Transformer-Based Transfer Learning for Battery State-of-Health Estimation
Alessandro Giuliano, Yuandi Wu, John Yawney, S. Andrew Gadsden
Abstract
The accurate prediction of batteries’ state of health has been an important research topic in recent years, given the surge in electric vehicle production. Dynamically assessing the current state of health of a battery can help predict how long the battery will last during the next discharge cycle, which is directly related to an electric vehicle’s autonomy calculations. Data-driven approaches have been successful in accurately estimating the state of health through machine learning-based models. Within this research topic, limited studies have been carried out to explore the transfer learning capabilities of these models to improve performance and reduce computational costs related to training. This paper aims to compare the performance of different machine learning models to adapt to diverse battery working conditions, as well as their transfer learning capabilities to batteries with different electrochemical compositions. A new transformer-based model is proposed for the SOH estimation problem. The results show that the proposed transformer model can improve its prediction performance through transfer learning when compared to the same model trained exclusively on the target dataset. When pre-trained on the NASA dataset and fine-tuned on the Oxford dataset, the transformer achieved an average RMSE of 0.01461, outperforming the best-performing model (an ANN with an RMSE of 0.01747) trained exclusively on the target data by 17%. On top of improving its performance, the model is also able to outperform a competing transformer model from the literature, which reported an RMSE of 0.90170 on a similar cross-composition transfer task.