Online condition monitoring and state of health estimation method for lithium-ion batteries based on time-ratio features

Abstract

Condition monitoring and state of health estimation are crucial for ensuring the secure operation of lithium-ion batteries. To address the difficulty in thoroughly capturing the changes in the state of the partial charging and discharging processes of the battery and the high demand for real-time performance of battery condition monitoring, a method for online condition monitoring and state of health estimation of lithium-ion batteries based on time-ratio features from the whole process is proposed. The health features are derived from the battery’s full charging and discharging curves, and the correlation methodology is used to analyse the relationship with the state of health. The proposed random forest-based online condition monitoring model and gated recurrent unit-based state of health estimation model are conducive to timely monitoring abnormal conditions and accurate estimation to ensure safe battery operation. Finally, the proposed method can realise accurate and real-time online condition monitoring with a classification accuracy of more than 0.93 and running time of less than 0.2 ms, and achieve better performance of state of health estimation under different ambient temperatures, with a root-mean-square error of less than 0.02 at room temperature and less than 0.01 at 43 °C.