Single Frequency Feature Point Derived from DRT for SOH Estimation of Lithium Ion Battery

Abstract High-efficient data feature extraction is crucial for the lithium ion battery state of health (SOH) evaluation with high accuracy and low cost. In this work, an evaluation model constructed by long short-term memory (LSTM) neural network processes the single-frequency impedance data as the feature data to predict the current health state of the battery. The feature data of electrochemical impedance spectroscopy is determined by the frequency (4.36 Hz) corresponding to the highest peak change in the distribution of relaxation time diagram during the cyclic process. The real and imaginary part values of this single frequency feature point are taken as an input set, and the corresponding SOH is taken as an output set. A battery SOH model based on the LSTM is constructed and the experimental results show that this model can accurately estimate the SOH of the lithium ion battery with the low root mean square error of 3.36% and mean absolute percentage error of 2.68%, indicating that this model displays the decreased computational load, high accuracy and good practicability.