Algorithm research on state of health of lithium battery based on electrochemical impedance spectroscopy and incremental capacity curve features

Abstract

This thesis presents a feature fusion state of health (SOH) prediction framework for lithium-ion batteries. The framework combines the health features of electrochemical impedance spectroscopy (EIS) and incremental capacity analysis (ICA), uses convolutional neural network (CNN) and improved long short-term memory network (TLSTM) to establish the mapping relationship between features and state of health. The parameters of the improved particle swarm optimization (IPSO) algorithm are optimized to build the IPSO-CNN-TLSTM model by modifying updating rules of the inertia weight and learning factor of the particle swarm optimization (PSO) algorithm to improve its optimization ability. Finally, numerical outcomes of the NASA PCoE datasets confirm this method’s applicability and efficacy.confirm this method’s applicability and efficacy.