HFCM-LSTM: A novel hybrid framework for state-of-health estimation of lithium-ion battery

Abstract

Accurate estimating the state of health (SOH) of lithium-ion battery plays a significant role in the safe operation of electric vehicles. With the development of deep learning, neural network-based methods have attracted much attention from researchers. While most of the existing SOH estimation methods are built by a single network, failing to sufficiently extract data information, and thus leading to the limited accuracy and generality (i.e., such a single network makes it difficult to estimate the SOH of battery, with different types and working conditions). Towards this issue, a novel hybrid network, called HFCM (Hierarchical Feature Coupled Module)-LSTM (Long–short-term memory), is designed to fully extract the original data information, making it more accurate to estimate the SOH of battery, with different types and working conditions. Specifically, the proposed HFCM-LSTM mainly consists of two components, HCFM and LSTM. The HCFM is proposed to comprehensively extract the original data feature information from the original samples. On the other hand, following the HFCM, a LSTM module is employed to model time series information. Based on this HFCM-LSTM network, the data obtained directly from the battery are fed into the model as input, enabling an end-to-end SOH estimation of the battery. A series of experiments are conducted on both NASA dataset and Oxford dataset, the experimental results demonstrate that the proposed SOH estimation algorithm outperforms several existing state-of-the-art methods, in terms of accuracy and generality.