Causal Anomaly Detection for Lithium-Ion Battery Degradation

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 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 TE(R→V ) correlates with charge-transfer resistance Rct (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.