Physics-Enhanced Transfer Learning for the State-of-Charge Estimation of Spacecraft Li-Ion Batteries Using Bi-LSTM Networks

In this work, the problem of state-of-charge (SOC) estimation for batteries with different chemical properties and different operating conditions is investigated based on a physics-enhanced bidirectional long short-term memory (PEBi-LSTM) network. The first physical layer of the PEBi-LSTM network learns the open-circuit and polarization voltages of batteries under various operating conditions by identifying a first-order RC equivalent circuit model, simplifying the mapping between measurement variables and SOC. Then, a two-stage transfer strategy is proposed to estimate the SOC of different lithium batteries. In the first stage, limited labeled data are used to fine-tune the Bi-LSTM layers to extract the effective features of new batteries and ensure model stability. Next, the mapping relationships in the fully connected layers are optimized by the adversarial transfer method, which reduces the data distribution differences between new and original batteries. The effectiveness of the PEBi-LSTM algorithm is verified by comparative experiments on the public dataset. In addition, experiments on a real spacecraft lithium battery platform show that the designed two-stage transfer method reduces errors at -20°C by 74.25% and 45.6% compared with the fine-tuning transfer method and the adversarial transfer method, respectively.