助
电池安全性仍是电动汽车（EV）大规模推广的主要瓶颈，而现场监测受困于一个根本性不对称问题：复杂电化学故障需通过稀疏、低频的电压测量进行诊断。现有方法在不升级硬件的情况下难以区分重叠故障模式间的信号歧义。本文证明这些不同的故障特征并未丢失，而是在电压快照中以拓扑形式折叠存在。我们提出DeFault，一种跨模态诊断框架，其数学上将一维电压序列展开为多维相空间拓扑。DeFault采用双向交叉注意力机制，作为自主的、符合物理规律的滤波器，可明确解码基于序列的方法根本无法察觉的复合故障模式。在来自99辆在用EV的1640万条现场数据记录上进行验证，仅用时长500秒（跨度<100 mV）的快照，我们的方法对四种故障类型的平均准确率达0.96，F1分数为0.84。本研究证明，无需新增传感器即可在现有车队上实现高保真、可解释的电化学诊断，为电池安全危机提供了可扩展的解决方案。
英文摘要
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.
