Abstract：Accurately estimating the state of health（SOH）of lithium-ion batteries is crucial for optimizing

energy storage systems' operation，management，and maintenance. Existing methods that extract health features

from single-stage charging data fail to exploit battery aging information fully，leading to a suboptimal estimation

accuracy. In addressing this issue，a SOH estimation method for energy storage systems based on the fusion of twostage charging data for lithium-ion batteries was proposed. Combining health features from both constant voltage charging and relaxation stages，the proposed method effectively mines aging information embedded in two-stage charging data，thereby improving SOH estimation accuracy. Additionally，the introduced health feature combination does not require the use of constant current charging stage data，making it less affected by the uncertainty of charging start points and more adaptable to practical energy storage conditions. Experimental results demonstrate that the proposed health feature combination significantly outperforms single-stage feature combinations，with an average absolute error of 0.66%，mean squared error of 0.85%，and an average coefficient of determination of 0.97.

      Key words：lithium-ion batteries；state of health（SOH）estimation；two-stage features fusion