Degradation-Aligned Self-Supervised Learning for State of Health Estimation of Lithium-Ion Batteries under Label Sparsity
2026-08-17 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors address the problem of estimating how healthy a battery is (its state of health) when there isn’t much labeled data available. They developed a new learning method that first trains a model to understand battery aging patterns from unlabeled data by ranking cycles in order. This helps the model learn useful features before fine-tuning it with very limited labeled data, leading to accurate battery health predictions. Their approach works well even when labeled examples are less than 1% of the data. They also studied how the way labeled data is spread out affects the model’s accuracy.
State of Health (SOH)Lithium-ion batteriesSelf-supervised learningConvolutional Neural Network (CNN)Gated Recurrent Unit (GRU)Cycle-order rankingPretrainingFine-tuningMean Absolute Error (MAE)Root Mean Square Error (RMSE)
Authors
Jiaqi Yao, Julia Kowal
Abstract
An accurate estimation of the state of health (SOH) underpins a safe and optimized use of the battery system. Although compelling, data-driven SOH estimation models typically require large amounts of high-quality labeled cycling data, while in practice such labels are often sparse in both quantity and coverage. Therefore, in this work, we propose a degradation-aligned self-supervised learning (SSL) framework based on a convolutional neural network-gated recurrent unit (CNN-GRU) model, which learns aging-consistent representations from unlabeled data through a cycle-order ranking objective as the pretext task for pretraining, thereby enabling robust SOH estimation after fine-tuning on sparsely labeled data. Test results showcase that the proposed ranking-based SSL approach proves to endow the pretrained model with degradation-aligned information from unlabeled data, and after fine-tuning the model can carry out accurate, robust SOH estimation, even when only an extremely limited amount of 1% of unevenly distributed labeled training data is available, where the MAE of 1.718% and RMSE of 2.329% can be achieved on the test cell. In addition, in-depth analyses are presented regarding the influences of label distribution of battery degradation data. We believe this work could shed new light on SOH estimation of lithium-ion batteries under label sparsity in real-world applications.