Invariant learning based multi-stage identification for Lithium-ion battery performance degradation
This work addresses battery health management for systems powered by batteries, but it is incremental as it builds on existing data-driven methods by adding mechanism analysis.
The paper tackles the problem of analyzing lithium-ion battery performance degradation by proposing an invariant learning-based method to identify whether degradation follows fixed behaviors, dividing the aging process into multiple stages based on consistent degradation speed, and validating the approach on a benchmark dataset.
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.