Lithium-Ion Battery State of Health Estimation Based on Electrochemical Impedance Spectroscopy and Cuckoo Search Algorithm Optimized Elman Neural Network

Abstract

Abstract In order to ensure the driving safety of electric vehicles and avoid potential failures, it is important to properly estimate the state of health (SOH) of lithium-ion batteries. In this paper, a method of lithium-ion battery SOH estimation based on electrochemical impedance spectroscopy (EIS) and an algorithm fused by Elman neural network and cuckoo search (CS-Elman) is proposed. First, by extracting 19 features of EIS and using principal component analysis to reduce dimension, we obtain four principal components as model inputs. Second, CS algorithm optimizes the weights and thresholds of Elman algorithm. Next, we use the CS-Elman model to estimate the battery SOH and verify the model with the remaining battery data. In addition, we propose a variable temperature estimation model and verify the feasibility of the model between 25 °C and 45 °C. Finally, the experimental results show that the mean absolute error of the method is less than 1.36%.