A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation. (arXiv:2209.00190v1 [cs.LG])

As a significant ingredient regarding health status, data-driven
state-of-health (SOH) estimation has become dominant for lithium-ion batteries
(LiBs). To handle data discrepancy across batteries, current SOH estimation
models engage in transfer learning (TL), which reserves apriori knowledge
gained through reusing partial structures of the offline trained model.
However, multiple degradation patterns of a complete life cycle of a battery
make it challenging to pursue TL. The concept of the stage is introduced to
describe the collection of continuous cycles that …

arxiv battery cycling health learning stage state