Boosting battery state of health estimation based on self-supervised learning

Abstract

State of health (SoH) estimation plays a key role in smart battery health prognostic and management. However, poor generalization, lack of labeled data, and unused measurements during aging are still major challenges to accurate SoH estimation. Toward this end, this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation. Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells, the proposed method achieves accurate and robust estimations using limited labeled data. A filter-based data preprocessing technique, which enables the extraction of partial capacity-voltage curves under dynamic charging profiles, is applied at first. Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder. The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data, which boosts the performance of the estimation framework. The proposed method has been validated under different battery chemistries, formats, operating conditions, and ambient. The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles, with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%, and robustness increases with aging. Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method. This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.