A Collaborative Gaussian Process Regression Model for Transfer Learning of Capacity Trends Between Li-Ion Battery Cells

A novel method is proposed for forecasting the capacity of lithium-ion battery cells. The method uses a Gaussian Process Regression model, a machine learning framework. Besides the high prediction accuracy and robustness the proposed method possesses, the method offers other advantages, namely, it provides uncertainty information, and it has the capability to cross-correlate capacity trends between different battery cells. These two merits make the proposed method a very reliable and practical solution for applications that use battery cell packs with a large number of interconnected battery cells. The proposed method is derived, verified, and compared to benchmark methods on three experimental lithium-ion battery cell datasets. The results show the effectiveness of the proposed method.