Battery Health State Prediction Based on Singular Spectrum Analysis and Transformer Network

Abstract

The failure of a battery may lead to a decline in the performance of electrical equipment, thus increasing the cost of use, so it is important to accurately evaluate the state of health (SOH) of the battery. Capacity degradation data for batteries are usually characterized by non-stationarity and non-linearity, which brings challenges for accurate prediction of battery health status. To tackle this problem, this paper proposes a battery prediction model based on singular spectrum analysis (SSA) and a transformer network. The model uses SSA to eliminate the effect of capacity regeneration, and a transformer network to automatically extract features from historical degraded data for the prediction. Specifically, the battery capacity sequence is used as the key index of performance degradation, which is decomposed by the SSA into trend components and periodic components. Then, the long-term dependence of capacity degradation is captured by the transformer network with a multi-head attention mechanism. Finally, two public lithium battery datasets were used to verify the validity of proposed model, and compared with mainstream models such as long-/short-term memory (LSTM) and convolutional neural networks (CNNs). The experimental results show that the proposed model has better prediction performance and extensive generalizability.