从实验室老化研究到现场预测：量化电池储能寿命预测中的不确定性

中文摘要 AI 辅助

预测电池储能系统能持续使用多久，对于保修设计、维护规划和投资决策至关重要，然而退化模型大多是确定性的，且很少针对真实现场数据进行验证。我们应用一个开源的概率退化框架，结合从电池单元到系统的近似方法，来弥合电池单元级实验室老化模型与系统级现场预测之间的差距，用于住宅电池储能系统，并带有量化的不确定性。该框架预测电池单元级健康状态的平均绝对误差在0.4%以内，约为先前模型在该数据集上误差的一半。当应用于现场运行数据时，该框架的预测与所有三个可用的系统级容量测量结果一致——这一基准对于开放的概率退化模型而言很少可用。电池单元级的异质性通过两种边界应力情景近似，这两种情景仅略有差异（温度差异5%，电流差异9%）。两种情景的平均退化轨迹达到寿命终点的时间相差10个月，而两种情景下预测的完整寿命终点范围跨度约为三年，约为预期系统寿命的三分之一。我们将这种不确定性归因于两个驱动因素。一是实验室测试条件与现场代表性运行应力之间的系统性不匹配。二是训练数据本身的变异性。这些见解转化为对未来老化研究设计的具体、资源高效的建议，支持在现实条件下对电池寿命进行更自信的预测。

英文摘要

Predicting how long a battery energy storage system will last is critical for warranty design, maintenance planning and investment decisions, yet degradation models are mostly deterministic and rarely validated against real field data. We apply an open-source probabilistic degradation framework, combined with a cell-to-system approximation, to bridge the gap between cell-level laboratory aging models and system-level field predictions for residential battery energy storage systems with quantified uncertainty. The framework predicts cell-level state-of-health to within 0.4 % mean absolute error, roughly half the error of prior models for this dataset. When applied to field operation data, the framework's predictions are consistent with all three available system-level capacity measurements - a benchmark rarely available for open probabilistic degradation models. Cell-level heterogeneity is approximated by two bounding stress scenarios differing only slightly (a 5 % spread in temperature and a 9 % spread in current). The mean degradation trajectories of the two scenarios reach end-of-life 10 months apart, while the full predicted end-of-life range across both scenarios spans approximately three years, about a third of the expected system lifetime. We link this uncertainty to two drivers. One is a systematic mismatch between laboratory test conditions and field-representative operating stress. The other is variability in the training data itself. These insights translate into concrete, resource-efficient recommendations for future aging study design, supporting more confident predictions of battery lifetime under real-world conditions.

发表机构

• Technical University of Munich（慕尼黑工业大学）
• RWTH Aachen University（亚琛工业大学）
• Kempten University of Applied Sciences（凯姆森应用科学大学）

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