Improve in-situ life prediction and classification performance by
  capturing both the present state and evolution rate of battery aging
Mingyuan Zhao Yongzhi Zhang
This study develops a methodology by capturing both the battery aging state
and degradation rate for improved life prediction performance. The aging state
is indicated by six physical features of an equivalent circuit model that are
extracted from the voltage relaxation data. And the degradation rate is
captured by two features extracted from the differences between the voltage
relaxation curves within a moving window (for life prediction), or the
differences between the capacity vs. voltage curves at different cycles (for
life classification). Two machine learning models, which are constructed based
on Gaussian Processes, are used to describe the relationships between these
physical features and battery lifetimes for the life prediction and
classification, respectively. The methodology is validated with the aging data
of 74 battery cells of three different types. Experimental results show that
based on only 3-12 minutes' sampling data, the method with novel features
predicts accurate battery lifetimes, with the prediction accuracy improved by
up to 67.09% compared with the benchmark method. And the batteries are
classified into three groups (long, medium, and short) with an overall accuracy
larger than 90% based on only two adjacent cycles' information, enabling the
highly efficient regrouping of retired batteries.