AI 中文总结
研究针对电池健康状态估计，通过在NASA电池老化数据集上用留一电池法对恒流、恒压及组合指标集进行系统比较，发现CC + CV组合方法性能最佳，且传统评估高估实际准确性，还给出了指标选择实用指南。
AI 中文摘要
准确的健康状态估计对于电池安全运行和经济高效维护至关重要。尽管已从恒流（CC）和恒压（CV）充电阶段得出众多健康指标，但在实际交叉电池验证下其有效性研究不足。本文通过对仅CC、仅CV和组合指标集进行系统比较，利用NASA电池老化数据集上严格的留一电池法（LOBO）验证解决这一差距。单独和组合评估了四个CV阶段指标和CC阶段持续时间。结果表明，CC + CV组合方法性能最佳（R2 = 0.874），证实CC和CV阶段捕捉互补降解信息。此外，标准5折交叉验证和LOBO验证之间存在119%的性能差距，表明传统评估高估了实际准确性。基于这些发现，给出了数据和计算约束下指标选择的实用指南。
英文摘要
Accurate State-of-Health estimation is essential for safe battery operation and cost-effective maintenance. Although numerous health indicators have been derived from constant-current (CC) and constant-voltage (CV) charging phases, their effectiveness under realistic cross-battery validation remains insufficiently studied. This work addresses this gap through a systematic comparison of CC-only, CV-only, and combined indicator sets using rigorous Leave-One-Battery-Out (LOBO) validation on the NASA battery aging dataset. Four CV-phase indicators and CC phase duration are evaluated individually and in combination. Results show that the combined CC+CV approach achieves the best performance (R2 = 0.874), confirming that CC and CV phases capture complementary degradation information. Moreover, a 119% performance gap is observed between standard 5-fold cross-validation and LOBO validation, indicating that conventional evaluation overestimates practical accuracy. Based on these findings, practical guidelines are provided for indicator selection under data and computational constraints.
CommentsThis paper has been published in Eksploatacja i Niezawodnosc. Please cite the published version
Journal refEksploatacja i Niezawodnosc 2026;28(4):220211
