  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.
