Battery capacity estimation based on a co-learning framework with few-labeled and noisy data

Abstract

Battery capacity estimation plays a crucial role in optimizing the performance and longevity of electric vehicles and stationary energy storage systems. However, accurately estimating battery capacity becomes challenging in real-world applications, particularly when dealing with unlabeled and noisy capacity data. To address this issue, this paper presents a co-learning framework incorporating both supervised and self-supervised learning for estimating battery capacity in the presence of few-labeled and noisy data. By training a shared encoder network with both self-supervised and supervised heads, the framework maximizes the agreement between the two heads in the latent space. The proposed approach demonstrates improved accuracy in battery capacity estimation in those challenging scenarios based on two public datasets. Comparative experiments show that the co-learning approach outperforms conventional end-to-end mapping methods, the average root mean square error of the proposed method is reduced at least by 36% and 19% under insufficient and noisy label conditions, respectively, leading to significant enhancements in estimation performance.