State of Health prediction of lithium-ion batteries based on temporal degeneration feature extraction with Deep Cycle Attention Network

Abstract

The State of Health (SOH) forecasting is essential for applying lithium-ion batteries in energy storage systems. The streaming sensor data collected by battery management systems (BMS) contain information about the battery SOH. Therefore, the battery degradation can be treated as the attention-based similarity in the battery cycles from the data point of view. The paper develops the Deep Cycle Attention Network (DCAN) with the attention mechanism to measure the battery state similarity in different cycles with the temporal patterns extracted from the streaming sensor data. The DCAN model can efficiently predict battery SOH under various operational working conditions with the attention mechanism. Essentially, the attention-based similarity regarding the first cycle can describe the battery SOH development in the charge and discharge cycles by extracting the degeneration-related temporal patterns from the streaming sensor data. Based on the MIT-Stanford dataset and NASA PCoE dataset, the prediction performance of the DCAN model is tested under constant current load profiles. Furthermore, the Oxford dataset is used to test the prediction performance with the characterization load profiles under the conditions of driving cycles. As a result, the proposed DCAN model demonstrates high accuracy and strong robustness for the two kinds of load profiles.