Combined Meta-Learning With CNN-LSTM Algorithms for State-of-Health Estimation of Lithium-Ion Battery

Due to the complexity of the actual operating conditions of lithium-ion batteries, accurately estimating the state-of-health (SOH) of them often requires a significant amount of battery data, but most of the current SOH estimation methods lack generalisability. To address this issue, this article proposes a meta-learning SOH estimation method, which combines the meta-learning model with the CNN-LSTM model to improve the generalization of lithium-ion battery SOH estimation. It not only possesses better generalization ability, but also has higher estimation accuracy. In addition, regardless of the four different types of CALCE datasets or lithium-ion battery datasets in the laboratory, the maximum root mean square error and mean absolute error of the proposed method is 2.31% and 2.03%, which indicates the good performance of the proposed method for SOH estimation. Compared with two prevalent deep learning methods, this method enhances the estimation accuracy by an average of 25% across different battery data.