Bayesian Changepoint Detection for Smart Sensing of Battery Degradation: Cycle-Level Health Indicators and PyMC Implementation

Abstract

Reliable detection of the onset of accelerated degradation is central to safe and cost-efficient operation of lithium-ion batteries. This paper presents a Bayesian single-changepoint model applied to a simple but physically meaningful cycle-level health indicator (HI), defined as the ratio of charge time to discharge time. The indicator is computed directly from voltage-current telemetry typically available in battery management systems (BMS), without access to raw waveforms. The changepoint model is implemented in PyMC using Hamiltonian Monte Carlo and produces posterior distributions for onset time and pre/post-degradation slopes, together with posterior predictive checks. Experiments on an open 18650-cell remaining useful life (RUL) dataset show consistent midlife changepoints with narrow highest-density intervals. The formulation is lightweight, interpretable, and amenable to smart-sensing deployment on embedded BMS platforms.