论文标题
基于不变学习的锂离子电池性能降低的多阶段识别
Invariant learning based multi-stage identification for Lithium-ion battery performance degradation
论文作者
论文摘要
通过通知准确的性能（例如容量），健康状态管理在保护电池及其动力系统中起着重要作用。尽管大多数当前方法主要基于数据驱动的方法，但缺乏对电池性能降解机制的深入分析可能会折现其性能。为了填补有关数据驱动的电池性能降解分析的研究差距，提出了一种基于不变的学习方法来研究电池性能降解是否遵循固定行为。首先，为了展开循环电池数据的隐藏动力，在相子空间中重建了测量值。接下来，提出了一种新颖的多阶段策略来判断存在多种退化行为的存在。然后将整个老化过程顺序分为几个段，其中以一致的降解速度分配了同一阶段的循环数据。对众所周知的基准测试验证了提出的多阶段识别策略的功效。提出的方法不仅可以从数据角度了解降解机制，而且还可以对相关主题（例如健康阶段）有所帮助。
By informing accurate performance (e.g., capacity), health state management plays a significant role in safeguarding battery and its powered system. While most current approaches are primarily based on data-driven methods, lacking in-depth analysis of battery performance degradation mechanism may discount their performances. To fill in the research gap about data-driven battery performance degradation analysis, an invariant learning based method is proposed to investigate whether the battery performance degradation follows a fixed behavior. First, to unfold the hidden dynamics of cycling battery data, measurements are reconstructed in phase subspace. Next, a novel multi-stage division strategy is put forward to judge the existent of multiple degradation behaviors. Then the whole aging procedure is sequentially divided into several segments, among which cycling data with consistent degradation speed are assigned in the same stage. Simulations on a well-know benchmark verify the efficacy of the proposed multi-stages identification strategy. The proposed method not only enables insights into degradation mechanism from data perspective, but also will be helpful to related topics, such as stage of health.
