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As a significant ingredient regarding health status, data-drivenstate-of-health (SOH) estimation has become dominant for lithium-ion batteries(LiBs). To handle data discrepancy across batteries, current SOH estimationmodels engage in transfer learning (TL), which reserves apriori knowledgegained through reusing partial structures of the offline trained model.However, multiple degradation patterns of a complete life cycle of a batterymake it challenging to pursue TL. The concept of the stage is introduced todescribe the collection of continuous cycles that present a similar degradationpattern. A transferable multi-stage SOH estimation model is proposed to performTL across batteries in the same stage, consisting of four steps. First, withidentified stage information, raw cycling data from the source battery arereconstructed into the phase space with high dimensions, exploring hiddendynamics with limited sensors. Next, domain invariant representation acrosscycles in each stage is proposed through cycling discrepancy subspace withreconstructed data. Third, considering the unbalanced discharge cycles amongdifferent stages, a switching estimation strategy composed of a lightweightmodel with the long short-term memory network and a powerful model with theproposed temporal capsule network is proposed to boost estimation accuracy.Lastly, an updating scheme compensates for estimation errors when the cyclingconsistency of target batteries drifts. The proposed method outperforms itscompetitive algorithms in various transfer tasks for a run-to-failure benchmarkwith three batteries.

A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation