通过因果分析提前402周期预警电池衰减，准确率100%。
Causal Anomaly Detection for Lithium-Ion Battery Degradation
- 用因果图与转移熵分析电压电流等数据，生成三类异常指标。
- 在七种电池上实现100%检测，最早可提前402周期发现衰减。
- 适合电池健康监测、故障预警场景，尤其长寿命电池适用。
可靠地早期检测锂离子电池衰减，需要可物理解释且能从常规充放电数据中计算的健康指标。我们提出 extsc{CausalHealth} 框架，利用因果图发现和 k-近邻转移熵分析每周期的电压、电流、温度和电阻时间序列，将十二个异常评分归纳为三个信号类别（幅值偏移、预测残差、复杂度熵）——孤立森林因可靠性不足单独报告——以评估在十种启用工况（5–30%）下的检测灵敏度。幅值偏移类在涵盖 LFP（MIT–Stanford MATR）和 LCO（NASA PCoE, CALCE CS2）化学体系的七种电池上实现100%检测，对渐进衰减电池可提前最多402周期达到传统容量阈值失效点。一个可靠性加权主健康指数（RWMHI）——基于五个高可靠性检测器按反变异系数加权融合——在长寿命电池上使提前量比类别中位数提升15–52周期，同时保持100%检测率。在一块NMC棱柱电池上通过电化学阻抗谱验证：转移熵 $\mathrm{TE}(R \to V)$ 与电荷转移电阻 $R_{\mathrm{ct}}$ 强相关（合并 $r = +0.990$；温控局部 $r = +0.898$），两者阿伦尼乌斯分析所得活化能与已发表的NMC电荷转移动力学一致。结果在七块电池和三个基准数据集上验证。
原文摘要 · Abstract (English)
Reliable early detection of lithium-ion battery degradation requires health indicators that are physically interpretable and computable from routine cycler telemetry without access to the degradation region. We introduce \textsc{CausalHealth}, a framework that applies causal graph discovery and $k$-nearest-neighbour transfer entropy to per-cycle voltage, current, temperature, and resistance time series, and organises twelve resulting anomaly scores into three signal-class bundles (Magnitude-shift, Predictive-residual, Complexity-entropy) -- with Isolation Forest reported separately as it falls below the bundle reliability threshold -- to characterise detection sensitivity across ten commissioning fractions (5--30\,\%). The Magnitude-shift class achieves 100\,\% detection across all seven tested cells spanning LFP (MIT--Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries, with a lead time of up to 402 cycles before conventional capacity-threshold failure on gradual-fade cells. A Reliability-Weighted Master Health Index (RWMHI) -- a cross-bundle fusion of five high-reliability detectors weighted by inverse coefficient of variation -- improves lead time by 15--52 cycles over the class median on long-lived cells while maintaining 100\,\% detection. Validation against electrochemical impedance spectroscopy on an NMC prismatic cell provides independent physical grounding: transfer entropy $\mathrm{TE}(R \!\to\! V)$ correlates with charge-transfer resistance $R_{\mathrm{ct}}$ (pooled $r = +0.990$; temperature-controlled partial $r = +0.898$), and an Arrhenius analysis of both quantities yields an activation energy consistent with published NMC charge-transfer kinetics. These results are evaluated on seven cells across three benchmark datasets.
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