Auxiliary-Label Enhanced Semi Supervised Learning With Selective Pseudolabeling for Battery Capacity Estimation

Recent advances in data-driven methods have significantly improved battery capacity estimation, yet most existing approaches remain constrained by their reliance on supervised learning, requiring substantial amounts of labeled cycling data that are often costly to obtain. To address this challenge, this study proposes a dual-branch network-based semi supervised framework that integrates self-supervised learning and transfer learning mechanisms. First, the framework derives meaningful degradation-aware auxiliary labels from both labeled and unlabeled samples, creating reliable self-supervised signal for model training. Second, the designed dual-branch neural network architecture employs a shared feature extractor that processes input data for both the primary capacity estimation task and the auxiliary label prediction task, enabling effective knowledge transfer between labeled and unlabeled domains through their common representation space. Third, a pseudolabel filtering strategy is proposed to dynamically select high-confidence samples from the unlabeled dataset for self-training, thereby effectively expanding the training set with high-quality pseudolabels and enhancing the capacity estimation accuracy. Finally, extensive experiments validate the framework’s superior performance, achieving a worst-case root-mean-square error of only 0.0143 Ah with merely 5% labeled data, representing a 27.04% reduction compared with the best-performing semi supervised baseline (Co-training) under the same conditions.