CyFormer: Accurate State-of-Health Prediction of Lithium-Ion Batteries via Cyclic Attention

TL;DR

CyFormer is a transformer-based model that accurately predicts lithium-ion battery health by capturing cyclic features and reducing domain gaps, outperforming existing methods with minimal data.

Contribution

This paper introduces CyFormer, a novel cyclic attention transformer model with transfer learning and pruning for improved battery SoH prediction.

Findings

Achieves an MAE of 0.75% with 10% data for fine-tuning.

Effectively captures intra-cycle and inter-cycle features.

Surpasses prior methods significantly in accuracy.

Abstract

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.