用物理约束和神经动态建模电池故障，提升电动车安全诊断精度。
SynForceNet: A Force-Driven Global-Local Latent Representation Framework for Lithium-Ion Battery Fault Diagnosis
- 结合物理约束与神经动力学，构建全局-局部潜在表示框架。
- 在20辆电动车860万数据上实现F1提升18.28%，AUC提升23.68%。
- 适合电池安全研究者及电动汽车故障诊断工程师参考。
电动汽车中锂离子电池的在线安全故障诊断在复杂罕见工况下至关重要。本文提出一种基于核一对一分类与最小体积估计的深度异常检测网络，引入机械约束和基于脉冲时序依赖可塑性（STDP）的动态表征，以增强复杂故障刻画能力并压缩正常状态边界。该方法在20辆电动车采集的860万有效数据点上验证，相比多个先进基线方法，平均提升7.59%的真阳性率（TPR）、27.92%的阳性预测值（PPV）、18.28%的F1分数和23.68%的AUC。分析显示，建模前后故障表示存在空间分离，通过学习潜在空间流形结构进一步提升框架鲁棒性。结果表明不同故障类型间可能存在共享因果结构，凸显融合深度学习与物理约束、神经动力学在电池安全诊断中的潜力。
原文摘要 · Abstract (English)
Online safety fault diagnosis is essential for lithium-ion batteries in electric vehicles(EVs), particularly under complex and rare safety-critical conditions in real-world operation. In this work, we develop an online battery fault diagnosis network based on a deep anomaly detection framework combining kernel one-class classification and minimum-volume estimation. Mechanical constraints and spike-timing-dependent plasticity(STDP)-based dynamic representations are introduced to improve complex fault characterization and enable a more compact normal-state boundary. The proposed method is validated using 8.6 million valid data points collected from 20 EVs. Compared with several advanced baseline methods, it achieves average improvements of 7.59% in TPR, 27.92% in PPV, 18.28% in F1 score, and 23.68% in AUC. In addition, we analyze the spatial separation of fault representations before and after modeling, and further enhance framework robustness by learning the manifold structure in the latent space. The results also suggest the possible presence of shared causal structures across different fault types, highlighting the promise of integrating deep learning with physical constraints and neural dynamics for battery safety diagnosis.
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