Feature-enhanced ensemble learning for accurate capacity estimation of lithium-ion batteries using partial discharging segments in initial stage based on second-order voltage derivatives

Abstract

Accurate and rapid capacity estimation is essential for efficient battery management in industrial settings particularly for cell grading, pack assembly, and second-life screening where throughput, cost, and energy efficiency are paramount. Conventional approaches require complete discharge cycles, leading to testing times of several hours per cell, which severely limits scalability and increases operational costs. To address this bottleneck, this paper proposes a fast capacity estimation method for battery capacity grading in the production process, which utilizes only the early-stage voltage measurements within the first 300-480 seconds of the initial discharge cycle during cell grading to accurately predict the cell’s nominal capacity, enabling reliable battery capacity grading within minutes instead of hours. Although real-world grading data from production lines are often inaccessible, this first-cycle setup serves as a well-controlled surrogate that replicates key aspects of factory-based capacity labeling. The method exploits early-voltage transients that encode degradation-sensitive electrochemical signatures such as lithium inventory loss and solid-electrolyte interphase (SEI) evolution arising from microscopic changes in charge-transfer resistance and ion transport dynamics. From this short window, we extract physically interpretable health indicators (HIs) that reflect underlying aging mechanisms. A nonlinear feature enhancement strategy is then applied to amplify subtle capacity-related patterns while suppressing manufacturing-induced variability. These engineered features feed into a Multi-Decision Ensemble Learning (MDEL) architecture, which adaptively fuses multiple regression pathways to improve robustness across diverse cell chemistries and aging stages. Evaluated on both in-lab cells, the public CALCE and MIT dataset spanning fresh to end-of-life capacity conditions, the proposed approach achieves a mean absolute error (MAE) of ≤0.0391 Ah (≤1.63% of nominal capacity), which is comparable to the methods with complete cycle data while reducing testing time by over 80%. This enables reliable capacity assessment in minutes rather than hours, offering a practical, scalable solution for high-throughput battery manufacturing, precise pack matching, and rapid second-life qualification. • Enables fast capacity estimation during battery grading with early discharge data. • Phase-aware segmentation captures capacity-sensitive features in initial discharge. • Nonlinear enhancement decouples health indicators for representation learning. • Multi-decision ensemble achieves high accuracy across our laboratory, CALCE, MIT datasets.

Topics & Concepts