Chuanpu Zhang, Qinghao Cao
Accurate battery health estimation from incomplete and irregular charging records remains challenging because local fragments differ not only in length but also in diagnostic quality. This study interprets random local charging fragments as reliability-varying partial health observations and develops RLCF-Net, which fuses local Q(V) response, masked voltage-location context, and scalar operating conditions. A training-only multi-evidence procedure selects a coverage-stable mother voltage interval, while category-level empirical reliability is defined as the inverse calibration RMSE for each span-region category and used for prediction-level aggregation. The framework is evaluated on two laboratory datasets comprising 121 NCA/NCM cells and a field dataset from 20 commercial EVs. On the main NCA dataset, validation-derived category reliability transferred strongly to held-out cells (Spearman 0.
@article{d0266240-953c-49bf-971d-7c1dd2381d15,
title={State of health estimation of lithium ion batteries from random 2027 Applie},
author={Chuanpu Zhang and Qinghao Cao},
year={2026},
language={en}
}TY - JOUR TI - State of health estimation of lithium ion batteries from random 2027 Applie AU - Chuanpu Zhang AU - Qinghao Cao PY - 2026 LA - en ER -
Jichao Hong, Lei Zhang
Voltage inconsistencies in lithium-ion battery packs are a primary driver of performance degradation and thermal runaway. Accurate diagnosis from real
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Accurate estimation of the state of charge (SOC) is critical for the safe and efficient operation of lithium-ion batteries. However, SOC estimation ba
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This chapter discusses metal casting processes, highlighting the diversity and common characteristics among them. The objective is to elucidate the fu
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