对比充放电阶段健康指标，发现组合使用更准
Charging Phase Health Indicators for Battery State-of-Health Estimation: A Systematic Comparison of CC, CV, and Combined Approaches under Cross-Battery Validation
- 用留一电池外验证法系统比较恒流、恒压及混合指标
- 混合方法预测准确率最高（R²=0.874），优于单一模式
- 提醒常规验证易高估性能，适合电池管理研发者参考
精确的电池健康状态估计对安全运行和经济维护至关重要。尽管已有大量基于恒流（CC）和恒压（CV）充电阶段的健康指标，但其在真实跨电池验证下的有效性仍缺乏充分研究。本文通过在NASA电池老化数据集上采用严格的留一电池外（LOBO）验证，系统比较了仅用CC、仅用CV以及组合使用两类指标的表现。评估了四个CV阶段指标及CC阶段持续时间，单独与组合使用。结果表明，组合使用CC+CV指标的方法表现最佳（R²=0.874），证实两者捕捉了互补的退化信息。此外，标准五折交叉验证与LOBO验证之间存在119%的性能差距，说明传统评估方法过高估计了实际精度。基于此，本文提出在数据与计算资源受限下的指标选择实用指南。
原文摘要 · Abstract (English)
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.
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