对比实验室与真实用车的电池测试差异，揭示性能评估的偏差来源。
Quantifying the Gap Between Laboratory Battery Test Patterns and Field Duty Profiles
- 用使用频率、强度、倍率和结构指数量化六类测试数据
- 实车日均行驶137.2公里，超半数充电至95%以上，与实验室差异显著
- 实验室老化测试寿命预估误差可达数倍，需明确披露工况条件
实验室电池测试是电池性能与退化研究的主要实证基础，但其运行模式并不直接反映实际使用场景。本文通过对比六类可获取的数据源——受控循环、驾驶循环测试、动态循环、NMC811实验室老化、真实电动车充电记录及车队级电动车健康状态（SOH）数据——量化了这一差距。分析综合了使用频率、使用强度、使用倍率及基于归一化电流分散度与爬坡率的工况结构指数（DSI）。代表性单段DSI范围为0.630（实车数据源）至2.936（牛津大学），使用倍率在0.14–0.40（帝国理工、NASA、斯坦福、现代）至2.00（牛津）之间。长期老化结果亦有差异：80%容量保持率出现在约351次NASA循环、6292次牛津检查点和1019次斯坦福循环。在化学一致的NMC/NCM数据中，帝国理工标准循环下保留0.813，驾驶循环下为0.865；而实车数据中位数SOH为0.889并具明显离散性。实车运行显示日均使用强度为137.2公里，56.9%的充电结束于或高于95%电量。结果表明，电池性能指标高度依赖生成它的工况；应用型研究应同时报告明确的工况描述，以及化学、容量和老化指标。
原文摘要 · Abstract (English)
Laboratory battery tests provide the main empirical basis for battery performance and degradation studies, but their operating patterns do not directly represent field duty profiles. This paper quantifies the gap by comparing six accessible evidence sources covering controlled cycling, drive-cycle testing, dynamic cycling, NMC811 laboratory ageing, a real electric-vehicle charging trace, and fleet-scale electric-vehicle state-of-health (SOH) data. The analysis combines usage frequency, usage intensity, usage C-rate, and a duty-structure index (DSI) based on normalized current dispersion and ramping. The representative single-segment DSI ranges from 0.630 for the field source trace and 0.699 for NASA to 2.936 for Oxford and 2.855 for Imperial, while usage C-rate ranges from 0.14-0.40 for Imperial, NASA, Stanford, and Hyundai to 2.00 for Oxford. Long-term ageing also differs: the 80 percent retention region occurs near 351 NASA cycles, 6292 Oxford checkpoints, and 1019 Stanford cycles. In chemistry-aligned NMC/NCM evidence, Imperial retains 0.813 under standard cycling and 0.865 under drive-cycle ageing, while the field source has median SOH 0.889 with visible dispersion. Field operation further shows a median use intensity of 137.2 km/day and 56.9 percent of charges ending at or above 95 percent SOC. These results show that battery performance metrics are conditional on the duty pattern that generated them; application-oriented studies should report explicit duty-profile descriptors together with chemistry, capacity, and ageing metrics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。