  Predicting the State-of-Health (SoH) of lithium-ion batteries is a
fundamental task of battery management systems on electric vehicles. It aims at
estimating future SoH based on historical aging data. Most existing deep
learning methods rely on filter-based feature extractors (e.g., CNN or Kalman
filters) and recurrent time sequence models. Though efficient, they generally
ignore cyclic features and the domain gap between training and testing
batteries. To address this problem, we present CyFormer, a transformer-based
cyclic time sequence model for SoH prediction. Instead of the conventional
CNN-RNN structure, we adopt an encoder-decoder architecture. In the encoder,
row-wise and column-wise attention blocks effectively capture intra-cycle and
inter-cycle connections and extract cyclic features. In the decoder, the SoH
queries cross-attend to these features to form the final predictions. We
further utilize a transfer learning strategy to narrow the domain gap between
the training and testing set. To be specific, we use fine-tuning to shift the
model to a target working condition. Finally, we made our model more efficient
by pruning. The experiment shows that our method attains an MAE of 0.75\% with
only 10\% data for fine-tuning on a testing battery, surpassing prior methods
by a large margin. Effective and robust, our method provides a potential
solution for all cyclic time sequence prediction tasks.
