From Short-Window Battery Reserve Estimation to Operational Decisions: Accuracy, Uncertainty, and Reliability Under Domain Shift
Year: 2026
Type: article
Abstract: Accurate battery-capacity predictions do not necessarily support reliable reserve-sufficiency decisions. This study jointly evaluates short-window capacity estimation, empirical uncertainty, remaining backup time, and operational decisions under domain shift. Eleven lithium-ion cells from the NASA PCoE Battery Data Set were evaluated using cell-held-out validation across domains differing in battery series, discharge current, and temperature. Available discharge capacity to 2.7 V was estimated from initial discharge windows of 1–15 min, with and without single-reference cell-specific correction; remaining backup time was evaluated under constant-current discharge. In the four-cell primary domain at T=10 min, representative zero-shot Random Forest and one-shot Ridge configurations achieved capacity MAEs of 0.0456 and 0.0335 Ah and backup-time MAEs of 1.472 and 1.135 min, respectively. Across the predefined model grid, lower prediction error did not consistently correspond to better empirical coverage or fewer unsafe permissions. Interval coverage was sensitive to calibration-cell composition and deteriorated under transfer for the representative configurations. At a 30 min reserve horizon, nominal-95% lower-bound decisions reduced observed unsafe permissions from 26 to 0 for Random Forest and from 5 to 0 for Ridge, while increasing false refusals from 28 to 145 and from 4 to 109, respectively, across 632 evaluated episodes. In a separate five-cell Orbital capacity-estimation experiment using models trained on voltage/current-only features, single-reference correction increased capacity MAE for both models on every cell. These findings support separate validation of accuracy, empirical coverage, and reserve-sufficiency decisions, with conclusions limited to the investigated cells and operating conditions.
Source: Batteries
Institution: Ural Federal University
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7225734723