A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm

• Health features are extracted from voltage and IC curves considering statistical and geometric metrics. • The hybrid datasets comprising labeled and unlabeled data are employed to improve the battery degradation model. • The small amount of labeled and unlabeled data can be achieved highly accurate SOH and RUL estimation. • Different amounts of labeled and unlabeled data are used to verify the proposed method. To ensure the safe operation of lithium-ion batteries, it is crucial to accurately predict their state of health (SOH) and remaining useful life (RUL). Addressing the issue of high costs and time consumption due to the reliance on large amounts of labeled data in existing models, this paper proposes a co-estimation framework that combines semi-supervised learning (SSL) with long short-term memory networks (LSTM), effectively utilizing unlabeled data. By selecting the most strongly correlated battery health features and constructing a degradation model using a hybrid dataset, the need for labeling is reduced. The verification results indicate SOH estimated error is reduced to 4% and the maximum root mean square error (RMSE) is 1.58%. When utilizing 75% SOH as the end-of-life criterion for battery cycle life, the mean absolute error (MAE) of the RUL predictions for the two tested batteries are 2.5281 and 0.0562 cycles, respectively. The results prove the framework enables accurate prediction and has wide practicability and universal applicability.