Domain Similarity Meta-Learning for Lithium-Ion Battery State-of-Health Estimation of Spacecraft Systems

Data limitation caused by the difficulty of data acquisition, the high cost of data collection, and the changes of working conditions is a serious obstacle to the accurate lithium-ion battery state of health (SOH) estimation for spacecrafts system. To achieve accurate few-shot lithium-ion battery SOH estimation and enhance the adaptability of learning methods across varying working conditions, a domain similarity model agnostic meta-learning method is proposed. First, we design a dynamic–static feature extraction method to adequately exploit information from the limited battery data and link the obtained features by gray correlation coefficients to collect sufficient information. Then, by calculating the domain similarity between the training tasks through the maximum mean discrepancy algorithm, the training tasks are ranked to reduce the retraining time. Finally, an long short-term memory (LSTM) model is added to the meta-learning framework to capture the long-term dependency relationships between SOH and time series of the voltage, and the ranked tasks are embedded in the meta-training process to improve adaptability in different working conditions. The effectiveness of the proposed method is validated by NASA and MIT datasets, and comparative experimental results illustrate that the proposed method average error is reduced by 73%, and the running speed is increased by 20% compared with traditional MAML in few-shot scenarios.