Hierarchical Bayesian Changepoint Analysis of Lithium-Ion Battery Degradation Under Incomplete Cycle Observations

Battery engineers often work with repeated cycle-level monitoring signals that are related to ageing but are not direct capacity or resistance measurements. This paper studies how such a signal can be used to detect faster change and to decide whether the measured record is long enough to locate the transition reliably. We analyse 14 lithium-ion cell records from a processed Hawaii Natural Energy Institute (HNEI) cycle-level table, using the charging-to-discharge duration ratio (C/D) as a practical charge/discharge-duration indicator. The corresponding original HNEI measurement files were checked to improve the cell description and to examine whether a capacity-based comparison was possible. They confirm substantial capacity fade, but they also contain non-physical capacity entries near cycle 370; these entries are not used as validation of C/D transition cycles. We compare a linear reference, broken-line regression, a smoothing-spline curvature check, an aggregate Bayesian smooth-transition model, and a battery-level hierarchical Bayesian model. The hierarchical model estimates a mean battery-level transition cycle of 553.9 cycles (95% credible interval: 547.1–560.9), with substantial battery-to-battery variation (standard deviation about 142 cycles). All 14 batteries show a positive increase in the rate of change of C/D. Randomly removing about half of the measurements while retaining the full test span widens uncertainty but preserves the acceleration conclusion and nearly preserves battery ordering. In contrast, cutting off the late part of the test record strongly destabilizes transition timing and extrapolation. The approach is therefore useful as a retrospective screening and test-interpretation tool for a chosen ageing-related signal. It is not a direct capacity-knee detector, a mechanism diagnosis, or a remaining-useful-life predictor.