通过不确定性量化预测电池储能系统的退化：一种概率框架
Predicting BESS Degradation with Uncertainty Quantification: A Probabilistic Framework for Battery Energy Storage Systems
AI总结：
研究针对电池储能系统退化预测问题，引入基于深度学习的概率框架，利用压力因素生成容量损失预测分布，通过随机轨迹传播不确定性，可扩展至全系统数据，经现场数据测试能有效模拟退化行为，为能源系统资产管理提供实用工具。
AI中文摘要：
准确且能感知不确定性的电池退化预测对储能系统的可靠运行和生命周期管理至关重要，传统确定性模型无法捕捉退化过程中的固有不确定性。本研究引入了一个用于概率性电池健康状态预测的框架。该框架利用深度学习模型，根据压力因素生成容量损失的预测分布。不确定性通过随机退化轨迹传播，即使在动态运行条件下也能进行稳健预测。关键进展是该框架可扩展到全系统数据，通过整合电池单元级预测与系统拓扑和实际运行变异性，为整个电池储能系统提供概率估计。使用来自住宅储能系统的多年现场数据对该方法进行了测试，展示了其模拟系统级退化行为的能力。该框架以95%的预测区间预测健康状态退化，与现场系统上进行的剩余容量测量结果吻合良好。这项工作弥合了实验室测试得出的电池单元老化模型与全系统运行数据评估之间的差距，为现代能源系统中数据驱动的资产管理提供了实用工具。
英文摘要：
Accurate and uncertainty-aware prediction of battery degradation is essential for the reliable operation and lifecycle management of energy storage systems, yet traditional deterministic models fail to capture the inherent uncertainty in degradation processes. This study introduces a framework for probabilistic battery state-of-health prediction. The framework leverages deep learning models to generate predictive distributions for capacity loss, conditioned on stress factors. Uncertainty is propagated through stochastic degradation trajectories, enabling robust predictions even under dynamic operating conditions. A key advancement is the framework's scalability to full-system data: by integrating cell-level predictions with system topology and real-world operational variability, it provides probabilistic estimates for entire battery energy storage systems. The approach is tested using multi-year field data from residential storage systems, demonstrating its ability to mimic system-level degradation behavior. The framework predicts SOH degradation with 95\% prediction intervals that align well with remaining capacity measurements performed on the field system. This work bridges the gap between laboratory test derived battery cell aging models and full-system operational data evaluation for degradation estimation, offering a practical tool for data-driven asset management in modern energy systems.
