Improve in-situ life prediction and classification performance by capturing both the present state and evolution rate of battery aging

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.