State of Health Estimation for Lithium-Ion Batteries Based on Elman Neural Network

Abstract

This paper proposes a state of health (SOH) estimation method with integration of grey relational analysis (GRA) with Elman neural network (NN). First, the experimental data of lithium-ion battery life attenuation are analyzed and the health factors (HFs) are extracted. Then, the correlation degree between HFs and SOH are analyzed by the GRA. Finally, the extracted HFs are considered as the model input, and the SOH as taken as a model target output for SOH prediction. The prediction results show that the proposed method has high prediction accuracy that it can be applied to the online SOH estimation.