Causal Anomaly Detection for Lithium-Ion Battery Degradation

• URL: http://arxiv.org/abs/2605.17334v1
• Date: Sun, 17 May 2026 08:53:16 GMT
• Title: Causal Anomaly Detection for Lithium-Ion Battery Degradation
• Abstract summary: scCausalHealth is a framework that applies causal graph discovery and $k$-nearest-neighbour transfer entropy to per-cycle voltage, current, temperature, and resistance time series.<n>It achieves 100,% detection across all seven tested cells spanning LFP (MIT--Stanford MATR) and LCO (NASA PCoE, CALCE CS2) chemistries.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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