An adaptive ensemble learning capacity prediction method with dual-modal data in lithium-ion battery manufacturing

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ABSTRACT To measure the capacity of lithium-ion batteries in manufacturing, it is necessary to go through the formation, standing, and grading processes. The existing data-driven capacity prediction methods can only reduce a portion of the grading process and do not comprehensively use both time series data and statistical feature data. In response to the above problems, this paper proposes an adaptive ensemble learning method, which does not go through the grading process and only uses the statistical features and time series of dual-modal data in the formation process to predict the capacity. The LightGBM algorithm is used to predict statistical feature data, while the multi-channel GRU and multi-head self-attention network are used to predict time series data. Then, the two parts are fused through adaptive ensemble learning to estimate the capacity. Furthermore, the proposed method could be flexibly applied to the prediction of single-modal dirty data as required. The method is verified using actual industrial production line data. The experimental results show that the MAE and MAPE of the test set are 0.948 Ah and 0.324%, respectively. For single-modal dirty data (accounting for about 4%), MAPE of its prediction results is less than 0.65%.