A Novel SOH Estimation Method With Attentional Feature Fusion Considering Differential Temperature Features for Lithium-Ion Batteries

It is vital to accurately estimate the state of health (SOH) of lithium-ion batteries of electrical vehicles. Despite the significant impact of temperature on the SOH, most conventional data-driven methods for SOH estimation neglect the influence of temperature-related characteristics. This article proposes a novel SOH estimation method with attentional feature fusion (AFF) considering differential temperature (DT) features for lithium-ion batteries. First, this article extracts DT features from the DT curves and employs the Pearson correlation coefficient (PCC) to select the health features (HFs) of the DT curve that exhibit the strongest correlation with SOH. Then, a novel SOH estimation model combining AFF and long short-term memory (LSTM) (AFF-LSTM) is proposed, which can automatically learn the influence weights of different types of HFs on SOH, thereby improving the accuracy of SOH estimation. The MAPE evaluation metric is approximately 0.126% on the Oxford dataset. Finally, various comparative analyses demonstrate that the proposed method can accurately estimate the SOH of lithium ion under different operating temperatures.